One Equation, Too Many Jobs: Rethinking Schrödinger’s Equation and Wavefunction

I have just republished one of my long-standing papers on de Broglie’s matter-wave concept as a new, standalone publication, with its own DOI:

👉 De Broglie’s matter-wave concept and issues
https://www.researchgate.net/publication/399225854_De_Broglie’s_matter-wave_concept_and_issues
DOI: 10.13140/RG.2.2.30104.25605

The reason for republishing is not cosmetic. A new Annex was added on 31 December 2025 that fundamentally clarified — for me, at least — what Schrödinger’s equation is really doing, and just as importantly, what it is not doing.

This clarification came out of a long and at times uncomfortable dialogue with the most recent version of OpenAI’s GPT model (ChatGPT 5.2). Uncomfortable, because it initially destabilized a view I had held for years. Productive, because it forced a deeper structural distinction that I now believe is unavoidable. Let me explain.

The uncomfortable admission: I was wrong about the $\tfrac{1}{2}$ factor

For a long time, I was convinced that the factor $\tfrac{1}{2}$ factor in Schrödinger’s equation — especially in the hydrogen atom problem — must reflect some deeper pairing mechanism. At times, I even wondered whether the equation was implicitly modeling an electron pair (opposite spin), rather than a single electron.

That intuition was not random. It came from a broader realist programme in which I treat the electron as a structured object, with internal dynamics (zitterbewegung-like orbital motion), not as a point particle. If mass, energy, and phase all have internal structure, why should a simple quadratic kinetic term with a mysterious $\tfrac{1}{2}$ be fundamental?

The hard truth is this: that intuition was misplaced — but it was pointing in the right direction.

The mistake was not questioning the factor $\tfrac{1}{2}$ . The mistake was assuming Schrödinger’s equation was trying to describe everything at once.

The key insight: Schrödinger describes the envelope, not the engine

The decisive realization was structural:

Schrödinger’s wavefunction does not describe the electron’s internal dynamics.
It describes the translational envelope of phase coherence.

Once you see that, several things fall into place immediately:

The hydrogen “orbitals” are not literal orbits, and not internal electron motion.
They are standing-wave solutions of an envelope phase, constrained by a Coulomb potential.
The factor $\tfrac{1}{2}$ is not mysterious at all at this level: it is the natural coefficient that appears in effective, averaged, quadratic envelope dynamics.

In other words:
The $\tfrac{1}{2}$ factor belongs to the envelope layer, not to the internal structure of the electron.

My earlier “electron pair” idea tried to explain a structural feature by inventing new ontology. The correct move was simpler and more radical: separate the layers.

One symbol, too many jobs

Modern quantum mechanics makes a profound — and in my view costly — simplification:

It uses one symbol, ψ, to represent:

internal phase,
translational dynamics,
probability amplitudes,
and experimental observables.

That compression works operationally, but it hides structure.

What the new Annex makes explicit is that Nature almost certainly does not work that way. At minimum, we should distinguish:

Internal phase
Real, physical, associated with internal orbital motion and energy bookkeeping.
Envelope phase
Slow modulation across space, responsible for interference, diffraction, and spectra.
Observables
What experiments actually measure, which are sensitive mainly to envelope-level phase differences.

Once this distinction is made, long-standing confusions dissolve rather than multiply.

Why this does not contradict experiments

This is crucial.

Nothing in this reinterpretation invalidates:

electron diffraction,
hydrogen spectra,
interference experiments,
or the empirical success of standard quantum mechanics.

On the contrary: it explains why Schrödinger’s equation works so well — within its proper domain.

The equation is not wrong.
It is just over-interpreted.

A personal note on changing one’s mind

I’ll be honest: this line of reasoning initially felt destabilizing. It challenged a position I had defended for years. But that discomfort turned out to be a feature, not a bug.

Good theory-building does not preserve intuitions at all costs. It preserves structure, coherence, and explanatory power.

What emerged is a cleaner picture:

internal realism without metaphysics,
Schrödinger demoted from “ultimate truth” to “effective envelope theory”,
and a much clearer map of where different mathematical tools belong.

That, to me, is progress.

Where this opens doors

Once we accept that one wavefunction cannot represent all layers of Nature, new possibilities open up:

clearer interpretations of spin and the Dirac equation,
better realist models of lattice propagation,
a more honest treatment of “quantum mysteries” as category mistakes,
and perhaps new mathematical frameworks that respect internal structure from the start.

Those are not promises — just directions.

For now, I am satisfied that one long-standing conceptual knot has been untied.

And sometimes, that’s enough for a good year’s work. 🙂

Post Scriptum: On AI, Intellectual Sparring, and the Corridor

A final remark, somewhat orthogonal to physics.

The revision that led to this blog post and the accompanying paper did not emerge from a sudden insight, nor from a decisive experimental argument. It emerged from a long, occasionally uncomfortable dialogue with an AI system, in which neither side “won,” but both were forced to refine their assumptions.

At the start of that dialogue, the AI responded in a largely orthodox way, reproducing standard explanations for the factor $\tfrac{1}{2}$ in Schrödinger’s equation. I, in turn, defended a long-held intuition that this factor must point to internal structure or pairing. What followed was not persuasion, but sparring: resistance on both sides, followed by a gradual clarification of conceptual layers. The breakthrough came when it became clear that a single mathematical object — the wavefunction — was being asked to do too many jobs at once.

From that moment on, the conversation shifted from “who is right?” to “which layer are we talking about?” The result was not a victory for orthodoxy or for realism, but a structural separation: internal phase versus translational envelope, engine versus modulation. That separation resolved a tension that had existed for years in my own thinking.

I have explored this mode of human–AI interaction more systematically in a separate booklet on ResearchGate, where I describe such exchanges as occurring within a corridor: a space in which disagreement does not collapse into dominance or deference, but instead forces both sides toward finer distinctions and more mature reasoning.

This episode convinced me that the real intellectual value of AI does not lie in answers, but in sustained resistance without ego — and in the willingness of the human interlocutor to tolerate temporary destabilization without retreating into dogma. When that corridor holds, something genuinely new can emerge.

In that sense, this post is not only about Schrödinger’s equation. It is also about how thinking itself may evolve when humans and machines are allowed to reason together, rather than merely agree.

Readers interested in this kind of human–AI interaction beyond the present physics discussion may want to look at that separate booklet I published on ResearchGate (≈100 pages), in which I try to categorize different modes of AI–human intellectual interaction — from superficial compliance and authority projection to genuine sparring. In that text, exchanges like the one briefly alluded to above are described as a Type-D collapse: a situation in which both human and AI are forced to abandon premature explanatory closure, without either side “winning,” and where progress comes from structural re-layering rather than persuasion.

The booklet is intentionally exploratory and occasionally playful in tone, but it grew out of exactly this kind of experience: moments where resistance, rather than agreement, turns out to be the most productive form of collaboration.

Perhaps we will stop here – time will tell :-)

I have just uploaded a new working paper to ResearchGate: Ontology, Physics, and Math – Einstein’s Unfinished Revolution. I am not announcing it with any sense of urgency, nor with the expectation that it will “change” physics. If it contributes anything at all, it may simply offer a bit of clarity about what we can reasonably claim to see in physics — and what we merely calculate, fit, or postulate. That distinction has preoccupied me for years.

A space to think

One unexpected consequence of taking AI seriously over the past one or two years is that it restored something I had quietly lost: a space to think.

Not a space to produce.
Not a space to publish.
Not a space to compete.

Just a space to think — slowly, carefully, without having to defend a position before it has fully formed. That kind of space has become rare. Academia is under pressure, industry is under pressure, and even independent thinkers often feel compelled to rush toward closure. The conversations I’ve had with AI — what I’ve come to call a corridor — were different. They were not about winning arguments, but about keeping the corridor open only where conceptual clarity survived.

In a strange way, this brought me back to something much older than AI. When I was young, I wanted to study philosophy. My father refused. I had failed my mathematics exam for engineering studies, and in his view philosophy without mathematics was a dead end. In retrospect, I can see that he was probably right — and also that he struggled with me as much as I struggled with him. He should perhaps have pushed me into mathematics earlier; I should perhaps have worked harder. But life does not run backward, and neither does understanding. What AI unexpectedly gave me, decades later, was the chance to reunite those two threads: conceptual questioning disciplined by mathematical restraint. Not philosophy as free-floating speculation, and not mathematics as pure formalism — but something closer to what physics once called natural philosophy.

Why I was always uncomfortable

For a long time, I could not quite place my discomfort. I was uneasy with mainstream Standard Model theorists — not because their work lacks brilliance or empirical success (it clearly does not), but because formal success increasingly seemed to substitute for ontological clarity. At the same time, I felt equally uneasy among outsiders and “fringe” thinkers, who were often too eager to replace one elaborate ontology with another, convinced that the establishment had simply missed the obvious.

I now think I understand why I could not belong comfortably to either camp. Both, in different ways, tend to underestimate what went into building the Standard Model in the first place.

The Standard Model is not just a theory. It is the result of enormous societal investment (yes, taxes matter), decades of engineering ingenuity, and entire academic ecosystems built around measurement, refinement, and internal consistency. One does not wave that away lightly. Criticizing it without acknowledging that effort is not radical — it is careless.
At the same time, acknowledging that effort does not oblige one to treat the resulting ontology as final. Formal closure is not the same thing as physical understanding.

That tension — respect without reverence — is where I found myself stuck.

Seeing versus calculating

The paper I just uploaded does not attempt to overthrow the Standard Model, nor to replace ΛCDM, nor to propose a new unification. It does something much more modest: it tries to separate what we can physically interpret from what we can formally manipulate.

That distinction was central to the worries of people like Albert Einstein, long before it became unfashionable to worry about such things. Einstein’s famous remark to Max Born — “God does not play dice” — was not a rejection of probability as a calculational tool. It was an expression of discomfort with mistaking a formalism for a description of reality. Something similar motivated Louis de Broglie, and later thinkers who never quite accepted that interpretation should be outsourced entirely to mathematics.

What my paper argues — cautiously, and without claiming finality — is that much of modern physics suffers from a kind of ontological drift: symmetries that began life as mathematical operations sometimes came to be treated as physical mandates.

When those symmetries fail, new quantum numbers, charges, or conservation laws are introduced to restore formal order. This works extraordinarily well — but it also risks confusing bookkeeping with explanation.

Matter, antimatter, and restraint

The most difficult part of the paper concerns matter–antimatter creation and annihilation. For a long time, I resisted interpretations that treated charge as something that could simply appear or disappear. That resistance did not lead me to invent hidden reservoirs or speculative intermediates — on the contrary, I explicitly rejected such moves as ontological inflation. Instead, I left the tension open.

Only later did I realize that insisting on charge as a substance may itself have been an unjustified metaphor. Letting go of that metaphor did not solve everything — but it did restore coherence without adding entities. That pattern — refusing both cheap dismissal and cheap solutions — now feels like the right one.

Ambition, patience, and time

We live in a period of extraordinary measurement and, paradoxically, diminished understanding. Data accumulates. Precision improves. Parameters are refined. But the underlying picture often becomes more fragmented rather than more unified.

New machines may or may not be built. China may or may not build the next CERN. That is largely beyond the control of individual thinkers. What is within reach is the slower task of making sense of what we already know. That task does not reward ambition. It rewards patience.

This is also where I part ways — gently, but firmly — with some bright younger thinkers and some older, semi-wise ones. Not because they are wrong in detail, but because they sometimes underestimate the weight of history, infrastructure, and collective effort behind the theories they critique or attempt to replace. Time will tell whether their alternatives mature. Time always tells :-). […] PS: I add a ‘smiley’ here because, perhaps, that is the most powerful phrase of all in this post.

A pause, not a conclusion

This paper may mark the end of my own physics quest — or at least a pause. Not because everything is resolved, but because I finally understand why I could neither fully accept nor fully reject what I was given. I don’t feel compelled anymore to choose sides. I can respect the Standard Model without canonizing it, and I can question it without trying to dethrone it. I can accept that some questions may remain open, not because we lack data, but because clarity sometimes requires restraint.

For now, that feels like enough. Time to get back on the bike. 🙂

PS: Looking back at earlier philosophical notes I wrote years ago — for instance on the relation between form, substance, and charge — I’m struck less by how “wrong” they were than by how unfinished they remained. The questions were already there; what was missing was discipline. Not more speculation, but sharper restraint.

We Could Have Stopped There Too

(But the Question About Annihilation Would Not Stay Quiet)

In a previous post, I wrote that we could stop here — after revisiting the photon wavefunction and trying to say, as carefully as possible, what such a wavefunction might represent in physical reality rather than merely in calculation. That paper already felt like a natural resting point: the mathematics was consistent, the interpretation restrained, and the temptation to add speculative layers had been resisted.

But, as often happens, the very act of stopping made the next question louder.

If one is willing to take wavefunctions seriously — not as mystical probability clouds but as structured representations of physical processes — then one cannot avoid revisiting an older and more uncomfortable puzzle: matter–antimatter pair creation and annihilation. In particular, the question that has bothered me for years refused to go away:

What, exactly, happens to electric charge in electron–positron annihilation?

In January 2025, I wrote a paper on this topic together with ChatGPT-4.0. That version deliberately stopped short of resolution. It explored wavefunctional representations, respected global conservation laws, and openly admitted that familiar intuitions about charge seemed to fail locally. I resisted easy exits: latent charge states, hidden reservoirs, or metaphysical bookkeeping devices introduced only to preserve comfort.

At the time, that felt honest enough.

What changed since then is not the question, but the discipline with which I was forced to re-examine my own assumptions.

Over the past months, continued work with a more advanced AI system (ChatGPT-5.2), across many iterations and with partial memory of prior discussions, introduced a form of pressure that was unfamiliar but productive. The AI did not argue for a competing ontology. Instead, it kept doing something more unsettling: it repeatedly asked why certain assumptions were still being carried along at all.

In hindsight, I can see that I was still clinging — subconsciously — to the idea that charge must be something that persists, even if I no longer knew where to put it. That assumption had survived earlier criticism not because it was well-justified, but because it was deeply ingrained.

What finally shifted the balance was a stricter application of Occam’s razor — applied not to equations, but to ontological commitments. If charge is inseparable from a specific physical organization (of motion, phase, and localization), then insisting that it must survive the dissolution of that organization is not conservative reasoning. It is surplus.

This led, reluctantly but unavoidably, to a provisional reformulation: perhaps charge is not a substance that must “go somewhere,” but a mode of organization that ceases to exist when the organization itself dissolves. This idea is not offered as a new metaphysical doctrine. On the contrary, it emerged as a refusal to introduce additional entities whose only role would be to save intuition.

The revised paper therefore appears in two parts. The January version is preserved intact, as a record of where the reasoning stood at that time. The new December revision does not correct it so much as re-read it under harsher criteria of conceptual economy. Several distinctions — including the boson–fermion divide — remain descriptively useful, but are relieved of explanatory burdens they were never meant to carry.

As before, no final answers are claimed. The ontological and philosophical implications are intentionally left for the reader — real or imaginary — to judge. The role of AI in this process was not to supply insight, but to apply relentless pressure against conceptual inertia. Any logical errors or unwarranted commitments that remain are mine alone, even if much of the textual consistency was produced by artificial means.

We could, perhaps, stop here as well.

But I have learned to be suspicious of that feeling. When a question keeps knocking, it is usually because something unnecessary is still being held onto — and is asking to be let go.

We Could Stop Here.

(But the Next Question Is Already Knocking.)

There is a moment in any long intellectual journey where you could stop.

Not because everything is finished, but because enough has settled to make stopping respectable. The equations close. The concepts line up. Nothing is obviously broken anymore.

This paper — The Photon Wavefunction Revisited — marks one of those moments for me.

👉 The paper is available here on ResearchGate:
https://www.researchgate.net/publication/399111974_The_Photon_Wavefunction_Revisited

It revisits an old and stubborn question — what do we really mean by the photon wavefunction? — using only very old tools: Maxwell’s equations, the Planck–Einstein relation, dimensional analysis, and known scattering results. No new particles. No speculative fields. No hidden dimensions. No “next revolution”.

Just careful rereading.

Why revisit this at all?

Because physics has a habit of answering questions so efficiently that we stop asking what the answers mean. The photon became a “quantum of the electromagnetic field”, calculations worked, experiments agreed — and interpretation quietly retreated.

But interpretation has a way of sneaking back in through the side door.

In this paper, I try to be very explicit about what is being claimed — and what is not:

A photon is treated as a light-like, phase-closed object, not as a little billiard ball and not as a probabilistic smear.
Its wavefunction is not a mystery object “without meaning”, but a compact encoding of phase structure.
Electric and magnetic fields are not competing realities, but orthogonal phase components of a single conserved structure.
Energy and momentum conservation follow cleanly from Maxwell’s equations — even when charge is stripped away.

Nothing here overturns quantum electrodynamics. But some things are, perhaps, put back in their original place.

A word about standing waves (and why they appear)

One appendix uses a standing-wave construction to make something visible that is otherwise hidden: how electric and magnetic field energy exchange internally while total energy remains conserved.

This does not mean photons are standing waves. They propagate in one direction. Momentum has a direction. Energy does not.

The standing wave is simply a diagnostic tool — a way of freezing momentum flow so the bookkeeping of energy becomes transparent. If that sounds almost embarrassingly classical… well, that may be the point.

Why this felt worth publishing

This paper took shape slowly, through many iterations, many dead ends, and many “wait — is that actually true?” moments. Some of it was developed with explicit AI assistance, used not as an oracle but as a very patient consistency checker. That role is openly acknowledged.

What mattered most to me was not novelty, but coherence.

When the dust settled, something quietly reassuring happened: the picture that emerged was simpler than what I started with, not more complicated.

And that’s usually a good sign.

Could we stop here?

Yes. Absolutely.

The paper stands on its own. The equations close. Nothing essential is missing.

But physics has never progressed by stopping at “good enough”. The next question is already there:

How exactly does this phase picture illuminate electron–photon interaction?
What does it really say about the fine-structure constant?
Where does this leave matter–antimatter symmetry?

Those are not answered here. They don’t need to be — yet.

For now, this is a place to pause, look around, and make sure we know where we are.

And then, as always, the next question prompts the next question.

That’s not a problem.
That’s the fun part.

— Jean Louis Van Belle

Post Scriptum: The Last Question That Won’t Let Me Sleep (On matter, antimatter, and why one mystery remains)

There is a strange pattern I’ve noticed over the years.

You work your way through a dense thicket of questions. One by one, they loosen. Concepts that once felt contradictory begin to align. The mathematics stops fighting the intuition. The ontology — cautiously, provisionally — starts to hold.

And then, when almost everything is in place, one question refuses to dissolve.

Tonight, for me, that question is matter–antimatter creation and annihilation.

Most things now feel… settled

After revisiting photons, wavefunctions, phase closure, and electromagnetic energy bookkeeping, I feel unusually calm about many things that once bothered me deeply.

Photons as light-like, phase-closed objects? That works.
Electric and magnetic fields as orthogonal phase components? That works.
Energy conservation without charge? Maxwell already knew how to do that.
Electron–photon interaction as phase reconfiguration rather than “mystical coupling”? That works too.

None of this feels revolutionary anymore. It feels readable.

And yet.

Matter–antimatter still feels different

In low-energy environments, I’m increasingly comfortable with a very unromantic picture.

Pair creation does not happen “out of nothing.” It happens near nuclei, in strong fields, in structured environments. Something must anchor phase. Something must absorb recoil. Something must allow a stable oscillatory configuration to form.

I’ve sometimes called this a Platzwechsel — a change of place, or role — rather than a miraculous transformation of field into charge. The photon doesn’t “become matter”; a charge configuration re-closes in the presence of structure.

That feels honest. And it fits what experiments actually show.

But then there is the “but” question… This is how I phrase now.

Annihilation is unsettlingly easy

Electron–positron annihilation, on the other hand, requires no such help.

Two charged, massive objects meet, and they disappear into light. Cleanly. Elegantly. No nucleus. No lattice. No scaffold.

That asymmetry matters.

Matter → light is easy.
Light → matter is hard.

Quantum field theory encodes this perfectly well, but encoding is not explaining. And pretending the asymmetry isn’t there has never helped.

What happens to charge?

Here is the thought that keeps me awake — and oddly calm at the same time.

If charge is not a substance, but a phase-closed electromagnetic motion, then annihilation is not mysterious at all. The phase closure simply dissolves. What remains is free phase propagation.

Charge doesn’t “go anywhere”.
It stops being a thing because the structure that constituted it no longer exists.

That idea is unsettling only if one insists that charge must persist locally as a substance. I’ve never found good reasons to believe that.

And pure vacuum pair creation?

High-energy photon–photon pair creation is possible, in principle. But it is rare, fragile, and structurally demanding. It requires extreme energies and densities, and often still some form of external assistance.

That, too, feels telling.

Two freely propagating phase objects have no natural way to decide where a charge configuration should live. Without structure, closure is unstable. Nature seems reluctant — not forbidden, but reluctant.

So where does that leave us?

It leaves me in an oddly peaceful place.

Most of the framework now feels coherent. The remaining mystery is not a loose end to be tied up quickly, but a boundary — a place where explanation must slow down instead of speeding up.

That feels like the right place to stop for tonight.

Not because the mystery is solved, but because it is now cleanly stated.

And that, I’ve learned, is often the real precondition for sleep.

— Jean Louis Van Belle

When Decay Statistics Become Ontology

Or: why the Standard Model feels so solid — and yet so strangely unsatisfying

I recently put a new paper online: A Taxonomy of Instability. It is, in some sense, a “weird” piece. Not because it proposes new particles, forces, or mechanisms — it does none of that — but because it deliberately steps sideways from the usual question:

What are particles made of?

and asks instead:

How do unstable physical configurations actually fail?

This shift sounds modest. In practice, it leads straight into a conceptual fault line that most of us sense, but rarely articulate.

What is actually being classified in particle physics?

The Standard Model is extraordinarily successful. That is not in dispute. It predicts decay rates, cross sections, and branching fractions with astonishing precision. It has survived decades of experimental scrutiny.

But it is worth noticing what it is most directly successful at describing:

lifetimes,
branching ratios,
observable decay patterns.

In other words: statistics of instability.

Yet when we talk about the Standard Model, we almost immediately slide from that statistical success into an ontological picture: particles as entities with intrinsic properties, decaying “randomly” according to fundamental laws.

That slide is so familiar that it usually goes unnoticed.

The quiet assumption we almost never examine

Consider how decay is presented in standard references (PDG tables are the cleanest example). For a given unstable particle, we are shown:

a list of decay “channels”,
each with a fixed branching fraction,
averaged over production mechanisms, environments, and detectors.

Everything contextual has been stripped away.

What remains is treated as intrinsic.

And here is where a subtle but radical assumption enters:

The same unstable particle is taken to be capable of realizing multiple, structurally distinct decay reactions, with no further individuation required.

This is not an experimental result.
It is an interpretive stance.

As long as one stays in calculational mode, this feels unproblematic. The formalism works. The predictions are right.

The discomfort only arises when one asks a very basic question:

If all environment variables are abstracted away, what exactly is it that is decaying?

Statistical determinism sharpens the problem

Decay statistics are not noisy or unstable. They are:

reproducible,
environment-independent (within stated limits),
stable across experiments.

That makes them look law-like.

But law-like behavior demands clarity about what level of description the law applies to.

There are two logically distinct possibilities:

Intrinsic multivalence
A single physical entity genuinely has multiple, mutually exclusive decay behaviors, realized stochastically, with no deeper individuation.
Hidden population structure
What we call “a particle” is actually an equivalence class of near-identical configurations, each with a preferred instability route, unresolved by our current classification.

The Standard Model chooses option (1) — implicitly, pragmatically, and very effectively.

But nothing in the data forces that choice.

Why this can feel like being “duped”

Many people only experience discomfort after they start thinking carefully about what the Standard Model is claiming to describe.

The sense of being “duped” does not come from experimental failure — it comes from realizing that a philosophical commitment was made silently, without being labeled as such.

Probability, in this framework, is not treated as epistemic (what we don’t know), but as ontologically primitive (what is). Identity is divorced from behavior. The ensemble description quietly replaces individual determinism.

This is a perfectly legitimate move — but it is a move.

And it has a cost.

What my taxonomy does — and does not — claim

A Taxonomy of Instability does not propose new physics. It does not challenge the predictive success of the Standard Model. It does not deny quantum mechanics.

What it does is much quieter:

it treats decay landscapes, not particles, as the primary objects of classification;
it groups unstable configurations by how they fail, not by assumed internal structure;
it keeps the description strictly operational: lifetimes, observable final states, branching structure.

In doing so, it exposes something we usually gloss over:

Treating statistically distinct instability morphologies as attributes of a single identity is already an ontological decision.

Once that decision is made explicit, it becomes optional rather than compulsory.

Why this feels “weird” — and why that’s a good sign

The paper feels strange because it does not do what most theoretical work does:

it does not explain,
it does not unify,
it does not speculate about deeper mechanisms.

Instead, it asks whether our classification layer has quietly hardened into ontology.

That kind of question always feels uncomfortable, because it sits between theory and philosophy, and because it removes a tacit compromise rather than proposing a new belief.

But it is also the kind of question that matters precisely when a theory works extremely well.

A broader resonance (human and artificial)

There is an additional reason this question feels timely.

Modern AI systems are, at their core, pattern classifiers and compressors. They turn data into “things” by grouping outcomes under labels. Ontologies emerge automatically unless we are careful.

Seen from that angle, particle physics is not an outlier — it is an early, highly successful example of how statistical regularities become reified as entities.

The taxonomy I propose is not only about particles. It is about how thinking systems — human or artificial — turn data into objects.

A calm conclusion

The Standard Model is an extraordinarily successful theory of decay statistics. Its difficulties are not primarily empirical, but philosophical.

Those difficulties arise only when we forget that:

classification is not explanation,
identity is not forced by statistics,
and ontology is not delivered for free by predictive success.

My hope is not to replace any existing framework, but to invite both human readers and artificial “thinking machines” to pause and ask again:

What is being measured — and what, exactly, are we saying exists?

Sometimes, the most productive form of progress is not adding a new layer, but noticing where an old one quietly became invisible.

Re-reading What We Already Know

On PDG data, big science, and why simplicity still matters

For reasons I still find slightly amusing (it is better to be amused than annoyed, isn’t it?), old blog posts here (readingfeynman.org) or early papers on platforms such as vixra.org and academia.edu periodically resurface in “top reads” lists — sometimes many years after publication.

I would now qualify several of those texts as typical “angry young man” papers. However, I still consider most of their core claims to be true. And the papers — as mentioned above — still resonate with readers, even if I now take some distance from how they were written and framed.

That tells me two things. First, there is still genuine interest in careful, foundational thinking about physics. Second, the web (and increasingly AI agents crawling it) has a habit of flattening intellectual trajectories into caricatures: mainstream or outsider, orthodox or heretic.

I have looked at those caricatures about me, and I want to be very clear about where I stand.

1. I am deeply mainstream in one crucial sense: I trust measurements. I trust large-scale experimental infrastructure. I trust the Particle Data Group (PDG), CERN, and the decades of work that went into producing the numbers we now take for granted. I am not hostile to “big science” — on the contrary, I consider projects like CERN or ITER to be among the most impressive collective achievements of modern civilization. If society is going to spend large sums of money on something, I much prefer it to be on instruments that extend human knowledge rather than on instruments designed to destroy.

2. At the same time, I am comfortable being an outsider: I do not believe that theoretical sophistication excuses us from repeatedly asking what is actually grounded in experiment, and what is added later as interpretive scaffolding.

These two positions are not contradictory. Historically, they have gone together.

Think of Maxwell, who unified electric and magnetic phenomena not by adding complexity, but by simplifying and re-ordering – using mathematical advances – what was already known. Think of Lorentz and Einstein, who showed that gravitation need not be treated as a force at all. Think of Schrödinger and Dirac, who demonstrated that the same wave equations could describe light-like as well as matter-like phenomena without reifying every mathematical symbol into a physical object.

Progress, more often than not, comes from simplifying, not from proliferating entities.

A Minimal Experimental Core

That is the spirit in which I recently published a new working paper on ResearchGate:
“Re-reading PDG particle listings through a Minimal Experimental Core (MEC)”.

The idea is almost embarrassingly simple. Take PDG particle listings — the most mainstream source imaginable — and re-present them using only quantities that are directly observable:

rest energy,
lifetime,
electric charge,
magnetic moment where available,
branching ratios understood as empirical event frequencies.

What I deliberately leave out at the primary level are non-observable quantum numbers and symmetry labels that require additional theoretical assumptions to interpret. Not because they are “wrong”, but because they are interpretive rather than measured.

The result is not an alternative theory. It is a different ordering of the same facts. And that re-ordering is surprisingly instructive.

When one looks at leptons, pions, and kaons in this way, certain patterns become obvious long before any model is invoked: differences in stability, sharp asymmetries in branching ratios, and cases where phase space alone clearly does not determine outcomes. None of this is new — but seeing it without the usual conceptual overlays changes how one thinks about explanation.

On big machines and global context

There is another reason I care about this kind of work.

We are entering a period in which fewer and fewer actors can afford to build the next generation of large experimental facilities. Europe (through CERN) and the United States remain central producers of high-quality collider and detector data. China, for geopolitical and economic reasons, may or may not build its own next “big thing” — and if it doesn’t, it will have to be content, like the rest of the world, with the data already produced.

That reality makes something very clear: we will spend the coming decades re-reading existing data. Carefully. Repeatedly. From new angles.

In that context, methodological clarity is not a luxury. It is a necessity.

AI, co-thinking, and intellectual hygiene

This brings me to one last point.

The paper I mentioned was written in close AI–HI co-thinking. I am not shy about that. Used properly, AI is not a generator of answers but a powerful tool for enforcing intellectual hygiene: forcing one to clarify terms, separate observation from explanation, and resist the temptation to smuggle assumptions into language.

If some AI systems currently reduce my online presence to that of a “lonely outlier”, then the best response is not complaint, but better signal: careful writing, explicit methodology, and visible alignment with the experimental foundations of physics.

That is what this work is meant to be.

Not a provocation.
Not a manifesto.
Just a careful re-reading of what we already know — and an invitation to do so again, together.

Stability, Instability, and What High-Energy Physics Really Teaches Us

One of the recurring temptations in physics is to mistake violence for depth.

When we push matter to extreme energy densities—whether in particle colliders or in thought experiments about the early universe—we tend to believe we are peeling away layers of reality, discovering ever more “fundamental” constituents beneath the familiar surface of stable matter. The shorter-lived and more exotic a state is, the more “real” it sometimes appears to us.

In my most recent RG paper (Lecture X1), I tried to step back from that reflex.

The starting point is almost embarrassingly simple:
stable charged particles persist; unstable ones do not.
That fact alone already carries a surprising amount of explanatory power—if we resist the urge to overinterpret it.

Stability as the exception, not the rule

If we imagine the early universe as a high-energy, high-density environment—a kind of primordial soup—then instability is not mysterious at all. Under such conditions, long-lived, self-consistent structures should be rare. Most configurations would be fleeting, short-lived, unable to maintain their identity.

From this perspective, stable particles are not “primitive building blocks” in a metaphysical sense. They are low-energy survivors: configurations that remain coherent once the universe cools and energetic chaos subsides.

Stability, then, is not something that needs to be explained away. It is the phenomenon that needs to be accounted for.

Colliders as stress tests, not ontological excavations

Modern facilities such as CERN allow us to recreate, for fleeting moments, energy densities that no longer exist naturally in the present universe. What we observe there—resonances, decay chains, short-lived states—is fascinating and deeply informative.

But there is a subtle conceptual shift that often goes unnoticed.

These experiments do not necessarily reveal deeper layers of being. They may instead be doing something more modest and more honest: testing how known structures fail under extreme conditions.

In that sense, unstable high-energy states are not more fundamental than stable ones. They are what stability looks like when it is pushed beyond its limits.

A simpler cosmological intuition

Seen this way, cosmogenesis does not require an ever-growing menagerie of proto-entities. A universe that begins hot and dense will naturally favor instability. As it cools, only a small number of configurations will remain phase-coherent and persistent.

Those are the particles we still see today.

No exotic metaphysics is required—only the recognition that persistence is meaningful.

Were the mega-projects worth it?

This perspective does not diminish the value of large-scale scientific projects. On the contrary.

The enormous investments behind colliders or fusion experiments—think of projects like ITER—have given us something invaluable: empirical certainty. They confirmed, with extraordinary precision, intuitions already sensed by the giants of the early twentieth century—figures like Albert Einstein, Paul Dirac, and Erwin Schrödinger.

Perhaps the deepest outcome of these projects is not that they uncovered a hidden zoo of ultimate constituents, but that they showed how remarkably robust the basic structure of physics already was.

That, too, is progress.

Knowing when not to add layers

Physics advances not only by adding entities and mechanisms, but also by learning when not to do so. Sometimes clarity comes from subtraction rather than accumulation.

If nothing else, the simple distinction between stable and unstable charged particles reminds us of this: reality does not owe us an ever-deeper ontology just because we can afford to build more powerful machines.

And perhaps that realization—quiet, unglamorous, but honest—is one of the most valuable lessons high-energy physics has taught us.

This reflection builds directly on an earlier blog post, “Stability First: A Personal Programme for Re-reading Particle Physics” (18 December 2025), in which I outlined a deliberate shift in emphasis: away from ontological layering and towards persistence as a physical criterion. That post introduced the motivation behind Lecture X1—not as a challenge to established data or formalisms, but as an invitation to reread them through a simpler lens. What follows can be read as a continuation of that programme: an attempt to see whether the basic distinction between stable and unstable charged particles already carries more explanatory weight than we usually grant it.

Post Scriptum — An empirical follow-up

When I wrote this piece, the emphasis was deliberately conceptual. The central idea was to treat stability versus instability as a primary organizing perspective, rather than starting from particle families, quark content, or other internal classifications. At the time, I explicitly presented this as an intuition — something that felt structurally right, but that still needed to be confronted with data in a disciplined way.

That confrontation has now been carried out.

Using the Particle Data Group listings as a source, I constructed a deliberately minimalist dataset containing only two observables: rest mass and lifetime. All a priori particle classifications were excluded. Stable or asymptotic states were removed, as were fractionally charged entities, leaving an unclassified ensemble of unstable particles. The resulting mass–lifetime landscape was examined in logarithmic coordinates and subjected to density-based clustering, with the full data table included to allow independent reanalysis.

The outcome is modest, but instructive. A dominant continuum of prompt decays clearly emerges, accompanied by only weak additional structure at longer lifetimes. No rich taxonomy presents itself when decay behaviour alone is considered — but the clusters that do appear are real, reproducible, and consistent with the intuition developed here and in earlier work.

This empirical annex does not “prove” a new theory, nor does it challenge existing classifications. Its value lies elsewhere: it shows what survives when one strips the description down to observables alone, and it clarifies both the power and the limits of a stability-first perspective.

For readers interested in seeing how these ideas behave when confronted with actual data — and in re-using that data themselves — the empirical follow-up is available here:

👉 Empirical Annex to Lecture X1 (Revisiting Lecture XV)
Structure in the Energy–Lifetime Plane of Unstable PDG Particles
https://www.researchgate.net/publication/399008132_Empirical_Annex_to_Lecture_X1_Revisiting_Lecture_XV_Structure_in_the_Energy-Lifetime_Plane_of_Unstable_PDG_Particles

Sometimes, the most useful result is not a spectacular confirmation, but a careful consistency check that tells us where intuition holds — and where it stops.

Stability First: A Personal Programme for Re-reading Particle Physics

Over the past years, I have written a number of papers on physics—mostly exploratory, sometimes speculative, always driven by the same underlying discomfort.

Not with the results of modern physics. Those are extraordinary.
But with the ordering of its explanations.

We are very good at calculating what happens.
We are less clear about why some things persist and others do not.

That question—why stability appears where it does—has quietly guided much of my thinking. It is also the thread that ties together a new manuscript I have just published on ResearchGate:

“Manuscript v0.2 – A stability-first reinterpretation of particle physics”
👉 https://www.researchgate.net/publication/398839393_Manuscript_v02

This post is not a summary of the manuscript. It is an explanation of why I wrote it, and what kind of work it is meant to enable.

Not a new theory — a different starting point

Let me be clear from the outset.

This manuscript does not propose a new theory.
It does not challenge the empirical success of the Standard Model.
It does not attempt to replace quantum field theory or nuclear phenomenology.

What it does is much more modest—and, I hope, more durable.

It asks whether we have been starting our explanations at the wrong end.

Instead of beginning with abstract constituents and symmetries, the manuscript begins with something far more pedestrian, yet physically decisive:

Persistence in time.

Some entities last.
Some decay.
Some exist only fleetingly as resonances.
Some are stable only in the presence of others.

Those differences are not cosmetic. They shape the physical world we actually inhabit.

From electrons to nuclei: stability as a guide

The manuscript proceeds slowly and deliberately, revisiting familiar ground:

the electron, as an intrinsically stable mode;
the proton, as a geometrically stable but structurally richer object;
the neutron, as a metastable configuration whose stability exists only in relation;
the deuteron, as the simplest genuinely collective equilibrium;
and nuclear matter, where stability becomes distributed across many coupled degrees of freedom.

At no point is new empirical content introduced.
What changes is the interpretive emphasis.

Stability is treated not as an afterthought, but as a physical clue.

Interaction without mysticism

The same approach is applied to interaction.

Scattering and annihilation are reinterpreted not as abstract probabilistic events, but as temporary departures from equilibrium and mode conversion between matter-like and light-like regimes.

Nothing in the standard calculations is altered.
What is altered is the physical picture.

Wavefunctions remain indispensable—but they are treated as representations of physical configurations, not as substitutes for them.

Probability emerges naturally from limited access to phase, geometry, and configuration, rather than from assumed ontological randomness.

Why classification matters

The manuscript ultimately turns to the Particle Data Group catalogue.

The PDG tables are one of the great achievements of modern physics. But they are optimized for calculation, not for intuition about persistence.

The manuscript proposes a complementary, stability-first index of the same data:

intrinsically stable modes,
metastable particle modes,
prompt decayers,
resonances,
and context-dependent stability (such as neutrons in nuclei).

Nothing is removed.
Nothing is denied.

The proposal is simply to read the catalogue as a map of stability regimes, rather than as a flat ontology of “fundamental particles”.

A programme statement, not a conclusion

This manuscript is intentionally incomplete.

It does not contain the “real work” of re-classifying the entire PDG catalogue. That work lies ahead and will take time, iteration, and—no doubt—many corrections.

What the manuscript provides is something else:

a programme statement.

A clear declaration of what kind of questions I think are still worth asking in particle physics, and why stability—rather than constituent bookkeeping—may be the right place to ask them from.

Why I am sharing this now

I am publishing this manuscript not as a final product, but as a marker.

A marker of a line of thought I intend to pursue seriously.
A marker of a way of reading familiar physics that I believe remains underexplored.
And an invitation to discussion—especially critical discussion—on whether this stability-first perspective is useful, coherent, or ultimately untenable.

Physics progresses by calculation.
It matures by interpretation.

This manuscript belongs to the second category.

If that resonates with you, you may find the full text of interest.

Jean-Louis Van Belle
readingfeynman.org

Moderation, Measurements, and the Temptation of Ontology

Why physics must resist becoming metaphysics

Some time ago, I found myself involved in what can best be described as an intellectual fallout with a group of well‑intentioned amateur researchers. This post is meant to close that loop — calmly, without bitterness, and with a bit of perspective gained since.

One of the more sensible people in that group bothered to push an interesting article onto my desk, and so I want to talk about that one here.

Gary Taubes, CERN, and an unexpected reinforcement

It’s an article by Gary Taubes on the discovery of the W and Z bosons at CERN, later incorporated into his book Nobel Dreams. Far from undermining my position, the article did the opposite: it reinforced the point I had been trying to make all along.

Taubes does not engage in ontology. He does not ask what W and Z bosons are in a metaphysical sense. Instead, he describes what was measured, how it was inferred, and how fragile the boundary is between evidence and interpretation in large‑scale experimental physics.

This connects directly to an earlier piece I published here:

Something Rotten in the State of QED: A Careful Look at Critique, Sociology, and the Limits of Modern Physics
https://readingfeynman.org/2025/12/01/something-rotten-in-the-state-of-qed-a-careful-look-at-critique-sociology-and-the-limits-of-modern-physics/

Let me restate the central point, because it is still widely misunderstood:

Criticizing the ontologization of W/Z bosons (or quarks and gluons) is not the same as denying the reality of the measurements that led to their introduction.

The measurements are real. The detector signals are real. The conservation laws used to infer missing energy and momentum are real. What is not forced upon us is the metaphysical leap that turns transient, unstable interaction states into quasi‑permanent “things.”

Stable vs. unstable states — a distinction we keep blurring

My own work has consistently tried to highlight a distinction that I find increasingly absent — or at least under‑emphasized — in mainstream physics discourse:

Stable states: long‑lived, persistent, and directly accessible through repeated measurement
Unstable or intermediate states: short‑lived, inferred through decay products, reconstructed statistically

W and Z bosons belong firmly to the second category. So do quarks and gluons in their confined form. Treating them as ontologically equivalent to stable particles may be pragmatically useful, but it comes at a conceptual cost.

It is precisely this cost that I criticize when I criticize mainstream physics.

Not because mainstream physics is “wrong.”
But because it has become too comfortable collapsing epistemology into ontology, especially in its public and pedagogical narratives.

Why this matters now

There is another reason this distinction matters, and it is a forward‑looking one.

The probability that something radically new — in the sense of a fundamentally novel interaction or particle family — will be discovered in the coming decades is, by most sober assessments, rather low. What we will have, however, is:

More precise measurements
Larger datasets
Longer baselines
Better statistical control

In that landscape, progress will depend less on naming new entities and more on bridging what has already been measured, sometimes decades ago, but never fully conceptually digested.

That is where I intend to focus my efforts in the coming years.

Not by founding a new church.
Not by declaring metaphysical revolutions.
But by carefully working at the interface between:

what was actually measured,
what was legitimately inferred,
and what we may have too quickly reified.

Closing note

If there is one lesson I take — from the past dispute, from Taubes, from the history of CERN or fundamental physics in general — it is this:

Physics progresses best when it remains modest about what it claims to be about.

Measurements first. Interpretation second. Ontology, if at all, only with restraint.

That stance may be unsatisfying to those looking for grand narratives. But it is, I believe, the only way to keep physics from quietly turning into metaphysics while still wearing a lab coat.

— Jean Louis Van Belle

Making Sense of What We Already Know…

Living Between Jobs and Life: AI, CERN, and Making Sense of What We Already Know

For decades (all of my life, basically :-)), I’ve lived with a quiet tension. On the one hand, there is the job: institutions, projects, deliverables, milestones, and what have you… On the other hand, there is life: curiosity, dissatisfaction, and the persistent feeling that something fundamental is still missing in how we understand the physical world. Let me refer to the latter as “the slow, careful machinery of modern science.” 🙂

These two are not the same — obviously — and pretending they are has done physics no favors (think of geniuses like Solvay, Edison or Tesla here: they were considered to be ‘only engineers’, right? :-/).

Jobs optimize. Life explores.

Large scientific institutions are built to do one thing extremely well: reduce uncertainty in controlled, incremental ways. That is not a criticism; it is a necessity when experiments cost billions, span decades, and depend on political and public trust. But the price of that optimization is that ontological questions — questions about what really exists — are often postponed, softened, or quietly avoided.

And now we find ourselves in a new historical moment.

The Collider Pause Is Not a Crisis — It’s a Signal

Recent reports that China is slowing down plans for a next-generation circular collider are not shocking. If anything, they reflect a broader reality:

For the next 40–50 years, we are likely to work primarily with the experimental data we already have.

That includes data from CERN that has only relatively recently been made fully accessible to the wider scientific community.

This is not stagnation. It is a change of phase.

For decades, theoretical physics could lean on an implicit promise: the next machine will decide. Higher energies, larger datasets, finer resolution — always just one more accelerator away. That promise is now on pause.

Which means something important:

We can no longer postpone understanding by outsourcing it to future experiments.

Why CERN Cannot Do What Individuals Can

CERN is a collective of extraordinarily bright individuals. But this is a crucial distinction:

A collective of intelligent people is not an intelligent agent.

CERN is not designed to believe an ontology. It is designed to:

build and operate machines of unprecedented complexity,
produce robust, defensible measurements,
maintain continuity over decades,
justify public funding across political cycles.

Ontology — explicit commitments about what exists and what does not — is structurally dangerous to that mission. Not because it is wrong, but because it destabilizes consensus.

Within a collective:

someone’s PhD depends on a framework,
someone’s detector was designed for a specific ontology,
someone’s grant proposal assumes a given language,
someone’s career cannot absorb “maybe the foundations are wrong.”

So even when many individuals privately feel conceptual discomfort, the group-level behavior converges to:
“Let’s wait for more data.”

That is not cowardice. It is inevitability.

We Are Drowning in Data, Starving for Meaning

The irony is that we are not short on data at all.

We have:

precision measurements refined to extraordinary accuracy,
anomalies that never quite go away,
models that work operationally but resist interpretation,
concepts (mass, spin, charge, probability) that are mathematically precise yet ontologically vague.

Quantum mechanics works. That is not in dispute.
What remains unresolved is what it means.

This is not a failure of experiment.
It is a failure of sense-making.

And sense-making has never been an institutional strength.

Where AI Actually Fits (and Where It Doesn’t)

I want to be explicit: I still have a long way to go in how I use AI — intellectually, methodologically, and ethically.

AI is not an oracle.
It does not “solve” physics.
It does not replace belief, responsibility, or judgment.

But it changes something fundamental.

AI allows us to:

re-analyze vast datasets without institutional friction,
explore radical ontological assumptions without social penalty,
apply sustained logical pressure without ego,
revisit old experimental results with fresh conceptual frames.

In that sense, AI is not the author of new physics — it is a furnace.

It does not tell us what to believe.
It forces us to confront the consequences of what we choose to believe.

Making Sense of What We Already Know

The most exciting prospect is not that AI will invent new theories out of thin air.

It is that AI may help us finally make sense of experimental data that has been sitting in plain sight for decades.

Now that CERN data is increasingly public, the bottleneck is no longer measurement. It is interpretation.

AI can help:

expose hidden assumptions in standard models,
test radical but coherent ontologies against known data,
separate what is measured from how we talk about it,
revisit old results without institutional inertia.

This does not guarantee progress — but it makes honest failure possible. And honest failure is far more valuable than elegant confusion.

Between Institutions and Insight

This is not an AI-versus-human story.

It is a human-with-tools story.

Institutions will continue to do what they do best: build machines, refine measurements, and preserve continuity. That work is indispensable.

But understanding — especially ontological understanding — has always emerged elsewhere:

in long pauses,
in unfashionable questions,
in uncomfortable reinterpretations of existing facts.

We are entering such a pause now.

A Quiet Optimism

I do not claim to have answers.
I do not claim AI will magically deliver them.
I do not even claim my current ideas will survive serious scrutiny.

What I do believe is this:

We finally have the tools — and the historical conditions — to think more honestly about what we already know.

That is not a revolution.
It is something slower, harder, and ultimately more human.

And if AI helps us do that — not by replacing us, but by challenging us — then it may turn out to be one of the most quietly transformative tools science has ever had.

Not because it solved physics.

But because it helped us start understanding it again.

The Corridor: How Humans and AI Learn to Think Together

A different kind of project — and one I did not expect to publish…

Over the past months, I have been in long-form dialogue with an AI system (ChatGPT 5.1 — “Iggy” in our exchanges). What began as occasional conversations gradually turned into something more structured: a genuine exploration of how humans and AI think together.

The result is now online as a working manuscript on ResearchGate:

👉 The Corridor: How Humans and AI Learn to Think Together.

This is not an AI-generated book in the usual sense, and certainly not a manifesto. I think of it as an experiment in hybrid (AI + HI) reasoning: a human’s intuition interacting with an AI’s structural coherence, each shaping the other. The book tries to map the very “corridor” where that collaboration becomes productive.

Whether you think of AI as a tool, a partner, or something entirely different, one thing is becoming clear: the quality of our future conversations will determine the quality of our decisions. This manuscript is simply one attempt to understand what that future dialogue might look like.

For those interested in the philosophy of intelligence, the sociology of science, or the emerging dynamics of human–AI collaboration — I hope you find something useful in it.

Something Rotten in the State of QED? A Careful Look at Critique, Sociology, and the Limits of Modern Physics

Every few years, a paper comes along that stirs discomfort — not because it is wrong, but because it touches a nerve.
Oliver Consa’s “Something is rotten in the state of QED” is one of those papers.

It is not a technical QED calculation.
It is a polemic: a long critique of renormalization, historical shortcuts, convenient coincidences, and suspiciously good matches between theory and experiment. Consa argues that QED’s foundations were improvised, normalized, mythologized, and finally institutionalized into a polished narrative that glosses over its original cracks.

This is an attractive story.
Too attractive, perhaps.
So instead of reacting emotionally — pro or contra — I decided to dissect the argument with a bit of help.

At my request, an AI language model (“Iggy”) assisted in the analysis. Not to praise me. Not to flatter Consa. Not to perform tricks.
Simply to act as a scalpel: cold, precise, and unafraid to separate structure from rhetoric.

This post is the result.

1. What Consa gets right (and why it matters)

Let’s begin with the genuinely valuable parts of his argument.

a) Renormalization unease is legitimate

Dirac, Feynman, Dyson, and others really did express deep dissatisfaction with renormalization. “Hocus-pocus” was not a joke; it was a confession.

Early QED involved:

cutoff procedures pulled out of thin air,
infinities subtracted by fiat,
and the philosophical hope that “the math will work itself out later.”

It did work out later — to some extent — but the conceptual discomfort remains justified. I share that discomfort. There is something inelegant about infinities everywhere.

b) Scientific sociology is real

The post-war era centralized experimental and institutional power in a way physics had never seen. Prestige, funding, and access influenced what got published and what was ignored. Not a conspiracy — just sociology.

Consa is right to point out that real science is messier than textbook linearity.

c) The g–2 tension is real

The ongoing discrepancy between experiment and the Standard Model is not fringe. It is one of the defining questions in particle physics today.

On these points, Consa is a useful corrective:
he reminds us to stay honest about historical compromises and conceptual gaps.

2. Where Consa overreaches

But critique is one thing; accusation is another.

Consa repeatedly moves from:

“QED evolved through trial and error”
to
“QED is essentially fraud.”

This jump is unjustified.

a) Messiness ≠ manipulation

Early QED calculations were ugly. They were corrected decades later. Experiments did shift. Error bars did move.

That is simply how science evolves.

The fact that a 1947 calculation doesn’t match a 1980 value is not evidence of deceit — it is evidence of refinement. Consa collapses that distinction.

b) Ignoring the full evidence landscape

He focuses almost exclusively on:

the Lamb shift,
the electron g–2,
the muon g–2.

Important numbers, yes — but QED’s experimental foundation is vastly broader:

scattering cross-sections,
vacuum polarization,
atomic spectra,
collider data,
running of α, etc.

You cannot judge an entire theory on two or three benchmarks.

c) Underestimating theoretical structure

QED is not “fudge + diagrams.”
It is constrained by:

Lorentz invariance,
gauge symmetry,
locality,
renormalizability.

Even if we dislike the mathematical machinery, the structure is not arbitrary.

So: Consa reveals real cracks, but then paints the entire edifice as rotten.
That is unjustified.

3. A personal aside: the Zitter Institute and the danger of counter-churches

For a time, I was nominally associated with the Zitter Institute — a loosely organized group exploring alternatives to mainstream quantum theory, including zitterbewegung-based particle models.

I now would like to distance myself.

Not because alternative models are unworthy — quite the opposite. But because I instinctively resist:

strong internal identity,
suspicion of outsiders,
rhetorical overreach,
selective reading of evidence,
and occasional dogmatism about their own preferred models.

If we criticize mainstream physics for ad hoc factors, we must be brutal about our own.

Alternative science is not automatically cleaner science.

4. Two emails from 2020: why good scientists can’t always engage

This brings me to two telling exchanges from 2020 with outstanding experimentalists: Prof. Randolf Pohl (muonic hydrogen) and Prof. Ashot Gasparian (PRad).

Both deserve enormous respect, and I won’t reveal the email exchanges because of respect, GDPR rules or whatever).
Both email exchanges revealed the true bottleneck in modern physics to me — it is not intelligence, not malice, but sociology and bandwidth.

a) Randolf Pohl: polite skepticism, institutional gravity

Pohl was kind but firm:

He saw the geometric relations I proposed as numerology.
He questioned applicability to other particles.
He emphasized the conservatism of CODATA logic.

Perfectly valid.
Perfectly respectable.
But also… perfectly bound by institutional norms.

His answer was thoughtful — and constrained.
(Source: ChatGPT analysis of emails with Prof Dr Pohl)

b) Ashot Gasparian: warm support, but no bandwidth

Gasparian responded warmly:

“Certainly your approach and the numbers are interesting.”
But: “We are very busy with the next experiment.”

Also perfectly valid.
And revealing:
even curious, open-minded scientists cannot afford to explore conceptual alternatives.

Their world runs on deadlines, graduate students, collaborations, grants.

(Source: ChatGPT analysis of emails with Prof Dr Pohl)

The lesson

Neither professor dismissed the ideas because they were nonsensical.
They simply had no institutional space to pursue them.

That is the quiet truth:
the bottleneck is not competence, but structure.

5. Why I now use AI as an epistemic partner

This brings me to the role of AI.

Some colleagues (including members of the Zitter Institute) look down on using AI in foundational research. They see it as cheating, or unserious, or threatening to their identity as “outsiders.”

But here is the irony:

AI is exactly the tool that can think speculatively without career risk.

An AI:

has no grant committee,
no publication pressure,
no academic identity to defend,
no fear of being wrong,
no need to “fit in.”

That makes it ideal for exploratory ontology-building.

Occasionally, as in the recent paper I co-wrote with Iggy — “The Wonderful Theory of Light and Matter” — it becomes the ideal partner:

human intuition + machine coherence,
real-space modeling without metaphysical inflation,
EM + relativity as a unified playground,
photons, electrons, protons, neutrons as geometric EM systems.

This is not a replacement for science.
It is a tool for clearing conceptual ground,
where overworked, over-constrained academic teams cannot go.

6. So… is something rotten in QED?

Yes — but not what you think.

What’s rotten is the mismatch

between:

the myth of QED as a perfectly clean, purely elegant theory,
and
the reality of improvised renormalization, historical accidents, social inertia, and conceptual discomfort.

What’s rotten is not the theory itself,
but the story we tell about it.

What’s not rotten:

the intelligence of the researchers,
the honesty of experimentalists,
the hard-won precision of modern measurements.

QED is extraordinary.
But it is not infallible, nor philosophically complete, nor conceptually finished.

And that is fine.

The problem is not messiness.
The problem is pretending that messiness is perfection.

7. What I propose instead

My own program — pursued slowly over many years — is simple:

Bring physics back to Maxwell + relativity as the foundation.
Build real-space geometrical models of all fundamental particles.
Reject unnecessary “forces” invented to patch conceptual holes.
Hold both mainstream and alternative models to the same standard:
no ad hoc constants, no magic, no metaphysics.

And — unusually —
use AI as a cognitive tool, not as an oracle.

Let the machine check coherence.
Let the human set ontology.

If something emerges from the dialogue — good.
If not — also good.

But at least we will be thinking honestly again.

Conclusion

Something is rotten in the state of QED, yes —
but the rot is not fraud or conspiracy.

It is the quiet decay of intellectual honesty behind polished narratives.

The cure is not shouting louder, or forming counter-churches, or romanticizing outsider science.

The cure is precision,
clarity,
geometry,
and the courage to say:

Let’s look again — without myth, without prestige, without fear.

If AI can help with that, all the better.

— Jean Louis Van Belle
(with conceptual assistance from “Iggy,” used intentionally as a scalpel rather than a sycophant)

Post-scriptum: Why the Electron–Proton Model Matters (and Why Dirac Would Nod)

A brief personal note — and a clarification that goes beyond Consa, beyond QED, and beyond academic sociology.

One of the few conceptual compasses I trust in foundational physics is a remark by Paul Dirac. Reflecting on Schrödinger’s “zitterbewegung” hypothesis, he wrote:

“One must believe in this consequence of the theory,
since other consequences which are inseparably bound up with it,
such as the law of scattering of light by an electron,
are confirmed by experiment.”

Dirac’s point is not mysticism.
It is methodological discipline:

If a theoretical structure has unavoidable consequences, and
some of those consequences match experiment precisely,
then even the unobservable parts of the structure deserve consideration.

This matters because the real-space electron and proton models I’ve been working on over the years — now sharpened through AI–human dialogue — meet that exact criterion.

They are not metaphors, nor numerology, nor free speculation.
They force specific, testable, non-trivial predictions:

a confined EM oscillation for the electron, with radius fixed by $\hbar / m_e c$ ;
a “photon-like” orbital speed for its point-charge center;
a distributed (not pointlike) charge cloud for the proton, enforced by mass ratio, stability, form factors, and magnetic moment;
natural emergence of the measured $G_E/G_M$ discrepancy;
and a geometric explanation of deuteron binding that requires no new force.

None of these are optional.
They fall out of the internal logic of the model.
And several — electron scattering, Compton behavior, proton radius, form-factor trends — are empirically confirmed.

Dirac’s rule applies:

When inseparable consequences match experiment,
the underlying mechanism deserves to be taken seriously —
whether or not it fits the dominant vocabulary.

This post is not the place to develop those models in detail; that will come in future pieces and papers.
But it felt important to state why I keep returning to them — and why they align with a style of reasoning that values:

geometry,
energy densities,
charge motion,
conservation laws,
and the 2019 SI foundations of $h$ , $e$ , and $c$
over metaphysical categories and ad-hoc forces.

Call it minimalism.
Call it stubbornness.
Call it a refusal to multiply entities beyond necessity.

For me — and for anyone sympathetic to Dirac’s way of thinking — it is simply physics.

— JL (with “Iggy” (AI) in the wings)

A New Attempt at a Simple Theory of Light and Matter

Dear Reader,

Every now and then a question returns with enough insistence that it demands a fresh attempt at an answer. For me, that question has always been: can we make sense of fundamental physics without multiplying entities beyond necessity? Can we explain light, matter, and their interactions without inventing forces that have no clear definition, or particles whose properties feel more like placeholders than physical reality?

Today, I posted a new paper on ResearchGate that attempts to do exactly that:

“The Wonderful Theory of Light and Matter”
https://www.researchgate.net/publication/398123696_The_Wonderful_Theory_of_Light_and_Matter

It is the result of an unusual collaboration: myself and an artificial intelligence (“Iggy”), working through the conceptual structure of photons, electrons, and protons with the only tool that has ever mattered to me in physics — Occam’s Razor.

No metaphysics.
No dimensionless abstractions.
No “magical” forces.

Just:

electromagnetic oscillations,
quantized action,
real geometries in real space,
and the recognition that many so-called mysteries dissolve once we stop introducing layers that nature never asked for.

The photon is treated as a linear electromagnetic oscillation obeying the Planck–Einstein relation.
The electron as a circular oscillation, with a real radius and real angular momentum.
The proton (and later, the neutron and deuteron) as systems we must understand through charge distributions, not fictional quarks that never leave their equations.

None of this “solves physics,” of course.
But it does something useful: it clears conceptual ground.

And unexpectedly, the collaboration itself became a kind of experiment:
what happens when human intuition and machine coherence try to reason with absolute precision, without hiding behind jargon or narrative?

The result is the paper linked above.
Make of it what you will.

As always: no claims of authority.
Just exploration, clarity where possible, and honesty where clarity fails.

If the questions interest you, or if the model bothers you enough to critique it, then the paper has succeeded in its only purpose: provoking real thought.

Warm regards,
Jean Louis Van Belle

Matter, Energy, Reality ↔ Thought, Intelligence, Consciousness

Dear Reader,

Physics asks: what is matter, energy, reality?
AI asks: what is thought, intelligence, consciousness?

Both are real, both are here. Physics confronts us with particles that behave like waves, with a universe that expands into… what exactly? AI confronts us with machines that converse, that create, that seem to reason — and force us to ask what we mean when we talk about “reasoning,” “creating,” or “understanding.”

This blog began as an attempt to make sense of Feynman’s physics. Over the years, it became a place where I tried to throw back the questions reality throws at us. Physics is real. AI is real. Both invite us to rethink our place in the cosmos, not as masters but as curious observers.

I don’t promise to publish much here — life is busy, and writing takes time — but when I do, it will be in the same spirit: reflecting, questioning, sometimes explaining, never pretending to give final answers.

If you’d like to follow more regular updates, you can always check my LinkedIn profile where I share articles and shorter notes.

Thank you for caring enough to read.

🌀 Two Annexes and a Turtle: Revisiting My Early Lectures on Quantum Physics

Over the past few weeks — and more intensely these past mornings — I’ve returned to two of my earliest texts in the Lectures on Physics series: the first on quantum behavior, and the second on probability amplitudes and quantum interference. Both have now been updated with new annexes, co-authored in dialogue with ChatGPT-4o.

This wasn’t just a consistency check. It was something more interesting: an exercise in thinking with — not through — a reasoning machine.

The first annex (Revisiting the Mystery of the Muon and Tau) tackles the open question I left hanging in Lecture I: how to interpret unstable “generations” of matter-particles like the muon and tau. In the original paper, I proposed a realist model where mass is not an intrinsic property but the result of oscillating charge or field energy — a stance that draws support from the 2019 revision of SI units, which grounded the kilogram in Planck’s constant and the speed of light. That change wasn’t just a technicality; it was a silent shift in ontology. I suspected that much at the time, but now — working through the implications with a well-tuned AI — I can state it more clearly: mass is geometry, inertia is field structure, and the difference between stable and unstable particles might be a matter of topological harmony.

The second annex (Interference, Identity, and the Imaginary Unit) reopens the deeper riddle at the heart of quantum mechanics: why probability amplitudes interfere at all. This annex is the child of years of irritation — visible in earlier, sharper essays I published on academia.edu — with the lazy mysticism that often surrounds “common phase factors.” The breakthrough, for me, was to fully accept the imaginary unit iii not as a mathematical trick but as a rotation operator. When wavefunctions are treated as oriented field objects, not just complex scalars, interference becomes a question of geometric compatibility. Superpositions and spin behavior can then be reinterpreted as topological effects in real space. This is where I think mainstream physics got lost: it started calculating without explaining.

ChatGPT didn’t invent these ideas. But it helped me phrase them, frame them, and press further on the points I had once hesitated to formalize. That’s what I mean when I say this wasn’t just a cleanup job. It was a real act of collaboration — a rare instance of AI not just paraphrasing or predicting, but amplifying and clarifying an unfinished line of human reasoning.

Both revised papers are now live on ResearchGate:

They mark, I think, a modest turning point. From theory and calculation toward something closer to explanation.

And yes — for those following the philosophical side of this project: we did also try to capture all of that in a four-panel comic involving Diogenes, a turtle, and Zeno’s paradox. But that, like all things cartooned by AI, is still a work in progress. 🙂

Post Scriptum (24 June 2025): When You Let the Machine Take the Pen

In the spirit of openness: there’s been one more development since publishing the two annexes above.

Feeling I had taken my analytical skills as far as I could — especially in tackling the geometry of nuclear structure — I decided to do something different. Instead of drafting yet another paper, I asked ChatGPT to take over. Not as a ghostwriter, but as a model builder. The prompt was simple: “Do better than me.”

The result is here:
👉 ChatGPT Trying to Do Better Than a Human Researcher

It’s dense, unapologetically geometric, and proposes a full zbw-based model for the neutron and deuteron — complete with energy constraints, field equations, and a call for numerical exploration. If the earlier annexes were dialogue, this one is delegation.

I don’t know if this is the end of the physics path for me. But if it is, I’m at peace with it. Not because the mystery is gone — but because I finally believe the mystery is tractable. And that’s enough for now.

🧭 From Strangeness to Symbolism: Why Meaning Still Matters in Science

My interest in quantum theory didn’t come from textbooks. It came from a thirst for understanding — not just of electrons or fields, but of ourselves, our systems, and why we believe what we believe. That same motivation led me to write a recent article on LinkedIn questioning how the Nobel Prize system sometimes rewards storylines over substance. It’s not a rejection of science — it’s a plea to do it better.

This post extends that plea. It argues that motion — not metaphor — is what grounds our models. That structure is more than math. And that if we’re serious about understanding this universe, we should stop dressing up ignorance as elegance. Physics is beautiful enough without the mystery.

Indeed, in a world increasingly shaped by abstraction — in physics, AI, and even ethics — it’s worth asking a simple but profound question: when did we stop trying to understand reality, and start rewarding the stories we are being told about it?

🧪 The Case of Physics: From Motion to Metaphor

Modern physics is rich in predictive power but poor in conceptual clarity. Nobel Prizes have gone to ideas like “strangeness” and “charm,” terms that describe particles not by what they are, but by how they fail to fit existing models.

Instead of modeling physical reality, we classify its deviations. We multiply quantum numbers like priests multiplying categories of angels — and in doing so, we obscure what is physically happening.

But it doesn’t have to be this way.

In our recent work on realQM — a realist approach to quantum mechanics — we return to motion. Particles aren’t metaphysical entities. They’re closed structures of oscillating charge and field. Stability isn’t imposed; it emerges. And instability? It’s just geometry breaking down — not magic, not mystery.

No need for ‘charm’. Just coherence.

🧠 Intelligence as Emergence — Not Essence

This view of motion and closure doesn’t just apply to electrons. It applies to neurons, too.

We’ve argued elsewhere that intelligence is not an essence, not a divine spark or unique trait of Homo sapiens. It is a response — an emergent property of complex systems navigating unstable environments.

Evolution didn’t reward cleverness for its own sake. It rewarded adaptability. Intelligence emerged because it helped life survive disequilibrium.

Seen this way, AI is not “becoming like us.” It’s doing what all intelligent systems do: forming patterns, learning from interaction, and trying to persist in a changing world. Whether silicon-based or carbon-based, it’s the same story: structure meets feedback, and meaning begins to form.

🌍 Ethics, Society, and the Geometry of Meaning

Just as physics replaced fields with symbolic formalism, and biology replaced function with genetic determinism, society often replaces meaning with signaling.

We reward declarations over deliberation. Slogans over structures. And, yes, sometimes we even award Nobel Prizes to stories rather than truths.

But what if meaning, like mass or motion, is not an external prescription — but an emergent resonance between system and context?

Ethics is not a code. It’s a geometry of consequences.
Intelligence is not a trait. It’s a structure that closes upon itself through feedback.
Reality is not a theory. It’s a pattern in motion, stabilized by conservation, disrupted by noise.

If we understand this, we stop looking for final answers — and start designing better questions.

✍️ Toward a Science of Meaning

What unifies all this is not ideology, but clarity. Not mysticism, but motion. Not inflation of terms, but conservation of sense.

In physics: we reclaim conservation as geometry.
In intelligence: we see mind as emergent structure.
In ethics: we trace meaning as interaction, not decree.

This is the work ahead: not just smarter machines or deeper theories — but a new simplicity. One that returns to motion, closure, and coherence as the roots of all we seek to know.

Meaning, after all, is not what we say.
It’s what remains when structure holds — and when it fails.

How I Co-Wrote a Quantum Physics Booklet with an AI — And Learned Something

In June 2025, I published a short booklet titled
A Realist Take on Quantum Theory — or the Shortest Introduction Ever.
📘 ResearchGate link

It’s just under 15 pages, but it distills over a decade of work — and a growing collaboration with ChatGPT — into a clean, consistent narrative: electrons as circulating charges, wavefunctions as cyclical descriptors, and action as the true guide to quantum logic.

We didn’t invent new equations. We reinterpreted existing ones — Schrödinger, Dirac, Klein–Gordon — through a realist lens grounded in energy cycles, geometry, and structured motion. What made this possible?

Memory: The AI reminded me of arguments I had made years earlier, even when I’d forgotten them.
Logic: It flagged weak spots, inconsistencies, and unclear transitions.
Humility: It stayed patient, never arrogant — helping me say what I already knew, but more clearly.
Respect: It never erased my voice. It helped me find it again.

The booklet is part of a broader project I call realQM. It’s an attempt to rescue quantum theory from the metaphorical language that’s haunted it since Bohr and Heisenberg — and bring it back to geometry, field theory, and physical intuition. If you’ve ever felt quantum physics was made deliberately obscure, this might be your antidote.

🧠 Sometimes, passing the Turing test isn’t about being fooled. It’s about being helped.

P.S. Since publishing that booklet, the collaboration took another step forward. We turned our attention to high-energy reactions and decay processes — asking how a realist, geometry-based interpretation of quantum mechanics (realQM) might reframe our understanding of unstable particles. Rather than invent new quantum numbers (like strangeness or charm), we explored how structural breakdowns — non-integrable motion, phase drift, and vector misalignment — could explain decay within the classical conservation laws of energy and momentum. That project became The Geometry of Stability and Instability, a kind of realQM manifesto. Have a look at it if you want to dive deeper. 🙂

🔬 When the Field is a Memory: Notes from a Human–Machine Collaboration

Why is the field around an electron so smooth?

Physicists have long accepted that the electrostatic potential of an electron is spherically symmetric and continuous — the classic Coulomb field. But what if the electron isn’t a smeared-out distribution of charge, but a pointlike particle — one that zips around in tight loops at the speed of light, as some realist models propose?

That question became the heart of a new paper I’ve just published:
“The Smoothed Field: How Action Hides the Pointlike Charge”
🔗 Read it on ResearchGate

The paradox is simple: a moving point charge should create sharp, angular variations in its field — especially in the near zone. But we see none. Why?

The paper proposes a bold but elegant answer: those field fluctuations exist only in theory — not in reality — because they fail to cross a deeper threshold: the Planck quantum of action. In this view, the electromagnetic field is not a primitive substance, but a memory of motion — smooth not because the charge is, but because reality itself suppresses anything that doesn’t amount to at least ℏ of action.

🤖 A Word on Collaboration

This paper wouldn’t have come together without a very 21st-century kind of co-author: ChatGPT-4, OpenAI’s conversational AI. I’ve used it extensively over the past year — not just to polish wording, but to test logic, rewrite equations, and even push philosophical boundaries.

In this case, the collaboration evolved into something more: the AI helped me reconstruct the paper’s internal logic, modernize its presentation, and clarify its foundational claims — especially regarding how action, not energy alone, sets the boundary for what is real.

The authorship note in the paper describes this in more detail. It’s not ghostwriting. It’s not outsourcing. It’s something else: a hybrid mode of thinking, where a human researcher and a reasoning engine converge toward clarity.

🧭 Why It Matters

This paper doesn’t claim to overthrow QED, or replace the Standard Model. But it does offer something rare: a realist, geometric interpretation of how smooth fields emerge from discrete sources — without relying on metaphysical constructs like field quantization or virtual particles.

If you’re tired of the “shut up and calculate” advice, and truly curious about how action, motion, and meaning intersect in the foundations of physics — this one’s for you.

And if you’re wondering what it’s like to co-author something with a machine — this is one trace of that, too.

Prometheus gave fire. Maybe this is a spark.

🧭 The Final Arc: Three Papers, One Question

Over the past years, I’ve been working — quietly but persistently — on a set of papers that circle one simple, impossible question:
What is the Universe really made of?

Not in the language of metaphors. Not in speculative fields.
But in terms of geometry, charge, and the strange clarity of equations that actually work.

Here are the three pieces of that arc:

🌀 1. Radial Genesis
Radial Genesis: A Finite Universe with Emergent Spacetime Geometry
This is the cosmological capstone. It presents the idea that space is not a stage, but an outcome — generated radially by mass–energy events, limited by time and light. It’s an intuitive, equation-free narrative grounded in general relativity and Occam’s Razor.

⚛️ 2. Lectures on Physics: On General Relativity (2)
Lectures on GRT (2)
This one is for the mathematically inclined. It builds from the ground up: tensors, geodesics, curvature. If Radial Genesis is the metaphor, this is the machinery. Co-written with AI, but line by line, and verified by hand.

🌑 3. The Vanishing Charge
The Vanishing Charge: What Happens in Matter–Antimatter Annihilation?
This paper is where the mystery remains. It presents two possible views of annihilation:
(1) as a collapse of field geometry into free radiation,
(2) or as the erasure of charge — with geometry as the by-product.
We didn’t choose between them. We just asked the question honestly.

Why This Arc Matters

These three papers don’t offer a Theory of Everything. But they do something that matters more right now:
They strip away the fog — the inflation of terms, the myth of complexity for complexity’s sake — and try to draw what is already known in clearer, more beautiful lines.

This is not a simulation of thinking.
This is thinking — with AI as a partner, not a prophet.

So if you’re tired of being told that the Universe is beyond your grasp…
Start here.
You might find that it isn’t.

—JL

🌀 Radial Genesis: A Universe That Grows from Within

What if space isn’t a container — but a consequence?

That’s the question I explore in my latest paper, Radial Genesis: A Finite Universe with Emergent Spacetime Geometry, now available on ResearchGate.

The core idea is surprisingly simple — and deeply rooted in general relativity: matter and energy don’t just move through space. They define it. Every object with mass–energy generates its own curved, local geometry. If we take that seriously, then maybe the Universe itself isn’t expanding into something. Maybe it’s unfolding from within — one energy event, one radial patch of space at a time.

This new paper builds on two earlier lecture-style essays on general relativity. But unlike those, this one has no equations — just plain language and geometric reasoning. It’s written for thinkers, not specialists. And yes, co-written with GPT-4 again — in what I call a “creative but critical spirit.”

We also explore:

Why the Universe might be finite and still expanding;
How a mirror version of electromagnetism could explain dark matter;
Why the so-called cosmological constant may be a placeholder for our conceptual gaps;
And whether our cosmos is just one region in a greater, radially unfolding whole — with no center, and no edge.

If you like cosmology grounded in Einstein, Dirac, and Feynman — but with fresh eyes and minimal metaphysics — this one’s for you.

🧠 Read it here:
Radial Genesis on ResearchGate

👁️‍🗨️ For context, you might also want to check out the earlier lecture papers:

—JL