Cleaning Up After Bell

On the limits of theorems, the sociology of prizes, and the slow work of intellectual maturity

When I re-read two older posts of mine on Bell’s Theorem — one written in 2020, at a moment when my blog was gaining unexpected traction, and another written in 2023 in reaction to what I then experienced as a Nobel Prize award controversy — I feel a genuine discomfort.

Not because I think the core arguments were wrong.
But because I now see more clearly what was doing the talking.

There is, in both texts, a mixture of three things:

A principled epistemic stance (which is still there);
A frustration with institutional dynamics in physics (also there);
But, yes, also a degree of rhetorical impatience that no longer reflects how I want to think — or be read.

This short text is an attempt to disentangle those layers.

1. Why I instinctively refused to “engage” with Bell’s Theorem

In the 2020 post, I wrote — deliberately provocatively — that I “did not care” about Bell’s Theorem. That phrasing was not chosen to invite dialogue; it was chosen to draw a boundary. At the time, my instinctive reasoning was this:

Bell’s Theorem is a mathematical theorem. Like any theorem, it tells us what follows if certain premises are accepted. Its physical relevance therefore depends entirely on whether those premises are physically mandatory, or merely convenient formalizations.

This is not a rejection of mathematics. It is a refusal to grant mathematics automatic ontological authority.

I was — and still am — deeply skeptical of the move by which a formal result is elevated into a metaphysical verdict about reality itself. Bell’s inequalities constrain a particular class of models (local hidden-variable models of a specific type). They do not legislate what Nature must be. In that sense, my instinct was aligned not only with Einstein’s well-known impatience with axiomatic quantum mechanics, but also with Bell himself, who explicitly hoped that a “radical conceptual renewal” might one day dissolve the apparent dilemma his theorem formalized.

Where I now see a weakness is not in the stance, but in its expression. Saying “I don’t care” reads as dismissal, while what I really meant — and should have said — is this:

I do not accept the premises as ontologically compulsory, and therefore I do not treat the theorem as decisive.

That distinction matters.

2. Bell, the Nobel Prize, and a sociological paradox

My 2023 reaction was sharper, angrier, and less careful — and that is where my current discomfort is strongest.

At the time, it seemed paradoxical to me that:

Bell was once close to receiving a Nobel Prize for a theorem he himself regarded as provisional,
and that nearly six decades later, a Nobel Prize was awarded for experiments demonstrating violations of Bell inequalities.

In retrospect, the paradox is not logical — it is sociological.

The 2022 Nobel Prize did not “disprove Bell’s Theorem” in a mathematical sense. It confirmed, experimentally and with great technical sophistication, that Nature violates inequalities derived under specific assumptions. What was rewarded was experimental closure, not conceptual resolution.

The deeper issue — what the correlations mean — remains as unsettled as ever.

What troubled me (and still does) is that the Nobel system has a long history of rewarding what can be stabilized experimentally, while quietly postponing unresolved interpretational questions. This is not scandalous; it is structural. But it does shape the intellectual culture of physics in ways that deserve to be named.

Seen in that light, my indignation was less about Bell, and more about how foundational unease gets ritualized into “progress” without ever being metabolized conceptually.

3. Authority, responsibility, and where my anger really came from

The episode involving John Clauser and climate-change denial pushed me from critique into anger — and here, too, clarity comes from separation.

The problem there is not quantum foundations.
It is the misuse of epistemic authority across domains.

A Nobel Prize in physics does not confer expertise in climate science. When prestige is used to undermine well-established empirical knowledge in an unrelated field, that is not dissent — it is category error dressed up as courage.

My reaction was visceral because it touched a deeper nerve: the responsibility that comes with public authority in science. In hindsight, folding this episode into a broader critique of Bell and the Nobel Prize blurred two distinct issues — foundations of physics, and epistemic ethics.

Both matter. They should not be confused.

4. Where I stand now

If there is a single thread connecting my current thinking to these older texts, it is this:

I am less interested than before in winning arguments, and more interested in clarifying where different positions actually part ways — ontologically, methodologically, and institutionally.

That shift is visible elsewhere in my work:

in a softer, more discriminating stance toward the Standard Model,
in a deliberate break with institutions and labels that locked me into adversarial postures,
and in a conscious move toward reconciliation where reconciliation is possible, and clean separation where it is not.

The posts on Bell’s Theorem were written at an earlier stage in that trajectory. I do not disown them. But I no longer want them to stand without context.

This text is that context.

Final notes

1. On method and collaboration

Much of the clarification in this essay did not emerge in isolation, but through extended dialogue — including with an AI interlocutor that acted, at times, less as a generator of arguments than as a moderator of instincts: slowing me down, forcing distinctions, and insisting on separating epistemic claims from emotional charge. That, too, is part of the story — and perhaps an unexpected one. If intellectual maturity means anything, it is not the abandonment of strong positions, but the ability to state them without needing indignation to carry the weight. That is the work I am now trying to do.

It is also why I want to be explicit about how these texts are currently produced: they are not outsourced to AI, but co-generated through dialogue. In that dialogue, I deliberately highlight not only agreements but also remaining disagreements — not on the physics itself, but on its ontological interpretation — with the AI agent I currently use (ChatGPT 5.2). Making those points of convergence and divergence explicit is, I believe, intellectually healthier than pretending they do not exist.

2. On stopping, without pretending to conclude

This post also marks a natural stopping point. Over the past weeks, several long-standing knots in my own thinking — Bell’s Theorem (what this post is about), the meaning of gauge freedom, the limits of Schrödinger’s equation as a model of charge in motion, or even very plain sociological considerations on how sciences moves forward — have either been clarified or cleanly isolated.

What remains most resistant is the problem of matter–antimatter pair creation and annihilation. Here, the theory appears internally consistent, while the experimental evidence, impressive as it is, still leaves a small but non-negligible margin of doubt — largely because of the indirect, assumption-laden nature of what is actually being measured. I do not know the experimental literature well enough to remove that last 5–10% of uncertainty, and I consider it a sign of good mental health not to pretend otherwise.

For now, that is where I leave it. Not as a conclusion, but as a calibration: knowing which questions have been clarified, and which ones deserve years — rather than posts — of further work.

3. Being precise on my use of AI: on cleaning up ideas, not outsourcing thinking

What AI did not do

Let me start with what AI did not do.

It did not:

supply new experimental data,
resolve open foundational problems,
replace reading, calculation, or judgment,
or magically dissolve the remaining hard questions in physics.

In particular, it did not remove my residual doubts concerning matter–antimatter pair creation. On that topic, I remain where I have been for some time: convinced that the theory is internally consistent, convinced that the experiments are impressive and largely persuasive, and yet unwilling to erase the remaining 5–10% of doubt that comes from knowing how indirect, assumption-laden, and instrument-mediated those experiments necessarily are. I still do not know the experimental literature well enough to close that last gap—and I consider it a sign of good mental health that I do not pretend otherwise.

What AI did do

What AI did do was something much more modest—and much more useful.

It acted as a moderator of instincts.

In the recent rewrites—most notably in this post (Cleaning Up After Bell)—AI consistently did three things:

It cut through rhetorical surplus.
Not by softening arguments, but by separating epistemic claims from frustration, indignation, or historical irritation.
It forced distinctions.
Between mathematical theorems and their physical premises; between experimental closure and ontological interpretation; between criticism of ideas and criticism of institutions.
It preserved the spine while sharpening the blade.
The core positions did not change. What changed was their articulation: less adversarial, more intelligible, and therefore harder to dismiss.

In that sense, AI did not “correct” my thinking. It helped me re-express it in a way that better matches where I am now—intellectually and personally.

Two primitives or one?

A good illustration is the remaining disagreement between myself and my AI interlocutor on what is ultimately primitive in physics.

I still tend to think in terms of two ontological primitives: charge and fields—distinct, but inseparably linked by a single interaction structure. AI, drawing on a much broader synthesis of formal literature, prefers a single underlying structure with two irreducible manifestations: localized (charge-like) and extended (field-like).

Crucially, this disagreement is not empirical. It is ontological, and currently underdetermined by experiment. No amount of rhetorical force, human or artificial, can settle it. Recognizing that—and leaving it there—is part of intellectual maturity.

Why I am stopping (again)

I have said before that I would stop writing, and I did not always keep that promise. This time, however, the stopping point feels natural.

Most of the conceptual “knots” that bothered me in the contemporary discourse on physics have now been:

either genuinely clarified,
or cleanly isolated as long-horizon problems requiring years of experimental and theoretical work.

At this point, continuing to write would risk producing more words than signal.

There are other domains that now deserve attention: plain work, family projects, physical activity, and the kind of slow, tangible engagement with the world that no theory—however elegant—can replace.

Closing

If there is a single lesson from this episode, it is this:

AI is most useful not when it gives answers, but when it helps you ask what you are really saying—and whether you still stand by it once the noise is stripped away.

Used that way, it does not diminish thinking.
It disciplines it.

For now, that is enough.

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.

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.

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.

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. 🙂

Using AI for sense-making once more…

As mentioned in my last post, I did a video (YouTube link here) on why I think the invention of new quantum numbers like strangeness, charm and beauty in the 1960s – and their later ontologization as quarks – makes no sense. As usual, I talk too much and the video is rather long-winding. I asked ChatGPT to make a summary of it, and I think it did a rather good job at that. I copy its summary unaltered below.

Beyond the Quark Hypothesis: A Call for Simplicity in High-Energy Physics

1. Introduction: A Personal Journey in Physics

In this video, I reflect on my path as an amateur physicist reaching 50,000 reads—a milestone that underscores both excitement and the challenge of tackling complex quantum theories. Over decades, physics has evolved from classical mechanics to intricate frameworks like quantum field theory and quantum chromodynamics, creating both insight and paradox. This reflection emerges from a deep sense of curiosity, shared by many, to understand not just what the universe is made of but how these theoretical structures genuinely map onto reality.

2. The Crisis of Modern Physics: From Classical Mechanics to the Quark Hypothesis

Moving through physics from classical theories into high-energy particle models reveals a stark contrast: classical mechanics offers clarity and empiricism, while modern particle theories, such as quarks and gluons, often feel abstract and detached from observable reality. The shift to “smoking gun physics”—observing particle jets rather than the particles themselves—highlights a methodological divide. While high-energy collisions produce vivid images and data, we must question whether these indirect observations validate quarks, or merely add complexity to our models.

3. Historical Context: Quantum Numbers and the Evolution of the Standard Model

The 1960s and 70s were pivotal for particle physics, introducing quantum numbers like strangeness, charm, and beauty to account for unexplained phenomena in particle interactions. Figures like Murray Gell-Mann and Richard Feynman attempted to classify particles by assigning these numbers, essentially ad hoc solutions to match data with theoretical expectations. However, as experiments push the boundaries, new data shows that these quantum numbers often fail to predict actual outcomes consistently.

One of the key criticisms of this approach lies in the arbitrary nature of these quantum numbers. When certain decays were unobserved, strangeness was introduced as a “conservation law,” but when that proved insufficient, additional numbers like charm were added. The Standard Model has thus evolved not from fundamental truths, but as a patchwork of hypotheses that struggle to keep pace with experimental findings.

4. The Nobel Prize and the Politics of Scientific Recognition

Scientific recognition, especially through the Nobel Prize, has reinforced certain theories by celebrating theoretical advances sometimes over empirical confirmation. While groundbreaking work should indeed be recognized, the focus on theoretical predictions has, at times, overshadowed the importance of experimental accuracy and reproducibility. This dynamic may have inadvertently constrained the scope of mainstream physics, favoring elaborate but tenuous theories over simpler, empirically grounded explanations.

For example, Nobel Prizes have been awarded to proponents of the quark model and the Higgs boson long before we fully understand these particles’ empirical foundations. In doing so, the scientific community risks prematurely canonizing incomplete or even incorrect theories, making it challenging to revisit or overturn these assumptions without undermining established reputations.

5. Indirect Evidence: The Limits of Particle Accelerators

Particle accelerators, particularly at scales such as CERN’s Large Hadron Collider, have extended our observational reach, yet the evidence remains indirect. High-energy collisions create secondary particles and jets rather than isolated quarks or gluons. In a sense, we are not observing the fundamental particles but rather the “smoking gun” evidence they purportedly leave behind. The data produced are complex patterns and distributions, requiring interpretations laden with theoretical assumptions.

This approach raises a fundamental question: if a theory only survives through indirect evidence, can it be considered complete or even valid? High-energy experiments reveal that the more energy we input, the more complex the decay products become, yet we remain without direct evidence of quarks themselves. This “smoking gun” approach diverges from the empirical rigor demanded in classical physics and undermines the predictive power we might expect from a true theory of fundamental particles.

6. The Particle Zoo: A Growing Complexity

The “particle zoo” has expanded over decades, complicating rather than simplifying our understanding of matter. Initial hopes were that quantum numbers and conservation laws like strangeness would organize particles in a coherent framework, yet the resulting classification scheme has only grown more convoluted. Today, particles such as baryons, mesons, and leptons are grouped by properties derived not from first principles but from empirical fits to data, leading to ad hoc conservation laws that seem arbitrary.

The “strangeness” quantum number, for instance, was initially introduced to prevent certain reactions from occurring. Yet, rare reactions that violate this rule have been observed, suggesting that the rule itself is more of a guideline than a fundamental conservation law. This trend continued with the addition of quantum numbers like charm, beauty, and even bottomness, yet these additions have not resolved the core issue: our inability to explain why certain reactions occur while others do not.

7. Disequilibrium States: Beyond the Particle Concept

One possible perspective is to reclassify many “particles” not as fundamental entities but as disequilibrium states—transient structures that emerge from the interactions of more fundamental components. Viewing particles in this way offers a pathway back to a simpler, more intuitive model, where only stable particles like electrons, protons, and photons are foundational. Such a model could focus on electromagnetic fields and forces, with high-energy states representing temporary disequilibrium configurations rather than new particle species.

This perspective aligns well with the principle of statistical determinism. In the same way that classical oscillators eventually dampen and settle into stable states, high-energy disequilibrium states would be expected to decay, producing stable configurations over time. This model not only reduces the need for numerous quantum numbers but also sidesteps the requirement for exotic forces like the strong and weak nuclear forces, allowing the electromagnetic force to assume a central role.

8. Statistical Determinism and Quantum Reality

Heisenberg and Bohr’s interpretation of quantum mechanics suggests we should accept statistical determinism—systems governed by probabilistic rules where precise knowledge of individual events is inaccessible. This idea does not necessitate mystical randomness but acknowledges our limited ability to track initial conditions in high-energy environments. Probabilities emerge not from an intrinsic unpredictability but from our practical inability to fully specify a system’s state.

From this viewpoint, quarks and gluons, as well as the numerous quantum numbers assigned to unstable particles, are secondary descriptors rather than primary components of nature. Stable particles are the true constants, while all else is a function of high-energy interactions. This interpretation keeps quantum mechanics grounded in empirical reality and sidesteps the need for complex, unverifiable entities.

9. Conclusion: Toward a Pragmatic and Local Realist Approach

This reflection does not dismiss the importance of high-energy physics but advocates a return to fundamental principles. By focusing on empirical evidence, statistical determinism, and electromagnetic interactions, we can build a model that is both pragmatic and intuitive. We need not abandon quantum mechanics, but we should strive to ensure that its interpretations are consistent with the observable universe. Instead of introducing additional quantum numbers or forces, we should ask if these are placeholders for deeper, more coherent explanations yet to be discovered.

The journey of science is, at its core, a journey back to simplicity. If physics is to move forward, it may do so by revisiting foundational assumptions, clarifying what can be empirically tested, and developing a model of matter that resonates with the simplicity we find in classical theories. As research continues, it is this blend of skepticism, open-mindedness, and empirical rigor that will pave the way for meaningful discoveries.

The ultimate proton model?

Today I made a major step towards a very different Zitterbewegung model of a proton. With different, I mean different from the usual toroidal or helical model(s). I had a first version of this paper but the hyperlink gives you the updated paper. The update is small but very important: I checked all the formulas with ChatGPT and, hence, consider that as confirmation that I am on the right track. To my surprise, ChatGPT first fed me the wrong formula for an orbital frequency formula. Because I thought it could not be wrong on such simple matters, I asked it to check and double-check. It came with rather convincing geometrical explanations but I finally found an error in its reasoning, and the old formula from an online engineering textbook turned out to be correct.

In any case, I now have a sparring partner – ChatGPT o1 – to further develop the model that we finally settled on. That is a major breakthrough in this realistic interpretation of quantum theory and particle models that I have been trying to develop: the electron model is fine, and so now all that is left is this proton model. And then, of course, a model for a neutron or the deuteron nucleus. That will probably be a retirement project, or something for my next life. 🙂

Post scriptum: I followed up. “A theory’s value lies in its utility and ability to explain phenomena, regardless of whether it’s mainstream or not.” That’s ChatGPT’s conclusion after various explorations and chats with it over the past few weeks: https://lnkd.in/ekAAbvwc. I think I tried to push its limits when discussing problems in physics, leading it to make a rather remarkable distinction between “it’s” perspective and mine (see point 6 of Annex I of https://lnkd.in/eFVAyHn8), but – frankly – it may have no limits. As far as I can see, ChatGPT-o1 is truly amazing: sheer logic. 🙂 hashtag#AI hashtag#ChatGPT hashtag#theoryofreality

Using AI to solve the 80-year-old problem of the anomaly of the electron magnetic moment?

Pre-scriptum (3 October 2024): I came back from holiday and, because this week-long up and down became quite convoluted, I did what I like to do in a case like that, and that is to take my Bamboo notebook and talk about it all in a video which I added to my Real Quantum Physics channel on YouTube. I also updated my paper on RG: as usual, it went through a few versions, but this one – with a summary co-authored by ChatGTP-4 (and ChatGPT-o1) – should be the final one: enjoy!

Indeed, instead of listening to the international news on the war with Russia and on what is happening in the Middle East (all very depressing), you may want to listen to this and read the latest theory. Perhaps you will be inspired by it to develop your own pet realist theory of what an electron might actually be. I can assure you that it is more fun than trying to understand Feynman diagrams and how QED calculations work. 🙂 But don’t think you will win a Nobel Prize if you do not have the right connections and pedigree and all of that: see this analysis of what makes Nobel Prize winners Nobel Prize winners. 🙂

Original post:

I asked some questions to ChatGPT about my geometric explanation of the anomaly in the electron’s magnetic moment. Here is the chat: https://chatgpt.com/share/66f91760-68b8-8004-8cb2-7d2d3624e0aa. To me, it confirms the ‘explanation’ of mainstream QED makes no sense. We can take Schwinger’s factor and build a series of converging terms using that factor. We can also take my first rough cut at a first-order correction (π(alpha)²/8, see my very early 2019 paper on a classical explanation of the amm), and use that.

You may wonder: why not ask ChatGPT about the best first-order factor to be used here considering the geometry of the situation? The fact is: I did, but the geometry is not all that easy. It first came up with the formula for a spherical cap, but that one does not do the trick. See the latter part of the conversation (link above).

I am on holiday now, and so I will switch off a while but I am thinking AI will do what two generations of ‘new’ quantum physicists did not do: come up with a model that is based on real physics and is easy to understand intuitively. 🙂

PS: Of course, I did another rapid-fire paper on ResearchGate to document it all (the logic step-by-step, so to speak). As the chat is public, feel free to continue the conversation. Note that I used the newest ChatGPT o1 version, now in preview but part of a subscription (which you may not have). Yet again a different beast! The older versions of ChatGPT may not be so smart. This conversation is totally worth the US$20/month I pay for my subscription. 🙂

PS 2: Now that I had it open, I also quickly queried it on my wildest hypothesis: a ‘mirror’ electromagnetic force explaining dark matter and dark energy. While it is totally wild (read: nuts), I entertain it because it does away with the need for an explanation in terms of some cosmological constant. Here is the conversation: https://chatgpt.com/share/66f92c7f-82a0-8004-a226-bde65085f18d. I like it that ChatGPT warns me a bit about privacy. It does look wild. However, it is nice to see how gentle ChatGPT is in pointing out what work needs to be done on a theory in order to make it look somewhat less wild. 🙂

PS 3 (yes, ChatGPT is addictive): I also queried it on the rather puzzling 8π/3 factor in the CODATA formula for the Thomson photon-electron scattering cross-section. See its response to our question in the updated chat: https://chatgpt.com/share/66f91760-68b8-8004-8cb2-7d2d3624e0aa. Just scroll down to the bottom. It took 31 seconds to generate the reply: I would be curious to know if that is just courtesy from ChatGPT (we all like to think our questions are complicated, don’t we?), or if this was effectively the time it needed to go through its knowledge base. Whatever the case might be, we think it is brilliant. 🙂 It is nothing to be afraid of, although I did feel a bit like: what’s left to learn to it but for asking intelligent questions. What if it starts really learning by asking intelligent questions itself to us? I am all ready for it. 🙂