Seeing the Unseen: The Proton Without Formulas express

Original: "Reconstruction of Gravitational Form Factors using Generative Machine Learning"
arXiv:2602.19267 · 2026-02-22 · CC BY 4.0 · 1 min
Scientists have learned to recover hidden details of the proton from scraps of data, much like a restorer reconstructing an old fresco.

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The proton is not a smooth ball but a tangle of quarks and gluons in constant motion, exchanging energy. Finding out exactly how energy, pressure, and spin are distributed inside it is impossible to do directly: measurements yield only sparse, noisy points. It's like trying to guess a face from a few blurry photos taken from different angles.

Scientists applied a method that learns from many examples: first, they generated thousands of plausible curves describing how proton properties change during particle scattering. Then a 'noisy' algorithm gradually turned random lines into credible ones, checking them against real data. In this way, they managed to reconstruct three key characteristics of the proton: its 'gravitational form factors'—across the entire energy range, even when only one or two data points were available.

The method resembles fresco restoration: from a few surviving fragments, the artist completes the whole picture, guided by knowledge of the era's style.

The reconstructed curves allowed them to extract important numbers — the so-called low-energy constants. Their values helped compute a quantity that shows how the proton 'resists' external pressure. This is a step toward understanding where the proton's mass comes from and how the strong force that binds quarks together works.

Original abstract

as written by the authors arXiv:2602.19267

We develop a generative framework based on denoising diffusion for the model-independent reconstruction of hadronic form factors from sparse and noisy data. The generative prior is built from a large ensemble of synthetic curves drawn from ten distinct functional classes rooted in different theoretical approaches to hadron structure. Applied to the proton gravitational form factors $$A(t)$$, $$J(t)$$, and $$D(t)$$, the framework yields non-parametric reconstructions consistent with lattice QCD across the full kinematic range $$0\le -t\le 2~\mathrm{GeV}^{2}$$, remaining robust even when only one or two conditioning points are retained. The densely sampled output enables a direct extraction of the chiral low-energy constants $$c_8=-4.6\pm 0.8~\mathrm{GeV}^{-1}$$ and $$c_9=-0.61\pm 0.19~\mathrm{GeV}^{-1}$$. Using these values at the physical pion mass, we obtain $$D(0)=-4.3\pm 0.8$$ for the nucleon $$D$$-term.

The proton is almost 99% energy from quark–gluon interactions, not from the mass of the quarks themselves, as if a person's weight were determined mostly by their internal tension, not their body.

In Liu Cixin's novel 'The Three-Body Problem,' aliens manipulate protons by unfolding them into multidimensional structures — recovering the proton's internal properties could be a first step toward such technologies.

Scientists
Emmy NoetherMax PlanckAlbert EinsteinWolfgang PauliEnrico FermiPaul Dirac
Concepts
generative model Generative Adversarial Networks Diffusion Models autoencoder predictive learning neural networks autoregressive model machine learning materials neural network data reconstruction lattice qcd gaussian process regression gravitational lensing modeling gravitational wave noise filtering neural network microlensing reconstruction normalizing flows reconstruction template predictive model reservoir computing