Inferring identified hadron production in $pp$ collisions with physics-informed machine learning at the LHC

Abstract

Machine learning has become a powerful tool in high-energy collider experiments, which enables the studies based on data-driven approaches to complex reconstruction and regression tasks. The study of identified hadron spectra in pseudorapidity regions beyond detector acceptance, which is limited to mid-rapidity regions, carries important information about particle production, yet remains unmeasured. In this work, we develop a physics-informed neural network, trained on PYTHIA8 $pp$ collisions at $\sqrt{s}=13.6$ TeV, to infer $p_{\mathrm{T}}$ spectra of $\ddot{I}{€}^{\pm }$, $K^{\pm }$, $p/\bar{p}$, $\hat{I}›/\bar{\hat{I}}›$, and $K_{\mathrm{s}}^0$ in different rapidity regions. Physics-motivated constraints, including particle yield ratios, spectral shape, and smoothness, are incorporated into the loss function. A staged hyperparameter optimization strategy is used to ensure stability. The model achieves yield uncertainties of $\sim 1.5\%$, $1.8\%$, and $5.83\%$ in the training, interpolation, and extrapolation regimes, respectively, outperforming XGBoost and LightGBM. It further reproduces key observables such as particle yield ratios, the multiplicity dependence of $⟨p_{\mathrm{T}}⟩$, and kinetic freeze-out parameters, indicating that the model captures the underlying physics and provides reliable predictions beyond the measured phase space.

18 pages and 10 captioned figures. Submitted for publication