Inferring identified hadron production in $pp$ collisions with physics-informed machine learning at the LHC
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arXiv
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2026
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| author | Gupta, Rishabh Goswami, Kangkan Prasad, Suraj Sahoo, Raghunath |
| author_facet | Gupta, Rishabh Goswami, Kangkan Prasad, Suraj Sahoo, Raghunath |
| contents | 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 $p_{\rm T}$ spectra of $π^{\pm}$, $K^{\pm}$, $p/\bar{p}$, $Λ/\barΛ$, and $K^{0}_{\mathrm{s}}$ 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 $\langle p_{\rm T} \rangle$, and kinetic freeze-out parameters, indicating that the model captures the underlying physics and provides reliable predictions beyond the measured phase space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09022 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Inferring identified hadron production in $pp$ collisions with physics-informed machine learning at the LHC Gupta, Rishabh Goswami, Kangkan Prasad, Suraj Sahoo, Raghunath High Energy Physics - Phenomenology High Energy Physics - Experiment High Energy Physics - Theory Nuclear Experiment Nuclear Theory 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 $p_{\rm T}$ spectra of $π^{\pm}$, $K^{\pm}$, $p/\bar{p}$, $Λ/\barΛ$, and $K^{0}_{\mathrm{s}}$ 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 $\langle p_{\rm T} \rangle$, and kinetic freeze-out parameters, indicating that the model captures the underlying physics and provides reliable predictions beyond the measured phase space. |
| title | Inferring identified hadron production in $pp$ collisions with physics-informed machine learning at the LHC |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment High Energy Physics - Theory Nuclear Experiment Nuclear Theory |
| url | https://arxiv.org/abs/2605.09022 |