Learning to Reconstruct: A Differentiable Approach to Muon Tracking at the LHC

TL;DR

This paper presents a novel end-to-end differentiable machine learning approach for charged particle track reconstruction and momentum estimation at the LHC, integrating physics priors directly into the model.

Contribution

It introduces a differentiable programming framework combining graph attention networks with clustering and fitting routines for improved particle tracking.

Findings

Differentiable connections enhance reconstruction performance.

The model accurately estimates transverse momenta.

Physics priors improve overall tracking resolution.

Abstract

Reconstructing the trajectories of charged particles in high-energy collisions requires high precision to ensure reliable event reconstruction and accurate downstream physics analyses. In particular, both precise hit selection and transverse momentum estimation are essential to improve the overall resolution of reconstructed physics observables. Enhanced momentum resolution also enables more efficient trigger threshold settings, leading to more effective data selection within the given data acquisition constraints. In this paper, we introduce a novel end-to-end tracking approach that employs the differentiable programming paradigm to incorporate physics priors directly into a machine learning model. This results in an optimized pipeline capable of simultaneously reconstructing tracks and accurately determining their transverse momenta. The model combines a graph attention network with differentiable clustering and fitting routines, and is trained using a composite loss that, due to its differentiable design, allows physical constraints to be back-propagated effectively through both the neural network and the fitting procedures. This proof of concept shows that introducing differentiable connections within the reconstruction process improves overall performance compared to an equivalent factorized and more standard-like approach, highlighting the potential of integrating physics information through differentiable programming.