Fast and Precise Track Fitting with Machine Learning

TL;DR

This paper introduces a machine learning-based method for fast, precise particle track fitting that significantly reduces computational cost while improving accuracy, enhancing various particle physics analysis tasks.

Contribution

It presents a novel ML approach transforming track fitting into a direct parameter regression task, achieving constant-time fitting without simplifying noise assumptions.

Findings

More precise parameter estimates in simulations

Over 1,000 times faster than traditional methods

Improved particle momentum and vertex estimation

Abstract

Efficient and accurate particle tracking is crucial for measuring Standard Model parameters and searching for new physics. This task consists of two major computational steps: track finding, the identification of a subset of all hits that are due to a single particle; and track fitting, the extraction of crucial parameters such as direction and momenta. Novel solutions to track finding via machine learning have recently been developed. However, track fitting, which traditionally requires searching for the best global solutions across a parameter volume plagued with local minima, has received comparatively little attention. Here, we propose a novel machine learning solution to track fitting. The per-track optimization task of traditional fitting is transformed into a single learning task optimized in advance to provide constant-time track fitting via direct parameter regression. This approach allows us to optimize directly for the true targets, i.e., the precise and unbiased estimates of the track parameters. This is in contrast to traditional fitting, which optimizes a proxy based on the distance between the track and the hits. In addition, our approach removes the requirement of making simplifying assumptions about the nature of the noise model. Most crucially, in the simulated setting described here, it provides more precise parameter estimates at a computational cost over 1,000 times smaller, which leads directly to improvements in particle momentum estimation, vertex finding, jet substructure, and anomaly detection.