Learning Selection Cuts With Gradients

TL;DR

This paper introduces a gradient descent-based method for optimizing selection cuts in high-energy physics analyses, enabling automated, smooth, and target-driven threshold tuning with a publicly available implementation.

Contribution

It presents a novel approach to cut optimization using gradient descent, allowing for automated, differentiable, and customizable threshold tuning in particle physics analyses.

Findings

Method achieves target signal efficiency effectively

Performance comparable to traditional classification tools

Implementation available as open-source Python package

Abstract

Many analyses in high-energy physics rely on selection thresholds (cuts) applied to detector, particle, or event properties. Initial cut values can often be guessed from physical intuition, but cut optimization, especially for multiple features, is commonly performed by hand, or skipped entirely in favor of multivariate algorithms like BDTs or neural networks. We revisit this problem, and develop a cut optimization approach based on gradient descent. Cut thresholds are learned as parameters of a network with a simple architecture, and can be tuned to achieve a target signal efficiency through the use of custom loss functions. Contractive terms in the loss can be used to ensure a smooth evolution of cuts as functions of efficiency, particle kinematics, or event features. The method is used to classify events in a search for Supersymmetry, and the performance is compared with common classification tools. An implementation of this approach is available in a public code repository and python package.