Learning New Physics from a Machine

TL;DR

This paper introduces a neural network-based, model-independent method for detecting deviations from reference models in data, useful for new physics searches and data validation, with promising initial performance results.

Contribution

It presents a novel, unbiased neural network algorithm utilizing likelihood-ratio tests for model-independent anomaly detection in physics data.

Findings

Good sensitivity to various signals

Performance is robust to prior signal region selection

Algorithm confirms model-independence and broad applicability

Abstract

We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The virtues of neural networks as unbiased function approximants make them particularly suited for this task. An algorithm that implements this idea is constructed, as a straightforward application of the likelihood-ratio hypothesis test. The algorithm compares observations with an auxiliary set of reference-distributed events, possibly obtained with a Monte Carlo event generator. It returns a p-value, which measures the compatibility of the reference model with the data. It also identifies the most discrepant phase-space region of the data set, to be selected for further investigation. The most interesting potential applications are model-independent new physics searches, although our approach could also be used to compare the theoretical predictions of different Monte Carlo event generators, or for data validation algorithms. In this work we study the performance of our algorithm on a few simple examples. The results confirm the model-independence of the approach, namely that it displays good sensitivity to a variety of putative signals. Furthermore, we show that the reach does not depend much on whether a favorable signal region is selected based on prior expectations. We identify directions for improvement towards applications to real experimental data sets.