Machine-Learning Compression for Particle Physics Discoveries

• URL: http://arxiv.org/abs/2210.11489v1
• Date: Thu, 20 Oct 2022 18:00:04 GMT
• Title: Machine-Learning Compression for Particle Physics Discoveries
• Authors: Jack H. Collins, Yifeng Huang, Simon Knapen, Benjamin Nachman, Daniel Whiteson
• Abstract summary: In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis. We propose a strategy that bridges these paradigms by compressing entire events for generic offline analysis but at a lower fidelity.
• Score: 4.432585853295899
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis. Typically, algorithms select individual collision events for preservation and store the complete experimental response. A relatively new alternative strategy is to additionally save a partial record for a larger subset of events, allowing for later specific analysis of a larger fraction of events. We propose a strategy that bridges these paradigms by compressing entire events for generic offline analysis but at a lower fidelity. An optimal-transport-based $\beta$ Variational Autoencoder (VAE) is used to automate the compression and the hyperparameter $\beta$ controls the compression fidelity. We introduce a new approach for multi-objective learning functions by simultaneously learning a VAE appropriate for all values of $\beta$ through parameterization. We present an example use case, a di-muon resonance search at the Large Hadron Collider (LHC), where we show that simulated data compressed by our $\beta$-VAE has enough fidelity to distinguish distinct signal morphologies.

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