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Particle-level Compression for New Physics Searches
Yifeng Huang · Jack Collins · Benjamin Nachman · Simon Knapen · Daniel Whiteson

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 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 β Variational Autoencoder (VAE) is used to automate the compression and the hyperparameter β controls the compression fidelity. We introduce a new approach for multi-objective learning functions by simultaneously learning a VAE appropriate for all values of β 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 β VAE has enough fidelity to distinguish distinct signal morphologies.

Author Information

Yifeng Huang (University of California, Irvine)
Jack Collins (SLAC National Accelerator Laboratory)
Benjamin Nachman (Lawrence Berkeley National Laboratory)
Simon Knapen (Lawrence Berkeley National Laboratory)
Daniel Whiteson (University of California Irvine)

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