9–12 Sept 2024
Imperial College London
Europe/London timezone

Anomaly aware machine learning for dark matter direct detection at the DARWIN experiment

Not scheduled
20m
Lecture Theatre 2, Blackett Laboratory (Imperial College London)

Lecture Theatre 2, Blackett Laboratory

Imperial College London

Contributed Talk

Speaker

Andre Joshua Scaffidi

Description

This talk presents a novel approach to dark matter direct detection using anomaly-aware machine learning techniques in the DARWIN next-generation dark matter direct detection experiment. I will introduce a semi-unsupervised deep learning pipeline that falls under the umbrella of generalized Simulation-Based Inference (SBI), an approach that allows one to effectively learn likelihoods straight from simulated data, without the need for complex functional dependence on systematics or nuisance parameters. I also present an inference procedure to detect non-background physics utilizing an anomaly function derived from the loss functions of the semi-unsupervised architecture. The pipeline's performance is evaluated using pseudo-data sets in a sensitivity forecasting task, and the results suggest that it offers improved sensitivity over traditional methods.

Primary Field of Research Particle Physics

Author

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