Model-independent searches of new physics in DARWIN with a semi-supervised deep learning pipeline
Model-independent searches of new physics in DARWIN with a semi-supervised deep learning pipeline
We present a novel deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next generation multi-ton scale liquid Xenon-based direct detection experiment, DARWIN. We train an anomaly detector comprising a variational autoencoder and a classifier on extensive, high-dimensional simulated detector response data …