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Abstract Identifying the nature of dark matter (DM) has long been a pressing question for particle physics. In the face of ever-more-powerful exclusions and null results from large-exposure searches for TeV-scale DM interacting with nuclei, a significant amount of attention has shifted to lighter (sub-GeV) DM candidates. Direct detection of the light DM in our galaxy by observing DM scattering off a target system requires new approaches compared to prior searches. Lighter DM particles have less available kinetic energy, and achieving a kinematic match between DM and the target mandates the proper treatment of collective excitations in condensed matter systems, such as charged quasiparticles or phonons. In this context, the condensed matter physics of the target material is crucial, necessitating an interdisciplinary approach. In this review, we provide a self-contained introduction to direct detection of keV–GeV DM with condensed matter systems. We give a brief survey of DM models and basics of condensed matter, while the bulk of the review deals with the theoretical treatment of DM-nucleon and DM-electron interactions. We also review recent experimental developments in detector technology, and conclude with an outlook for the field of sub-GeV DM detection over the next decade. 

A bstract Autoencoders have been proposed as a powerful tool for model-independent anomaly detection in high-energy physics. The operating principle is that events which do not belong to the space of training data will be reconstructed poorly, thus flagging them as anomalies. We point out that in a variety of examples of interest, the connection between large reconstruction error and anomalies is not so clear. In particular, for data sets with nontrivial topology, there will always be points that erroneously seem anomalous due to global issues. Conversely, neural networks typically have an inductive bias or prior to locally interpolate such that undersampled or rare events may be reconstructed with small error, despite actually being the desired anomalies. Taken together, these facts are in tension with the simple picture of the autoencoder as an anomaly detector. Using a series of illustrative low-dimensional examples, we show explicitly how the intrinsic and extrinsic topology of the dataset affects the behavior of an autoencoder and how this topology is manifested in the latent space representation during training. We ground this analysis in the discussion of a mock “bump hunt” in which the autoencoder fails to identify an anomalous “signal” for reasons tied to the intrinsic topology of n -particle phase space.