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Graphical models are all you need: Per-interaction reconstruction uncertainties in a dark matter detection experiment

Peters, C.; Higuera, A.; Liang, S.; Roy, V.; Bajwa, W.U.; Shatkay, H.; Tunnell, C.D. (December 2022, Proc. NeurIPS Workshop on Machine Learning and the Physical Sciences)

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GPU-based optical simulation of the DARWIN detector

https://doi.org/10.1088/1748-0221/17/07/P07018

Althueser, L.; Antunović, B.; Aprile, E.; Bajpai, D.; Baudis, L.; Baur, D.; Baxter, A.L.; Bellagamba, L.; Biondi, R.; Biondi, Y.; et al (July 2022, Journal of Instrumentation)

Abstract Understanding propagation of scintillation light is critical for maximizing the discovery potential of next-generation liquid xenon detectors that use dual-phase time projection chamber technology. This work describes a detailed optical simulation of the DARWIN detector implemented using Chroma, a GPU-based photon tracking framework. To evaluate the framework and to explore ways of maximizing efficiency and minimizing the time of light collection, we simulate several variations of the conventional detector design. Results of these selected studies are presented. More generally, we conclude that the approach used in this work allows one to investigate alternative designs faster and in more detail than using conventional Geant4 optical simulations, making it an attractive tool to guide the development of the ultimate liquid xenon observatory.
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The XENON1T data acquisition system

https://doi.org/10.1088/1748-0221/14/07/P07016

Aprile, E.; Aalbers, J.; Agostini, F.; Alfonsi, M.; Althueser, L.; Amaro, F.D.; Antochi, V.C.; Arneodo, F.; Barge, D.; Baudis, L.; et al (July 2019, Journal of Instrumentation)

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