Speaker
Description
The XENON series of experiments has played a leading role in developing the liquid-xenon time-projection-chamber technology toward becoming the most sensitive way to detect WIMPs. Within the technologies being developed, we have also pushed the frontiers of what is possible in data analysis as we have tackled core problems in applying machine learning techniques to astroparticle experiments. This talk will discuss the main ways that we use novel techniques to increase the physics reach of our experiment. We use probabilistic techniques to reconceptualize how we think about fiducial volumes and also (in our recent paper) show how we can improve our electronic-recoil efficiency. I will explain how our use case of extreme rare-event searches benefits from recent breakthroughs related to anomaly detection.
| Submitted on behalf of a Collaboration? | Yes |
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