Physical Sciences and Mathematics Commons^™

Machine Learning In Xenon1t Analysis, Dillon A. Davis, Rafael F. Lang, Darryl P. Masson

The Summer Undergraduate Research Fellowship (SURF) Symposium

In process of analyzing large amounts of quantitative data, it can be quite time consuming and challenging to uncover populations of interest contained amongst the background data. Therefore, the ability to partially automate the process while gaining additional insight into the interdependencies of key parameters via machine learning seems quite appealing. As of now, the primary means of reviewing the data is by manually plotting data in different parameter spaces to recognize key features, which is slow and error prone. In this experiment, many well-known machine learning algorithms were applied to a dataset to attempt to semi-automatically identify known populations, …

The Search For Dark Matter In Xenon: Innovative Calibration Strategies And Novel Search Channels, Shayne Edward Reichard

Open Access Dissertations

The direct detection dark matter experiment XENON1T became operational in early 2016, heralding the era of tonne-scale dark matter detectors. Direct detection experiments typically search for elastic scatters of dark matter particles off target nuclei. XENON1T's larger xenon target provides the advantage of stronger dark matter signals and lower background rates compared to its predecessors, XENON10 and XENON100; but, at the same time, calibration of the detector's response to backgrounds with traditional external sources becomes exceedingly more difficult.

A 220Rn source is deployed on the XENON100 dark matter detector in order to address the challenges in calibration of tonne-scale liquid …