Novel ideas and techniques for large dark matter detectors

As dark matter detection experiments continue to report null results, the need for larger and more sensitive detectors means even more stringent design requirements. New calibration techniques are required and better calibration methods become possible with increased detector size. Additionally, previously ignored detector features such as convection become important, especially as internal, dissolvable sources become more common. Furthermore, convection also offers the possibility for reduction of the 222Rn backrounds via an offline analysis where atoms of 214Pb are tagged and followed throughout the detector via a technique dubbed the “radon self-veto”. In this thesis, we present the characterization of a deuterium-deuterium plasma fusion neutron generator optimized to perform the nuclear recoil calibration of XENON1T. Part of this characterization is done with liquid organic scintillator detectors, which are sensitive to both electonic and nuclear recoil interactions. We develop a new algorithm for discriminating between these two signal types using Laplace transforms and show that it performs better than traditional algorithms. A multipurpose source of dissolvable 220Rn is presented and measurements made of long-lived contaminants from this source. Finally, we present the first measurement of convection in XENON1T and report the results of a simple convection-agnostic implementation of the radon self-veto

Machine Learning in XENON1T Analysis

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, and potentially identify other features of interest such as detector artefacts. Additionally, using the results of the machine learning process it became possible to cross-check the results of the XENON1T selection cuts. Clustering algorithms were used to segment the dataset into populations, which then recursively split those into additional subpopulations. Upon capturing a subpopulation, a classifier was trained and used to predict if other data could potentially belong to the same population. From this process, it was observed that there were two clustering algorithms that were capable of identifying the electronic recoil band accurately. It was also seen that a few XENON1T selection cuts may need relaxed. These algorithms may be able to be used to tweak the cuts, or continue in search of artefacts. The process of automating the analysis stage by means of machine learning could be further extended by automating the recognition of waveforms using neural networks

AxFoundation/strax: v1.5.4

What's Changed Split compare_metadata into utils.compare_meta by @dachengx in https://github.com/AxFoundation/strax/pull/754 Change endtime - time >= 0 to endtime >= time by @JYangQi00 in https://github.com/AxFoundation/strax/pull/756 Mandatorily wrap _read_chunk in a check_chunk_n decorator by @dachengx in https://github.com/AxFoundation/strax/pull/758 New Contributors @JYangQi00 made their first contribution in https://github.com/AxFoundation/strax/pull/756 Full Changelog: https://github.com/AxFoundation/strax/compare/v1.5.3...v1.5.