The existence of dark matter has been inferred through many astrophysical evidences. However, much about its nature is unknown to this day. The several decades-long search for dark matter has given rise to many experiments and even more dark matter candidates. SuperCDMS is a direct detection experiment which uses cryogenic detectors to probe interactions of dark matter particles with Standard Model particles.
Work done towards advancing two frontiers of the SuperCDMS SNOLAB experiment will be the main points of discussion in this thesis – (a) the characterization of the new SuperCDMS SNOLAB detectors, and, (b) the development of novel event reconstruction techniques for the experiment.
After introducing the motivation for dark matter and the current experimental techniques used to detect it, the thesis will introduce SuperCDMS detection principles and pre-requisite knowledge to motivate and understand most of the work described within. First, preliminary results from testing and characterizing SuperCDMS detector towers at the Cryogenic Underground TEst facility, a low background test facility at SNOLAB in Sudbury, Canada will be presented. The remainder of this thesis will focus on novel reconstruction techniques critical to achieving the experiment’s projected sensitivity post commissioning.
This discussion is sub-divided into two. The first aspect will present a detailed discussion of advanced reconstruction algorithms to fit data sampled at non-uniform speeds to keep within the bandwidths of the readout electronics and maintain low trigger thresholds at SuperCDMS SNOLAB. The second major development discussed will be a novel reconstruction technique called the N×M filter, which fits N channels with M shapes/templates simultaneously and develops a pipeline which integrates machine learning to achieve excellent resolution improvement.
The key outcomes of this thesis are (a) capability demonstration of the SuperCDMS SNOLAB detectors, (b) development of a less memory intensive algorithm to process non-uniformly sampled data, and, (c) demonstration of a two-fold improvement and nearly a four-fold improvement in energy resolution in old and new data sets using the N×M filter, respectively.
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2024-07-29T09:00:002024-07-29T11:00:00Novel reconstruction techniques for detecting low mass dark matter in the SuperCDMS experiment and characterization of SuperCDMS SNOLAB detectorsEvent Information:
Abstract:
The existence of dark matter has been inferred through many astrophysical evidences. However, much about its nature is unknown to this day. The several decades-long search for dark matter has given rise to many experiments and even more dark matter candidates. SuperCDMS is a direct detection experiment which uses cryogenic detectors to probe interactions of dark matter particles with Standard Model particles.
Work done towards advancing two frontiers of the SuperCDMS SNOLAB experiment will be the main points of discussion in this thesis – (a) the characterization of the new SuperCDMS SNOLAB detectors, and, (b) the development of novel event reconstruction techniques for the experiment.
After introducing the motivation for dark matter and the current experimental techniques used to detect it, the thesis will introduce SuperCDMS detection principles and pre-requisite knowledge to motivate and understand most of the work described within. First, preliminary results from testing and characterizing SuperCDMS detector towers at the Cryogenic Underground TEst facility, a low background test facility at SNOLAB in Sudbury, Canada will be presented. The remainder of this thesis will focus on novel reconstruction techniques critical to achieving the experiment’s projected sensitivity post commissioning.
This discussion is sub-divided into two. The first aspect will present a detailed discussion of advanced reconstruction algorithms to fit data sampled at non-uniform speeds to keep within the bandwidths of the readout electronics and maintain low trigger thresholds at SuperCDMS SNOLAB. The second major development discussed will be a novel reconstruction technique called the N×M filter, which fits N channels with M shapes/templates simultaneously and develops a pipeline which integrates machine learning to achieve excellent resolution improvement.
The key outcomes of this thesis are (a) capability demonstration of the SuperCDMS SNOLAB detectors, (b) development of a less memory intensive algorithm to process non-uniformly sampled data, and, (c) demonstration of a two-fold improvement and nearly a four-fold improvement in energy resolution in old and new data sets using the N×M filter, respectively.Event Location:
Zoom - https://ubc.zoom.us/j/68355025780?pwd=QCKYuMaLKywTlUaZiwJu6H4obKhniI.1 Passcode: 8223191