Purdue University Graduate School
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Computational techniques for the direct detection of dark matter

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posted on 2023-05-10, 12:25 authored by Juehang QinJuehang Qin

Dark matter makes up a vast majority of the matter in the universe, however, the composition of dark matter is unknown. One approach to uncover the composition of dark matter is via the direct detection of dark matter. This thesis focuses on the use of computational techniques to enhance or enable the direct detection of dark matter by the XENON experiment and the Windchime project.

First, a software veto for radon chain backgrounds using measurements of the convective motion in XENON1T is detailed and demonstrated. It is shown that this can reduce the radon chain background in the XENON1T and XENONnT detectors, and with potential for improved performance in larger detectors with reduced convection. The design and operation of a Y-88/Be photo-neutron source is then discussed. It is shown that in the calibration data collected there is a significant population of neutron events that can be used to calibrate the detector response. Following that is a discussion of the methods to conduct analysis and make sensitivity projections for the Windchime project. A semi-analytical method for sensitivity projections that does not require a full simulation of the sensor array is shown, and both Bayesian and frequentist approaches to track-finding in sensor arrays are demonstrated. The latter includes an estimate of the look-elsewhere effect in such a dark matter search. This leads to an exploration of the use of Gaussian random fields for the estimation of the look-elsewhere effect, wherein it is shown that Gaussian random fields can be sampled quickly to compute the look-elsewhere effect corrections.


Degree Type

  • Doctor of Philosophy


  • Physics and Astronomy

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Rafael Lang

Additional Committee Member 2

David Koltick

Additional Committee Member 3

Nima Lashkari

Additional Committee Member 4

Daniel Carney

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