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Machine learning accelerated likelihood-free event reconstruction in dark matter direct detection
Stockholm University, Faculty of Science, Department of Physics.
Stockholm University, Faculty of Science, Department of Physics.
Stockholm University, Faculty of Science, Department of Physics.
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Number of Authors: 52019 (English)In: Journal of Instrumentation, ISSN 1748-0221, E-ISSN 1748-0221, Vol. 14, article id P03004Article in journal (Refereed) Published
Abstract [en]

Reconstructing the position of an interaction for any dual-phase time projection chamber (TPC) with the best precision is key to directly detecting Dark Matter. Using the likelihood-free framework, a newalgorithm to reconstruct the 2-D (x; y) position and the size of the charge signal (e) of an interaction is presented. The algorithm uses the secondary scintillation light distribution (S2) obtained by simulating events using a waveform generator. To deal with the computational effort required by the likelihood-free approach, we employ the Bayesian Optimization for LikelihoodFree Inference (BOLFI) algorithm. Together with BOLFI, prior distributions for the parameters of interest (x; y; e) and highly informative discrepancy measures to performthe analyses are introduced. We evaluate the quality of the proposed algorithm by a comparison against the currently existing alternative methods using a large-scale simulation study. BOLFI provides a natural probabilistic uncertainty measure for the reconstruction and it improved the accuracy of the reconstruction over the next best algorithm by up to 15% when focusing on events at large radii (R > 30 cm, the outer 37% of the detector). In addition, BOLFI provides the smallest uncertainties among all the tested methods.

Place, publisher, year, edition, pages
2019. Vol. 14, article id P03004
Keywords [en]
Analysis and statistical methods, Dark Matter detectors (WIMPs, axions, etc.), Simulation methods and programs, Time projection Chambers (TPC)
National Category
Physical Sciences
Research subject
Physics
Identifiers
URN: urn:nbn:se:su:diva-167624DOI: 10.1088/1748-0221/14/03/P03004ISI: 000460721500001OAI: oai:DiVA.org:su-167624DiVA, id: diva2:1304379
Available from: 2019-04-12 Created: 2019-04-12 Last updated: 2020-03-23Bibliographically approved
In thesis
1. Enhancing Direct Searches for Dark Matter: Spatial-Temporal Modeling and Explicit Likelihoods
Open this publication in new window or tab >>Enhancing Direct Searches for Dark Matter: Spatial-Temporal Modeling and Explicit Likelihoods
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Astronomical and cosmological observations on different scales point to the existence of dark matter. In the current cosmological paradigm this dark matter accounts for about 26% of the energy-density of the universe, yet has not been directly observed. Weakly Interacting Massive Particles (WIMPs) and axions are two candidates among the many theories and particles proposed to explain dark matter. Direct detection experiments aim to detect the scattering or coupling of dark matter to the detector medium. The event rate in such experiments is expected to exhibit an annual modulation due to the motion of the Earth through the Galactic dark matter halo. The XENON collaboration built several experiments that have searched for WIMP dark matter by looking for WIMPs scattering on xenon nuclei. The heart of these detectors consists of a Time Projection Chamber (TPC) which records the scintillation light (S1) and ionization charge (S2) signal following a recoiling xenon nucleus as well as its position and time. Using these ultra-low background detectors, the XENON collaboration has set world-leading exclusion limits on the WIMP-nucleon scattering cross-section. In this thesis several different ways of enhancing direct detection experiments are presented, involving time dependent signal models, event reconstruction and a method enhancing statistical inference. First, during a search for event rate modulation, spanning almost 4 years of XENON100 data, no oscillation was found to be compatible with the expected signature. This thesis presents a verification of the correctness of the test statistic distribution used in this analysis using dedicated simulations. Second, the positions of interactions in XENON detectors are used for detector volume fiducialization as well as for modeling the position dependent detector response. This thesis presents the position reconstruction methods used during the first XENON1T science analysis. Third, a new algorithm for position and energy reconstruction using the likelihood-free paradigm is presented. This simulator-based method increases the accuracy of the previous method by up to 15% and can simultaneously infer the transverse position and size of the charge signal. Fourth, to enhance the physics reach of future dark matter searches using xenon TPCs, a new method for computing differential rates is developed. This method replaces the calculation usually performed by Monte-Carlo simulations with an equivalent analytic expression. This enables the use of higher dimensional explicit (profile) likelihood functions, resulting in better signal-background discrimination. The new method uses time dependent signal models (encoding annual modulation) as well as spatially non-uniform sources such as a radiogenic neutron background and fully accounts for the non-uniform detector response. This method can significantly reduce the exposure needed for a potential dark matter discovery in future detectors such as XENONnT. Lastly, the amplitude of the axion dark matter field is expected to exhibit stochastic behavior. Experiments whose measurements are shorter than the coherence time of the field need to include this effect in their data analysis and inference. This thesis presents an analysis of a simulated axion signal in a CASPEr-ZULF-like detector, showing that exclusion limits on the axion amplitude are too strong by a factor ~4 when not including the axion amplitude fluctuation.

Place, publisher, year, edition, pages
Stockholm: Department of Physics, Stockholm University, 2020. p. 83
Keywords
Dark matter, direct detection, statistical analysis
National Category
Astronomy, Astrophysics and Cosmology
Research subject
Physics
Identifiers
urn:nbn:se:su:diva-179974 (URN)978-91-7911-056-7 (ISBN)978-91-7911-057-4 (ISBN)
Public defence
2020-05-07, sal FB42, AlbaNova universitetscentrum, Roslagstullsbacken 21, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: Manuscript. Paper 5: Manuscript.

Available from: 2020-04-14 Created: 2020-03-19 Last updated: 2020-05-26Bibliographically approved

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