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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
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: 2019-04-12Bibliographically approved

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Pelssers, BartBarge, DerekConrad, Jan
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  • apa
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  • de-DE
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