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Machine learning accelerated likelihood-free event reconstruction in dark matter direct detection
Stockholms universitet, Naturvetenskapliga fakulteten, Fysikum.
Stockholms universitet, Naturvetenskapliga fakulteten, Fysikum.
Stockholms universitet, Naturvetenskapliga fakulteten, Fysikum.
Vise andre og tillknytning
Rekke forfattare: 52019 (engelsk)Inngår i: Journal of Instrumentation, ISSN 1748-0221, E-ISSN 1748-0221, Vol. 14, artikkel-id P03004Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2019. Vol. 14, artikkel-id P03004
Emneord [en]
Analysis and statistical methods, Dark Matter detectors (WIMPs, axions, etc.), Simulation methods and programs, Time projection Chambers (TPC)
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Identifikatorer
URN: urn:nbn:se:su:diva-167624DOI: 10.1088/1748-0221/14/03/P03004ISI: 000460721500001OAI: oai:DiVA.org:su-167624DiVA, id: diva2:1304379
Tilgjengelig fra: 2019-04-12 Laget: 2019-04-12 Sist oppdatert: 2019-04-12bibliografisk kontrollert

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