Machine learning accelerated likelihood-free event reconstruction in dark matter direct detection

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http://hdl.handle.net/10138/313054

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Simola , U , Pelssers , B , Barge , D , Conrad , J & Corander , J 2019 , ' Machine learning accelerated likelihood-free event reconstruction in dark matter direct detection ' , Journal of Instrumentation , vol. 14 , 03004 . https://doi.org/10.1088/1748-0221/14/03/P03004

Title: Machine learning accelerated likelihood-free event reconstruction in dark matter direct detection
Author: Simola, U.; Pelssers, B.; Barge, D.; Conrad, J.; Corander, J.
Contributor: University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, Department of Mathematics and Statistics
Date: 2019-03-06
Language: eng
Number of pages: 28
Belongs to series: Journal of Instrumentation
ISSN: 1748-0221
URI: http://hdl.handle.net/10138/313054
Abstract: 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.
Subject: Analysis and statistical methods
Dark Matter detectors (WIMPs, axions, etc.)
Simulation methods and programs
Time projection Chambers (TPC)
APPROXIMATE BAYESIAN COMPUTATION
SEQUENTIAL MONTE-CARLO
PARAMETER
EVOLUTION
INFERENCE
111 Mathematics
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