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Position Reconstruction in the DEAP-3600 Dark Matter Search Experiment
by
Waqar, M
, Florian, S
, Hearns, C
, Baldwin, M
, d, R J
, McLaughlin, J B
, Ward, M
, Graham, K
, Pal, S
, Grobov, A
, Retière, F
, Adhikari, P
, Levashko, N
, Giménez-Alcázar, A
, Kuźniak, M
, Stone, C
, Murray, A
, Schuckman, F G
, Cai, B
, Crampton, R
, LePage-Bourbonnais, J
, Cranshaw, D
, Hallin, A L
, M -C Piro
, Viel, S
, Pesudo, V
, Cárdenas-Montes, M
, Jillings, C J
, Sobotkiewich, K
, Ellingwood, E
, Ajaj, R
, Majewski, P
, Anstey, J
, Goeldi, D
, I Rodríguez García
, Giampa, P
, Peeters, S J M
, Mirasola, L
, Butcher, A
, Duncan, F A
, Ouellet, C
, P Di Stefano
, Willis, J
, Langrock, S
, Smith, B
, Haskins, S
, P García Abia
, Lock, J
, McDonald, A B
, Stringer, M
, Rad, F
, Soukup, J
, Farahani, S S
, Luzzi, L
, Hamstra, M
, Lai, M
, Zuñiga-Reyes, A
, Stanic, G
, Moharana, A
, Doria, L
, Sur, B
, Benmansour, H
, Corning, J M
, Veloce, L
, Dunford, M
, Bonivento, W M
, Rand, E T
, Araujo, G R
, Erlandson, A
, Fatemighomi, N
, Seeburn, N
, Kamaev, O
, DelGobbo, P
, Lidgard, J
, Walczak, M
, Bina, C E
, McElroy, T
, The DEAP Collaboration
, Kaur, G
, Turcotte-Tardif, R
, Boulay, M G
, Monroe, J
, Grant, D R
, Joy, A
, Rethmeier, C
, Garg, S
, Skensved, P
, Vyas, B
, Golovko, V V
, Dolganov, G
, Santorelli, R
, Kemp, A
, Dering, K
, Nadeau, P
, Papi, D
, B
in
Algorithms
/ Argon
/ Dark matter
/ Machine learning
/ Photoelectrons
/ Photomultiplier tubes
/ Reconstruction
/ Spatial distribution
2025
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Position Reconstruction in the DEAP-3600 Dark Matter Search Experiment
by
Waqar, M
, Florian, S
, Hearns, C
, Baldwin, M
, d, R J
, McLaughlin, J B
, Ward, M
, Graham, K
, Pal, S
, Grobov, A
, Retière, F
, Adhikari, P
, Levashko, N
, Giménez-Alcázar, A
, Kuźniak, M
, Stone, C
, Murray, A
, Schuckman, F G
, Cai, B
, Crampton, R
, LePage-Bourbonnais, J
, Cranshaw, D
, Hallin, A L
, M -C Piro
, Viel, S
, Pesudo, V
, Cárdenas-Montes, M
, Jillings, C J
, Sobotkiewich, K
, Ellingwood, E
, Ajaj, R
, Majewski, P
, Anstey, J
, Goeldi, D
, I Rodríguez García
, Giampa, P
, Peeters, S J M
, Mirasola, L
, Butcher, A
, Duncan, F A
, Ouellet, C
, P Di Stefano
, Willis, J
, Langrock, S
, Smith, B
, Haskins, S
, P García Abia
, Lock, J
, McDonald, A B
, Stringer, M
, Rad, F
, Soukup, J
, Farahani, S S
, Luzzi, L
, Hamstra, M
, Lai, M
, Zuñiga-Reyes, A
, Stanic, G
, Moharana, A
, Doria, L
, Sur, B
, Benmansour, H
, Corning, J M
, Veloce, L
, Dunford, M
, Bonivento, W M
, Rand, E T
, Araujo, G R
, Erlandson, A
, Fatemighomi, N
, Seeburn, N
, Kamaev, O
, DelGobbo, P
, Lidgard, J
, Walczak, M
, Bina, C E
, McElroy, T
, The DEAP Collaboration
, Kaur, G
, Turcotte-Tardif, R
, Boulay, M G
, Monroe, J
, Grant, D R
, Joy, A
, Rethmeier, C
, Garg, S
, Skensved, P
, Vyas, B
, Golovko, V V
, Dolganov, G
, Santorelli, R
, Kemp, A
, Dering, K
, Nadeau, P
, Papi, D
, B
in
Algorithms
/ Argon
/ Dark matter
/ Machine learning
/ Photoelectrons
/ Photomultiplier tubes
/ Reconstruction
/ Spatial distribution
2025
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Position Reconstruction in the DEAP-3600 Dark Matter Search Experiment
by
Waqar, M
, Florian, S
, Hearns, C
, Baldwin, M
, d, R J
, McLaughlin, J B
, Ward, M
, Graham, K
, Pal, S
, Grobov, A
, Retière, F
, Adhikari, P
, Levashko, N
, Giménez-Alcázar, A
, Kuźniak, M
, Stone, C
, Murray, A
, Schuckman, F G
, Cai, B
, Crampton, R
, LePage-Bourbonnais, J
, Cranshaw, D
, Hallin, A L
, M -C Piro
, Viel, S
, Pesudo, V
, Cárdenas-Montes, M
, Jillings, C J
, Sobotkiewich, K
, Ellingwood, E
, Ajaj, R
, Majewski, P
, Anstey, J
, Goeldi, D
, I Rodríguez García
, Giampa, P
, Peeters, S J M
, Mirasola, L
, Butcher, A
, Duncan, F A
, Ouellet, C
, P Di Stefano
, Willis, J
, Langrock, S
, Smith, B
, Haskins, S
, P García Abia
, Lock, J
, McDonald, A B
, Stringer, M
, Rad, F
, Soukup, J
, Farahani, S S
, Luzzi, L
, Hamstra, M
, Lai, M
, Zuñiga-Reyes, A
, Stanic, G
, Moharana, A
, Doria, L
, Sur, B
, Benmansour, H
, Corning, J M
, Veloce, L
, Dunford, M
, Bonivento, W M
, Rand, E T
, Araujo, G R
, Erlandson, A
, Fatemighomi, N
, Seeburn, N
, Kamaev, O
, DelGobbo, P
, Lidgard, J
, Walczak, M
, Bina, C E
, McElroy, T
, The DEAP Collaboration
, Kaur, G
, Turcotte-Tardif, R
, Boulay, M G
, Monroe, J
, Grant, D R
, Joy, A
, Rethmeier, C
, Garg, S
, Skensved, P
, Vyas, B
, Golovko, V V
, Dolganov, G
, Santorelli, R
, Kemp, A
, Dering, K
, Nadeau, P
, Papi, D
, B
in
Algorithms
/ Argon
/ Dark matter
/ Machine learning
/ Photoelectrons
/ Photomultiplier tubes
/ Reconstruction
/ Spatial distribution
2025
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Position Reconstruction in the DEAP-3600 Dark Matter Search Experiment
Paper
Position Reconstruction in the DEAP-3600 Dark Matter Search Experiment
2025
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Overview
In the DEAP-3600 dark matter search experiment, precise reconstruction of the positions of scattering events in liquid argon is key for background rejection and defining a fiducial volume that enhances dark matter candidate events identification. This paper describes three distinct position reconstruction algorithms employed by DEAP-3600, leveraging the spatial and temporal information provided by photomultipliers surrounding a spherical liquid argon vessel. Two of these methods are maximum-likelihood algorithms: the first uses the spatial distribution of detected photoelectrons, while the second incorporates timing information from the detected scintillation light. Additionally, a machine learning approach based on the pattern of photoelectron counts across the photomultipliers is explored.
Publisher
Cornell University Library, arXiv.org
Subject
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