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Application and modeling of an online distillation method to reduce krypton and argon in XENON1T
by
Messina, M
, Wenz, D
, Zavattini, G
, Baxter, A L
, Pierre, M
, Semeria, F
, Rupp, N
, Kavrigin, P
, Lopes, J A M
, Yang, L
, Morå, K
, Jakob, J
, Pienaar, J
, Selvi, M
, Diglio, S
, Colijn, A P
, Xu, Z
, Decowski, M P
, Manfredini, A
, Gaemers, P
, Liang, S
, Form, S
, Lavina, L Scotto
, Di Giovanni, A
, Lombardi, F
, Macolino, C
, Terliuk, A
, Mancuso, A
, Shagin, P
, Shockley, E
, Long, J
, Toschi, F
, Mosbacher, Y
, Molinario, A
, Iacovacci, M
, Marignetti, F
, Baudis, L
, Higuera, A
, Moriyama, S
, Qin, J
, Agostini, F
, Ma, Y
, Fulgione, W
, Yamashita, M
, Paetsch, B
, Tunnell, C
, Gaior, R
, Takeda, A
, Wolf, T
, Tan, P-L
, Simgen, H
, Zhang, Y
, Galloway, M
, Masbou, J
, Bruno, G
, Liu, K
, Arneodo, F
, Yuan, L
, Huhmann, C
, Martin, D Antón
, Lang, R F
, Schumann, M
, Di Pede, S
, Plante, G
, Wei, Y
, Thers, D
, Trinchero, G
, Joy, A
, Grigat, J
, Eißing, H Schulze
, Aprile, E
, Clark, M
, Conrad, J
, Oberlack, U
, Tönnies, F
, Kazama, S
, Undagoitia, T Marrodán
, Di Gangi, P
, Landsman, H
, Mahlstedt, J
, Murra, M
, Pizzella, V
, Weiss, M
, Cardoso, J M R
, Cichon, D
, García, D Ramírez
, Peres, R
, Capelli, C
, Di Stefano, R
, Martens, K
, Zhu, T
, Masson, D
, Masson, E
, Volta, G
, Alfonsi, M
, Manenti, L
, Bernard, A
, Kopec, A
, Reichard, S
, dos Santos, J M F
in
Sensors
2022
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Application and modeling of an online distillation method to reduce krypton and argon in XENON1T
by
Messina, M
, Wenz, D
, Zavattini, G
, Baxter, A L
, Pierre, M
, Semeria, F
, Rupp, N
, Kavrigin, P
, Lopes, J A M
, Yang, L
, Morå, K
, Jakob, J
, Pienaar, J
, Selvi, M
, Diglio, S
, Colijn, A P
, Xu, Z
, Decowski, M P
, Manfredini, A
, Gaemers, P
, Liang, S
, Form, S
, Lavina, L Scotto
, Di Giovanni, A
, Lombardi, F
, Macolino, C
, Terliuk, A
, Mancuso, A
, Shagin, P
, Shockley, E
, Long, J
, Toschi, F
, Mosbacher, Y
, Molinario, A
, Iacovacci, M
, Marignetti, F
, Baudis, L
, Higuera, A
, Moriyama, S
, Qin, J
, Agostini, F
, Ma, Y
, Fulgione, W
, Yamashita, M
, Paetsch, B
, Tunnell, C
, Gaior, R
, Takeda, A
, Wolf, T
, Tan, P-L
, Simgen, H
, Zhang, Y
, Galloway, M
, Masbou, J
, Bruno, G
, Liu, K
, Arneodo, F
, Yuan, L
, Huhmann, C
, Martin, D Antón
, Lang, R F
, Schumann, M
, Di Pede, S
, Plante, G
, Wei, Y
, Thers, D
, Trinchero, G
, Joy, A
, Grigat, J
, Eißing, H Schulze
, Aprile, E
, Clark, M
, Conrad, J
, Oberlack, U
, Tönnies, F
, Kazama, S
, Undagoitia, T Marrodán
, Di Gangi, P
, Landsman, H
, Mahlstedt, J
, Murra, M
, Pizzella, V
, Weiss, M
, Cardoso, J M R
, Cichon, D
, García, D Ramírez
, Peres, R
, Capelli, C
, Di Stefano, R
, Martens, K
, Zhu, T
, Masson, D
, Masson, E
, Volta, G
, Alfonsi, M
, Manenti, L
, Bernard, A
, Kopec, A
, Reichard, S
, dos Santos, J M F
in
Sensors
2022
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Application and modeling of an online distillation method to reduce krypton and argon in XENON1T
by
Messina, M
, Wenz, D
, Zavattini, G
, Baxter, A L
, Pierre, M
, Semeria, F
, Rupp, N
, Kavrigin, P
, Lopes, J A M
, Yang, L
, Morå, K
, Jakob, J
, Pienaar, J
, Selvi, M
, Diglio, S
, Colijn, A P
, Xu, Z
, Decowski, M P
, Manfredini, A
, Gaemers, P
, Liang, S
, Form, S
, Lavina, L Scotto
, Di Giovanni, A
, Lombardi, F
, Macolino, C
, Terliuk, A
, Mancuso, A
, Shagin, P
, Shockley, E
, Long, J
, Toschi, F
, Mosbacher, Y
, Molinario, A
, Iacovacci, M
, Marignetti, F
, Baudis, L
, Higuera, A
, Moriyama, S
, Qin, J
, Agostini, F
, Ma, Y
, Fulgione, W
, Yamashita, M
, Paetsch, B
, Tunnell, C
, Gaior, R
, Takeda, A
, Wolf, T
, Tan, P-L
, Simgen, H
, Zhang, Y
, Galloway, M
, Masbou, J
, Bruno, G
, Liu, K
, Arneodo, F
, Yuan, L
, Huhmann, C
, Martin, D Antón
, Lang, R F
, Schumann, M
, Di Pede, S
, Plante, G
, Wei, Y
, Thers, D
, Trinchero, G
, Joy, A
, Grigat, J
, Eißing, H Schulze
, Aprile, E
, Clark, M
, Conrad, J
, Oberlack, U
, Tönnies, F
, Kazama, S
, Undagoitia, T Marrodán
, Di Gangi, P
, Landsman, H
, Mahlstedt, J
, Murra, M
, Pizzella, V
, Weiss, M
, Cardoso, J M R
, Cichon, D
, García, D Ramírez
, Peres, R
, Capelli, C
, Di Stefano, R
, Martens, K
, Zhu, T
, Masson, D
, Masson, E
, Volta, G
, Alfonsi, M
, Manenti, L
, Bernard, A
, Kopec, A
, Reichard, S
, dos Santos, J M F
in
Sensors
2022
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Application and modeling of an online distillation method to reduce krypton and argon in XENON1T
Journal Article
Application and modeling of an online distillation method to reduce krypton and argon in XENON1T
2022
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Overview
Abstract
A novel online distillation technique was developed for the XENON1T dark matter experiment to reduce intrinsic background components more volatile than xenon, such as krypton or argon, while the detector was operating. The method is based on a continuous purification of the gaseous volume of the detector system using the XENON1T cryogenic distillation column. A krypton-in-xenon concentration of (360 ± 60) ppq was achieved. It is the lowest concentration measured in the fiducial volume of an operating dark matter detector to date. A model was developed and fitted to the data to describe the krypton evolution in the liquid and gas volumes of the detector system for several operation modes over the time span of 550 days, including the commissioning and science runs of XENON1T. The online distillation was also successfully applied to remove 37Ar after its injection for a low-energy calibration in XENON1T. This makes the usage of 37Ar as a regular calibration source possible in the future. The online distillation can be applied to next-generation liquid xenon time projection chamber experiments to remove krypton prior to, or during, any science run. The model developed here allows further optimization of the distillation strategy for future large-scale detectors.
Publisher
Oxford University Press
Subject
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