Application and modeling of an online distillation method to reduce krypton and argon in XENON1T
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 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 fit 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 Ar-37 after its injection for a low energy calibration in XENON1T. This makes the usage of Ar-37 as a regular calibration source possible in the future. The online distillation can be applied to next-generation 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.
Keywords
Cite
@article{arxiv.2112.12231,
title = {Application and modeling of an online distillation method to reduce krypton and argon in XENON1T},
author = {E. Aprile and K. Abe and F. Agostini and S. Ahmed Maouloud and M. Alfonsi and L. Althueser and E. Angelino and J. R. Angevaare and V. C. Antochi and D. Antón Martin and F. Arneodo and L. Baudis and A. L. Baxter and L. Bellagamba and A. Bernard and R. Biondi and A. Bismark and A. Brown and S. Bruenner and G. Bruno and R. Budnik and C. Capelli and J. M. R. Cardoso and D. Cichon and B. Cimmino and M. Clark and A. P. Colijn and J. Conrad and J. J. Cuenca-García and J. P. Cussonneau and V. D'Andrea and M. P. Decowski and P. Di Gangi and S. Di Pede and A. Di Giovanni and R. Di Stefano and S. Diglio and A. Elykov and S. Farrell and A. D. Ferella and H. Fischer and S. Form and W. Fulgione and P. Gaemers and R. Gaior and M. Galloway and F. Gao and R. Glade-Beucke and L. Grandi and J. Grigat and A. Higuera and C. Hils and K. Hiraide and L. Hoetzsch and J. Howlett and C. Huhmann and M. Iacovacci and Y. Itow and J. Jakob and F. Joerg and N. Kato and P. Kavrigin and S. Kazama and M. Kobayashi and G. Koltman and A. Kopec and H. Landsman and R. F. Lang and L. Levinson and I. Li and S. Liang and S. Lindemann and M. Lindner and K. Liu and F. Lombardi and J. Long and J. A. M. Lopes and Y. Ma and C. Macolino and J. Mahlstedt and A. Mancuso and L. Manenti and A. Manfredini and F. Marignetti and T. Marrodán Undagoitia and K. Martens and J. Masbou and D. Masson and E. Masson and S. Mastroianni and M. Messina and K. Miuchi and K. Mizukoshi and A. Molinario and S. Moriyama and K. Morå and Y. Mosbacher and M. Murra and K. Ni and U. Oberlack and J. Palacio and R. Peres and J. Pienaar and M. Pierre and V. Pizzella and G. Plante and J. Qi and J. Qin and D. Ramírez García and S. Reichard and A. Rocchetti and N. Rupp and L. Sanchez and J. M. F. dos Santos and G. Sartorelli and J. Schreiner and D. Schulte and H. Schulze Eißing and M. Schumann and L. Scotto Lavina and M. Selvi and F. Semeria and P. Shagin and E. Shockley and M. Silva and H. Simgen and A. Takeda and P. -L. Tan and A. Terliuk and C. Therreau and D. Thers and F. Toschi and G. Trinchero and C. Tunnell and F. Tönnies and K. Valerius and G. Volta and Y. Wei and C. Weinheimer and M. Weiss and D. Wenz and J. Westermann and C. Wittweg and T. Wolf and Z. Xu and M. Yamashita and L. Yang and J. Ye and L. Yuan and G. Zavattini and Y. Zhang and M. Zhong and T. Zhu and J. P. Zopounidis},
journal= {arXiv preprint arXiv:2112.12231},
year = {2022}
}