Simulation of charge readout with segmented tiles in nEXO

Li, Z.; Cen, W. R.; Robinson, A.; Moore, David C.; Wen, L.; Odian, A.; Kharusi, S. Al; Anton, G.; Arnquist, I. J.; Badhrees, I.; Barbeau, P. S.; Beck, D.; Belov, V.; Bhatta, Trilochan; Brodsky, J.; Brown, E.; Brunner, T.; Caden, E.; Cao, G. F.; Cao, Liqiang; Chambers, Claire; Chana, B.; Charlebois, Serge A.; Chiu, M.; Cleveland, B. T.; Coon, Minor J.; Craycraft, A.; Dalmasson, J.; Daniels, T.; Darroch, L.; Daugherty, S. J.; Croix, A. De St.; Mesrobian-Kabakian, A. Der; DeVoe, R.; Vacri, M. L. Di; Dilling, J.; Ding, Ying; Dolinski, M. J.; Dragone, A.; Echevers, J.; Elbeltagi, M.; Fabris, L.; Fairbank, D.; Fairbank, W. M.; Farine, J.; Ferrara, Silvia; Feyzbakhsh, S.; Fontaine, R.; Fucarino, A.; Gallina, G.; Gautam, P.; Giacomini, G.; Goeldi, D.; Gornea, R.; Gratta, G.; Hansen, E. V.; Heffner, M.; Hoppe, E. W.; Hößl, J.; House, A.; Hughes, M.; Iverson, A.; Jamil, A.; Jewell, M. J.; Jiang, X. S.; Karelin, A.; Kaufman, L. J.; Kodroff, D.; Koffas, T.; Krücken, R.; Kuchenkov, A.; Kumar, Krishna; Lan, Y.; Larson, A. C.; Leach, K. G.; Lenardo, B. G.; Leonard, D. S.; Li, G.; Li, S.; Licciardi, C.; Lin, Yi-Hsuan; Lv, Pin; MacLellan, R.; McElroy, Thomas; Medina-Peregrina, M.; Michel, Thilo; Mong, B.; Murray, K.; Nakarmi, P.; Natzke, C. R.; Newby, J.; Ning, Z.; Njoya, O.; Nolet, F.; Nusair, O.; Odgers, K.; Oriunno, M.; Orrell, J. L.; Ortega, G. S.; Ostrovskiy, I.; Overman, C.T.; Parent, Samuel; Piepke, A.; Pocar, A.; Pratte, J.‐F.; Radeka, V.; Raguzin, Е.; Rescia, S.; Retière, F.; Richman, M.; Rossignol, T.; Rowson, P. C.; Roy, N.; Runge, J.; Saldanha, R.; Sangiorgio, S.; Skarpaas, K.; Soma, A. K.; St-Hilaire, G.; Stekhanov, V.; Stiegler, T.; Sun, X.; Tarka, M.; Todd, J.; Tolba, T.; Totev, T. I.; Tsang, R.; Tsang, T.; Vachon, F.; Veeraraghavan, V.; Viel, S.; Visser, G.; Vivo-Vilches, Carlos; Vuilleumier, J. L.; Wagenpfeil, M.; Walent, M.; Wang, Q.; Ward, M.; Watkins, J.; Weber, M.; Wei, W.; Wichoski, U.; Wu, Sen; Wu, Wei; Wu, Xiaomeng; Xia, Qi; Hong-jun, Yang; Yang, L.; Yen, Y.-R.; Zeldovich, O.; Zhao, Junsuo; Zhou, Y.; Ziegler, T.

doi:10.1088/1748-0221/14/09/p09020

Cited by 11 publications

(17 citation statements)

References 22 publications

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“…More details about the waveform simulation can be found in Ref. [40]. Figure 2 shows the simulated waveforms for an example multi-site event with all energy deposits localized in three charge tiles.…”

Section: Charge Signal Detectionmentioning

confidence: 99%

“…The decay of its daughter 42 K results in a β emission with endpoint energy of 3525 keV, as well as γ radiation with an energy of 2424 keV (with a branching fraction of 0.02%). Multiple factors play a role in estimating the expected levels of 42 Ar in nEXO, some of which are not well known, including the abundance of 42 Ar relative to 40 Ar after enrichment, which is difficult to estimate quantitatively because they are both very far from the mass cut used. Here we assume a factor nine enhancement of the isotope 42 Ar abundance based on the equivalent value for 136 Xe separation relative to lighter xenon isotopes.…”

Section: Intrinsic Radioactivity Of Detector Materialsmentioning

confidence: 99%

“…In an initial analysis [39], nEXO was shown to reach a 0νββ sensitivity of 9.2 × 10 27 yr when using a multi-parameter analysis similar to that featured in early analyses of EXO-200 data [32,33]. This report presents an improved sensitivity estimate, applying a more realistic model that reflects advances in the design and understanding of the nEXO performance obtained through ongoing R&D [36,39,40,41,42,43,44,45,46,47]. It also includes improved knowledge of the ionization and scintillation production in LXe at MeV energies [48] and increased sensitivity in material assay measurements.…”

Section: Introductionmentioning

confidence: 99%

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nEXO: neutrinoless double beta decay search beyond 10²⁸ year half-life sensitivity

Adhikari

Kharusi

Angelico

et al. 2021

J. Phys. G: Nucl. Part. Phys.

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The nEXO neutrinoless double beta (0νββ) decay experiment is designed to use a time projection chamber and 5000 kg of isotopically enriched liquid xenon to search for the decay in 136Xe. Progress in the detector design, paired with higher fidelity in its simulation and an advanced data analysis, based on the one used for the final results of EXO-200, produce a sensitivity prediction that exceeds the half-life of 1028 years. Specifically, improvements have been made in the understanding of production of scintillation photons and charge as well as of their transport and reconstruction in the detector. The more detailed knowledge of the detector construction has been paired with more assays for trace radioactivity in different materials. In particular, the use of custom electroformed copper is now incorporated in the design, leading to a substantial reduction in backgrounds from the intrinsic radioactivity of detector materials. Furthermore, a number of assumptions from previous sensitivity projections have gained further support from interim work validating the nEXO experiment concept. Together these improvements and updates suggest that the nEXO experiment will reach a half-life sensitivity of 1.35 × 1028 yr at 90% confidence level in 10 years of data taking, covering the parameter space associated with the inverted neutrino mass ordering, along with a significant portion of the parameter space for the normal ordering scenario, for almost all nuclear matrix elements. The effects of backgrounds deviating from the nominal values used for the projections are also illustrated, concluding that the nEXO design is robust against a number of imperfections of the model.

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Section: Charge Signal Detectionmentioning

confidence: 99%

Section: Intrinsic Radioactivity Of Detector Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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nEXO: neutrinoless double beta decay search beyond 10²⁸ year half-life sensitivity

Adhikari

Kharusi

Angelico

et al. 2021

J. Phys. G: Nucl. Part. Phys.

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“…Drawing on successful image recognition techniques in industry and neutrino experiments [213], rare event searches have implemented 2D CNNs to take full advantage of spatial (or spatiotemporal) information using light and charge detector hit patterns. This approach is particularly effective at topological signal/background discrimination in Xe time projection chambers (TPCs) for 0νββ [245], including NEXT [246,247], nEXO [248,249], and PandaX-III [250], as well as the KamLAND-Zen 0νββ scintillator experiment [251] and even nuclear emulsion DM searches [252]. 2D detector hit patterns have also been used directly as inputs to CNNs, fully-connected networks, or Bayesian optimization methods [253] for more precise position reconstruction in noble element TPCs, such as EXO-200 [254], XENON1T [255], and the Ar-based DarkSide DM experiments [256,257].…”

Section: Shallow Discriminatorsmentioning

confidence: 99%

Machine Learning in the Search for New Fundamental Physics

Karagiorgi¹,

Kasieczka²,

Kravitz³

et al. 2021

Preprint

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Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments, including the Large Hadron Collider, rare event searches, and neutrino experiments. While machine learning has a long history in these fields, the deep learning revolution (early 2010s) has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present review.

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“…An algorithm groups energy deposits within 3 mm of each other into clusters. The 3 mm cluster size is chosen to emulate the discrimination ability projected for nEXO using a separate detailed charge transport simulation [17]. To be labeled SS, an event can only have one reconstructed cluster inside the TPC LXe.…”

Section: Event Reconstruction In An Open-cage Tpc Designmentioning

confidence: 99%

Event Reconstruction in a Liquid Xenon Time Projection Chamber with an Optically-Open Field Cage

Stiegler,

Sangiorgio,

Brodsky

et al. 2020

Preprint

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nEXO is a proposed tonne-scale neutrinoless double beta decay (0νββ) experiment using liquid 136 Xe (LXe) in a Time Projection Chamber (TPC) to read out ionization and scintillation signals. Between the field cage and the LXe vessel, a layer of LXe ("skin" LXe) is present, where no ionization signal is collected. Only scintillation photons are detected, owing to the lack of optical barrier around the field cage. In this work, we show that the light originating in the skin LXe region can be used to improve background discrimination by 5% over previous published estimates. This improvement comes from two elements. First, a fraction of the γ-ray background is removed by identifying light from interactions with an energy deposition in the skin LXe. Second, background from 222 Rn dissolved in the skin LXe can be efficiently rejected by tagging the α decay in the 214 Bi-214 Po chain in the skin LXe.

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Simulation of charge readout with segmented tiles in nEXO

Cited by 11 publications

References 22 publications

nEXO: neutrinoless double beta decay search beyond 10²⁸ year half-life sensitivity

nEXO: neutrinoless double beta decay search beyond 10²⁸ year half-life sensitivity

Machine Learning in the Search for New Fundamental Physics

Event Reconstruction in a Liquid Xenon Time Projection Chamber with an Optically-Open Field Cage

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Simulation of charge readout with segmented tiles in nEXO

Cited by 11 publications

References 22 publications

nEXO: neutrinoless double beta decay search beyond 1028 year half-life sensitivity

nEXO: neutrinoless double beta decay search beyond 1028 year half-life sensitivity

Machine Learning in the Search for New Fundamental Physics

Event Reconstruction in a Liquid Xenon Time Projection Chamber with an Optically-Open Field Cage

Contact Info

Product

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nEXO: neutrinoless double beta decay search beyond 10²⁸ year half-life sensitivity

nEXO: neutrinoless double beta decay search beyond 10²⁸ year half-life sensitivity