Search for<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>2</mml:mn><mml:mi>ν</mml:mi><mml:mi>β</mml:mi><mml:mi>β</mml:mi></mml:mrow></mml:math>decay of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mi>Xe</mml:mi><mml:mprescripts/><mml:none/><mml:mn>136</mml:mn></mml:mmultiscripts></mml:math>to the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mn>0</mml:mn><mml:mn>1</mml:mn><mml:mo>+</mml:mo></mml:msubsup></mml:math>ex

Albert, J.; Auty, D. J.; Barbeau, P. S.; Beck, D.; Belov, V.; Breidenbach, M.; Brunner, T.; Burenkov, A.; Cao, G. F.; Chambers, Claire; Chaves, J.; Cleveland, B. T.; Coon, Minor J.; Craycraft, A.; Daniels, T.; Danilov, M.; Daugherty, S. J.; Davis, J.; Delaquis, S.; Mesrobian-Kabakian, A. Der; DeVoe, R.; Didberidze, T.; Dilling, J.; Dolgolenko, A.; Dolinski, M. J.; Dunford, M.; Fairbank, W. M.; Farine, J.; Feldmeier, W.; Feyzbakhsh, S.; Fierlinger, P.; Fudenberg, D.; Gornea, R.; Graham, K.; Gratta, G.; Hall, C.; Hughes, M.; Jewell, M. J.; Johnson, A. S.; Johnson, T. N.; Johnston, S.; Karelin, A.; Kaufman, L. J.; Killick, Ryan; King, J.D.; Koffas, T.; Kravitz, S.; Krücken, R.; Kuchenkov, A.; Kumar, Krishna; Leonard, D. S.; Licciardi, C.; Lin, Yi-Hsuan; MacLellan, R.; Marino, M. G.; Mong, B.; Moore, David C.; Njoya, O.; Nelson, Robert H.; Odian, A.; Ostrovskiy, I.; Piepke, A.; Pocar, A.; Prescott, C.Y.; Retière, F.; Rowson, P. C.; Russell, J. J.; Schubert, A.; Sinclair, D.; Smith, E.; Stekhanov, V.; Tarka, M.; Tolba, T.; Tsang, R.; Twelker, K.; Vogel, P.; Vuilleumier, J. L.; Waite, A. P.; Walton, J.; Walton, T.; Weber, M.; Wen, L.; Wichoski, U.; Winick, Tristan; Wood, J. L.; Xu, Qingyang; Yang, L.; Yen, Y.-R.; Zeldovich, O.

doi:10.1103/physrevc.93.035501

Cited by 17 publications

(7 citation statements)

References 27 publications

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“…Existing DM and neutrino experiments have demonstrated improvements in background rejection using simple neural networks and boosted decision trees [16]. Deep learning techniques have also been successfully employed for reconstruction of physical quantities, such as energy and position [36,53], as well as particle identification [25,6] and signal/background discrimination [21].…”

Section: Machine Learning: the Current And Future Landscapementioning

confidence: 99%

Dark-matter And Neutrino Computation Explored (DANCE) Community Input to Snowmass

Roberts¹,

Tunnell²,

Krosigk³

et al. 2022

Preprint

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This paper summarizes the needs of the dark matter and neutrino communities as it relates to computation. The scope includes data acquisition, triggers, data management and processing, data preservation, simulation, machine learning, data analysis, software engineering, career development, and equity and inclusion. Beyond identifying our community needs, we propose actions that can be taken to strengthen this community and to work together to overcome common challenges.

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Section: Machine Learning: the Current And Future Landscapementioning

confidence: 99%

Dark-matter And Neutrino Computation Explored (DANCE) Community Input to Snowmass

Roberts¹,

Tunnell²,

Krosigk³

et al. 2022

Preprint

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“…Similar approaches have been used to remove noise pulses in solid state DM detectors such as SuperCDMS [234] and COSINE-100 [235]. In the EXO-200 0νββ experiment, this approach was taken a step further by combining the BDT output with other variables such as measured energy in the final two-dimensional fit [236][237][238]. Time Series Analysis.…”

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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“…limit derived is T 0ν 1/2 > 3.5 × 10 25 y [151]. Other modes and searches have also been explored [152][153][154][155]. The nEXO observatory (with five tons of enriched xenon) is the next phase of the project, with the aim to improve the sensitivity two orders of magnitude [156].…”

Section: Xenon Dbd Experimentsmentioning

confidence: 99%

Cosmogenic Activation in Double Beta Decay Experiments

Cebrián

2020

Universe

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Double beta decay is a very rare nuclear process and, therefore, experiments intended to detect it must be operated deep underground and in ultra-low background conditions. Long-lived radioisotopes produced by the previous exposure of materials to cosmic rays on the Earth’s surface or even underground can become problematic for the required sensitivity. Here, the studies developed to quantify and reduce the activation yields in detectors and materials used in the set-up of these experiments will be reviewed, considering target materials like germanium, tellurium and xenon together with other ones commonly used like copper, lead, stainless steel or argon. Calculations following very different approaches and measurements from irradiation experiments using beams or directly cosmic rays will be considered for relevant radioisotopes. The effect of cosmogenic activation in present and future double beta decay projects based on different types of detectors will be analyzed too.

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Search for2νββdecay ofXe136to the01+ex

Cited by 17 publications

References 27 publications

Dark-matter And Neutrino Computation Explored (DANCE) Community Input to Snowmass

Dark-matter And Neutrino Computation Explored (DANCE) Community Input to Snowmass

Machine Learning in the Search for New Fundamental Physics

Cosmogenic Activation in Double Beta Decay Experiments

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