Cosmic Ray Background Removal With Deep Neural Networks in SBND.
Cosmic Ray Background Removal With Deep Neural Networks in SBND.
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DOI：
10.3389/frai.2021.649917
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发表时间：
2021
影响因子：
4
通讯作者：
Zglam A
中科院分区：
文献类型：
--
作者：
Acciarri R;Adams C;Andreopoulos C;Asaadi J;Babicz M;Backhouse C;Badgett W;Bagby L;Barker D;Basque V;Bazetto MCQ;Betancourt M;Bhanderi A;Bhat A;Bonifazi C;Brailsford D;Brandt AG;Brooks T;Carneiro MF;Chen Y;Chen H;Chisnall G;Crespo-Anadón JI;Cristaldo E;Cuesta C;de Icaza Astiz IL;De Roeck A;de Sá Pereira G;Del Tutto M;Di Benedetto V;Ereditato A;Evans JJ;Ezeribe AC;Fitzpatrick RS;Fleming BT;Foreman W;Franco D;Furic I;Furmanski AP;Gao S;Garcia-Gamez D;Frandini H;Ge G;Gil-Botella I;Gollapinni S;Goodwin O;Green P;Griffith WC;Guenette R;Guzowski P;Ham T;Henzerling J;Holin A;Howard B;Jones RS;Kalra D;Karagiorgi G;Kashur L;Ketchum W;Kim MJ;Kudryavtsev VA;Larkin J;Lay H;Lepetic I;Littlejohn BR;Louis WC;Machado AA;Malek M;Mardsen D;Mariani C;Marinho F;Mastbaum A;Mavrokoridis K;McConkey N;Meddage V;Méndez DP;Mettler T;Mistry K;Mogan A;Molina J;Mooney M;Mora L;Moura CA;Mousseau J;Navrer-Agasson A;Nicolas-Arnaldos FJ;Nowak JA;Palamara O;Pandey V;Pater J;Paulucci L;Pimentel VL;Psihas F;Putnam G;Qian X;Raguzin E;Ray H;Reggiani-Guzzo M;Rivera D;Roda M;Ross-Lonergan M;Scanavini G;Scarff A;Schmitz DW;Schukraft A;Segreto E;Soares Nunes M;Soderberg M;Söldner-Rembold S;Spitz J;Spooner NJC;Stancari M;Stenico GV;Szelc A;Tang W;Tena Vidal J;Torretta D;Toups M;Touramanis C;Tripathi M;Tufanli S;Tyley E;Valdiviesso GA;Worcester E;Worcester M;Yarbrough G;Yu J;Zamorano B;Zennamo J;Zglam A
在暴露于中微子束的液态氩时间投影室中，在表面水平上或附近运行，宇宙μ子和其他宇宙粒子入射到探测器上，同时记录单个中微子引发的事件。在实践中，这意味着来自表面液态氩时间投射室的数据将由宇宙粒子主导，既作为事件触发源，也作为真正中微子触发事件中的大多数粒子计数。在这项工作中，我们展示了深度学习技术的一种新应用，通过对来自SBND探测器的完整探测器图像应用深度学习来去除这些背景粒子，SBND探测器是费米实验室短基线中微子计划中的近探测器。我们使用这种技术来识别，在一个像素一个像素的水平上，是否记录的活动起源于宇宙粒子或中微子相互作用。
In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons, and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.
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DOI：
10.1016/j.nima.2009.12.009
发表时间：
2010-02-21
影响因子：
1.4
作者：
Andreopoulos, C.;Bell, A.;Yang, T.
通讯作者：
Yang, T.
影响因子：
1.3
作者：
Adams, C.;An, R.;Zhang, C.
通讯作者：
Zhang, C.
影响因子：
1.3
作者：
Anderson, C.;Antonello, M.;Zeller, G. P.
通讯作者：
Zeller, G. P.
影响因子：
1.3
作者：
Qian, X.;Zhang, C.;Diwan, M.
通讯作者：
Diwan, M.
影响因子：
4.4
作者：
Acciarri, R.;Adams, C.;Zhang, C.
通讯作者：
Zhang, C.