Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment

被引：18

作者：

Kekic, M. ^{[19
,20
,22
]}

Adams, C. ^{[2
]}

Woodruff, K. ^{[3
]}

Renner, J. ^{[19
,20
,22
]}

Church, E. ^{[21
]}

Del Tutto, M. ^{[5
]}

Hernando Morata, J. A. ^{[22
]}

Gomez-Cadenas, J. J. ^{[9
,16
]}

Alvarez, V ^{[23
]}

Arazi, L. ^{[6
]}

Arnquist, I. J. ^{[21
]}

Azevedo, C. D. R. ^{[4
]}

Bailey, K. ^{[2
]}

Ballester, F. ^{[23
]}

Benlloch-Rodriguez, J. M. ^{[16
,19
,20
]}

Borges, F. I. G. M. ^{[14
]}

Byrnes, N. ^{[3
]}

Carcel, S. ^{[19
,20
]}

Carrion, J., V ^{[19
,20
]}

Cebrian, S. ^{[24
]}

Conde, C. A. N. ^{[14
]}

Contreras, T. ^{[11
]}

Diaz, G. ^{[22
]}

Diaz, J. ^{[19
,20
]}

Diesburg, M. ^{[5
]}

Escada, J. ^{[14
]}

Esteve, R. ^{[23
]}

Felkai, R. ^{[6
,7
,19
,20
]}

Fernandes, A. F. M. ^{[13
]}

Fernandes, L. M. P. ^{[13
]}

Ferrario, P. ^{[9
,16
]}

Ferreira, A. L. ^{[4
]}

Freitas, E. D. C. ^{[13
]}

Generowicz, J. ^{[16
]}

Ghosh, S. ^{[11
]}

Goldschmidt, A. ^{[8
]}

Gonzalez-Diaz, D. ^{[22
]}

Guenette, R. ^{[11
]}

Gutierrez, R. M. ^{[10
]}

Haefner, J. ^{[11
]}

Hafidi, K. ^{[2
]}

Hauptman, J. ^{[1
]}

Henriques, C. A. O. ^{[13
]}

Herrero, P. ^{[16
]}

Herrero, V ^{[23
]}

Ifergan, Y. ^{[6
,7
]}

Jones, B. J. P. ^{[3
]}

Labarga, L. ^{[18
]}

Laing, A. ^{[3
]}

Lebrun, P. ^{[5
]}

机构：

[1] Iowa State Univ, Dept Phys & Astron, 12 Phys Hall, Ames, IA 50011 USA

[2] Argonne Natl Lab, 9700 S Cass Ave, Argonne, IL 60439 USA

[3] Univ Texas Arlington, Dept Phys, POB 19059, Arlington, TX 76019 USA

[4] Univ Aveiro, Inst Nanostruct Nanomodelling & Nanofabricat i3N, Campus Santiago, P-3810193 Aveiro, Portugal

[5] Fermilab Natl Accelerator Lab, POB 500, Batavia, IL 60510 USA

[6] Ben Gurion Univ Negev, Fac Engn Sci, Nucl Engn Unit, POB 653, IL-8410501 Beer Sheva, Israel

[7] Nucl Res Ctr Negev, IL-84190 Beer Sheva, Israel

[8] Lawrence Berkeley Natl Lab, 1 Cyclotron Rd, Berkeley, CA 94720 USA

[9] Ikerbasque, Basque Fdn Sci, E-48013 Bilbao, Spain

[10] Univ Antonio Narino, Ctr Invest Ciencias Basicas & Aplicadas, Sede Circunvalar, Carretera 3 Este 47 A-15, Bogota, Colombia

[11] Harvard Univ, Dept Phys, Cambridge, MA 02138 USA

[12] Lab Subterraneo Canfranc, Paseo Ayerbe S-N, E-22880 Canfranc Estacion, Spain

[13] Univ Coimbra, Phys Dept, LIBPhys, Rua Larga, P-3004516 Coimbra, Portugal

[14] Univ Coimbra, Dept Phys, LIP, P-3004516 Coimbra, Portugal

[15] Texas A&M Univ, Dept Phys & Astron, College Stn, TX 77843 USA

[16] Donostia Int Phys Ctr DIPC, Paseo Manuel Lardizabal 4, E-20018 Donostia San Sebastian, Spain

[17] Univ Girona, Escola Politecn Super, Av Montilivi S-N, E-17071 Girona, Spain

[18] Univ Autonoma Madrid, Dept Fis Teor, Campus Cantoblanco, E-28049 Madrid, Spain

[19] CSIC, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran 2, E-46980 Paterna, Spain

[20] Univ Valencia, Calle Catedrat Jose Beltran 2, E-46980 Paterna, Spain

[21] Pacific Northwest Natl Lab, Richland, WA 99352 USA

[22] Univ Santiago de Compostela, Inst Gallego Fis Altas Energias, Campus Sur,Rua Xose Maria Suarez Nunez S-N, E-15782 Santiago De Compostela, Spain

[23] Univ Politecn Valencia, Ctr Mixto, CSIC, Inst Instrumentac Imagen Mol I3M, Camino Vera S-N, E-46022 Valencia, Spain

[24] Univ Zaragoza, Ctr Astroparticulas & Fis Altas Energias CAPA, Calle Pedro Cerbuna 12, E-50009 Zaragoza, Spain

[25] Weizmann Inst Sci, Rehovot, Israel

[26] Univ Texas Austin, Austin, TX 78712 USA

来源：

JOURNAL OF HIGH ENERGY PHYSICS | 2021年 / 2021卷 / 01期

基金：

欧洲研究理事会;

关键词：

Dark Matter and Double Beta Decay (experiments);

D O I：

10.1007/JHEP01(2021)189

中图分类号：

O412 [相对论、场论]; O572.2 [粒子物理学];

学科分类号：

摘要：

Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses.

引用

页数：22

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