Identification of cosmic ray electrons and positrons by neural networks

被引：5

作者：

Aversa, F

Barbiellini, G

Basini, G

Bellotti, R

Bidoli, V

Bocciolini, M

Bravar, U

Boezio, M

Cafagna, F

Candusso, M

Casolino, M

Castellano, M

Circella, M

Colavita, A

DeCataldo, G

DeMarzo, C

DePascale, MP

Finetti, N

Fratnik, F

Giglietto, N

Golden, RL

Grimani, C

Hof, M

Marangelli, B

Brancaccio, FM

Menn, W

Mitchell, JW

Morselli, A

Papini, P

Perego, A

Piccardi, S

Picozza, P

Raino, A

Ricci, M

Schiavon, P

Simon, M

Sparvoli, R

Spillantini, P

Spinelli, P

Stephens, SA

Stochaj, SJ

Streitmatter, RE

Vacchi, A

Zampa, N

机构：

[1] IST NAZL FIS NUCL,I-70126 BARI,ITALY

[2] UNIV ROMA TOR VERGATA,I-00133 ROME,ITALY

[3] IST NAZL FIS NUCL,I-00133 ROME,ITALY

[4] UNIV TRIESTE,I-34147 TRIESTE,ITALY

[5] IST NAZL FIS NUCL,I-34147 TRIESTE,ITALY

[6] UNIV FLORENCE,I-50125 FLORENCE,ITALY

[7] IST NAZL FIS NUCL,I-50125 FLORENCE,ITALY

[8] IST NAZL FIS NUCL,LAB NAZL FRASCATI,I-00044 FRASCATI,ITALY

[9] UNIV PERUGIA,I-06100 PERUGIA,ITALY

[10] IST NAZL FIS NUCL,I-06100 PERUGIA,ITALY

[11] NEW MEXICO STATE UNIV,LAS CRUCES,NM 88003

[12] TATA INST FUNDAMENTAL RES,BOMBAY 400005,MAHARASHTRA,INDIA

[13] NASA,GODDARD SPACE FLIGHT CTR,GREENBELT,MD 20771

[14] UNIV SIEGEN,SIEGEN,GERMANY

来源：

ASTROPARTICLE PHYSICS | 1996年 / 5卷 / 02期

关键词：

D O I：

10.1016/0927-6505(96)00009-6

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

A data analysis based on artificial neural network classifiers has been done to identify cosmic ray electrons and positrons detected with the balloon-borne NMSU/Wizard-TS93 experiment. The information is provided by two ancillary and independent particle detectors: a transition radiation detector and a silicon-tungsten imaging calorimeter, Electrons and positrons measured during the flight have been identified with background rejection factors of 80 +/- 3 and 500 +/- 37 at signal efficiencies of 72 +/- 3% and 86 +/- 2% for the transition radiation detector and silicon-tungsten imaging calorimeter, respectively, The ability of the artificial neural network classifiers to perform a careful multidimensional analysis surpasses the results achieved by conventional methods.

引用

页码：111 / 117

页数：7

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