Learning to identify electrons - INSPIRE

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Learning to identify electrons

Julian Collado
(
- UC, Irvine (main)
)
,
Jessica N. Howard
(
- UC, Irvine
)
,
Taylor Faucett
(
- UC, Irvine
)
,
Tony Tong
(
- UC, Irvine
)
,
Pierre Baldi
(
- UC, Irvine (main)
)

Nov 3, 2020

10 pages

Published in:

Phys.Rev.D 103 (2021) 11, 116028

Published: Jun 1, 2021

e-Print:

2011.01984 [physics.data-an]

DOI:

10.1103/PhysRevD.103.116028 (publication)

View in:

ADS Abstract Service

reference search17 citations

Citations per year

Abstract: (APS)

We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information. A deep convolutional neural network analysis of electromagnetic and hadronic calorimeter deposits is compared to the performance of typical features, revealing a

\approx 5 %

gap which indicates that these lower-level data do contain untapped classification power. To reveal the nature of this unused information, we use a recently developed technique to map the deep network into a space of physically interpretable observables. We identify two simple calorimeter observables which are not typically used for electron identification, but which mimic the decisions of the convolutional network and nearly close the performance gap.

Note:

10 pages, lots of figures, v2 for submission

calorimeter: hadronic
jet: background
electron: particle identification
particle identification: performance
network
gap
calorimeter: electromagnetic
neural network
data analysis method

References(44)

Figures(26)

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(, Cambridge, England, to be published)

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(
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(
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(
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(
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(
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