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Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network

Publication ,  Journal Article
Aad, G; Aakvaag, E; Abbott, B; Abdelhameed, S; Abeling, K; Abicht, NJ; Abidi, SH; Aboelela, M; Aboulhorma, A; Abramowicz, H; Abulaiti, Y ...
Published in: Scipost Physics
January 1, 2025
Published version (DOI)

The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach not only yields a continuous and smooth calibration function that improves performance relative to the standard calibration but also provides uncertainties on the calibrated energies for each topo-cluster. The results obtained by using a trained BNN are compared to the standard local hadronic calibration and to a calibration provided by training a deep neural network. The uncertainties predicted by the BNN are interpreted in the context of a fractional contribution to the systematic uncertainties of the trained calibration. They are also compared to uncertainty predictions obtained from an alternative estimator employing repulsive ensembles.

Duke Scholars

Ashutosh V. Kotwal
Author Ashutosh V. Kotwal Physics
Mark C. Kruse
Author Mark C. Kruse Physics

Published In

Scipost Physics

DOI

10.21468/SciPostPhys.19.6.155

EISSN

2542-4653

Publication Date

January 1, 2025

Volume

19

Issue

6

Related Subject Headings

  • 51 Physical sciences
  • 49 Mathematical sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Aad, G., Aakvaag, E., Abbott, B., Abdelhameed, S., Abeling, K., Abicht, N. J., … Aoki, T. (2025). Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network. Scipost Physics, 19(6). https://doi.org/10.21468/SciPostPhys.19.6.155
Aad, G., E. Aakvaag, B. Abbott, S. Abdelhameed, K. Abeling, N. J. Abicht, S. H. Abidi, et al. “Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network.” Scipost Physics 19, no. 6 (January 1, 2025). https://doi.org/10.21468/SciPostPhys.19.6.155.
Aad G, Aakvaag E, Abbott B, Abdelhameed S, Abeling K, Abicht NJ, et al. Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network. Scipost Physics. 2025 Jan 1;19(6).
Aad, G., et al. “Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network.” Scipost Physics, vol. 19, no. 6, Jan. 2025. Scopus, doi:10.21468/SciPostPhys.19.6.155.
Aad G, Aakvaag E, Abbott B, Abdelhameed S, Abeling K, Abicht NJ, Abidi SH, Aboelela M, Aboulhorma A, Abramowicz H, Abulaiti Y, Acharya BS, Ackermann A, Bourdarios CA, Adamczyk L, Addepalli SV, Addison MJ, Adelman J, Adiguzel A, Adye T, Affolder AA, Afik Y, Agaras MN, Aggarwal A, Agheorghiesei C, Ahmadov F, Ahuja S, Ai X, Aielli G, Aikot A, Tamlihat MA, Aitbenchikh B, Akbiyik M, Åkesson TPA, Akimov AV, Akiyama D, Akolkar NN, Aktas S, Alberghi GL, Albert J, Albicocco P, Albouy GL, Alderweireldt S, Alegria ZL, Aleksa M, Aleksandrov IN, Alexa C, Alexopoulos T, Alfonsi F, Algren M, Alhroob M, Ali B, Ali HMJ, Ali S, Alibocus SW, Aliev M, Alimonti G, Alkakhi W, Allaire C, Allbrooke BMM, Allen JS, Allen JF, Allport PP, Aloisio A, Alonso F, Alpigiani C, Alsolami ZMK, Fernandez AA, Cardoso MA, Alviggi MG, Aly M, Coutinho YA, Ambler A, Amelung C, Amerl M, Ames CG, Amidei D, Amini B, Amirie KJ, Amirkhanov A, Santos SPAD, Amos KR, Amperiadou D, An S, Ananiev V, Anastopoulos C, Andeen T, Anders JK, Anderson AC, Andreazza A, Angelidakis S, Angerami A, Anisenkov AV, Annovi A, Antel C, Antipov E, Antonelli M, Anulli F, Aoki M, Aoki T. Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network. Scipost Physics. 2025 Jan 1;19(6).

Published In

Scipost Physics

DOI

10.21468/SciPostPhys.19.6.155

EISSN

2542-4653

Publication Date

January 1, 2025

Volume

19

Issue

6

Related Subject Headings

  • 51 Physical sciences
  • 49 Mathematical sciences