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Electromagnetic Shower Reconstruction and Identification in FASER's Emulsion Detector for LHC Forward Neutrino Measurements
Abstract
We present methods for electromagnetic shower reconstruction and identification in the FASERnu emulsion detector using 100 GeV and 200 GeV electron test-beam data from the CERN SPS H4 beamline. The reconstruction employs a clustering-based algorithm without energy-dependent tuning to determine shower axes. A multi-level identification chain comprising track pre-selection, a cut-based selection, and a BDT classifier achieves combined background rejection rates of 99.99% (100 GeV) and 99.94% (200 GeV). The method reaches total reconstruction and identification efficiencies of 58.9% (100 GeV) and 70.8% (200 GeV) evaluated from simulated samples. Energy reconstruction using the total number of reconstructed segments as the calorimetric estimator yields relative biases of +0.6% (100 GeV) and -0.8% (200 GeV), with resolutions of 25.4% and 22.6%, respectively. Systematic uncertainties on the energy reconstruction are dominated by variations in emulsion film detection efficiency, with totals of (+10.9%/-8.2%) at 100 GeV and (+10.3%/-6.9%) at 200 GeV. The methodology provides a validated framework for electron neutrino identification with the FASERnu detector at the LHC.
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BibTeX
@misc{collaboration2026electromagnetic,
title = {{Electromagnetic Shower Reconstruction and Identification in FASER's Emulsion Detector for LHC Forward Neutrino Measurements}},
author = {FASER Collaboration and Roshan Mammen Abraham and Xiaocong Ai and Saul Alonso Monsalve and John Anders and Emma Kate Anderson and Akitaka Ariga and Tomoko Ariga and Jeremy Atkinson and Florian U. Bernlochner and Jianming Bian and Tobias Boeckh and Eliot Bornand and Jamie Boyd and Lydia Brenner and Angela Burger and Franck Cadoux and Roberto Cardella and David W. Casper and Charlotte Cavanagh and Shiyang Chen and Xin Chen and Xing Cheng and Dhruv Chouhan and Andrea Coccaro and Fabio Cufino and Stephane Débieux and Ansh Desai and Sergey Dmitrievsky and Radu Dobre and Monica D'Onofrio and Sinead Eley and Yannick Favre and Jonathan L. Feng and Carlo Alberto Fenoglio and Didier Ferrere and Max Fieg and Wissal Filali and Elena Firu and Haruhi Fujimori and Edward Galantay and Stephen Gibson and Sergio Gonzalez-Sevilla and Yuri Gornushkin and Yotam Granov and Jinjing Gu and Carl Gwilliam and Elie Hammou and Daiki Hayakawa and Michael Holzbock and Shih-Chieh Hsu and Zhen Hu and Giuseppe Iacobucci and Tomohiro Inada and Luca Iodice and Sune Jakobsen and Cesar Jesus-Valls and Arash Jofrehei and Hans Joos and Enrique Kajomovitz},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2606.18517},
eprint = {2606.18517},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.18517},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Electromagnetic Shower Reconstruction and Identification in FASER's Emulsion Detector for LHC Forward Neutrino Measurements
AU - FASER Collaboration
AU - Roshan Mammen Abraham
AU - Xiaocong Ai
AU - Saul Alonso Monsalve
AU - John Anders
AU - Emma Kate Anderson
AU - Akitaka Ariga
AU - Tomoko Ariga
AU - Jeremy Atkinson
AU - Florian U. Bernlochner
AU - Jianming Bian
AU - Tobias Boeckh
AU - Eliot Bornand
AU - Jamie Boyd
AU - Lydia Brenner
AU - Angela Burger
AU - Franck Cadoux
AU - Roberto Cardella
AU - David W. Casper
AU - Charlotte Cavanagh
AU - Shiyang Chen
AU - Xin Chen
AU - Xing Cheng
AU - Dhruv Chouhan
AU - Andrea Coccaro
AU - Fabio Cufino
AU - Stephane Débieux
AU - Ansh Desai
AU - Sergey Dmitrievsky
AU - Radu Dobre
AU - Monica D'Onofrio
AU - Sinead Eley
AU - Yannick Favre
AU - Jonathan L. Feng
AU - Carlo Alberto Fenoglio
AU - Didier Ferrere
AU - Max Fieg
AU - Wissal Filali
AU - Elena Firu
AU - Haruhi Fujimori
AU - Edward Galantay
AU - Stephen Gibson
AU - Sergio Gonzalez-Sevilla
AU - Yuri Gornushkin
AU - Yotam Granov
AU - Jinjing Gu
AU - Carl Gwilliam
AU - Elie Hammou
AU - Daiki Hayakawa
AU - Michael Holzbock
AU - Shih-Chieh Hsu
AU - Zhen Hu
AU - Giuseppe Iacobucci
AU - Tomohiro Inada
AU - Luca Iodice
AU - Sune Jakobsen
AU - Cesar Jesus-Valls
AU - Arash Jofrehei
AU - Hans Joos
AU - Enrique Kajomovitz
PY - 2026
DA - 2026/08/24
PB - arXiv
DO - 10.48550/arxiv.2606.18517
UR - https://arxiv.org/abs/2606.18517
AB - We present methods for electromagnetic shower reconstruction and identification in the FASERnu emulsion detector using 100 GeV and 200 GeV electron test-beam data from the CERN SPS H4 beamline. The reconstruction employs a clustering-based algorithm without energy-dependent tuning to determine shower axes. A multi-level identification chain comprising track pre-selection, a cut-based selection, and a BDT classifier achieves combined background rejection rates of 99.99% (100 GeV) and 99.94% (200 GeV). The method reaches total reconstruction and identification efficiencies of 58.9% (100 GeV) and 70.8% (200 GeV) evaluated from simulated samples. Energy reconstruction using the total number of reconstructed segments as the calorimetric estimator yields relative biases of +0.6% (100 GeV) and -0.8% (200 GeV), with resolutions of 25.4% and 22.6%, respectively. Systematic uncertainties on the energy reconstruction are dominated by variations in emulsion film detection efficiency, with totals of (+10.9%/-8.2%) at 100 GeV and (+10.3%/-6.9%) at 200 GeV. The methodology provides a validated framework for electron neutrino identification with the FASERnu detector at the LHC.
ER -