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BibTex format

@article{Aamir:2024:11/P11025,
author = {Aamir, M and Adamov, G and Adams, T and Adloff, C and Afanasiev, S and Agrawal, C and Agrawal, C and Ahmad, A and Ahmed, H and Akbar, S and Akchurin, N and Akgul, B and Akgun, B and Akpinar, R and Aktas, E and Al, Kadhim A and Alexakhin, V and Alimena, J and Alison, J and Alpana, A and Alshehri, W and Alvarez, Dominguez P and Alyari, M and Amendola, C and Amir, R and Andersen, S and Andreev, Y and Antoszczuk, P and Aras, U and Ardila, L and Aspell, P and Avila, M and Awad, I and Aydilek, O and Azimi, Z and Aznar, Pretel A and Bach, O and Bainbridge, R and Bakshi, A and Bam, B and Banerjee, S and Barney, D and Bayraktar, O and Beaudette, F and Beaujean, F and Becheva, E and Behera, P and Belloni, A and Bergauer, T and Besancon, M and Bessidskaia, Bylund O and Bhatt, L and Bhattacharya, S and Bhowmil, D and Blekman, F and Blinov, P and Bloch, P and Bodek, A and Boger, A and Bonnemaison, A and Bouyjou, F and Brennan, L and Brondolin, E and Brusamolino, A and Bubanja, I and Buchot, Perragu},
doi = {11/P11025},
journal = {Journal of Instrumentation},
title = {Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter},
url = {http://dx.doi.org/10.1088/1748-0221/19/11/P11025},
volume = {19},
year = {2024}
}
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TY  - JOUR
AB  - A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.
AU  - Aamir,M
AU  - Adamov,G
AU  - Adams,T
AU  - Adloff,C
AU  - Afanasiev,S
AU  - Agrawal,C
AU  - Agrawal,C
AU  - Ahmad,A
AU  - Ahmed,H
AU  - Akbar,S
AU  - Akchurin,N
AU  - Akgul,B
AU  - Akgun,B
AU  - Akpinar,R
AU  - Aktas,E
AU  - Al,Kadhim A
AU  - Alexakhin,V
AU  - Alimena,J
AU  - Alison,J
AU  - Alpana,A
AU  - Alshehri,W
AU  - Alvarez,Dominguez P
AU  - Alyari,M
AU  - Amendola,C
AU  - Amir,R
AU  - Andersen,S
AU  - Andreev,Y
AU  - Antoszczuk,P
AU  - Aras,U
AU  - Ardila,L
AU  - Aspell,P
AU  - Avila,M
AU  - Awad,I
AU  - Aydilek,O
AU  - Azimi,Z
AU  - Aznar,Pretel A
AU  - Bach,O
AU  - Bainbridge,R
AU  - Bakshi,A
AU  - Bam,B
AU  - Banerjee,S
AU  - Barney,D
AU  - Bayraktar,O
AU  - Beaudette,F
AU  - Beaujean,F
AU  - Becheva,E
AU  - Behera,P
AU  - Belloni,A
AU  - Bergauer,T
AU  - Besancon,M
AU  - Bessidskaia,Bylund O
AU  - Bhatt,L
AU  - Bhattacharya,S
AU  - Bhowmil,D
AU  - Blekman,F
AU  - Blinov,P
AU  - Bloch,P
AU  - Bodek,A
AU  - Boger,A
AU  - Bonnemaison,A
AU  - Bouyjou,F
AU  - Brennan,L
AU  - Brondolin,E
AU  - Brusamolino,A
AU  - Bubanja,I
AU  - Buchot,Perraguin A
AU  - Bunin,P
AU  - Burazin,Misura A
AU  - Butler-Nalin,A
AU  - Cakir,A
AU  - Callier,S
AU  - Campbell,S
AU  - Candemir,Y
AU  - Canderan,K
AU  - Cankocak,K
AU  - Cappati,A
AU  - Caregari,S
AU  - Carron,S
AU  - Carty,C
AU  - Cauchois,A
AU  - Ceard,L
AU  - Cerci,S
AU  - Chang,P
AU  - Chatterjee,R
AU  - Chatterjee,S
AU  - Chattopadhyay,P
AU  - Chatzistavrou,T
AU  - Chaudhary,M
AU  - Chen,J
AU  - Chen,J
AU  - Chen,Y
AU  - Cheng,K
AU  - Cheung,H
AU  - Chhikara,J
AU  - Chiron,A
AU  - Chiusi,M
AU  - Chokheli,D
AU  - Chudasama,R
AU  - Clement,E
AU  - Coco,Mendez S
DO  - 11/P11025
PY  - 2024///
TI  - Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter
T2  - Journal of Instrumentation
UR  - http://dx.doi.org/10.1088/1748-0221/19/11/P11025
VL  - 19
ER  -
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