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Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter
M. Aamir, B. Acar, G. Adamov, T. Adams, C. Adloff, S. Afanasiev, C. Agrawal, C. Agrawal, A. Ahmad, H. A. Ahmed, S. Akbar, N. Akchurin, B. Akgul, B. Akgun, R. O. Akpinar, E. Aktas, A. AlKadhim, V. Alexakhin, J. Alimena, J. Alison, et al (555)
Jun 19 2024 physics.ins-det hep-ex physics.data-an arXiv:2406.11937v2

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H.~Sert and M.~Shahid and P.~Sharma and R.~Sharma and S.~Sharma and M.~Shelake and A.~Shenai and C.~W.~Shih and R.~Shinde and D.~Shmygol and R.~Shukla and E.~Sicking and P.~Silva and C.~Simsek and E.~Simsek and B.~K.~Sirasva and Y.~Sirois and S.~Song and Y.~Song and G.~Soudais and S.~Sriram and R.~R.~StJacques and A.~G.~StahlLeiton and A.~Steen and J.~Stein and J.~Strait and N.~Strobbe and X.~Su and E.~Sukhov and A.~Suleiman and D.~Sunar Cerci and P.~Suryadevara and K.~Swain and C.~Syal and B.~Tali and K.~Tanay and W.~Tang and A.~Tanvir and J.~Tao and A.~Tarabini and T.~Tatli and R.~Taylor and Z.~C.~Taysi and G.~Teafoe and C.~Z.~Tee and W.~Terrill and D.~Thienpont and R.~Thomas and M.~Titov and C.~Todd and E.~Todd and M.~Toms and A.~Tosun and J.~Troska and L.~Tsai and Z.~Tsamalaidze and D.~Tsionou and G.~Tsipolitis and M.~Tsirigoti and R.~Tu and S.~N.~Tural Polat and S.~Undleeb and E.~Usai and E.~Uslan and V.~Ustinov and E.~Vernazza and O.~Viahin and O.~Viazlo and P.~Vichoudis and A.~Vijay and T.~Virdee and E.~Voirin and M.~Vojinovic and N.~Voytishin and T.~Á.~Vámi and A.~Wade and D.~Walter and C.~Wang and F.~Wang and J.~Wang and K.~Wang and X.~Wang and X.~Wang and Y.~Wang and Z.~Wang and E.~Wanlin and M.~Wayne and J.~Wetzel and A.~Whitbeck and R.~Wickwire and D.~Wilmot and J.~Wilson and H.~Wu and M.~Xiao and J.~Yang and B.~Yazici and Y.~Ye and T.~Yetkin and R.~Yi and R.~Yohay and T.~Yu and C.~Yuan and X.~Yuan and O.~Yuksel and I.~YushmanoV and I.~Yusuff and A.~Zabi and D.~Zareckis and A.~Zarubin and P.~Zehetner and A.~Zghiche and C.~Zhang and D.~Zhang and H.~Zhang and J.~Zhang and J.~Zhang and Z.~Zhang and X.~Zhao and J.~Zhong and Y.~Zhou and Ç.~Zorbilmez}, title = {{U}sing graph neural networks to reconstruct charged pion showers in the {CMS} {H}igh {G}ranularity {C}alorimeter}, year = {2024}, eprint = {2406.11937}, note = {arXiv:2406.11937v2} }
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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.