Software Compensation for Highly Granular Calorimeters Using Machine Learning
S. Lai, J. Utehs, A. Wilhahn,O. Bach,E. Brianne,A. Ebrahimi,K. Gadow,P. Goettlicher,O. Hartbrich,D. Heuchel,A. Irles,K. Krueger,J. Kvasnicka,S. Lu,C. Neubueser,A. Provenza,M. Reinecke,F. Sefkow,S. Schuwalow,M. De Silva,Y. Sudo,H. L. Tran,E. Buhmann,E. Garutti,S. Huck,G. Kasieczka,S. Martens,J. Rolph,J. Wellhausen,G. C. Blazey,A. Dyshkant,K. Francis,V Zutshi,B. Bilki,D. Northacker,Y. Onel,F. Hummer,F. Simon,K. Kawagoe, T. Onoe,T. Suehara,S. Tsumura,T. Yoshioka,M. C. Fouz,L. Emberger,C. Graf, M. Wagner,R. Poschl,F. Richard,D. Zerwas,V Boudry,J-C Brient,J. Nanni,H. Videau,L. Liu,R. Masuda, T. Murata,W. Ootani, T. Takatsu,N. Tsuji,M. Chadeeva,M. Danilov,S. Korpachev,V Rusinov JOURNAL OF INSTRUMENTATION（2024）
Key words
Large detector-systems performance,Pattern recognition,cluster finding,calibration and fitting methods,Performance of High Energy Physics Detectors
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