Tootfinder

Software Compensation for Highly Granular Calorimeters using Machine Learning
S. Lai, J. Utehs, A. Wilhahn, O. Bach, E. Brianne, A. Ebrahimi, K. Gadow, P. G\"ottlicher, O. Hartbrich, D. Heuchel, A. Irles, K. Kr\"uger, J. Kvasnicka, S. Lu, C. Neub\"user, 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.…

Software Compensation for Highly Granular Calorimeters using Machine Learning
A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale…