25 citations · 200 across the 16 of their papers we have counts for
18 papers
Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning
S. Lai, J. Utehs, A. Wilhahn +48
To achieve state-of-the-art jet energy resolution for Particle Flow, sophisticated energy clustering algorithms must be developed that can fully exploit available information to se…
Software Compensation for Highly Granular Calorimeters using Machine Learning
S. Lai, J. Utehs, A. Wilhahn +61
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 in…
Fully Depleted Monolithic Active Microstrip Sensors: TCAD simulation study of an innovative design concept
Lorenzo De Cilladi, Thomas Corradino, Gian-Franco Dalla Betta +2
The paper presents the simulation studies of 10 m pitch microstrips on a fully depleted monolithic active CMOS technology and describes their potential to provide a new and cost…
Optimising longitudinal and lateral calorimeter granularity for software compensation in hadronic showers using deep neural networks
Coralie Neubüser, Jan Kieseler, Paul Lujan
We investigate the effect of longitudinal and transverse calorimeter segmentation on event-by-event software compensation for hadronic showers. To factorize out sampling and electr…
Sensor design optimization of innovative low-power, large area MAPS for HEP and applied science
Coralie Neubüser, Thomas Corradino, Gian-Franco Dalla Betta +2
Fully Depleted Monolithic Active Pixels (FD-MAPS) represent a state-of-the-art detector technology and profit from a low material budget and cost for high energy physics experiment…
Calorimeters for the FCC-hh
M. Aleksa, P. Allport, R. Bosley +14
The future proton-proton collider (FCC-hh) will deliver collisions at a center of mass energy up to TeV at an unprecedented instantaneous luminosity of …