258 citations · 6.6k across the 165 of their papers we have counts for
174 papers
Optimisation of a silicon-tungsten electromagnetic calorimeter energy response to photons
Yukun Shi, Vincent Boudry
An innovative path for the detectors at future colliders to achieve higher performances is to use a Particle Flow approach, which requires highly granular calorimeters to image ind…
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…
Energy and Time Histograms: Fluxes determination in calorimeters
Khalid Hassouna, Vincent Boudry
In high-granularity calorimetry, as proposed for detectors at future Higgs factories, the requirements on electronics can have a strong impact on the design of the detector, especi…
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…
Technical Design Report for the LUXE Experiment
H. Abramowicz, M. Almanza Soto, M. Altarelli +131
This Technical Design Report presents a detailed description of all aspects of the LUXE (Laser Und XFEL Experiment), an experiment that will combine the high-quality and high-energ…
Unbinned Deep Learning Jet Substructure Measurement in High ep collisions at HERA
The H1 collaboration, V. Andreev, M. Arratia +142
The radiation pattern within high energy quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force as well as an environment f…