4 citations · 6 across the 6 of their papers we have counts for
6 papers
End-to-End Detector Optimization with Diffusion models: A Case Study in Sampling Calorimeters
Kylian Schmidt, Nikhil Kota, Jan Kieseler +16
Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics…
Neuromorphic Readout for Hadron Calorimeters
Enrico Lupi, Abhishek, Max Aehle +17
We simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter to investigate how the resulting light yield and its temporal structure, as detected by an array o…
Hadron Identification Prospects With Granular Calorimeters
Andrea De Vita, Abhishek, Max Aehle +15
In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy depositions in dense matter, along with the time…
Unsupervised Particle Tracking with Neuromorphic Computing
Emanuele Coradin, Fabio Cufino, Muhammad Awais +6
We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-…
Learning the Approach During the Short-loading Cycle Using Reinforcement Learning
Carl Borngrund, Ulf Bodin, Henrik Andreasson +1
The short-loading cycle is a repetitive task performed in high quantities, making it a great alternative for automation. In the short-loading cycle, an expert operator navigates to…
Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
Max Aehle, Lorenzo Arsini, R. Belén Barreiro +27
In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models…