2 citations · 6 across the 7 of their papers we have counts for
8 papers
Leakage-Safe Machine Learning for Hydrogen Embrittlement Detection in 316L Stainless Steel: A Region-Held-Out Evaluation of Texture and Deep Features in SEM Micrographs
Muhammad Awais, Muhammad Yaseen, Abdul Shakoor +3
Scanning electron microscopy (SEM) is routinely used to characterize the microstructural changes caused by hydrogen embrittlement (HE) in structural steels. Machine learning can au…
On the Codesign of Scientific Experiments and Industrial Systems
Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas +84
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for in…
Differentiable Surrogate for Detector Simulation and Design with Diffusion Models
Xuan Tung Nguyen, Long Chen, Tommaso Dorigo +7
In this work, we present a conditional denoising-diffusion surrogate for electromagnetic calorimeter showers that is trained to generate high-fidelity energy-deposition maps condit…
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-…
On the Utility Function of Experiments in Fundamental Science
Tommaso Dorigo, Michele Doro, Max Aehle +7
The majority of experiments in fundamental science today are designed to be multi-purpose: their aim is not simply to measure a single physical quantity or process, but rather to e…