2 citations · 2 across the 1 of their papers we have counts for
5 papers
Inferring astrophysical X-ray polarization with deep learning
Nikita Moriakov, Ashwin Samudre, Michela Negro +3
We investigate the use of deep learning in the context of X-ray polarization detection from astrophysical sources as will be observed by the Imaging X-ray Polarimetry Explorer (IXP…
Deep-learning enhancement of large scale numerical simulations
Caspar van Leeuwen, Damian Podareanu, Valeriu Codreanu +11
Traditional simulations on High-Performance Computing (HPC) systems typically involve modeling very large domains and/or very complex equations. HPC systems allow running large mod…
Les Houches 2019 Physics at TeV Colliders: New Physics Working Group Report
G. Brooijmans, A. Buckley, S. Caron +87
This report presents the activities of the `New Physics' working group for the `Physics at TeV Colliders' workshop (Les Houches, France, 10--28 June, 2019). These activities includ…
Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming
Marco Chianese, Adam Coogan, Paul Hofma +2
Since upcoming telescopes will observe thousands of strong lensing systems, creating fully-automated analysis pipelines for these images becomes increasingly important. In this wor…
Constraining the Parameters of High-Dimensional Models with Active Learning
Sascha Caron, Tom Heskes, Sydney Otten +1
Constraining the parameters of physical models with parameters is a widespread problem in fields like particle physics and astronomy. The generation of data to explore this…