113 citations · 328 across the 8 of their papers we have counts for
9 papers
Machine Learning and LHC Event Generation
Anja Butter, Tilman Plehn, Steffen Schumann +48
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predicti…
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
The DarkMachines High Dimensional Sampling Group, Csaba Balázs, Melissa van Beekveld +18
Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensi…
Interactive web-based visualization of multi-dimensional physical and astronomical data
Faruk Diblen, Luc Hendriks, Bob Stienen +3
In this manuscript, we propose to expand the use of scientific repositories such as Zenodo and HEP Data, in particular in order to better examine multi-parametric solutions of phys…
Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
Bob Stienen, Rob Verheyen
We explore the use of autoregressive flows, a type of generative model with tractable likelihood, as a means of efficient generation of physical particle collider events. The usual…
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…
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…