13 citations · 28 across the 6 of their papers we have counts for
6 papers
Differentiable quantum-trajectory simulation of Lindblad dynamics for QGP transport-coefficient inference
Lukas Heinrich, Tom Magorsch
We study parameter estimation for the transport coefficients of the quark-gluon plasma by differentiating open-quantum-system-based Monte Carlo simulations of quarkonium suppressio…
Reinterpretation and preservation of data and analyses in HEP
Jon Butterworth, Sabine Kraml, Harrison Prosper +145
Data from particle physics experiments are unique and are often the result of a very large investment of resources. Given the potential scientific impact of these data, which goes…
Hierarchical Neural Simulation-Based Inference Over Event Ensembles
Lukas Heinrich, Siddharth Mishra-Sharma, Chris Pollard +1
When analyzing real-world data it is common to work with event ensembles, which comprise sets of observations that collectively constrain the parameters of an underlying model of i…
Learning Optimal Test Statistics in the Presence of Nuisance Parameters
Lukas Heinrich
The design of optimal test statistics is a key task in frequentist statistics and for a number of scenarios optimal test statistics such as the profile-likelihood ratio are known.…
Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper
Tommaso Dorigo, Andrea Giammanco, Pietro Vischia +33
The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensi…
neos: End-to-End-Optimised Summary Statistics for High Energy Physics
Nathan Simpson, Lukas Heinrich
The advent of deep learning has yielded powerful tools to automatically compute gradients of computations. This is because training a neural network equates to iteratively updating…