237 citations · 2.3k across the 83 of their papers we have counts for
25 papers · 1 filter
Bayesian Optimization of Pythia8 Tunes
Ali Al Kadhim, Harrison B Prosper, Stephen Mrenna
A new tune (set of model parameters) is found for the six most important parameters of the Pythia8 final state parton shower and hadronization model using Bayesian optimization. Th…
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…
SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning
Abdulhakim Alnuqaydan, Sergei Gleyzer, Harrison Prosper
The cross section is one of the most important physical quantities in high-energy physics and the most time consuming to compute. While machine learning has proven to be highly suc…
Analysis Description Language: A DSL for HEP Analysis
Harrison B. Prosper, Sezen Sekmen, Gokhan Unel
We propose to adopt a declarative domain specific language for describing the physics algorithm of a high energy physics (HEP) analysis in a standard and unambiguous way decoupled…
Publishing statistical models: Getting the most out of particle physics experiments
Kyle Cranmer, Sabine Kraml, Harrison B. Prosper +30
The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In…
Analysis Description Languages for the LHC
Sezen Sekmen, Philippe Gras, Lindsey Gray +6
An analysis description language is a domain specific language capable of describing the contents of an LHC analysis in a standard and unambiguous way, independent of any computing…