264 citations · 825 across the 13 of their papers we have counts for
15 papers
Scaling MadMiner with a deployment on REANA
Irina Espejo, Sinclert Pérez, Kenyi Hurtado +2
MadMiner is a Python package that implements a powerful family of multivariate inference techniques that leverage matrix element information and machine learning. This multivariate…
Data and Analysis Preservation, Recasting, and Reinterpretation
Stephen Bailey, Christian Bierlich, Andy Buckley +15
We make the case for the systematic, reliable preservation of event-wise data, derived data products, and executable analysis code. This preservation enables the analyses' long-ter…
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…
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…
Software Training in HEP
Sudhir Malik, Samuel Meehan, Kilian Lieret +44
Long term sustainability of the high energy physics (HEP) research software ecosystem is essential for the field. With upgrades and new facilities coming online throughout the 2020…
HL-LHC Computing Review: Common Tools and Community Software
HEP Software Foundation, :, Thea Aarrestad +107
Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will b…
Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2
Waleed Abdallah, Shehu AbdusSalam, Azar Ahmadov +139
We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpret…
Extending RECAST for Truth-Level Reinterpretations
Alex Schuy, Lukas Heinrich, Kyle Cranmer +1
RECAST is an analysis reinterpretation framework; since analyses are often sensitive to a range of models, RECAST can be used to constrain the plethora of theoretical models withou…
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Atılım Güneş Baydin, Lei Shao, Wahid Bhimji +14
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remai…
Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider
Juliette Alimena, James Beacham, Martino Borsato +198
Particles beyond the Standard Model (SM) can generically have lifetimes that are long compared to SM particles at the weak scale. When produced at experiments such as the Large Had…
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji +12
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which all…
Machine Learning in High Energy Physics Community White Paper
Kim Albertsson, Piero Altoe, Dustin Anderson +125
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by a…
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio +8
We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from po…
A Roadmap for HEP Software and Computing R&D for the 2020s
Johannes Albrecht, Antonio Augusto Alves, Guilherme Amadio +307
Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facil…
Yadage and Packtivity - analysis preservation using parametrized workflows
Kyle Cranmer, Lukas Heinrich
Preserving data analyses produced by the collaborations at LHC in a parametrized fashion is crucial in order to maintain reproducibility and re-usability. We argue for a declarativ…