Zobrazeno 1 - 10
of 2 346
pro vyhledávání: '"Prosper , Harrison"'
Autor:
Smith, Nick, Spitzbart, Daniel, Dickinson, Jennet, Wilson, Jon, Gray, Lindsey, Mohrman, Kelci, Bhattacharya, Saptaparna, Piccinelli, Andrea, Roy, Titas, Paspalaki, Garyfallia, Fontes, Duarte, Martin, Adam, Shepherd, William, Cruz, Sergio Sánchez, Goncalves, Dorival, Gritsan, Andrei, Prosper, Harrison, Junk, Tom, Cranmer, Kyle, Peskin, Michael, Gilbert, Andrew, Langford, Jonathon, Petriello, Frank, Mantani, Luca, Wightman, Andrew, Knight, Charlotte, Shyamsundar, Prasanth, Basnet, Aashwin, Boldrini, Giacomo, Lannon, Kevin
The LPC EFT workshop was held April 25-26, 2024 at the University of Notre Dame. The workshop was organized into five thematic sessions: "how far beyond linear" discusses issues of truncation and validity in interpretation of results with an eye towa
Externí odkaz:
http://arxiv.org/abs/2408.11229
Autor:
Kadhim, Ali Al, Prosper, Harrison B.
Simulation-based inference methods that feature correct conditional coverage of confidence sets based on observations that have been compressed to a scalar test statistic require accurate modeling of either the p-value function or the cumulative dist
Externí odkaz:
http://arxiv.org/abs/2405.02488
High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. When coupled with machine learning, a simulator makes it possible to infer the parameters of a theoretical model directly from real a
Externí odkaz:
http://arxiv.org/abs/2306.07769
Publikováno v:
Mach. Learn.: Sci. Technol. 4 (2023) 015007
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 successful in numerical calculations in high-energy physics, analytical c
Externí odkaz:
http://arxiv.org/abs/2206.08901
Publikováno v:
PoS(EPS-HEP2021)906
Data analysis at the LHC has a very steep learning curve, which erects a formidable barrier between data and anyone who wishes to analyze data, either to study an idea or to simply understand how data analysis is performed. To make analysis more acce
Externí odkaz:
http://arxiv.org/abs/2203.13302
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 from analysis software frameworks, and argue that this approach provid
Externí odkaz:
http://arxiv.org/abs/2203.09886
Reliable modeling of conditional densities is important for quantitative scientific fields such as particle physics. In domains outside physics, implicit quantile neural networks (IQN) have been shown to provide accurate models of conditional densiti
Externí odkaz:
http://arxiv.org/abs/2111.11415
Autor:
Cranmer, Kyle, Kraml, Sabine, Prosper, Harrison B., Bechtle, Philip, Bernlochner, Florian U., Bloch, Itay M., Canonero, Enzo, Chrzaszcz, Marcin, Coccaro, Andrea, Conrad, Jan, Cowan, Glen, Feickert, Matthew, Iachellini, Nahuel Ferreiro, Fowlie, Andrew, Heinrich, Lukas, Held, Alexander, Kuhr, Thomas, Kvellestad, Anders, Madigan, Maeve, Mahmoudi, Farvah, Morå, Knut Dundas, Neubauer, Mark S., Pierini, Maurizio, Rojo, Juan, Sekmen, Sezen, Silvestrini, Luca, Sanz, Veronica, Stark, Giordon, Torre, Riccardo, Thorne, Robert, Waltenberger, Wolfgang, Wardle, Nicholas, Wittbrodt, Jonas
Publikováno v:
SciPost Phys. 12, 037 (2022)
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 this paper, we make the scientific case for systematically publishing
Externí odkaz:
http://arxiv.org/abs/2109.04981
This paper presents an overview and features of an Analysis Description Language (ADL) designed for HEP data analysis. ADL is a domain specific, declarative language that describes the physics content of an analysis in a standard and unambiguous way,
Externí odkaz:
http://arxiv.org/abs/2108.00857