Publishing statistical models: Getting the most out of particle physics experiments

Kyle Cranmer; Sabine Kraml; Harrison B. Prosper; Philip Bechtle; Florian U. Bernlochner; Itay M. Bloch; Enzo Canonero; Marcin Chrzaszcz; Andrea Coccaro; Jan Conrad; Glen Cowan; Matthew Feickert; Nahuel F. Iachellini; Andrew Fowlie; Lukas Heinrich; Alexander Held; Thomas Kuhr; Anders Kvellestad; Maeve Madigan; Farvah Mahmoudi; Knut D. Morå; Mark S. Neubauer; Maurizio Pierini; Juan Rojo; Sezen Sekmen; Luca Silvestrini; Veronica Sanz; Giordon Stark; Riccardo Torre; Robert Thorne; Wolfgang Waltenberger; Nicholas Wardle; Jonas Wittbrodt

doi:10.21468/SciPostPhys.12.1.037

Publishing statistical models: Getting the most out of particle physics experiments

Kyle Cranmer^*
, Sabine Kraml^*
, Harrison B. Prosper^*
, Philip Bechtle
, Florian U. Bernlochner
, Itay M. Bloch
, Enzo Canonero
, Marcin Chrzaszcz
, Andrea Coccaro
, Jan Conrad
, Glen Cowan
, Matthew Feickert
, Nahuel F. Iachellini
, Andrew Fowlie
, Lukas Heinrich
, Alexander Held
, Thomas Kuhr
, Anders Kvellestad
, Maeve Madigan
, Farvah Mahmoudi

Knut D. Morå, Mark S. Neubauer, Maurizio Pierini, Juan Rojo, Sezen Sekmen, Luca Silvestrini, Veronica Sanz, Giordon Stark, Riccardo Torre, Robert Thorne, Wolfgang Waltenberger, Nicholas Wardle, Jonas Wittbrodt

^*Corresponding author for this work

New York University
Laboratoire de Physique Subatomique et de Cosmologie de Grenoble
Florida State University
University of Bonn
Tel Aviv University
University of Genoa
Polish Academy of Sciences
National Institute for Nuclear Physics
Stockholm University
Royal Holloway University of London
University of Illinois at Urbana-Champaign
Max Planck Institute for Physics (Werner Heisenberg Institute)
Excellence Cluster ORIGINS
Nanjing Normal University
CERN
Ludwig Maximilian University of Munich
University of Oslo
University of Cambridge
Université de Lyon
Columbia University
Vrije Universiteit Amsterdam
Kyungpook National University
University of Sussex
University of Valencia
University of California at Santa Cruz
University College London
University of Vienna
Imperial College London
Lund University

Research output: Contribution to journal › Article › peer-review

33 Citations (Scopus)

Abstract

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 the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases - including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits - we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.

Original language	English
Article number	037
Journal	SciPost Physics
Volume	12
Issue number	1
DOIs	https://doi.org/10.21468/SciPostPhys.12.1.037
Publication status	Published - Jan 2022
Externally published	Yes

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10.21468/SciPostPhys.12.1.037

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Cranmer, K., Kraml, S., Prosper, H. B., Bechtle, P., Bernlochner, F. U., Bloch, I. M., Canonero, E., Chrzaszcz, M., Coccaro, A., Conrad, J., Cowan, G., Feickert, M., Iachellini, N. F., Fowlie, A., Heinrich, L., Held, A., Kuhr, T., Kvellestad, A., Madigan, M., ... Wittbrodt, J. (2022). Publishing statistical models: Getting the most out of particle physics experiments. SciPost Physics, 12(1), Article 037. https://doi.org/10.21468/SciPostPhys.12.1.037

@article{2879923a962a4bd4ae466cf4e31b4b44,

title = "Publishing statistical models: Getting the most out of particle physics experiments",

abstract = "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 the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases - including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits - we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.",

author = "Kyle Cranmer and Sabine Kraml and Prosper, \{Harrison B.\} and Philip Bechtle and Bernlochner, \{Florian U.\} and Bloch, \{Itay M.\} and Enzo Canonero and Marcin Chrzaszcz and Andrea Coccaro and Jan Conrad and Glen Cowan and Matthew Feickert and Iachellini, \{Nahuel F.\} and Andrew Fowlie and Lukas Heinrich and Alexander Held and Thomas Kuhr and Anders Kvellestad and Maeve Madigan and Farvah Mahmoudi and Mor{\aa}, \{Knut D.\} and Neubauer, \{Mark S.\} and Maurizio Pierini and Juan Rojo and Sezen Sekmen and Luca Silvestrini and Veronica Sanz and Giordon Stark and Riccardo Torre and Robert Thorne and Wolfgang Waltenberger and Nicholas Wardle and Jonas Wittbrodt",

note = "Publisher Copyright: {\textcopyright} The Author(s), 2021.",

year = "2022",

month = jan,

doi = "10.21468/SciPostPhys.12.1.037",

language = "English",

volume = "12",

journal = "SciPost Physics",

issn = "2542-4653",

number = "1",

}

Cranmer, K, Kraml, S, Prosper, HB, Bechtle, P, Bernlochner, FU, Bloch, IM, Canonero, E, Chrzaszcz, M, Coccaro, A, Conrad, J, Cowan, G, Feickert, M, Iachellini, NF, Fowlie, A, Heinrich, L, Held, A, Kuhr, T, Kvellestad, A, Madigan, M, Mahmoudi, F, Morå, KD, Neubauer, MS, Pierini, M, Rojo, J, Sekmen, S, Silvestrini, L, Sanz, V, Stark, G, Torre, R, Thorne, R, Waltenberger, W, Wardle, N & Wittbrodt, J 2022, 'Publishing statistical models: Getting the most out of particle physics experiments', SciPost Physics, vol. 12, no. 1, 037. https://doi.org/10.21468/SciPostPhys.12.1.037

TY - JOUR

T1 - Publishing statistical models

T2 - Getting the most out of particle physics experiments

AU - Cranmer, Kyle

AU - Kraml, Sabine

AU - Prosper, Harrison B.

AU - Bechtle, Philip

AU - Bernlochner, Florian U.

AU - Bloch, Itay M.

AU - Canonero, Enzo

AU - Chrzaszcz, Marcin

AU - Coccaro, Andrea

AU - Conrad, Jan

AU - Cowan, Glen

AU - Feickert, Matthew

AU - Iachellini, Nahuel F.

AU - Fowlie, Andrew

AU - Heinrich, Lukas

AU - Held, Alexander

AU - Kuhr, Thomas

AU - Kvellestad, Anders

AU - Madigan, Maeve

AU - Mahmoudi, Farvah

AU - Morå, Knut D.

AU - Neubauer, Mark S.

AU - Pierini, Maurizio

AU - Rojo, Juan

AU - Sekmen, Sezen

AU - Silvestrini, Luca

AU - Sanz, Veronica

AU - Stark, Giordon

AU - Torre, Riccardo

AU - Thorne, Robert

AU - Waltenberger, Wolfgang

AU - Wardle, Nicholas

AU - Wittbrodt, Jonas

PY - 2022/1

Y1 - 2022/1

N2 - 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 the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases - including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits - we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.

AB - 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 the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases - including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits - we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.

UR - https://www.scopus.com/pages/publications/85124422901

U2 - 10.21468/SciPostPhys.12.1.037

DO - 10.21468/SciPostPhys.12.1.037

M3 - Article

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SN - 2542-4653

VL - 12

JO - SciPost Physics

JF - SciPost Physics

IS - 1

M1 - 037

ER -

Publishing statistical models: Getting the most out of particle physics experiments

Abstract

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