Built on

A. Buckley, D. Kar and K. Nordström, Fast simulation of detector effects in Rivet , SciPost Phys. 8
Original
1910
Earlier work this paper cites.
C. Bierlich et al., Robust Independent Validation of Experiment and Theory: Rivet version 3 , SciPost Phys. 8
Original
1912
Earlier work this paper cites.
S. Kraml et al., Searches for New Physics: Les Houches Recommendations for the Presentation of LHC Results , Eur. Phys. J. C 72
Original
1976
Earlier work this paper cites.
LHC Reinterpretation Forum
Original
2003
Earlier work this paper cites.
J.Y. Araz, B. Fuks and G. Polykratis, Simplified fast detector simulation in MADANALYSIS 5 , Eur. Phys. J. C 81
Original
2006
Earlier work this paper cites.
A. Buckley and M. Whalley, HepData reloaded: Reinventing the HEP data archive , PoS ACAT2010
Original
2010
Earlier work this paper cites.
ATLAS Collaboration, Search for squarks and gluinos in final states with jets and missing transverse momentum using 139 fb -1
Original
2010
Earlier work this paper cites.
K. Cranmer and I. Yavin, RECAST: Extending the Impact of Existing Analyses , JHEP 04
Original
2011
Earlier work this paper cites.
DELPHES 3
Original
2014
Earlier work this paper cites.
ATLAS Collaboration, Performance of b b -Jet Identification in the ATLAS Experiment , JINST 11
Original
2016
Earlier work this paper cites.
E. Maguire, L. Heinrich and G. Watt, HEPData: a repository for high energy physics data , J. Phys. Conf. Ser. 898
Original
2017
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D. Dercks, N. Desai, J.S. Kim, K. Rolbiecki, J. Tattersall and T. Weber, CheckMATE 2: From the model to the limit , Comput. Phys. Commun. 221
Original
2017
Earlier work this paper cites.
D. Guest, K. Cranmer and D. Whiteson, Deep Learning and its Application to LHC Physics , Ann. Rev. Nucl. Part. Sci. 68
Original
2018
Earlier work this paper cites.
K. Albertsson et al., Machine Learning in High Energy Physics Community White Paper , J. Phys. Conf. Ser. 1085
Original
2018
Earlier work this paper cites.
A. Radovic, M. Williams, D. Rousseau, M. Kagan, D. Bonacorsi, A. Himmel et al., Machine learning at the energy and intensity frontiers of particle physics , Nature 560
2018
Earlier work this paper cites.

Similar

E. Conte and B. Fuks, Confronting new physics theories to LHC data with MADANALYSIS 5 , Int. J. Mod. Phys. A 33
Original
2018
Cited alongside, same era.
ATLAS Collaboration, Search for pair production of heavy vector-like quarks decaying into hadronic final states in p p pp collisions at s = 13 \sqrt{s}=13 TeV with the ATLAS detector , Phys. Rev. D 98
Original
2018
Cited alongside, same era.
CMS Collaboration, Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV , JINST 13
Original
2018
Cited alongside, same era.
P. Shanahan et al., Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning , 2209.07559
Original
2021
Cited alongside, same era.
ATLAS Collaboration, “ ZeroLepton2018-SRBDT-weight.tar.gz

Then

H.B. Prosper, S. Sekmen and G. Unel, Analysis Description Language: A DSL for HEP Analysis , in Snowmass 2021 , 3, 2022 [ 2203.09886 ]
Original
2022
Later among the works it cites.
“7th LHC BSM Reinterpretation Forum Workshop.” https://indico.cern.ch/event/1197680/ , 12–15 Dec 2022, CERN
2022
Later among the works it cites.
ATLAS Collaboration, Search for flavour-changing neutral-current interactions of a top quark and a gluon in pp collisions at s = 13 \sqrt{s}=13 TeV with the ATLAS detector , Eur. Phys. J. C 82
Original
2022
Later among the works it cites.
ATLAS Collaboration, Search for supersymmetry in final states with missing transverse momentum and three or more b b -jets in 139 fb -1
Original
2023
Closest in time.
ATLAS Collaboration, “ ONNX.tar.gz
2023
Closest in time.

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2023

Les Houches guide to reusable ML models in LHC analyses

Araz, Jack Y., Buckley, Andy, Kasieczka, Gregor et al.

Understand

With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse.

The complexity of these models creates practical issues for both reporting them accurately and for ensuring the stability of their behaviours in different environments and over extended timescales.
In this note we discuss the current state of affairs, highlighting specific practical issues and focusing on the most promising technical and strategic approaches to ensure trustworthy analysis-preservation.
This material originated from discussions in the LHC Reinterpretation Forum and the 2023 PhysTeV workshop at Les Houches.

Built on

A. Buckley, D. Kar and K. Nordström, Fast simulation of detector effects in Rivet , SciPost Phys. 8
Original
1910
Earlier work this paper cites.
C. Bierlich et al., Robust Independent Validation of Experiment and Theory: Rivet version 3 , SciPost Phys. 8
Original
1912
Earlier work this paper cites.
S. Kraml et al., Searches for New Physics: Les Houches Recommendations for the Presentation of LHC Results , Eur. Phys. J. C 72
Original
1976
Earlier work this paper cites.
LHC Reinterpretation Forum
Original
2003
Earlier work this paper cites.
J.Y. Araz, B. Fuks and G. Polykratis, Simplified fast detector simulation in MADANALYSIS 5 , Eur. Phys. J. C 81
Original
2006
Earlier work this paper cites.
A. Buckley and M. Whalley, HepData reloaded: Reinventing the HEP data archive , PoS ACAT2010
Original
2010
Earlier work this paper cites.
ATLAS Collaboration, Search for squarks and gluinos in final states with jets and missing transverse momentum using 139 fb -1
Original
2010
Earlier work this paper cites.
K. Cranmer and I. Yavin, RECAST: Extending the Impact of Existing Analyses , JHEP 04
Original
2011
Earlier work this paper cites.
DELPHES 3
Original
2014
Earlier work this paper cites.
ATLAS Collaboration, Performance of b b -Jet Identification in the ATLAS Experiment , JINST 11
Original
2016
Earlier work this paper cites.
E. Maguire, L. Heinrich and G. Watt, HEPData: a repository for high energy physics data , J. Phys. Conf. Ser. 898
Original
2017
Earlier work this paper cites.
D. Dercks, N. Desai, J.S. Kim, K. Rolbiecki, J. Tattersall and T. Weber, CheckMATE 2: From the model to the limit , Comput. Phys. Commun. 221
Original
2017
Earlier work this paper cites.
D. Guest, K. Cranmer and D. Whiteson, Deep Learning and its Application to LHC Physics , Ann. Rev. Nucl. Part. Sci. 68
Original
2018
Earlier work this paper cites.
K. Albertsson et al., Machine Learning in High Energy Physics Community White Paper , J. Phys. Conf. Ser. 1085
Original
2018
Earlier work this paper cites.
A. Radovic, M. Williams, D. Rousseau, M. Kagan, D. Bonacorsi, A. Himmel et al., Machine learning at the energy and intensity frontiers of particle physics , Nature 560
2018
Earlier work this paper cites.

Similar

E. Conte and B. Fuks, Confronting new physics theories to LHC data with MADANALYSIS 5 , Int. J. Mod. Phys. A 33
Original
2018
Cited alongside, same era.
ATLAS Collaboration, Search for pair production of heavy vector-like quarks decaying into hadronic final states in p p pp collisions at s = 13 \sqrt{s}=13 TeV with the ATLAS detector , Phys. Rev. D 98
Original
2018
Cited alongside, same era.
CMS Collaboration, Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV , JINST 13
Original
2018
Cited alongside, same era.
P. Shanahan et al., Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning , 2209.07559
Original
2021
Cited alongside, same era.
ATLAS Collaboration, “ ZeroLepton2018-SRBDT-weight.tar.gz

Then

H.B. Prosper, S. Sekmen and G. Unel, Analysis Description Language: A DSL for HEP Analysis , in Snowmass 2021 , 3, 2022 [ 2203.09886 ]
Original
2022
Later among the works it cites.
“7th LHC BSM Reinterpretation Forum Workshop.” https://indico.cern.ch/event/1197680/ , 12–15 Dec 2022, CERN
2022
Later among the works it cites.
ATLAS Collaboration, Search for flavour-changing neutral-current interactions of a top quark and a gluon in pp collisions at s = 13 \sqrt{s}=13 TeV with the ATLAS detector , Eur. Phys. J. C 82
Original
2022
Later among the works it cites.
ATLAS Collaboration, Search for supersymmetry in final states with missing transverse momentum and three or more b b -jets in 139 fb -1
Original
2023
Closest in time.
ATLAS Collaboration, “ ONNX.tar.gz
2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2021

Cited alongside, same era.

ATLAS Collaboration, “ ZeroLeptonBDT2018.cxx

2021

Cited alongside, same era.

ATLAS Collaboration, “ SUSY-2019-04-ONNX.tgz

2021

Cited alongside, same era.

ATLAS Collaboration, “ SUSY-2019-04.cxx

2021

Cited alongside, same era.

K. Uno, “Material for the ATLAS 0-lepton gluino/squark search with 139/fb (incl. BDT weights!). Reinterpretation: Auxiliary Material Presentation (RAMP) seminar, 2021-04-09.” https://cds.cern.ch/record/2763449 , 2021

2021

Cited alongside, same era.

L. Corpe, “Material from the ATLAS EXOT-2019-23 (neutral LLP to displaced jets in calo) analysis. Reinterpretation: Auxiliary Material Presentation (RAMP) seminar, 2023-01-16.” https://indico.cern.ch/event/1233294/ , 2021

2021

Cited alongside, same era.

G. Unel, S. Sekmen, A.M. Toon, B. Gokturk, B. Orgen, A. Paul et al., CutLang v2: Advances in a Runtime-Interpreted Analysis Description Language for HEP Data , Front. Big Data 4

Original

2021

Cited alongside, same era.

D.H. Guest, J.W. Smith, M. Paganini, M. Kagan, M. Lanfermann, A. Krasznahorkay et al., “lwtnn/lwtnn: Version 2.13.” https://doi.org/10.5281/zenodo.6467676 , Apr., 2022

2022

Cited alongside, same era.

ATLAS Collaboration, “ BDTS.tar.gz

2023

Closest in time.

ATLAS Collaboration, “Data from Figures 11 and 12 of Aux. Material of Ref. [ 26 ] (HEPData record, version 3).” https://doi.org/10.17182/hepdata.115578.v3/t48 , https://doi.org/10.17182/hepdata.115578.v3/t49 , 2023

2023

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ATLAS Collaboration, “ readMap.py

2023

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ATLAS Collaboration, “ VRNN_weights.pth

2023

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ATLAS Collaboration, “ VRNN_model.py

2023

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J.M. Berlingen, “Material from the ATLAS SUSY RPV lepton(s) + jets analysis (ATLAS-SUSY-2019-04). Reinterpretation: Auxiliary Material Presentation (RAMP) seminar, 2021-11-19.” https://indico.cern.ch/event/1083851/ , 2023

2023

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“8th LHC BSM Reinterpretation Forum Workshop.” https://conference.ippp.dur.ac.uk/event/1178/ , 29 Aug. – 1 Sep. 2023, Durham Univ., Durham, UK

2023

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ATLAS Collaboration, Search for pair-produced scalar and vector leptoquarks decaying into third-generation quarks and first- or second-generation leptons in pp collisions with the ATLAS detector , JHEP 2306

Original

2023

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S. An, L. Moneta, S. Sengupta, A. Hamdan, F. Sossai and A. Saxena, C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA , J. Phys. Conf. Ser. 2438

2023

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