Publication of statistical models: hands-on workshop

Name: Publication of statistical models: hands-on workshop
Start: 2021-11-08T14:00:00+01:00
End: 2021-11-12T19:00:00+01:00
Location: CERN (online only)

8 Nov 2021, 14:00 → 12 Nov 2021, 19:00 Europe/Zurich

CERN (online only)

Sabine Kraml (LPSC Grenoble)

Description

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 arXiv:2109.04981, we made the scientific case for systematically publishing the full statistical models; we discussed the technical developments that make this practical, and illustrated by a variety of physics cases how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results

This workshop is intended as the first in a series to discuss in more detail practical issues for publishing statistical models and likelihoods, and work towards concrete solutions.

In this context note also the PHYSTAT workshop on systematics (Nov 1-3 + Nov 10) and in particular the talk by Kyle Cranmer on "A call to action: Honoring PHYSTAT's 20 year old agreement" at 6 pm CET on Nov 1st there, which will also in part set the stage for our workshop here.

Overall, apart from the first two days the workshop addresses a rather specialized audience, i.e. people want to who work on technical solutions for publishing and/or (re)using statistical models and likelihoods.

Slides and recordings of all sessions are available via the timetable.

Registration

Registration and expression of interests

Participants

Adil Jueid
Adinda De Wit
Ajay Kumar
Akram Khan
Alexander Held
Ali Dokhani
Andre Lessa
Andre Sznajder
Andrea Coccaro
Andrew Fowlie
Andrew Gilbert
Andy Buckley
Arnab Purohit
Arnau Morancho
Aurore Savoy Navarro
Benjamin Fuks
Boyang Zhang
Caspar Schmitt
Chang-Seong Moon
Clemens Lange
Clement Helsens
David F. Renteria-Estrada
David Richard Shope
Dimitri Bourilkov
Edmund Xiang Lin Ting
Emanuele Angelo Bagnaschi
Enzo Canonero
Florian Urs Bernlochner
Frederic Engelke
Gaël Alguero
Giordon Holtsberg Stark
Glen Cowan
Henrik Junkerkalefeld
Humberto Reyes-González
Ingo Schienbein
Isabel Dominguez
Itay Bloch
Jack Araz
Jaco ter Hoeve
Jacob Julian Kempster
Jamie Yellen
Javier Mauricio Duarte
Jieun Yoo
Jing-Ge Shiu
Joaquin Hoya
Jonas Eschle
Jonas Wittbrodt
Jonathan Butterworth
Jonathon Mark Langford
Juan José López
Juan Rojo
Judita Mamuzic
Juhi Dutta
Juhi Dutta
Karri Folan Di Petrillo
KC Kong
Louie Dartmoor Corpe
Luca Silvestrini
Lukas Alexander Heinrich
Maeve Madigan
Maria Moreno Llacer
Mario Arndt
Mark Neubauer
Massimiliano Galli
Matteo Bonanomi
Matthew Feickert
Maximilian Horzela
Michael Eliachevitch
Mohammed Mahmoud Mohammed
Monika Mittal
nahuel ferreiro
Nahuel Ferreiro Iachellini
Nicholas Wardle
Nick Manganelli
Nick Smith
Nicolas Berger
Oliver Majersky
Oliver Schulz
Pietro Vischia
Priyanka Cheema
Raghav Kansal
Ramon Orlando Ruiz Olais
Ravindra Kumar Verma
Riccardo Torre
Sabine Kraml
Sam Kaveh
Saroj Pokharel
Sebastian Hoof
Sezen Sekmen
Shehu AbdusSalam
Shiva Bikram Thapa
Tamas Almos Vami
Thomas Kuhr
Tim Adye
Tim Herrmann
Timothée Pascal
Tomas Dado
Vasiliki Mitsou
Veronica Sanz Gonzalez
Vincent Alexander Croft
Wolfgang Waltenberger
Zhuolin Zhang

Monday, 8 November
- 1
  
  Worksop opening
  
  Speaker: Sabine Kraml (LPSC Grenoble)
  
  ws-opening.pdf
- 2
  
  Introductory talk: statistical models and likelihoods
  
  Speaker: Lukas Alexander Heinrich (CERN)
  
  PubLhoodIntro.pdf
  
  Recording (mp4)
- Presentation of use cases
  
  Convener: Sabine Kraml (LPSC Grenoble)
  
  Recording: MA5 SModelS Protomodelling Flavour talks (mp4)
  
  Recording: PDF EFT Higgs talks (mp4)
  - 3
    
    Parton Distribution Functions
    
    Speaker: Juan Rojo (VU Amsterdam and Nikhef)
    
    rojo-PDF-likelihoods.pdf
  - 4
    
    EFT fits
    
    Speaker: Veronica Sanz Gonzalez (Universities of Valencia and Sussex)
    
    publiclikelihoods.pdf
  - 5
    
    Higgs measurements
    
    Speaker: Jonas Wittbrodt (Lund University)
    
    wittbrodt.pdf
  - 15:30
    
    Break
  - 6
    
    Full and simplified likelihoods in MadAnalysis 5
    
    Speaker: Jack Araz (IPPP - Durham University)
    
    Full & simplified likelihoods in MadAnalysis5
    
    MadAnalysis 5 - Home page for talks and tutorials
    
    MadAnalysis 5 - Launchpad for questions and discussions
    
    Public Analysis Database
  - 7
    
    Full and simplified likelihoods in SModelS
    
    Speaker: Sabine Kraml (LPSC Grenoble)
    
    smodels.pdf
  - 8
    
    Analysis combinations and proto-modelling
    
    Speaker: Wolfgang Waltenberger (Austrian Academy of Sciences (AT))
    
    08nov2021.pdf
  - 9
    
    Heavy flavor physics
    
    Speaker: Florian Bernlochner (Uni Bonn)
    
    Likelihood_Flavor_Talk.pdf
- 10
  
  General discussion
Tuesday, 9 November
- 11
  Hands on pyhf
  Planned outline through pyhf tutorial material:
  - Introduction to HistFactory
  - Introduction to Workspaces
  - Modifiers
  - Workspace Manipulations
  - Using HEPData
  - Introduction to HistFactory Models with pyhf
  Speaker: Matthew Feickert (Univ. Illinois at Urbana Champaign (US))
  
  pyhf tutorial
  
  pyhf tutorial GitHub repository
- 16:30
  
  Break
- 12
  Hands on Combine
  - differences wrt HistFactory
  - serialisation of Combine statistical models
  - usage/extension of pyhf JSON format?
  - ....
  Speaker: Andrew Gilbert (Northwestern University (US))
  
  Combine-LikelihoodWorkshop.pdf
Wednesday, 10 November
- 13
  
  Summary talks from PHYSTAT workshop
  
  We join the PHYSTAT workshop to follow their summary talks and discussion --> https://indico.cern.ch/event/1051224/timetable/
- 16:30
  
  Break
- Discussion on simplified likelihoods
  
  Approaches, schemes, limitations; simplifying and pruning full statistical models (cf 3rd bullet point of Section 5 in arXiv:2109.04981)
  
  Conveners: Andy Buckley (University of Glasgow (GB)), Nicholas Wardle (Imperial College (GB))
  
  Recording (mp4)
  - 14
    
    Simplified likelihood frameworks
    
    Speaker: Nicholas Wardle (Imperial College (GB))
    
    paper ref.
    
    simplifiedlikelihoods.pdf
    
    SL code example
  - 15
    
    simplify
    
    simplify-discussion.pdf
  - 16
    
    Fast approximate likelihoods of complex models
    
    Speaker: Nicolas Berger (Centre National de la Recherche Scientifique (FR))
    
    likelihoods_20211110.pdf
  - 17
    
    Discussion on simplified likelihoods
Thursday, 11 November
- 18
  
  Free discussion and working session
  
  This is a free session for those who want to discuss something. No fixed program or topic. Join main Zoom room and move to separate discussion room if needed.
- 19
  
  Reinterpretation and likelihoods sessions of the LLP workshop
  
  see https://indico.cern.ch/event/1042226/timetable/
  
  to join:
  URL: https://cern.zoom.us/j/66746428033?pwd=ZGYvZWo1dlExT3ZnamlRbzdlcHdoZz09
  Meeting ID: 66746428033
  Passcode: 96028080
  
  LLP workshop timetable
- 16:30
  
  Break
- Joint session with LLP workshop
  
  This is a joint session with the parallel "Long-lived Particle Community Workshop" where publication of likelihoods will be discussed. Please note the alternative indigo page and zoom details !
  
  https://cern.zoom.us/j/69432885993?pwd=VWFEMnJFVFVxaW5JMnY2Rk53bzEvZz09
  Passcode: 96028080
  
  https://indico.cern.ch/event/1042226/timetable/#b-440156-re-interpretations-an
  
  Convener: Louie Dartmoor Corpe (CERN)
Friday, 12 November
- Machine learning likelihoods (and statistical models)
  
  We intend to discuss the main ideas related to interpolating likelihoods and statistical models using (Deep) Neural Networks. The main topics and open questions/issues are:
  - Bayesian vs Frequentist statistical approaches and their relations to the neural network representation of the Likelihood (e.g. combination of likelihoods and double counting of constraint terms vs priors, likelihood vs statistical model)
  - Interpolation of full statistical models through NN vs other established approaches
  - Regression vs density estimation (supervised vs unsupervised Likelihood learning)
  - Practical implementations within experiments
  - Practical implementations outside experiments (fitting groups)
  - Examples
  
  Conveners: Andrea Coccaro (INFN Genova (IT)), Riccardo Torre (INFN e Universita Genova (IT))
  
  2021-11-12_Publication-Statistical-Models.pdf
  
  GMT20211112-140831_Recording_1482x740.mp4
  
  paper ref
- 16:30
  
  break
- Discussion on measurement-unfolding tools and combinations of LHC searches and measurements
  
  Convener: Andy Buckley (University of Glasgow (GB))
  
  Recording (mp4)
  - 20
    
    Intro
    
    Speaker: Andy Buckley (University of Glasgow (GB))
    
    2021-11 PubStatModels Unfolding.pdf
  - 21
    
    RooUnfold (esp IBU)
    
    Speaker: Vincent Alexander Croft (Tufts University (US))
    
    RooUnfoldSimplifiedWorkshop.pdf
  - 22
    
    TUnfold
    
    Speaker: Stefan Schmitt (Deutsches Elektronen-Synchrotron (DE))
  - 23
    
    PyFBU
    
    Speaker: Clement Helsens (CERN)
    
    pyFBU_12-11-2021.pdf
  - 24
    
    TRExFitter
    
    Speaker: Michele Pinamonti (Universita degli Studi di Udine (IT))
  - 25
    
    Convino
    
    Speaker: Jan Kieseler (CERN)
  - 26
    
    Input from measurements with public stat models
  - 27
    
    Inputs from search, combination, and recasting

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Publication of statistical models: hands-on workshop

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