Bayesian Inference in High-Energy Physics

Europe/London
OC218 (IPPP)

OC218

IPPP

Ian Vernon (Durham University), Jernej Kamenik (Jozef Stefan Institute, Ljubljana), Manuel Szewc (ICAS-UNSAM & IJS), Michael Spannowsky (IPPP, Durham University), Stephen Jones (IPPP, Durham), ezequiel alvarez (ICAS, UNSAM)
Description


Bayesian inference methods consist in finding out probability distribution over unknowns given some observations and some knowledge of the model that connects them.  These techniques not only look very suitable for inquiring Nature in High-Energy Physics, but also they have been highly boosted and developed in the last decades through a broad spectrum of Machine Learning applications.  Our challenge is to reach the state-of-the-art of these techniques within High-Energy Physics applications to study their scope in better understanding non-perturbative phenomena, improving Monte Carlo techniques, improving experimental setups, modelling and eventually finding New Physics, and many other LHC-related disciplines, as well as other HEP areas.

The workshop consists in mainly three aspects, on which we aim to trigger discussions that construct connecting and developing bridges.  From one side, the known and in-research HEP cases, in theory, phenomenology and experiment,  in which inference methods provide relevant results.  From another side, how Monte Carlo simulations could be assisted through Bayesian methods.  And from the final side, we want to discuss with Computer Science specialists who foster the latest developments and tools on how these can be best used in HEP.

The workshop is in-person at the Durham IPPP (UK) and we aim to have ~25 participants.  Sessions will mix talks, coffee, and discussion slots.  If you are interested in attending the workshop please apply by registering here.


Follow us on twitter @IPPP_Durham

Participants
  • Alexander Rothkopf
  • Andy Buckley
  • Barry Dillon
  • Daniel Reichelt
  • Darren Wilkinson
  • David Yallup
  • ezequiel alvarez
  • Hailiang Du
  • Ian Jermyn
  • Ian Vernon
  • Jack Araz
  • Jon-Ivar Skullerud
  • Jordan Melendez
  • Krzysztof Graczyk
  • Lukas Heinrich
  • Manuel Szewc
  • Michael Goldstein
  • Michael Spannowsky
  • Mike Walmsley
  • Muhammad M Hasan
  • Sebastian Schmon
  • Shashi Kumar Samdarshi
  • Steffen Bass
  • Stephen Jones
  • Taylor Faucett
  • Theo Heimel
  • Tom Stone
    • 10:00 AM 11:30 AM
      Durham Locals Session: Intro to Bayesian Inference (for our local PhD / PostDoc / Staff) 1h 30m OC218

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      Attendance from workshop participants is welcome but not expected. This session is aimed at introducing some of the basic ideas of Bayesian Inference to our local PhD/ PostDocs and interested staff members.

    • 1:55 PM 2:00 PM
      Workshop Official Start & Welcome 5m OC218

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    • 2:00 PM 6:00 PM
      Results talks OC218

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      Talks on existing papers, results, etc. by some of its authors

      • 2:00 PM
        Mapping Machine-Learned Physics into a Human-Readable Space 35m
        Speaker: Taylor Faucett (UC, Irvine)
      • 2:35 PM
        Bayesian Inference of QCD splittings with Invertible Networks 35m
        Speaker: Theo Heimel (U. Heidelberg, ITP)
      • 3:10 PM
        How well do we know the neutron-matter equation of state at the densities inside neutron stars? A Bayesian approach with correlated uncertainties 35m
        Speaker: Jordan Melendez (The Ohio State University)
      • 3:45 PM
        Coffee Break 30m
      • 4:15 PM
        A graphical multi-fidelity Gaussian process model, with application to emulation of expensive computer simulations 35m
        Speaker: Steffen Bass (Duke University)
      • 4:50 PM
        Exploring phase space with Nested Sampling 35m
        Speaker: David Yallup (University of Cambridge)
    • 9:00 AM 12:50 PM
      Computer Science and Statistics: Mathematical aspects of Bayesian inference, by the Statistics group @ Durham OC218

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      Talks on Computer Science tools

      Convener: Prof. Ian Vernon (Durham)
      • 9:00 AM
        Fully Bayesian parameter inference for Markov processes 40m
        Speaker: Darren Wilkinson (Department of Mathematical Sciences at Durham University)
      • 9:40 AM
        Bayes linear strategies for emulation and history matching for complex computer models 40m
        Speaker: Michael Goldstein (Department of Mathematical Sciences at Durham University)
      • 10:20 AM
        Multilevel Emulation of Complex Computer Models, with application to EAGLE, a Galaxy Formation Simulation 40m
        Speaker: Ian Vernon (Department of Mathematical Sciences at Durham University)
      • 11:00 AM
        Coffee break 20m
      • 11:20 AM
        Enforcing stationarity through the prior in vector autoregressions 40m
        Speaker: Sarah Heaps (Department of Mathematical Sciences at Durham University)
      • 12:00 PM
        Contemporaneous MCMC 30m
        Speaker: Louis Aslett (Department of Mathematical Sciences at Durham University)
    • 1:00 PM 2:30 PM
      Buffet Lunch 1h 30m OC218

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    • 2:40 PM 6:40 PM
      Topics talks on Bayesian Inference in HEP OC218

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      Talks on HEP topics from the Bayesian Inference point of view

      • 2:40 PM
        Differentiable programming and its applications 40m
        Speaker: Lukas Heinrich (Technical University Munich)
      • 3:20 PM
        HEP Simulator issues tackled with Bayesian Inference 40m
        Speaker: Manuel Szewc (IJS & ICAS-UNSAM)
      • 4:00 PM
        Coffee Break 30m
      • 4:30 PM
        Quantifying Uncertainty in Deep Learning 40m
        Speaker: Mike Walmsley (University of Manchester)
      • 5:10 PM
        The inverse problem challenge in lattice QCD 40m
        Speaker: Alexander Rothkopf (University of Stavanger)
    • 7:00 PM 9:00 PM
      Conference Dinner 2h Alishaan Durham

      Alishaan Durham

      https://goo.gl/maps/SNUbcwgXbqBDp9EB9
    • 9:00 AM 11:00 AM
      Topics talks on Bayesian Inference in HEP OC218

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      IPPP

      Talks on HEP topics from the Bayesian Inference point of view

      • 9:00 AM
        Causal Inference 40m
        Speaker: Ezequiel Alvarez (ICAS, UNSAM)
      • 9:40 AM
        Representation Learning for HEP 40m
        Speaker: Barry Dillon (University of Heidelberg)
      • 10:20 AM
        Bayesian community criteria for anomaly evaluation 40m
        Speaker: Jernej Kamenik (Jozef Stefan Institute & Ljubljana University)
    • 11:00 AM 11:30 AM
      Coffee 30m OC218

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    • 11:30 AM 12:30 PM
      Open discussion OC218

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      Conference summary and open discussion