Outreach¶
We are always interested in talking about pyhf
. See the abstract and a list of previously given presentations and feel free to invite us to your next conference/workshop/meeting!
Abstract¶
The HistFactory p.d.f. template [CERN-OPEN-2012-016] is per-se independent of its implementation in ROOT and it is useful to be able to run statistical analysis outside of the ROOT, RooFit, RooStats framework. pyhf is a pure-python implementation of that statistical model for multi-bin histogram-based analysis and its interval estimation is based on the asymptotic formulas of “Asymptotic formulae for likelihood-based tests of new physics” [1007.1727]. pyhf supports modern computational graph libraries such as TensorFlow and PyTorch in order to make use of features such as auto-differentiation and GPU acceleration.
The HistFactory p.d.f. template \href{https://cds.cern.ch/record/1456844}{[CERN-OPEN-2012-016]} is per-se independent of its implementation in ROOT and it is useful to be able to run statistical analysis outside of the ROOT, RooFit, RooStats framework. pyhf is a pure-python implementation of that statistical model for multi-bin histogram-based analysis and its interval estimation is based on the asymptotic formulas of "Asymptotic formulae for likelihood-based tests of new physics" \href{https://arxiv.org/abs/1007.1727}{[arXiv:1007.1727]}. pyhf supports modern computational graph libraries such as TensorFlow and PyTorch in order to make use of features such as autodifferentiation and GPU acceleration.
Presentations¶
This list will be updated with talks given on pyhf
:
Matthew Feickert. Likelihood preservation and statistical reproduction of searches for new physics. CHEP 2019, Nov 2019. URL: https://indico.cern.ch/event/773049/contributions/3476143/.
Matthew Feickert. pyhf: pure-Python implementation of HistFactory. PyHEP 2019 Workshop, Oct 2019. URL: https://indico.cern.ch/event/833895/contributions/3577824/.
Matthew Feickert, Lukas Heinrich, Giordon Stark, and Kyle Cranmer. pyhf: a pure Python implementation of HistFactory with tensors and autograd. DIANA Meeting - pyhf, October 2018. URL: https://indico.cern.ch/event/759480/.
Matthew Feickert, Lukas Heinrich, Giordon Stark, and Kyle Cranmer. pyhf: pure-Python implementation of HistFactory models with autograd. (Internal) Joint Machine Learning & Statistics Fora Meeting, September 2018. URL: https://indico.cern.ch/event/757657/contributions/3141134/.
Lukas Heinrich. Gaussian Process Shape Estimation and Systematics. (Internal) Joint Machine Learning & Statistics Fora Meeting, Dec 2018. URL: https://indico.cern.ch/event/777561/contributions/3234669/.
Lukas Heinrich. HEP in the Cloud Computing and Open Science Era. EP-IT Data science seminar, Oct 2019. URL: https://indico.cern.ch/event/840837/.
Lukas Heinrich. Likelihoods associated with statistical fits used in searches for new physics on HEPData and use of RECAST. (Internal) ATLAS Weekly Meeting, Nov 2019. URL: https://indico.cern.ch/event/864395/contributions/3642165/.
Lukas Heinrich. Traditional inference with machine learning tools. 1st Pan-European Advanced School on Statistics in High Energy Physics, Oct 2019. URL: https://indico.desy.de/indico/event/22731/session/4/contribution/19.
Lukas Heinrich. pyhf: Full Run-2 ATLAS likelihoods. (Internal) Joint Machine Learning & Statistics Fora Meeting, May 2019. URL: https://indico.cern.ch/event/817483/contributions/3412907/.
Lukas Heinrich, Matthew Feickert, Giordon Stark, and Kyle Cranmer. pyhf: A standalone HistFactory Implementation. (Re)interpreting the results of new physics searches at the LHC Workshop, May 2018. URL: https://indico.cern.ch/event/702612/contributions/2958658/.
Giordon Stark. Likelihood Preservation and Reproduction. West Coast LHC Jamboree 2019, Oct 2019. URL: https://indico.cern.ch/event/848030/contributions/3616614/.
Giordon Stark. New techniques for use of public likelihoods for reinterpretation of search results. 27th International Conference on Supersymmetry and Unification of Fundamental Interactions (SUSY2019), May 2019. URL: https://indico.cern.ch/event/746178/contributions/3396797/.
Tutorials¶
This list will be updated with tutorials and schools given on pyhf
:
Lukas Heinrich. Introduction to pyhf. (Internal) ATLAS Induction Day + Software Tutorial, Oct 2019. URL: https://indico.cern.ch/event/831761/contributions/3484275/.
Posters¶
This list will be updated with posters presented on pyhf
:
Matthew Feickert, Lukas Heinrich, Giordon Stark, and Kyle Cranmer. pyhf: a pure Python statistical fitting library for High Energy Physics with tensors and autograd. July 2019. 18th Scientific Computing with Python Conference (SciPy 2019). URL: http://conference.scipy.org/proceedings/scipy2019/slides.html, doi:10.25080/Majora-7ddc1dd1-019.
Matthew Feickert, Lukas Heinrich, Giordon Stark, and Kyle Cranmer. pyhf: pure Python implementation of HistFactory. November 2019. 24th International Conference on computing in High Energy & Nuclear Physics (CHEP 2019). URL: https://indico.cern.ch/event/773049/contributions/3476180/.
Lukas Heinrich, Matthew Feickert, Giordon Stark, and Kyle Cranmer. pyhf: auto-differentiable binned statistical models. 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019), March 2019. URL: https://indico.cern.ch/event/708041/contributions/3272095/.
In the Media¶
This list will be updated with media publications featuring pyhf
:
Katarina Anthony. New open release streamlines interactions with theoretical physicists. ATLAS News, December 2019. URL: https://atlas.cern/updates/atlas-news/new-open-likelihoods.
Katarina Anthony. New open release allows theorists to explore LHC data in a new way. CERN News, January 2020. URL: https://home.cern/news/news/knowledge-sharing/new-open-release-allows-theorists-explore-lhc-data-new-way.