nuisance is hosted by Hepforge, IPPP Durham
NUISANCE

NUISANCE

NUISANCE aims to provide a coherent framework for comparing neutrino generators to external data. NUISANCE can also tune cross-section parameters to available data.

Latest validated release : v2r8

Authors: Luke Pickering, Patrick Stowell, Callum Wilkinson, Clarence Wret

Developer mailing list: nuisance@projects.hepforge.org

The NUISANCE Slack chat: nuisance-xsec.slack.com. If you want access without a @fnal.gov, please email us.


Installing NUISANCE

Documentation on the NUISANCE installation procedure is available here.

We have over 250 cross-section distributions publicly available for GENIE, NuWro, NEUT and GiBUU. For the full list of distributions, please see here.


  • Dec 2018:
    We're working with T2K and NOvA collaborators for the upcoming T2K-NOvA workshop at FNAL in February 2019
  • Oct 2018:
    The NOvA-MINERvA cross section workshop at FNAL was a success and we're happy to have be working with new people!
  • Sep 2018:
    We're preparing for T2K interaction model tuning for 2019+ analyses. We're also weasled a central role in developing the DUNE interaction model for the Technical Design Report, hurray!
  • July 2018:
    We've (hopefully) recovered from the HepForge migration from trac to phabricator. Unfortunately this means our wiki is no longer supported, although the information is still stored here. We're working on setting up the github mirror again and commit messages to Slack. Additionally, all NUISANCE authors have now finished their PhDs, woho! Onto summer conferences!
  • Jun 2017:
    NUISANCE v2r0 is validated and released! Check it out by:
    $ git clone http://nuisance.hepforge.org/git/nuisance.git -b v2r0
  • 11 May 2017:
    We've set up a public Github so external users can contribute to the package
  • March 2017:
    There is silence during the T2K cross-section inputs tuning...
  • 22 Jan 2017:
    The NUISANCE overview paper has been published in JINST.
    Thanks everyone for the help and support. Stay tuned for multi-generator tuning papers!
  • 3 Jan 2017:
    We're busy filling the wiki with tutorials and howtos. Check it out in the sidebar!
  • 21 Dec 2016:
    The NUISANCE paper is available on arxiv and has been submitted to JINST
  • 15 Dec 2016:
    NUISANCE v1r0 is validated and released! Check it out by:
    $ git clone http://nuisance.hepforge.org/git/nuisance.git -b v1r0

NUISANCE Features

Multiple Generator Inputs

NUISANCE can currently handle inputs from the following generators:

  • GENIE
  • NEUT
  • NuWro
  • GiBUU (with third party event converter)
  • NUANCE (Shape-only)
If there is a different generator you would like us to implement please let us know.

Raw Generator Comparisons

The structure of the NUISANCE core classes promotes consistent comparison of different generators by converting each format into a common NUISANCE event format. Tools are included to convert these events into simple 'flat tree' formats that can be analysed with ROOT alone.

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Figure 1: Raw Generator comparisons with NUISANCE for the MiniBooNE flux with CC1pi+ final state.
(left) Shape comparison. (right) Normalised cross-section comparison.

Data/MC Comparisons

The NUISANCE tool "nuiscomp" can be used to generate comparisons of any of the different generators supported and any dataset class already implemented into the framework.

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Figure 2: Model comparisons with NUISANCE.
(left) MiniBooNE CCQE numu. (right) MINERvA CC1pi numu.

Automated Parameter Tuning

Generator ReWeight dials can be provided to NUISANCE to produce modified cross-section predictions. The "nuismin" application interfaces these reweight dials with ROOT's minimizer libraries to support automated model parameter tuning using various possible parameter estimation routines.

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Figure 3: ANL Automated Tuning of the CCQE Axial Mass Parameter in NEUT.
(left) Likelihood Scan. (right) Enu ANL Cross-section Data.

Cross-section Systematic Tools

The NUISANCE tool "nuissyst" is provided to support the study of cross-section systematics for neutrino experiments. Different routines are implemented to validate reweight parameter responses and generate systematic error bands from arbritrary covariance matrices for those parameters.

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Figure 4: GENIE MC Toy Throws used to create systematic error bands.
(left) Example 1-sigma reweight dial variations. (right) Likelihood distribution for all toy throws.

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Figure 5: GENIE MC Systematic Error Bands for ArgoNeuT anti-neutrino data.
(left) Muon Momentum. (right) Muon Angle.