blueice: Build Likelihoods Using Efficient Interpolations and monte-Carlo generated Events

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Source code: https://github.com/JelleAalbers/blueice

Documentation: http://blueice.readthedocs.io/en/latest/index.html

About

This package allows you to do parametric inference using likelihood functions, in particular likelihoods derived from Monte-Carlo or calibration sources.

Especially when connected to a Monte Carlo, blueice lets you make likelihood functions which measure agreement between data and theory with flexibility: you choose which settings to vary (which parameters the likelihood functions has) and in which space the agreement is measured.

This package contains only generic code: you’ll need a few things to make it useful for a particular experiment. Originally this code was developed for XENON1T only; the XENON1T models have since been split off to the laidbax repository. XENONnT is still developing alea which is based on blueice.

Contributors

  • Jelle Aalbers

  • Knut Dundas Moraa

  • Bart Pelssers

1.2.2 (2026/8/13)

  • Cubic PChip interpolation by @Ananthu-Ravindran (#49)

  • Brentq argument passing fix (#50)

1.2.1 (2024/12/11)

1.2.0 (2024/01/13)

  • Prevent negative rates being passed to Barlow-Beeston equation, and allow per-event weights (#32)

  • Add likelihood that takes coupling as shape parameters (#34)

  • Patch for tests (#37)

  • Use scipy stats for PoissonLL (#40)

  • Do not scale mus when livetime_days is 0 (#41)

1.1.0 (2021/01/07)

  • Likelihood sum wrapper (#17)

  • emcee bestfit and multicore precomputation (#18)

  • LogAncillaryLikelihood for constraint terms (#19)

  • HistogramPDFSource simulation, order shape parameter dict (#20)

  • Efficiency shape parameter, LogLikelihoodSum enhancements (#23)

  • Use scipy as default optimizer (#24)

  • Minuit support for bounds and errors (#26, #27)

  • Per-source efficiencies, weighted LogLikelihoodSum (#28)

  • Use atomicwrites for cache to prevent race conditions (#30)

1.0.0 (2016/10/01)

  • Binned likelihoods (#7)

  • Argument validation for LogLikelihood function (#8)

  • Automatic handling of statistical uncertainty due to finite MC/calibration statistics (#9): * Adjustment of expected counts per bin using Beeston-Barlow method for one source * Generalized to multiple sources, but only one with finite statistics. * Only for binned likelihoods.

  • iminuit integration, use as default minimizer if installed (#10, #13)

  • compute_pdf option to do full likelihood model computation on the fly (#11)

  • HistogramPDF to provide just histogram lookup/interpolation from DensityEstimatingSource (#12)

  • inference functions -> LogLikelihood methods

  • Most-used functions/classes available under blueice (blueice.Source, blueice.UnbinnedLogLikelihood, …)

  • compute_pdf auto-called, consistent handling of events_per_day

  • Start of documentation, readthedocs integration

0.4.0 (2016/08/22)

  • Big internal refactor, some API changes (#5)

  • DensityEstimatingSource

  • Bugfixes, more tests

0.3.0 (2016/08/21)

  • Renamed to blueice, XENON stuff renamed to laidbax

  • Experimental radial template morphing (#4)

  • Tests, several bugfixes (e.g. #3)

  • Rate parameters are now rate multipliers

  • Linear interpolation of density estimator

  • Parallel model initialization

0.2.0 (2016/07/31)

  • Complete makeover centered around LogLikelihood function

  • Separation of XENON stuff and general code

  • PDF caching

  • Example notebooks

0.1.0 (2016/07/14)

  • First release in separate repository

  • Model and Source, pdf sampling.

0.0.1 (2015/12/18)

  • First release in XeAnalysisScripts

Project links

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Credits

Author: Jelle Aalbers

GitHub Statistics

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The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

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blueice

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