blueice 1.2.0

Build Likelihoods Using Efficient Interpolations from monte-Carlo generated Events

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

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

1.2.0 (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