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Build Likelihoods Using Efficient Interpolations and monte-Carlo generated Events

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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.

Contributors

  • Jelle Aalbers
  • Knut Dundas Moraa
  • Bart Pelssers

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Build Likelihoods Using Efficient Interpolations and monte-Carlo generated Events

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  • Jupyter Notebook 57.6%
  • Python 42.4%