Metadata-Version: 2.1
Name: flamedisx
Version: 1.4.1
Summary: Fast likelihood analysis in more dimensions for xenon TPCs
Home-page: https://github.com/FlamTeam/flamedisx
Author: Jelle Aalbers, Bart Pelssers, Cristian Antochi
License: UNKNOWN
Description: Flamedisx
        ==========
        
        Fast likelihood analysis in more dimensions for xenon TPCs.
        
        ![Build Status](https://github.com/FlamTeam/flamedisx/actions/workflows/test_flamedisx.yml/badge.svg)
        [![Documentation Status](https://readthedocs.org/projects/flamedisx/badge/?version=latest)](https://flamedisx.readthedocs.io/en/latest/?badge=latest)
        [![DOI](https://zenodo.org/badge/176141558.svg)](https://zenodo.org/badge/latestdoi/176141558)
        [![ArXiv number](https://img.shields.io/badge/physics.ins--det-arXiv%3A2003.12483-%23B31B1B)](https://arxiv.org/abs/2003.12483)
        [![Join the chat at https://gitter.im/AxFoundation/strax](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/FlamTeam/flamedisx)
        
        
        Flamedisx aims to increase the practical number of dimensions and parameters in likelihoods for liquid-xenon (LXe) detectors, which are leading the field of direct dark matter detection. 
        
        Traditionally, particle physicists compute signal and background models by filling histogram 'templates' with high-statistics Monte Carlo (MC) simulations. However, the LXe model can also be computed with a series of (large) matrix multiplications, equivalent to the integral approximated by the MC simulation. Using TensorFlow makes this computation differentiable and GPU-scalable, so it can be used practically for fitting and statistical inference.
        
        The result is a better sensitivity, since the likelihood can use all observables, and more robust fits, because using simultaneous correlated nuisance parameters no longer requires challenging interpolation and template morphing.
        
        
        
        Getting started
        ---------------------------
        
        To get started, [Launch our tutorial on Colaboratory](https://colab.research.google.com/github/FlamTeam/flamedisx-notebooks/blob/master/Tutorial.ipynb), or view it statically on [GitHub](https://github.com/FlamTeam/flamedisx-notebooks/blob/master/Tutorial.ipynb) or [ReadTheDocs](https://flamedisx.readthedocs.io/en/latest/tutorial.html).
        
        Our [paper](https://arxiv.org/abs/2003.12483) gives a detailed description of Flamedisx, and compares Flamedisx quantitatively to traditional template-based methods.
        
        If you want all the details, see the [Flamedisx Documentation](https://flamedisx.readthedocs.io) and our [Notebooks repository](https://github.com/FlamTeam/flamedisx-notebooks).
        
        
        1.4.1 / 2021-04-20
        ------------------
        - Stabilize default optimizer with better parameter scaling (#114)
        - XENONnT: Support reading data from private repository (#115)
        - XENON1T: Variable elife (#118)
        - XENON1T: Npz resource reading (#123)
        
        1.4.0 / 2021-03-05
        ------------------
        - Fix 'sticky defaults' bug (#110)
        - Enable GitHub Actions and Dependabot (#109)
        - Documentation updates (#92, [notebooks#3](https://github.com/FlamTeam/flamedisx-notebooks/pull/3))
        - Likelihood `defaults` support, simulate argument fixes (#103) 
        - SpatialRateEnergySpectrum: Simplify API (#100) and fix draw_positions (#105)
        - WIMPEnergySpectrum: Accept event times slightly out of range (#99)
        - Do not round photons_detected_mle (#91)
        - XENON1T: fix S2 acceptance (#97) and name reconstruction efficiency pivots (#102)
        
        1.3.0 / 2020-08-25
        ------------------
        - Block system (#81)
        - Documentation (#81)
        - Bugfixes (#83, #87, #89)
        
        1.2.0 / 2020-07-21
        ------------------
        - Access BBF data and XENON-utilities (#80)
        - Double photoelectron emission modeling (#78)
        - Optimization improvements (#76)
        - Bugfix (#79)
        
        1.1.0 / 2020-07-09
        ------------------
        - Nonlinear constraint limit setting (experimental) (#70)
        - Dimension scaling inside optimizers (#72)
        - Auto-guess rate multipliers (#74)
        - Python 3.8 builds (#73)
        - Add sanity checks on input and guess (#69)
        
        1.0.0 / 2020-03-26
        ------------------
        - Fiducial volume specification (#64)
        - Added default cS1 cut (#63)
        - Cleanup and optimizations (#63, #64, #65)
        
        0.5.0 / 2020-01-31
        ------------------
        - Autographed Hessian; use Hessian in the optimizer (#62)
        - Check for optimizer failures (#61) 
        - Trace single-batch likelihood, but use numpy thereafter (#61)
        - Fix simulation/data discrepancy in recombination fluctuation
        - Adjust optimizer defaults
        - Option to use time-averaged WIMP spectra
        
        0.4.0 / 2020-01-15
        -------------------
        - Many changes to objectives and inference (#59, #60)
        - Add tilt to objective for interval/limit searches
        - one_parameter_interval -> limit and interval methods
        - Optimizers use bounds
        - Tolerance option homogenization (first pass)
        - Auto-guess limits
        
        0.3.1 / 2019-11-26
        ------------------
        - Performance improvements and cleanup (#58)
        - Improve one_parameter_interval arguments (#56)
        - Add Tutorial output to flamedisx-notebooks (#56)
        - Bugfixes (#57)
        
        0.3.0 / 2019-11-19
        ------------------
        - Split off notebook folder to flamedisx-notebooks
        - Pass source specific parameters correctly (#51)
        - Flexible event padding (#54)
        - SciPy optimizer and optimizer settings (#54)
        - one_parameter_interval (#54)
        - Bugfixes (#46, #55, #51)
        - Unify optimizers (#54)
        
        0.2.2 / 2019-10-30
        ------------------
        - Minuit optimizer (#40)
        - Likelihood simulator (#43, #44)
        - Updates to NRSource (#40)
        
        0.2.1 / 2019-10-24
        ------------------
        - Workaround for numerical errors (#38, #39)
        
        0.2.0 / 2019-10-11
        ------------------
        - Spatially dependent rates (#27)
        - Time dependent energy spectra (#24)
        - XENON1T SR1-like model / fixes (#22, #32)
        - Switch optimizer to BFGS + Hessian (#19)
        - Multiple source support (#14)
        - Optimization (#13)
        - Bugfixes / refactor (#18, #20, #21, #28, #30, #31, #35)
        
        0.1.2 / 2019-07-24
        -------------------
        - Speedup ER computation, add tutorial (#11)
        - Optimize lookup-axis1 (#10)
        
        0.1.1 / 2019-07-21
        -------------------
        - 5x speedup for Hessian (#9)
        - Fix pip install
        
        0.1.0 / 2019-07-16
        -------------------
        - Batching (#7)
        - Inference (#6)
        - Ported to tensorflow / GPU support (#1, #2, #3, #5)
        
        0.0.1 / 2019-03-17
        ------------------
        - Initial numpy-based version
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: docs
Provides-Extra: strict-deps
