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User-defined science module for the Fink broker.

Project description

pypi Sentinel PEP8 codecov

Fink Science

This repository contains science modules used to generate added values to alert collected by the Fink broker. It currently contains:

  • xmatch: perform a crossmatch of the stream with external catalogs
  • random_forest_snia: returns the probability of an alert to be a SNe Ia using a Random Forest Classifier (binary classification)
  • snn: returns the probability of an alert to be a SNe Ia using SuperNNova. Two pre-trained models:
    • snn_snia_vs_nonia: Ia vs core-collapse SNe
    • snn_sn_vs_all: SNe vs. anything else (variable stars and other categories in the training)
  • microlensing: returns the predicted class (among microlensing, variable star, cataclysmic event, and constant event) & probability of an alert to be a microlensing event in each band using LIA.
  • asteroids: Determine if the alert is a Solar System Object (experimental).
  • nalerthist: Number of detections contained in each alert (current+history). Upper limits are not taken into account.
  • kilonova: returns the probability of an alert to be a kilonova using a Random Forest Classifier (binary classification).

You will find README in each subfolder describing the module.

How to contribute

Learn how to design your science module, and integrate it inside the Fink broker.


If you want to install the package (broker deployment), you can just pip it:

pip install fink_science

Project details

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