User-defined science module for the Fink broker.
Project description
Fink Science
This repository contains science modules used to generate added values to alert collected by the Fink broker.
ZTF alert stream
It currently contains:
| Source | Field in Fink alerts | Type | Contents |
|---|---|---|---|
| fink_science/xmatch | cdsxmatch++ |
string | Counterpart (cross-match) from any CDS catalog or database using the CDS xmatch service. Contains also crossmatch to the General Catalog of Variable Stars and the International Variable Star Index, 3HSP, 4LAC DR3, Mangrove |
| fink_science/random_forest_snia | rf_snia_vs_nonia |
float | Probability to be a rising SNe Ia based on Random Forest classifier (1 is SN Ia). Based on https://arxiv.org/abs/2111.11438 |
| fink_science/snn | snn_snia_vs_nonia |
float | Probability to be a SNe Ia based on SuperNNova classifier (1 is SN Ia). Based on https://arxiv.org/abs/1901.06384 |
| fink_science/snn | snn_sn_vs_all |
float | Probability to be a SNe based on SuperNNova classifier (1 is SNe). Based on https://arxiv.org/abs/1901.06384 |
| fink_science/microlensing | mulens |
float | Probability score to be a microlensing event by LIA |
| fink_science/asteroids | roid |
int | Determine if the alert is a Solar System object |
| fink_science/kilonova | rf_kn_vs_nonkn |
float | probability of an alert to be a kilonova using a Random Forest Classifier (binary classification). |
| fink_science/nalerthist | nalerthist |
int | Number of detections contained in each alert (current+history). Upper limits are not taken into account. |
| fink_science/ad_features | lc_* |
dict[int, array] | Numerous light curve features used in astrophysics. |
| fink_science/agn | rf_agn_vs_nonagn |
float | Probability to be an AGN based on Random Forest classifier (1 is AGN). |
| fink_science/anomaly_detection | anomaly_score |
float | Anomaly score (lower values mean more anomalous observations) |
| fink_science/t2 | t2 |
dic[str, float] | Classifier based on Transformers. Based on https://arxiv.org/abs/2105.06178 |
You will find README in each subfolder describing the module.
ELASTiCC stream (Rubin-like simulated data)
These modules are being tested for Rubin era on the LSST-DESC ELASTiCC data challenge:
| Source | Field in Fink alerts | Type | Contents |
|---|---|---|---|
| fink_science/agn_elasticc | rf_agn_vs_nonagn |
float | Probability to be an AGN based on Random Forest classifier (1 is AGN). |
| fink_science/random_forest_snia | rf_snia_vs_nonia |
float | Probability to be a rising SNe Ia based on Random Forest classifier (1 is SN Ia). Based on https://arxiv.org/abs/2111.11438 |
| fink_science/snn | snn_snia_vs_nonia |
float | Probability to be a SNe Ia based on SuperNNova classifier (1 is SN Ia). Based on https://arxiv.org/abs/1901.06384 |
| fink_science/snn | broad |
array[float] | Broad classifier based on SNN. Returns [class, max(prob)]. |
| fink_science/cats | fine |
array[float] | Fine classifier based on the CBPF Algorithm for Transient Search. Returns [class, max(prob)]. |
How to contribute
Learn how to design your science module, and integrate it inside the Fink broker.
Installation
If you want to install the package (broker deployment), you can just pip it:
pip install fink_science
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