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Fink science modules

In addition to the information contained in the incoming raw alerts, Fink deploys science modules whose task is to add further details to better characterise the event.

graph LR
  A(RA, DEC, flux) --> B((Science module #1));
  B -..-> C((Science module #N));
  C --> D(RA, DEC, flux, labels + ML scores + flags, ...);

The science modules are provided by the scientific community and encompass a dozen modules that focus on a wide range of scientific cases, from Solar System science to galactic and extragalactic studies. These modules can share information, allowing the input of one module to utilize the output of one or more other modules.

Open source and open data

Each science module provides added values in form of extra fields inside the alert packet, and these fields are freely accessible by anyone. The code sources of science modules can be found at https://github.com/astrolabsoftware/fink-science.

ZTF science modules

Below we summarise the fields added by the Fink/ZTF science modules.

Cross-match

For each alert, we look for counterparts in various databases or catalogs (spatial match). Note that ZTF already performs associations with Gaia DR1, PanSTARRS, and the Minor Planet Center.

Field in Fink alerts Type Contents Available from
cdsxmatch string Counterpart (cross-match) from the SIMBAD database using the CDS xmatch service if exists within 1.5 arcsec. Labels can be found at http://simbad.u-strasbg.fr/simbad/sim-display?data=otypes 2019/11
gcvs string Counterpart (cross-match) to the General Catalog of Variable Stars if exists within 1.5 arcsec. 2022/07
vsx string Counterpart (cross-match) to the International Variable Star Index if exists within 1.5 arcsec. 2022/07
Plx float Absolute stellar parallax (in milli-arcsecond) of the closest source from Gaia catalog; if exists within 1 arcsec. 2022/07
e_Plx float Standard error of the stellar parallax (in milli-arcsecond) of the closest source from Gaia catalog; if exists within 1 arcsec. 2022/07
DR3Name string Unique source designation of closest source from Gaia catalog; if exists within 1 arcsec. 2022/07
x4lac string Counterpart (cross-match) to the 4LAC DR3 catalog if exists within 1 arcminute. 2023/01
x3hsp string Counterpart (cross-match) to the 3HSP catalog if exists within 1 arcminute. 2023/01
mangrove dic[str, str] Counterpart (cross-match) to the Mangrove catalog if exists within 1 arcminute. 2023/01
spicy_id int Unique source designation of closest source from the SPICY catalog hosted at CDS; if exists within 1.2 arcsec. 2024/01
spicy_class str Class name of closest source from the SPICY catalog hosted at CDS; if exists within 1.2 arcsec. 2024/01

Please feel free to suggest any other catalogs. If they are available at CDS, we can integrate them directly. For external catalogs, depending on their size, we can consider hosting them ourselves.

Fail XXX

If there is a failure with the xmatch service from CDS, the fields can have values Fail XXX. XXX is a 3-digit number corresponding to the failure type (see HTTP status codes). Note that the next time the object emits an alert, if the xmatch service is up, these values will be updated with their correct values.

Machine and deep learning

In Fink, you can upload pre-trained models, and each alert will receive a score. We have binary models focusing on specific class of transients (e.g. SN Ia vs the rest of the world), or broad classifiers that output a vector of probabilities for a variety of classes.

Field in Fink alerts Type Contents Available from
rf_snia_vs_nonia float Probability to be a rising SNe Ia based on Random Forest classifier (1 is SN Ia). Based on 2111.11438 2019/11
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 2019/11
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 2019/11
mulens float Probability score to be a microlensing event by LIA 2019/11
rf_kn_vs_nonkn float Probability of an alert to be a kilonova using a Random Forest Classifier (1 is KN). Based on 2210.17433. 2019/11
t2 dic[str, float] Vector of probabilities (class, prob) using Transformers (arxiv:2105.06178) 2023/01
lc_* dict[int, array] Numerous light curve features used in astrophysics. 2023/01
anomaly_score float Probability of an alert to be anomalous (lower values mean more anomalous observations) based on lc_* 2023/01

Standard modules

Standard modules typically issue flags or aggregated information to ease the processing later.

Field in Fink alerts Type Contents Available from
roid int Determine if the alert is a Solar System object 2019/11
nalerthist int Number of detections contained in each alert (current+history). Upper limits are not taken into account. 2019/11
jd_first_real_det double first variation time at 5 sigma contains in the alert history 2023/12
jdstarthist_dt double delta time between jd_first_real_det and the first variation time at 3 sigma (jdstarthist). If jdstarthist_dt > 30 days then the first variation time at 5 sigma is False (accurate for fast transient). 2023/12
mag_rate double magnitude rate (mag/day) 2023/12
sigma_rate double magnitude rate error estimation (mag/day) 2023/12
lower_rate double 5% percentile of the magnitude rate sampling used for the error computation (sigma_rate) 2023/12
upper_rate double 95% percentile of the magnitude rate sampling used for the error computation (sigma_rate) 2023/12
delta_time double delta time between the the two measurement used for the magnitude rate mag_rate 2023/12
from_upper bool if True, the magnitude rate mag_rate has been computed using the last upper limit and the current measurement 2023/12

Post-processing modules

There are also modules applied after the observing night:

Field name Type Contents Available from
tracklet string Tracklet ID in the Fink database. Tracklets are typically derelict satellites or rocket bodies, collision debris, or spacecraft payloads. See 2202.05719 and 2310.17322 for more information. 2020/08
kstest_static float Determine if an alert is hostless (based on 2404.18165) 2024/07

DESC-ELAsTiCC science modules

These modules are being tested for Rubin era on the LSST-DESC ELAsTiCC data challenge:

Field in Fink alerts Type Contents
rf_agn_vs_nonagn float Probability to be an AGN based on Random Forest classifier (1 is AGN).
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
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
preds_snn array[float] Broad classifier based on SNN. Returns [class, max(prob)].
cbpf_preds array[float] Fine classifier based on the CBPF Algorithm for Transient Search. Returns [class, max(prob)].

LSST science modules

To come!