Probabilistic classification of the Fermi-LAT 4FGL-DR3 catalog sources
Creators
- 1. Erlangen Centre for Astroparticle Physics
- 2. University of Potsdam
Description
Multi-class classification of Fermi LAT 4FGL-DR3 sources into six or nine classes using random forest (RF) and neural network (NN) algorithms.
Probabilistic catalogs are in files:
4FGL-DR3_6class_GMM_nmin100_prob_cat.csv
4FGL-DR3_6class_RF_nmin100_prob_cat.csv
4FGL-DR3_9class_GMM_nmin15_prob_cat.csv
Files containing definition of the six or nine groups of physical classes of the 4FGL-DR3 catalog and summary of the expected numbers of associated and unassociated sources in each of the six or nine groups:
4FGL-DR3_6class_GMM_nmin100_summary.csv
4FGL-DR3_6class_RF_nmin100_summary.csv
4FGL-DR3_9class_GMM_nmin15_summary.csv
The identifies in file names, GMM_nmin100, RF_nmin100, GMM_nmin15, refer to the method how the groups of physical classes were determined: (1) using the Gaussian mixture model (GMM) or random forest (RF), (2) requiring at least 100 associated sources in a group (nmin100) or at least 15 sources in a group (nmin15).
A detailed description can be found at: http://arxiv.org/abs/2301.07412
Files
4FGL-DR3_probabilistic_classification.zip
Files
(3.8 MB)
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