VizieR Online Data Catalog: New lens candidates from GaSNets (Zhong+, 2022)

The new algorithm is made of different CNNs, dubbed Galaxy Spectra convolutional neural Networks (GaSNets). These are optimized to work together to provide SGL candidates, but can also perform classification and regression tasks independently. As such, they are extremely suitable for further applications in large databases of tens to hundreds of millions of spectra, like the ones expected from the next generation spectroscopic surveys (4MOST, DESI, EUCLID, CSST).

In this paper, we have started by applying these new tools to the strong lensing search in the eBOSS/DR16 database (Ahumada et al., 2020ApJS..249....3A, Cat. V/154). To this aim we have introduced: (1) GaSNet-L1 giving to each eBOSS spectrum the probability to be an SGL event (PL); (2) GaSNet-L2 estimating the redshift of background sources (zPE) from a series of pre-selected emission lines and (3) GaSNet-L3 estimating the redshift of the galaxy itself (zPG), using the information it learns from the continuous spectrum, including local absorption/emission features.

We have collected ~930 candidates that have been further cleaned by misclassified SGL events, via visual inspection. The final sample of visual HQ candidates is made of 497 spectroscopic selected objects. This catalog has been a posteriori compared to the most extended catalog of spectroscopic selected lens candidates from Talbot et al. (2021MNRAS.502.4617T) and found an overlap of only 68 candidates, meaning that 429 of our candidates are newly found.

(1 data file).