Pan-STARRS lens candidates from neural networks

Second, we trained a convolutional neural network (CNN) on Pan-STARRS gri image cutouts to classify this sample and obtain sets of 105760 and 12382 lens candidates with scores of pCNN>0.5 and >0.9, respectively. Extensive tests showed that CNN performances rely heavily on the design of lens simulations and the choice of negative examples for training, but little on the network architecture. The CNN correctly classified 14 out of 16 test lenses, which are previously confirmed lens systems above the detection limit of Pan-STARRS.

Finally, we visually inspected all galaxies with pCNN>0.9 to assemble a final set of 330 high-quality newly-discovered lens candidates while recovering 23 published systems. For a subset, SDSS spectroscopy on the lens central regions proves that our method correctly identifies lens LRGs at z~0.1-0.7. Five spectra also show robust signatures of high-redshift background sources, and Pan-STARRS imaging confirms one of them as a quadruply-imaged red source at z_s_=1.185, which is likely a recently quenched galaxy strongly lensed by a foreground LRG at z_d_=0.3155.

In the future, high-resolution imaging and spectroscopic follow-up will be required to validate Pan-STARRS lens candidates and derive strong lensing models. We also expect that the efficient and automated two-step classification method presented in this paper will be applicable to the ~4 mag deeper gri stacks from the Rubin Observatory Legacy Survey of Space and Time (LSST) with minor adjustments.