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First author: G. Angora Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological simulations. We search for galaxy-galaxy strong-lensing systems in HST multi-band imaging of galaxy cluster cores from the CLASH and HFF programs by exploring the classification capabilities of deep learning techniques. Convolutional neural networks are trained utilising highly-realistic simulations of galaxy-scale strong lenses injected into the HST cluster fields around cluster members.

First author: Hina Goto To better understand the formation of large, low surface brightness galaxies, we measure the correlation function between ultra-diffuse galaxy (UDG) candidates and Milky Way analogs (MWAs). We find that (1) the projected radial distribution of UDG satellites (projected surface density $\propto r^{-0.84\pm0.06}$) is consistent with that of normal satellite galaxies, (2) the number of UDG satellites per MWA ($S_{\rm UDG}$) is $\sim 0.5\pm0.1$ over projected radii from 20 to 250 kpc and $-17< M_r < -13.