187 citations · 917 across the 12 of their papers we have counts for
12 papers
Building the largest spectroscopic sample of ultra-compact massive galaxies with the Kilo Degree Survey
Diana Scognamiglio, Crescenzo Tortora, Marilena Spavone +14
Ultra-compact massive galaxies UCMGs, i.e. galaxies with stellar masses and effective radii kpc, are very rare systems, in partic…
Identifying Galaxy Mergers in Observations and Simulations with Deep Learning
W. J. Pearson, L. Wang, J. W. Trayford +2
Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, p…
Deep Learning for Galaxy Mergers in the Galaxy Main Sequence
William J. Pearson, Lingyu Wang, James Trayford +2
Starburst galaxies are often found to be the result of galaxy mergers. As a result, galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlation b…
LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
C. E. Petrillo, C. Tortora, G. Vernardos +17
We present a new sample of galaxy-scale strong gravitational-lens candidates, selected from 904 square degrees of Data Release 4 of the Kilo-Degree Survey (KiDS), i.e., the "Lenses…
K2-140b and K2-180b - Characterization of a hot Jupiter and a mini-Neptune from the K2 mission
J. Korth, Sz. Csizmadia, D. Gandolfi +40
We report the independent discovery and characterization of two K2 planets: K2-180b, a mini-Neptune-size planet in an 8.9-day orbit transiting a V = 12.6 mag, metal-poor ([Fe/H] =$…
Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS
C. E. Petrillo, C. Tortora, S. Chatterjee +7
Convolutional Neural Networks (ConvNets) are one of the most promising methods for identifying strong gravitational lens candidates in survey data. We present two ConvNet lens-find…