340 citations · 1.4k across the 23 of their papers we have counts for
7 papers · 1 filter
The Influence of Age on the Relative Frequency of Super-Earths and Sub-Neptunes
Angeli Sandoval, Gabriella Contardo, Trevor J. David
There is growing evidence that the population of close-in planets discovered by the Kepler mission was sculpted by atmospheric loss, though the typical timescale for this evolution…
Evolution of the Exoplanet Size Distribution: Forming Large Super-Earths Over Billions of Years
Trevor J. David, Gabriella Contardo, Angeli Sandoval +9
The radius valley, a bifurcation in the size distribution of small, close-in exoplanets, is hypothesized to be a signature of planetary atmospheric loss. Such an evolutionary pheno…
Anomaly Detection for Multivariate Time Series of Exotic Supernovae
V. Ashley Villar, Miles Cranmer, Gabriella Contardo +2
Supernovae mark the explosive deaths of stars and enrich the cosmos with heavy elements. Future telescopes will discover thousands of new supernovae nightly, creating a need to fla…
The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel +19
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project. CAMELS is a suite of 4,233 cosmological simulations of volu…
Dalek -- a deep-learning emulator for TARDIS
Wolfgang E. Kerzendorf, Christian Vogl, Johannes Buchner +3
Supernova spectral time series contain a wealth of information about the progenitor and explosion process of these energetic events. The modeling of these data requires the explora…
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network
Ademola Oladosu, Tony Xu, Philip Ekfeldt +5
This paper presents a meta-learning framework for few-shots One-Class Classification (OCC) at test-time, a setting where labeled examples are only available for the positive class,…
Gravitational wave population inference with deep flow-based generative network
Kaze W. K. Wong, Gabriella Contardo, Shirley Ho
We combine hierarchical Bayesian modeling with a flow-based deep generative network, in order to demonstrate that one can efficiently constraint numerical gravitational wave (GW) p…