340 citations · 1.4k across the 23 of their papers we have counts for
23 papers
Prospects for Detecting Gaps in Globular Cluster Stellar Streams in External Galaxies with the Nancy Grace Roman Space Telescope
Christian Aganze, Sarah Pearson, Tjitske Starkenburg +5
Stellar streams form through the tidal disruption of satellite galaxies or globular clusters orbiting a host galaxy. Globular cluster streams are exciting since they are thin (dyna…
First Impressions: Early-Time Classification of Supernovae using Host Galaxy Information and Shallow Learning
Alexander Gagliano, Gabriella Contardo, Daniel Foreman-Mackey +2
Substantial effort has been devoted to the characterization of transient phenomena from photometric information. Automated approaches to this problem have taken advantage of comple…
Emulating radiation transport on cosmological scale using a denoising Unet
Mosima P. Masipa, Sultan Hassan, Mario G. Santos +2
Semi-numerical simulations are the leading candidates for evolving reionization on cosmological scales. These semi-numerical models are efficient in generating large-scale maps of…
The Eighteenth Data Release of the Sloan Digital Sky Surveys: Targeting and First Spectra from SDSS-V
Andrés Almeida, Scott F. Anderson, Maria Argudo-Fernández +151
The eighteenth data release of the Sloan Digital Sky Surveys (SDSS) is the first one for SDSS-V, the fifth generation of the survey. SDSS-V comprises three primary scientific progr…
Further Evidence of Modified Spin-down in Sun-like Stars: Pileups in the Temperature-Period Distribution
Trevor J. David, Ruth Angus, Jason L. Curtis +5
We combine stellar surface rotation periods determined from NASA's Kepler mission with spectroscopic temperatures to demonstrate the existence of pileups at the long-period and sho…
The emptiness inside: Finding gaps, valleys, and lacunae with geometric data analysis
Gabriella Contardo, David W. Hogg, Jason A. S. Hunt +2
Discoveries of gaps in data have been important in astrophysics. For example, there are kinematic gaps opened by resonances in dynamical systems, or exoplanets of a certain radius…
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar +25
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containi…
Finding universal relations in subhalo properties with artificial intelligence
Helen Shao, Francisco Villaescusa-Navarro, Shy Genel +7
We use a generic formalism designed to search for relations in high-dimensional spaces to determine if the total mass of a subhalo can be predicted from other internal properties s…
A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
V. Ashley Villar, Miles Cranmer, Edo Berger +4
There is a shortage of multi-wavelength and spectroscopic followup capabilities given the number of transient and variable astrophysical events discovered through wide-field, optic…
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…
From Dark Matter to Galaxies with Convolutional Neural Networks
Jacky H. T. Yip, Xinyue Zhang, Yanfang Wang +7
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations. However, to co…
The Quijote simulations
Francisco Villaescusa-Navarro, ChangHoon Hahn, Elena Massara +26
The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7,000 cosmological models in the hyperplane.…
From Dark Matter to Galaxies with Convolutional Networks
Xinyue Zhang, Yanfang Wang, Wei Zhang +5
Cosmological surveys aim at answering fundamental questions about our Universe, including the nature of dark matter or the reason of unexpected accelerated expansion of the Univers…
A Meta-Learning Approach to One-Step Active Learning
Gabriella Contardo, Ludovic Denoyer, Thierry Artieres
We consider the problem of learning when obtaining the training labels is costly, which is usually tackled in the literature using active-learning techniques. These approaches prov…
Sequential Cost-Sensitive Feature Acquisition
Gabriella Contardo, Ludovic Denoyer, Thierry Artières
We propose a reinforcement learning based approach to tackle the cost-sensitive learning problem where each input feature has a specific cost. The acquisition process is handled th…
Representation Learning for cold-start recommendation
Gabriella Contardo, Ludovic Denoyer, Thierry Artieres
A standard approach to Collaborative Filtering (CF), i.e. prediction of user ratings on items, relies on Matrix Factorization techniques. Representations for both users and items a…
Learning States Representations in POMDP
Gabriella Contardo, Ludovic Denoyer, Thierry Artieres +1
We propose to deal with sequential processes where only partial observations are available by learning a latent representation space on which policies may be accurately learned.