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07/24/2023
Learnable wavelet neural networks for cosmological inference
Convolutional neural networks (CNNs) have been shown to both extract mor...
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03/23/2023
Predicting the Initial Conditions of the Universe using Deep Learning
Finding the initial conditions that led to the current state of the univ...
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11/24/2022
Learning Integrable Dynamics with Action-Angle Networks
Machine learning has become increasingly popular for efficiently modelli...
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11/08/2022
A Neural Network Subgrid Model of the Early Stages of Planet Formation
Planet formation is a multi-scale process in which the coagulation of μ ...
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10/24/2022
: Learning Galaxy Properties from Merger Trees
Efficiently mapping baryonic properties onto dark matter is a major chal...
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10/05/2022
Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning
We demonstrate the utility of deep learning for modeling the clustering ...
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09/05/2022
The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
Ionized gas in the halo circumgalactic medium leaves an imprint on the c...
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07/18/2022
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
Simulation-based inference (SBI) is rapidly establishing itself as a sta...
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06/09/2022
Field Level Neural Network Emulator for Cosmological N-body Simulations
We build a field level emulator for cosmic structure formation that is a...
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06/09/2022
Simple lessons from complex learning: what a neural network model learns about cosmic structure formation
We train a neural network model to predict the full phase space evolutio...
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04/15/2022
Wavelet Moments for Cosmological Parameter Estimation
Extracting non-Gaussian information from the non-linear regime of struct...
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02/28/2022
Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks
Theoretical uncertainty limits our ability to extract cosmological infor...
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02/04/2022
Rediscovering orbital mechanics with machine learning
We present an approach for using machine learning to automatically disco...
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01/04/2022
Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter
Complex systems (stars, supernovae, galaxies, and clusters) often exhibi...
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12/31/2021
Learned Coarse Models for Efficient Turbulence Simulation
Turbulence simulation with classical numerical solvers requires very hig...
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11/11/2021
Super-resolving Dark Matter Halos using Generative Deep Learning
Generative deep learning methods built upon Convolutional Neural Network...
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06/02/2021
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
Among the most extreme objects in the Universe, active galactic nuclei (...
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03/22/2021
A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
There is a shortage of multi-wavelength and spectroscopic followup capab...
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01/11/2021
A Bayesian neural network predicts the dissolution of compact planetary systems
Despite over three hundred years of effort, no solutions exist for predi...
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10/29/2020
deep21: a Deep Learning Method for 21cm Foreground Removal
We seek to remove foreground contaminants from 21cm intensity mapping ob...
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10/21/2020
Anomaly Detection for Multivariate Time Series of Exotic Supernovae
Supernovae mark the explosive deaths of stars and enrich the cosmos with...
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07/08/2020
Meta-Learning One-Class Classification with DeepSets: Application in the Milky Way
We explore in this paper the use of neural networks designed for point-c...
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06/19/2020
Discovering Symbolic Models from Deep Learning with Inductive Biases
We develop a general approach to distill symbolic representations of a l...
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03/10/2020
Lagrangian Neural Networks
Accurate models of the world are built upon notions of its underlying sy...
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10/17/2019
From Dark Matter to Galaxies with Convolutional Neural Networks
Cosmological simulations play an important role in the interpretation of...
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10/09/2019
Learning neutrino effects in Cosmology with Convolutional Neural Networks
Measuring the sum of the three active neutrino masses, M_ν, is one of th...
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09/12/2019
Learning Symbolic Physics with Graph Networks
We introduce an approach for imposing physically motivated inductive bia...
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08/21/2019
Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates
We demonstrate an algorithm for learning a flexible color-magnitude diag...
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04/29/2019
HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks
One of the most promising ways to observe the Universe is by detecting t...
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02/15/2019
From Dark Matter to Galaxies with Convolutional Networks
Cosmological surveys aim at answering fundamental questions about our Un...
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11/15/2018
Learning to Predict the Cosmological Structure Formation
Matter evolved under influence of gravity from minuscule density fluctua...
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08/14/2018
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
Deep learning is a promising tool to determine the physical model that d...
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05/01/2018
Detecting Galaxy-Filament Alignments in the Sloan Digital Sky Survey III
Previous studies have shown the filamentary structures in the cosmic web...
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11/06/2017
Estimating Cosmological Parameters from the Dark Matter Distribution
A grand challenge of the 21st century cosmology is to accurately estimat...
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06/07/2015
