b'Shirley Ho'

research

∙ 07/24/2023

Learnable wavelet neural networks for cosmological inference

Convolutional neural networks (CNNs) have been shown to both extract mor...

0 Christian Pedersen, et al. ∙

research

∙ 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...

0 Vaibhav Jindal, et al. ∙

research

∙ 11/24/2022

Learning Integrable Dynamics with Action-Angle Networks

Machine learning has become increasingly popular for efficiently modelli...

0 Ameya Daigavane, et al. ∙

research

∙ 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 ...

0 Thomas Pfeil, et al. ∙

research

∙ 10/24/2022

: Learning Galaxy Properties from Merger Trees

Efficiently mapping baryonic properties onto dark matter is a major chal...

0 Christian Kragh Jespersen, et al. ∙

research

∙ 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 ...

0 Yan-Mong Chan, et al. ∙

research

∙ 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...

0 Digvijay Wadekar, et al. ∙

research

∙ 07/18/2022

Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks

Simulation-based inference (SBI) is rapidly establishing itself as a sta...

16 Pablo Lemos, et al. ∙

research

∙ 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...

0 Drew Jamieson, et al. ∙

research

∙ 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...

0 Drew Jamieson, et al. ∙

research

∙ 04/15/2022

Wavelet Moments for Cosmological Parameter Estimation

Extracting non-Gaussian information from the non-linear regime of struct...

0 Michael Eickenberg, et al. ∙

research

∙ 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...

0 Leander Thiele, et al. ∙

research

∙ 02/04/2022

Rediscovering orbital mechanics with machine learning

We present an approach for using machine learning to automatically disco...

0 Pablo Lemos, et al. ∙

research

∙ 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...

0 Digvijay Wadekar, et al. ∙

research

∙ 12/31/2021

Learned Coarse Models for Efficient Turbulence Simulation

Turbulence simulation with classical numerical solvers requires very hig...

5 Kimberly Stachenfeld, et al. ∙

research

∙ 11/11/2021

Super-resolving Dark Matter Halos using Generative Deep Learning

Generative deep learning methods built upon Convolutional Neural Network...

0 David Schaurecker, et al. ∙

research

∙ 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 (...

9 Ji-won Park, et al. ∙

research

∙ 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...

0 V. Ashley Villar, et al. ∙

research

∙ 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...

270 Miles Cranmer, et al. ∙

research

∙ 10/29/2020

deep21: a Deep Learning Method for 21cm Foreground Removal

We seek to remove foreground contaminants from 21cm intensity mapping ob...

1 T. Lucas Makinen, et al. ∙

research

∙ 10/21/2020

Anomaly Detection for Multivariate Time Series of Exotic Supernovae

Supernovae mark the explosive deaths of stars and enrich the cosmos with...

0 V. Ashley Villar, et al. ∙

research

∙ 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...

12 Ademola Oladosu, et al. ∙

research

∙ 06/19/2020

Discovering Symbolic Models from Deep Learning with Inductive Biases

We develop a general approach to distill symbolic representations of a l...

12 Miles Cranmer, et al. ∙

research

∙ 03/10/2020

Lagrangian Neural Networks

Accurate models of the world are built upon notions of its underlying sy...

9 Miles Cranmer, et al. ∙

research

∙ 10/17/2019

From Dark Matter to Galaxies with Convolutional Neural Networks

Cosmological simulations play an important role in the interpretation of...

0 Jacky H. T. Yip, et al. ∙

research

∙ 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...

0 Elena Giusarma, et al. ∙

research

∙ 09/12/2019

Learning Symbolic Physics with Graph Networks

We introduce an approach for imposing physically motivated inductive bia...

0 Miles D. Cranmer, et al. ∙

research

∙ 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...

7 Miles D. Cranmer, et al. ∙

research

∙ 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...

0 Juan Zamudio-Fernandez, et al. ∙

research

∙ 02/15/2019

From Dark Matter to Galaxies with Convolutional Networks

Cosmological surveys aim at answering fundamental questions about our Un...

0 Xinyue Zhang, et al. ∙

research

∙ 11/15/2018

Learning to Predict the Cosmological Structure Formation

Matter evolved under influence of gravity from minuscule density fluctua...

0 Siyu He, et al. ∙

research

∙ 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...

0 Amrita Mathuriya, et al. ∙

research

∙ 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...

0 Yen-Chi Chen, et al. ∙

research

∙ 11/06/2017

Estimating Cosmological Parameters from the Dark Matter Distribution

A grand challenge of the 21st century cosmology is to accurately estimat...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 06/07/2015

Optimal Ridge Detection using Coverage Risk

We introduce the concept of coverage risk as an error measure for densit...

0 Yen-Chi Chen, et al. ∙

Shirley Ho

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