David N. Spergel

According to our database, David N. Spergel authored at least 20 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

The optical and infrared are connected.

Christian Kragh Jespersen, Peter Melchior, David N. Spergel, Andy D. Goulding, ChangHoon Hahn, Kartheik Iyer

CoRR, March, 2025

2023

SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering.

Pablo Lemos, Liam Holden Parker, ChangHoon Hahn, Shirley Ho, Michael Eickenberg, Jiamin Hou, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, Bruno Régaldo-Saint Blancard, David N. Spergel

CoRR, 2023

Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects.

Natalí S. M. de Santi, Francisco Villaescusa-Navarro, L. Raul Abramo, Helen Shao, Lucia A. Perez, Tiago Castro, Yueying Ni, Christopher C. Lovell, Elena Hernandez-Martinez, Federico Marinacci, David N. Spergel, Klaus Dolag, Lars Hernquist, Mark Vogelsberger

CoRR, 2023

2022

Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks.

Leander Thiele, Miles D. Cranmer, William R. Coulton, Shirley Ho, David N. Spergel

Mach. Learn. Sci. Technol., 2022

The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback.

Digvijay Wadekar, Leander Thiele, J. Colin Hill, Shivam Pandey, Francisco Villaescusa-Navarro, David N. Spergel, Miles D. Cranmer, Daisuke Nagai, Daniel Anglés-Alcázar, Shirley Ho, Lars Hernquist

CoRR, 2022

Field Level Neural Network Emulator for Cosmological N-body Simulations.

Drew Jamieson, Yin Li, Renan Alves de Oliveira, Francisco Villaescusa-Navarro, Shirley Ho, David N. Spergel

CoRR, 2022

Simple lessons from complex learning: what a neural network model learns about cosmic structure formation.

Drew Jamieson, Yin Li, Siyu He, Francisco Villaescusa-Navarro, Shirley Ho, Renan Alves de Oliveira, David N. Spergel

CoRR, 2022

Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter.

Digvijay Wadekar, Leander Thiele, Francisco Villaescusa-Navarro, J. Colin Hill, David N. Spergel, Miles D. Cranmer, Nicholas Battaglia, Daniel Anglés-Alcázar, Lars Hernquist, Shirley Ho

CoRR, 2022

The CAMELS project: public data release.

Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés-Alcázar, Lucia A. Perez, Pablo Villanueva-Domingo, Digvijay Wadekar, Helen Shao, Faizan G. Mohammad, Sultan Hassan, Emily Moser, Erwin T. Lau, Luis Fernando Machado Poletti Valle, Andrina Nicola, Leander Thiele, Yongseok Jo, Oliver H. E. Philcox, Benjamin D. Oppenheimer, Megan Tillman, ChangHoon Hahn, Neerav Kaushal, Alice Pisani, Matthew Gebhardt, Ana Maria Delgado, Joyce Caliendo, Christina Kreisch, Kaze W. K. Wong, William R. Coulton, Michael Eickenberg, Gabriele Parimbelli, Yueying Ni, Ulrich P. Steinwandel, Valentina La Torre, Romeel Dave, Nicholas Battaglia, Daisuke Nagai, David N. Spergel, Lars Hernquist, Blakesley Burkhart, Desika Narayanan, Benjamin D. Wandelt, Rachel S. Somerville, Greg L. Bryan, Matteo Viel, Yin Li, Vid Irsic, Katarina Kraljic, Mark Vogelsberger

CoRR, 2022

2021

Weighing the Milky Way and Andromeda with Artificial Intelligence.

Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés-Alcázar, Lars Hernquist, Federico Marinacci, David N. Spergel, Mark Vogelsberger, Desika Narayanan

CoRR, 2021

Inferring halo masses with Graph Neural Networks.

Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel, Federico Marinacci, David N. Spergel, Lars Hernquist, Mark Vogelsberger, Romeel Dave, Desika Narayanan

CoRR, 2021

The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence.

Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar, Leander Thiele, Romeel Dave, Desika Narayanan, Andrina Nicola, Yin Li, Pablo Villanueva-Domingo, Benjamin D. Wandelt, David N. Spergel, Rachel S. Somerville, José Manuel Zorrilla Matilla, Faizan G. Mohammad, Sultan Hassan, Helen Shao, Digvijay Wadekar, Michael Eickenberg, Kaze W. K. Wong, Gabriella Contardo, Yongseok Jo, Emily Moser, Erwin T. Lau, Luis Fernando Machado Poletti Valle, Lucia A. Perez, Daisuke Nagai, Nicholas Battaglia, Mark Vogelsberger

CoRR, 2021

Robust marginalization of baryonic effects for cosmological inference at the field level.

Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar, David N. Spergel, Yin Li, Benjamin D. Wandelt, Leander Thiele, Andrina Nicola, José Manuel Zorrilla Matilla, Helen Shao, Sultan Hassan, Desika Narayanan, Romeel Dave, Mark Vogelsberger

CoRR, 2021

Multifield Cosmology with Artificial Intelligence.

Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel, David N. Spergel, Yin Li, Benjamin D. Wandelt, Andrina Nicola, Leander Thiele, Sultan Hassan, José Manuel Zorrilla Matilla, Desika Narayanan, Romeel Dave, Mark Vogelsberger

CoRR, 2021

A Bayesian neural network predicts the dissolution of compact planetary systems.

Miles D. Cranmer, Daniel Tamayo, Hanno Rein, Peter W. Battaglia, Samuel Hadden, Philip J. Armitage, Shirley Ho, David N. Spergel

CoRR, 2021

2020

Fast and Accurate Non-Linear Predictions of Universes with Deep Learning.

Renan Alves de Oliveira, Yin Li, Francisco Villaescusa-Navarro, Shirley Ho, David N. Spergel

CoRR, 2020

deep21: a Deep Learning Method for 21cm Foreground Removal.

T. Lucas Makinen, Lachlan Lancaster, Francisco Villaescusa-Navarro, Peter Melchior, Shirley Ho, Laurence Perreault Levasseur, David N. Spergel

CoRR, 2020

Lagrangian Neural Networks.

Miles D. Cranmer, Sam Greydanus, Stephan Hoyer, Peter W. Battaglia, David N. Spergel, Shirley Ho

CoRR, 2020

Discovering Symbolic Models from Deep Learning with Inductive Biases.

Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates.

Miles D. Cranmer, Richard Galvez, Lauren Anderson, David N. Spergel, Shirley Ho

CoRR, 2019