GeometricImageNet: Extending convolutional neural networks to vector and tensor images

1 code implementation • 21 May 2023 • Wilson Gregory, David W. Hogg, Ben Blum-Smith, Maria Teresa Arias, Kaze W. K. Wong, Soledad Villar

We use representation theory to quantify the dimension of the space of equivariant polynomial functions on 2-dimensional vector images.

The CAMELS project: public data release

no code implementations • 4 Jan 2022 • 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 Wandelt, Rachel S. Somerville, Greg L. Bryan, Matteo Viel, Yin Li, Vid Irsic, Katarina Kraljic, Mark Vogelsberger

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning.

BIG-bench Machine Learning

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

1 code implementation • 22 Sep 2021 • Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar, Leander Thiele, Romeel Dave, Desika Narayanan, Andrina Nicola, Yin Li, Pablo Villanueva-Domingo, Benjamin Wandelt, David N. Spergel, Rachel S. Somerville, Jose 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

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 containing many different properties of cosmic gas, dark matter, and stars from 2, 000 distinct simulated universes at several cosmic times.

BIG-bench Machine Learning

Constraining the primordial black hole scenario with Bayesian inference and machine learning: the GWTC-2 gravitational wave catalog

no code implementations • 3 Nov 2020 • Kaze W. K. Wong, Gabriele Franciolini, Valerio De Luca, Vishal Baibhav, Emanuele Berti, Paolo Pani, Antonio Riotto

Primordial black holes (PBHs) might be formed in the early Universe and could comprise at least a fraction of the dark matter.

Bayesian Inference General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology

Gravitational-wave signal-to-noise interpolation via neural networks

1 code implementation • 20 Jul 2020 • Kaze W. K. Wong, Ken K. Y. Ng, Emanuele Berti

Computing signal-to-noise ratios (SNRs) is one of the most common tasks in gravitational-wave data analysis.

High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology

Machine-learning interpolation of population-synthesis simulations to interpret gravitational-wave observations: a case study

no code implementations • 13 Sep 2019 • Kaze W. K. Wong, Davide Gerosa

We report on advances to interpret current and future gravitational-wave events in light of astrophysical simulations.

High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology

Multiband gravitational-wave event rates and stellar physics

1 code implementation • 31 Jan 2019 • Davide Gerosa, Sizheng Ma, Kaze W. K. Wong, Emanuele Berti, Richard O'Shaughnessy, Yanbei Chen, Krzysztof Belczynski

We use population synthesis simulations of isolated binary stars to explore some of the stellar physics that could be constrained with multiband events, and we show that specific formation pathways might be overrepresented in multiband events compared to ground-only detections.

High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology