Explanipedia

Predicting Stellar Parameters of Massive Stars from Light Curves with Machine Learning Open

Rachel C. Zhang, Kaze W. K. Wong, G. Holgado, Matteo Cantiello · 2025

High-resolution spectroscopic measurements of OB stars are important for understanding processes like stellar evolution but require labor-intensive observations. In contrast, photometric missions like the Transiting Exoplanet Survey Satell…

Predicting Stellar Parameters of Massive Stars from Light Curves with Machine Learning Open

Rachel C. Zhang, Kaze W. K. Wong, G. Holgado, Matteo Cantiello · 2025

High-resolution spectroscopic measurements of OB stars are important for understanding processes like stellar evolution, but require labor-intensive observations. In contrast, photometric missions like the Transiting Exoplanet Survey Satel…

ginjax: E(d)-Equivariant CNN for Tensor Images Open

Wilson G. Gregory, Kaze W. K. Wong, David W. Hogg, Soledad Villar · 2025

Equivariant geometric convolutions for dynamical systems on vector and tensor images Open

Wilson G. Gregory, David W. Hogg, Ben Blum-Smith, M. Arias, Kaze W. K. Wong , et al. · 2025

Machine learning methods are increasingly being employed as surrogate models in place of computationally expensive and slow numerical integrators for a bevy of applications in the natural sciences. However, while the laws of physics are re…

Gravitational-wave Parameter Estimation in Non-Gaussian Noise Using Score-based Likelihood Characterization Open

Ronan Legin, M. Isi, Kaze W. K. Wong, Yashar Hezaveh, Laurence Perreault-Levasseur · 2025

Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled on a case-by-case basis, e.g., through bespoke procedures to…

Peering into the Black Box: Forward Modeling of the Uncertainty Budget of High-resolution Spectroscopy of Exoplanet Atmospheres Open

Arjun B. Savel, Megan Bedell, Eliza M.-R. Kempton, Peter Smith, Jacob L. Bean , et al. · 2025

Ground-based high-resolution cross-correlation spectroscopy (HRCCS; R ≳ 15,000) is a powerful complement to space-based studies of exoplanet atmospheres. By resolving individual spectral lines, HRCCS can precisely measure chemical abundanc…

Peering into the black box: forward-modeling the uncertainty budget of high-resolution spectroscopy of exoplanet atmospheres Open

Arjun B. Savel, Megan Bedell, Eliza M.-R. Kempton, Peter K. Smith, Jacob L. Bean , et al. · 2024

Ground-based high-resolution cross-correlation spectroscopy (HRCCS; R >~ 15,000) is a powerful complement to space-based studies of exoplanet atmospheres. By resolving individual spectral lines, HRCCS can precisely measure chemical abundan…

Super-Resolution without High-Resolution Labels for Black Hole Simulations Open

Thomas Helfer, T. Edwards, Jessica Dafflon, Kaze W. K. Wong, Matthew Lyle Olson · 2024

Generating high-resolution simulations is key for advancing our understanding of one of the universe's most violent events: Black Hole mergers. However, generating Black Hole simulations is limited by prohibitive computational costs and sc…

Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows Open

Alicja Polanska, Thibeau Wouters, P. T. H. Pang, Kaze W. K. Wong, Jason D. McEwen · 2024

We present an accelerated pipeline, based on high-performance computing techniques and normalizing flows, for joint Bayesian parameter estimation and model selection and demonstrate its efficiency in gravitational wave astrophysics. We int…

Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization Open

Ronan Legin, M. Isi, Kaze W. K. Wong, Yashar Hezaveh, Laurence Perreault-Levasseur · 2024

Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled on a case-by-case basis, e.g., through bespoke procedures to…

Differentiable and hardware-accelerated waveforms for gravitational wave data analysis Open

T. Edwards, Kaze W. K. Wong, Kelvin K. H. Lam, Adam Coogan, Daniel Foreman-Mackey , et al. · 2024

We propose the use of automatic differentiation through the programming framework for accelerating a variety of analysis tasks throughout gravitational wave (GW) science. Firstly, we demonstrate that complete waveforms which cover the insp…

Birefringence tests of gravity with multimessenger binaries Open

Macarena Lagos, Leah Jenks, M. Isi, Kenta Hotokezaka, Brian D. Metzger , et al. · 2024

Extensions to General Relativity (GR) allow the polarization of gravitational waves (GW) from astrophysical sources to suffer from amplitude and velocity birefringence, which respectively induce changes in the ellipticity and orientation o…

AspGap: Augmented Stellar Parameters and Abundances for 37 Million Red Giant Branch Stars from Gaia XP Low-resolution Spectra Open

Jiadong Li, Kaze W. K. Wong, David W. Hogg, Hans‐Walter Rix, Vedant Chandra · 2024

We present AspGap, a new approach to inferring stellar labels from the low-resolution Gaia XP spectra, including precise [ α /M] estimates—the first time these are obtained by such an approach. AspGap is a neural-network-based regression m…

The high energy X-ray probe (HEX-P): constraining supermassive black hole growth with population spin measurements Open

Joanna M. Piotrowska, J. A. García, D. J. Walton, Ricarda S. Beckmann, Daniel Stern , et al. · 2024

Constraining the primary growth channel of supermassive black holes (SMBHs) remains one the most actively debated questions in the context of cosmological structure formation. Owing to the expected connection between SMBH spin parameter ev…

Birefringence tests of gravity with multi-messenger binaries Open

Macarena Lagos, Leah Jenks, M. Isi, Kenta Hotokezaka, Brian D. Metzger , et al. · 2024

Extensions to General Relativity (GR) allow the polarization of gravitational waves (GW) from astrophysical sources to suffer from amplitude and velocity birefringence, which respectively induce changes in the ellipticity and orientation o…

Fast Gravitational-wave Parameter Estimation without Compromises Open

Kaze W. K. Wong, M. Isi, T. Edwards · 2023

We present a lightweight, flexible, and high-performance framework for inferring the properties of gravitational-wave events. By combining likelihood heterodyning, automatically differentiable, and accelerator-compatible waveforms, and gra…

AspGap: Augmented Stellar Parameters and Abundances for 23 million RGB stars from Gaia XP low-resolution spectra Open

Jiadong Li, Kaze W. K. Wong, David W. Hogg, Hans‐Walter Rix, Vedant Chandra · 2023

We present AspGap, a new approach to infer stellar labels from low-resolution Gaia XP spectra, including precise [$α$/M] estimates for the first time. AspGap is a neural-network based regression model trained on APOGEE spectra. In the trai…

Recalibrating Gravitational Wave Phenomenological Waveform Model Open

Kelvin K. H. Lam, Kaze W. K. Wong, T. Edwards · 2023

We investigate the possibility of improving the accuracy of the phenomenological waveform model, IMRPhenomD, by jointly optimizing all the calibration coefficients at once, given a set of numerical relativity (NR) waveforms. When IMRPhenom…

Backward Population Synthesis: Mapping the Evolutionary History of Gravitational-wave Progenitors Open

Kaze W. K. Wong, Katelyn Breivik, Will M. Farr, Rodrigo Luger · 2023

One promising way to extract information about stellar astrophysics from a gravitational-wave catalog is to compare the catalog to the outputs of stellar population synthesis modeling with varying physical assumptions. The parameter space …

Equivariant geometric convolutions for emulation of dynamical systems Open

Wilson G. Gregory, David W. Hogg, Ben Blum-Smith, M. Arias, Kaze W. K. Wong , et al. · 2023

Machine learning methods are increasingly being employed as surrogate models in place of computationally expensive and slow numerical integrators for a bevy of applications in the natural sciences. However, while the laws of physics are re…

Constraining gravitational wave amplitude birefringence with GWTC-3: Data Release Open

Thomas C. K. Ng, Maximiliano Isi, Kaze W. K. Wong, W. M. Farr · 2023

Dataset release accompanying Constraining gravitational wave amplitude birefringence with GWTC-3.

Constraining gravitational wave amplitude birefringence with GWTC-3: Data Release Open

Thomas C. K. Ng, M. Isi, Kaze W. K. Wong, Will M. Farr · 2023

Dataset release accompanying Constraining gravitational wave amplitude birefringence with GWTC-3.

Constraining gravitational wave amplitude birefringence with GWTC-3: Data Release Open

Thomas C. K. Ng, M. Isi, Kaze W. K. Wong, Will M. Farr · 2023

Dataset release accompanying Constraining gravitational wave amplitude birefringence with GWTC-3.

The CAMELS Project: Public Data Release Open

Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés‐Alcázar, Lucia A. Perez, Pablo Villanueva-Domingo , et al. · 2023

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. CAMELS contains 4233 co…

flowMC: Normalizing flow enhanced sampling package forprobabilistic inference in JAX Open

Kaze W. K. Wong, Marylou Gabrié, Daniel Foreman-Mackey · 2023

International audience

Nonlinear Effects in Black Hole Ringdown Open

Mark Ho-Yeuk Cheung, Vishal Baibhav, Emanuele Berti, Vítor Cardoso, G. Carullo , et al. · 2023

We report evidence for nonlinear modes in the ringdown stage of the gravitational waveform produced by the merger of two comparable-mass black holes. We consider both the coalescence of black hole binaries in quasicircular orbits and high-…

ripple: Differentiable and Hardware-Accelerated Waveforms for Gravitational Wave Data Analysis Open

T. Edwards, Kaze W. K. Wong, Kelvin K. H. Lam, Adam Coogan, Daniel Foreman-Mackey , et al. · 2023

We propose the use of automatic differentiation through the programming framework jax for accelerating a variety of analysis tasks throughout gravitational wave (GW) science. Firstly, we demonstrate that complete waveforms which cover the …

Fast gravitational wave parameter estimation without compromises Open

Kaze W. K. Wong, M. Isi, T. Edwards · 2023

We present a lightweight, flexible, and high-performance framework for inferring the properties of gravitational-wave events. By combining likelihood heterodyning, automatically-differentiable and accelerator-compatible waveforms, and grad…

Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study Open

David Ruhe, Kaze W. K. Wong, Miles Cranmer, Patrick Forré · 2022

We propose parameterizing the population distribution of the gravitational wave population modeling framework (Hierarchical Bayesian Analysis) with a normalizing flow. We first demonstrate the merit of this method on illustrative experimen…

flowMC: Normalizing-flow enhanced sampling package for probabilistic inference in Jax Open

Kaze W. K. Wong, Marylou Gabrié, Daniel Foreman-Mackey · 2022

flowMC is a Python library for accelerated Markov Chain Monte Carlo (MCMC) leveraging deep generative modeling. It is built on top of the machine learning libraries JAX and Flax. At its core, flowMC uses a local sampler and a learnable glo…

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