1 code implementation • 22 Feb 2024 • Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul
We analyze the convergence of BaM when the target distribution is Gaussian, and we prove that in the limit of infinite batch size the variational parameter updates converge exponentially quickly to the target mean and covariance.
1 code implementation • 6 Feb 2024 • Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier, Carolina Cuesta-Lazaro, Simon Ding, Axel Lapel, Pablo Lemos, Christopher C. Lovell, T. Lucas Makinen, Chirag Modi, Viraj Pandya, Shivam Pandey, Lucia A. Perez, Benjamin Wandelt, Greg L. Bryan
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology.
no code implementations • 23 Oct 2023 • Pablo Lemos, Liam Parker, ChangHoon Hahn, Shirley Ho, Michael Eickenberg, Jiamin Hou, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, Bruno Regaldo-Saint Blancard, David Spergel
We demonstrate the robustness of our analysis by showcasing our ability to infer unbiased cosmological constraints from a series of test simulations that are constructed using different forward models than the one used in our training dataset.
2 code implementations • NeurIPS 2023 • Chirag Modi, Charles Margossian, Yuling Yao, Robert Gower, David Blei, Lawrence Saul
We study how GSM-VI behaves as a function of the problem dimensionality, the condition number of the target covariance matrix (when the target is Gaussian), and the degree of mismatch between the approximating and exact posterior distribution.
no code implementations • 28 Jun 2022 • Chirag Modi, Yin Li, David Blei
We show that after a short initial warm-up and training phase, VBS generates better quality of samples than simple VI approaches and reduces the correlation length in the sampling phase by a factor of 10-50 over using only HMC to explore the posterior of initial conditions in 64$^3$ and 128$^3$ dimensional problems, with larger gains for high signal-to-noise data observations.
no code implementations • 1 Oct 2021 • Chirag Modi, Alex Barnett, Bob Carpenter
The efficiency of Hamiltonian Monte Carlo (HMC) can suffer when sampling a distribution with a wide range of length scales, because the small step sizes needed for stability in high-curvature regions are inefficient elsewhere.
2 code implementations • 22 Oct 2020 • Chirag Modi, Francois Lanusse, Uros Seljak
We present FlowPM, a Particle-Mesh (PM) cosmological N-body code implemented in Mesh-TensorFlow for GPU-accelerated, distributed, and differentiable simulations.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
no code implementations • 16 Oct 2019 • Chirag Modi, Uros Seljak
We combine this Gaussian prior with the data likelihood given by the pre-treatment data of the single unit, to obtain the synthetic prediction of the unit post-treatment, which minimizes the error variance of synthetic prediction.
2 code implementations • 15 Oct 2019 • Vanessa Boehm, Chirag Modi, Emanuele Castorina
We find that the relative size of lensing corrections depends on the respective redshift distributions of the lensing sources and galaxies, but that they are generally small for high signal-to-noise correlations.
Cosmology and Nongalactic Astrophysics
no code implementations • 4 Jul 2019 • Chirag Modi, Martin White, Anze Slosar, Emanuele Castorina
For redshifts $z=2$ and 4, we are able to reconstruct 21cm field with cross correlation, $r_c > 0. 8$ on all scales for both our optimistic and pessimistic assumptions about foreground contamination and for different levels of thermal noise.
Cosmology and Nongalactic Astrophysics
2 code implementations • 26 Apr 2019 • Chirag Modi, Emanuele Castorina, Yu Feng, Martin White
This paper introduces the Hidden Valley simulations, a set of trillion-particle N-body simulations in gigaparsec volumes aimed at intensity mapping science.
Cosmology and Nongalactic Astrophysics
no code implementations • 6 May 2018 • Chirag Modi, Yu Feng, Uros Seljak
Our method relies on following the gradients of forward model and since the standard way to identify halos is non-differentiable and results in a discrete sample of objects, we propose a framework to model the halo position and mass field starting from the non-linear matter field using Neural Networks.
Cosmology and Nongalactic Astrophysics
2 code implementations • 15 Dec 2017 • Nick Hand, Yu Feng, Florian Beutler, Yin Li, Chirag Modi, Uros Seljak, Zachary Slepian
The package is extensively documented at http://nbodykit. readthedocs. io, which also includes an interactive set of example recipes for new users to explore.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
1 code implementation • 10 Jun 2017 • Chirag Modi, Martin White, Zvonimir Vlah
A new generation of surveys will soon map large fractions of sky to ever greater depths and their science goals can be enhanced by exploiting cross correlations between them.
Cosmology and Nongalactic Astrophysics