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Found 23 papers, 8 papers with code
Deterministic Langevin Unconstrained Optimization with Normalizing Flows
We introduce a global, gradient-free surrogate optimization strategy for expensive black-box functions inspired by the Fokker-Planck and Langevin equations.
Multiscale Flow for Robust and Optimal Cosmological Analysis
We propose Multiscale Flow, a generative Normalizing Flow that creates samples and models the field-level likelihood of two-dimensional cosmological data such as weak lensing.
A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time Series
We construct a physically-parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of type Ia supernovae (SNe Ia) from a sparse set of spectral time series.
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
We propose a general purpose Bayesian inference algorithm for expensive likelihoods, replacing the stochastic term in the Langevin equation with a deterministic density gradient term.
Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis
TRENF is also a generative model of the data, and we show that TRENF samples agree well with the N-body simulations it trained on, and that the inverse mapping of the data agrees well with a Gaussian white noise both visually and on various summary statistics: when this is perfectly achieved the resulting p(x|y) likelihood analysis becomes optimal.
Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
Anomaly detection is a key application of machine learning, but is generally focused on the detection of outlying samples in the low probability density regions of data.
FlowPM: Distributed TensorFlow Implementation of the FastPM Cosmological N-body Solver
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
Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning
In this work we propose Lagrangian Deep Learning (LDL) for this purpose, applying it to learn outputs of cosmological hydrodynamical simulations.
Sliced Iterative Normalizing Flows
We develop an iterative (greedy) deep learning (DL) algorithm which is able to transform an arbitrary probability distribution function (PDF) into the target PDF.
Ranked #2 on
Image Generation
on Fashion-MNIST
Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics
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.
Posterior inference unchained with EL_2O
Statistical inference of analytically non-tractable posteriors is a difficult problem because of marginalization of correlated variables and stochastic methods such as MCMC and VI are commonly used.
Disconnected Covariance of 2-point Functions in Large-Scale Structure
We verify the analytic covariance against the sample covariance from the galaxy mock simulations in two test cases: (1) the power spectrum multipole covariance, and (2) the joint covariance of the projected correlation function and the correlation function multipoles.
Cosmology and Nongalactic Astrophysics
The Simons Observatory: Science goals and forecasts
With up to an order of magnitude lower polarization noise than maps from the Planck satellite, the high-resolution sky maps will constrain cosmological parameters derived from the damping tail, gravitational lensing of the microwave background, the primordial bispectrum, and the thermal and kinematic Sunyaev-Zel'dovich effects, and will aid in delensing the large-angle polarization signal to measure the tensor-to-scalar ratio.
Cosmology and Nongalactic Astrophysics
Cosmological Reconstruction From Galaxy Light: Neural Network Based Light-Matter Connection
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
nbodykit: an open-source, massively parallel toolkit for large-scale structure
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
An optimal FFT-based anisotropic power spectrum estimator
Here, we present a faster, optimal means of using FFTs for this measurement.
Cosmology and Nongalactic Astrophysics
Intrinsic alignment-lensing interference as a contaminant of cosmic shear
The first model predicts a gravitational-intrinsic (GI) correlation that can be much greater than the intrinsic-intrinsic (II) correlation for broad redshift distributions, and that remains when galaxies pairs at similar redshifts are rejected.
astro-ph
The Sunyaev-Zel'dovich angular power spectrum as a probe of cosmological parameters
It receives a dominant contribution from cluster region between 20-40% of the virial radius and is thus insensitive to the poorly known gas physics in the cluster centre, such as cooling or (pre)heating.
astro-ph
CMBFAST for spatially closed universes
We extend the cosmological linear perturbation theory code CMBFAST to closed geometries.
astro-ph
Gravitational Lensing Effect on Cosmic Microwave Background Polarization
As in the case of temperature spectrum gravitational lensing leads to smoothing of narrow features and enhancement of power on the damping tail of the power spectrum.
astro-ph
A Complete Treatment of CMB Anisotropies in a FRW Universe
We generalize the total angular momentum method for computing Cosmic Microwave Background anisotropies to Friedman-Robertson-Walker (FRW) spaces with arbitrary geometries.
astro-ph
A Line of Sight Approach to Cosmic Microwave Background Anisotropies
The source term can be expressed in terms of photon, baryon and metric perturbations, all of which can be calculated using a small number of differential equations.
astro-ph
GRAVITATIONAL LENSING EFFECT ON COSMIC MICROWAVE BACKGROUND ANISOTROPIES: A POWER SPECTRUM APPROACH
The effect of gravitational lensing on cosmic microwave background (CMB) anisotropies is investigated using the power spectrum approach.
astro-ph General Relativity and Quantum Cosmology
