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Found 18 papers, 10 papers with code
Unified framework for diffusion generative models in SO(3): applications in computer vision and astrophysics
In this work, we develop extensions of both score-based generative models (SGMs) and Denoising Diffusion Probabilistic Models (DDPMs) to the Lie group of 3D rotations, SO(3).
xVal: A Continuous Number Encoding for Large Language Models
Large Language Models have not yet been broadly adapted for the analysis of scientific datasets due in part to the unique difficulties of tokenizing numbers.
Multiple Physics Pretraining for Physical Surrogate Models
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling.
AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models
We present AstroCLIP, a strategy to facilitate the construction of astronomical foundation models that bridge the gap between diverse observational modalities.
Towards solving model bias in cosmic shear forward modeling
As the volume and quality of modern galaxy surveys increase, so does the difficulty of measuring the cosmological signal imprinted in galaxy shapes.
Probabilistic Mass Mapping with Neural Score Estimation
We introduce a novel methodology allowing for efficient sampling of the high-dimensional Bayesian posterior of the weak lensing mass-mapping problem, and relying on simulations for defining a fully non-Gaussian prior.
Adaptive wavelet distillation from neural networks through interpretations
Moreover, interpretable models are concise and often yield computational efficiency.
Real-Time Likelihood-Free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation
Fast and automated inference of binary-lens, single-source (2L1S) microlensing events with sampling-based Bayesian algorithms (e. g., Markov Chain Monte Carlo; MCMC) is challenged on two fronts: high computational cost of likelihood evaluations with microlensing simulation codes, and a pathological parameter space where the negative-log-likelihood surface can contain a multitude of local minima that are narrow and deep.
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems
Deep neural networks have proven extremely efficient at solving a wide rangeof inverse problems, but most often the uncertainty on the solution they provideis hard to quantify.
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
Automating Inference of Binary Microlensing Events with Neural Density Estimation
Automated inference of binary microlensing events with traditional sampling-based algorithms such as MCMC has been hampered by the slowness of the physical forward model and the pathological likelihood surface.
Likelihood-free inference with neural compression of DES SV weak lensing map statistics
We present likelihood-free cosmological parameter inference using weak lensing maps from the Dark Energy Survey (DES) SV data, using neural data compression of weak lensing map summary statistics.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Transformation Importance with Applications to Cosmology
Machine learning lies at the heart of new possibilities for scientific discovery, knowledge generation, and artificial intelligence.
Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems
We present a Bayesian machine learning architecture that combines a physically motivated parametrization and an analytic error model for the likelihood with a deep generative model providing a powerful data-driven prior for complex signals.
The Role of Machine Learning in the Next Decade of Cosmology
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
Core Cosmology Library: Precision Cosmological Predictions for LSST
As a result, predictions for correlation functions of galaxy clustering, galaxy-galaxy lensing and cosmic shear are demonstrated to be within a fraction of the expected statistical uncertainty of the observables for the models and in the range of scales of interest to LSST.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
We find on our simulated data set that for a rejection rate of non-lenses of 99%, a completeness of 90% can be achieved for lenses with Einstein radii larger than 1. 4" and S/N larger than 20 on individual $g$-band LSST exposures.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images
To this end, we study the application of deep conditional generative models in generating realistic galaxy images.
