arxiv_cv 95% Match Research Paper Astronomers,Machine Learning Researchers,Computer Vision Engineers 1 month ago

Neural Posterior Estimation with Autoregressive Tiling for Detecting Objects in Astronomical Images

computer-vision › object-detection

📄 Abstract

Abstract: Upcoming astronomical surveys will produce petabytes of high-resolution images of the night sky, providing information about billions of stars and galaxies. Detecting and characterizing the astronomical objects in these images is a fundamental task in astronomy -- and a challenging one, as most of these objects are faint and many visually overlap with other objects. We propose an amortized variational inference procedure to solve this instance of small-object detection. Our key innovation is a family of spatially autoregressive variational distributions that partition and order the latent space according to a $K$-color checkerboard pattern. By construction, the conditional independencies of this variational family mirror those of the posterior distribution. We fit the variational distribution, which is parameterized by a convolutional neural network, using neural posterior estimation (NPE) to minimize an expectation of the forward KL divergence. Using images from the Sloan Digital Sky Survey, our method achieves state-of-the-art performance. We further demonstrate that the proposed autoregressive structure greatly improves posterior calibration.

Key Contributions

Proposes a novel amortized variational inference procedure for object detection in astronomical images. Introduces a family of spatially autoregressive variational distributions that partition the latent space using a K-color checkerboard pattern, mirroring the posterior distribution's conditional independencies. This approach aims to improve the detection of faint and overlapping objects in large-scale astronomical surveys.

Business Value

Enables more accurate and efficient analysis of vast astronomical datasets, potentially leading to new discoveries in cosmology and astrophysics. Improves the foundational capabilities for scientific research in space observation.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Requires significant computational resources for training and inference, but the methods are standard deep learning techniques applicable to large image datasets.

Limitations Addressed

Challenges in detecting faint and overlapping objects in high-resolution astronomical images, which are common in large-scale surveys.

Technical Tags

Variational InferenceAutoregressive ModelsObject DetectionImage AnalysisDeep LearningConvolutional Neural NetworksBayesian InferencePosterior EstimationAstronomical ImagingLatent Space Partitioning

Research Topics

Computer VisionMachine LearningAstronomyStatistical ModelingGenerative Models

Methods & Architectures

Amortized Variational InferenceNeural Posterior Estimation (NPE)Convolutional Neural Networks (CNNs)Autoregressive Variational DistributionsKL Divergence Minimization Convolutional Neural Networks

Applications & Tasks

Astronomy Image Processing Object DetectionImage AnalysisStatistical Inference Detecting faint and overlapping objects in astronomical images

Datasets & Benchmarks

Datasets

Sloan Digital Sky Survey (SDSS)

Related Fields

AstrophysicsStatistical Machine LearningImage RecognitionBayesian Deep Learning

Keywords

object detectionastronomyvariational inferenceautoregressive modelsdeep learningconvolutional neural networksimage analysisBayesian methodsNPElatent spaceastronomical imagessky surveysfaint objectsoverlapping objects

Academic Context

#Computer Vision#Machine Learning#Astronomy#Statistical Modeling#Generative Models

Commercial Potential

Potential Products

Automated astronomical object cataloging systemsTools for analyzing large-scale sky survey data

Target Industries

Scientific ResearchAcademia

Use Case Examples

Identifying galaxies and stars in images from upcoming astronomical surveys

Competitive Edge

Offers a novel variational distribution family and fitting procedure (NPE) that specifically addresses the challenges of object detection in complex astronomical imagery, potentially outperforming existing methods that may not capture the specific conditional independencies of the posterior.

Resource Requirements

Compute Needs

High, for training deep neural networks on large image datasets.

Data Requirements

Large, high-resolution astronomical image datasets.

Deployment Constraints

Computational cost for inference on massive datasets.

Scalability

Designed for large-scale astronomical surveys, implying scalability is a key consideration.

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