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We propose a novel approximate method for the probabilistic catalog matching problem that provides better solutions than previously used heuristics and scales well for large real-world applications. We also improve probabilistic catalog matching by including a simple but powerful prior and optimizing the posterior instead of just the likelihood as in previous formulations. Our new approach uses constrained clustering, specifically COP-KMeans, to provide near-optimal solutions in a fraction of the time of previous methods. We empirically demonstrate our constrained clustering’s efficacy through simulations and data from the Hubble Source Catalog.

We propose a novel exact method to solve the probabilistic catalog matching problem faster than previously possible. Our new approach uses mixed integer programming and introduces quadratic constraints to shrink the problem by multiple orders of magnitude. We also provide a method to use a feasible solution to dramatically speed up our algorithm. This gain in performance is dependent on how close to optimal the feasible solution is. Also, we are able to provide good solutions by stopping our mixed integer programming solver early. Using simulated catalogs, we empirically show that our new mixed integer program with quadratic constraints is able to be set up and solved much faster than previous large linear formulations. We also demonstrate our new approach on real-world data from the Hubble Source Catalog. This paper is accompanied by publicly available software to demonstrate the proposed method.

Fitting the spectral energy distributions (SEDs) of galaxies is an almost universally used technique that has matured significantly in the last decade. Model predictions and fitting procedures have improved significantly over this time, attempting to keep up with the vastly increased volume and quality of available data. We review here the field of SED fitting, describing the modelling of ultraviolet to infrared galaxy SEDs, the creation of multiwavelength data sets, and the methods used to fit model SEDs to observed galaxy data sets. We touch upon the achievements and challenges in the major ingredients of SED fitting, with a special emphasis on describing the interplay between the quality of the available data, the quality of the available models, and the best fitting technique to use in order to obtain a realistic measurement as well as realistic uncertainties. We conclude that SED fitting can be used effectively to derive a range of physical properties of galaxies, such as redshift, stellar masses, star formation rates, dust masses, and metallicities, with care taken not to over-interpret the available data. Yet there still exist many issues such as estimating the age of the oldest stars in a galaxy, finer details of dust properties and dust-star geometry, and the influences of poorly understood, luminous stellar types and phases. The challenge for the coming years will be to improve both the models and the observational data sets to resolve these uncertainties. The present review will be made available on an interactive, moderated web page ( sedfitting.org ), where the community can access and change the text. The intention is to expand the text and keep it up to date over the coming years.

A key processing step in ground-based astronomy involves combining multiple noisy and blurry exposures to produce an image of the night sky with an improved signal-to-noise ratio. Typically, this is achieved via image coaddition, and can be undertaken such that the resulting night sky image has enhanced spatial resolution. Yet, this task remains a formidable challenge despite decades of advancements. In this paper, we introduce ImageMM: a new framework based on the majorization–minimization (MM) algorithm for joint multi-frame astronomical image restoration and super-resolution. ImageMM uses multiple registered astronomical exposures to produce a nonparametric latent image of the night sky, prior to the atmosphere’s impact on the observed exposures. Our framework also features a novel variational approach to compute refined point-spread functions of arbitrary resolution for the restoration and super-resolution procedure. Our algorithms, implemented in TensorFlow, leverage graphics processing unit acceleration to produce latent images in near real time, even when processing high-resolution exposures. We tested ImageMM on Hyper Suprime-Cam (HSC) exposures, which are a precursor of the upcoming imaging data from the Rubin Observatory. The results are encouraging: ImageMM produces sharp latent images, in which spatial features of bright sources are revealed in unprecedented detail (e.g., showing the structure of spiral galaxies), and where faint sources that are usually indistinguishable from the noisy sky background also become discernible, thus pushing the detection limits. Moreover, aperture photometry performed on the HSC pipeline coadd and ImageMM’s latent images yielded consistent source detection and flux measurements, thereby demonstrating ImageMM’s suitability for cutting-edge photometric studies with state-of-the-art astronomical imaging data.

Building on previous Bayesian approaches, we introduce a novel formulation of probabilistic cross-identification, where detections are directly associated to (hypothesized) astronomical objects in a globally optimal way. We show that this new method scales better for processing multiple catalogs than enumerating all possible candidates, especially in the limit of crowded fields, which is the most challenging observational regime for new-generation astronomy experiments such as the Rubin Observatory Legacy Survey of Space and Time. Here we study simulated catalogs where the ground truth is known and report on the statistical and computational performance of the method. The paper is accompanied by a public software tool to perform globally optimal catalog matching based on directional data.

Massively multiplexed spectrographs will soon gather large statistical samples of stellar spectra. The accurate estimation of uncertainties on derived parameters, such as the line-of-sight velocity v los, especially for spectra with low signal-to-noise ratios (S/Ns), is paramount. We generated an ensemble of simulated optical spectra of stars as if they were observed with low- and medium-resolution fiber-fed instruments on an 8 m class telescope, similar to the Subaru Prime Focus Spectrograph, and determined v los by fitting stellar templates to the simulated spectra. We compared the empirical errors of the derived parameters—calculated from an ensemble of simulations—to the asymptotic errors determined from the Fisher matrix, as well as from Monte Carlo sampling of the posterior probability. We confirm that the uncertainty of v los scales with the inverse square root of the S/N, but also show how this scaling breaks down at low S/N and analyze the error and bias caused by template mismatch. We outline a computationally optimized algorithm to fit multiexposure data and provide a mathematical model of stellar spectrum fitting that maximizes the so called significance, which allows for calculating the error from the Fisher matrix analytically. We also introduce the effective line count, and provide a scaling relation to estimate the errors of v los measurements based on stellar type. Our analysis covers a range of stellar types with parameters that are typical of the Galactic outer disk and halo, together with analogs of stars in M31 and in satellite dwarf spheroidal galaxies around the Milky Way.

Recovering high-fidelity images of the night sky from blurred observations is a fundamental problem in astronomy, where traditional methods typically fall short. In ground-based astronomy, combining multiple exposures to enhance signal-to-noise ratios is further complicated by variations in the point-spread function caused by atmospheric turbulence. In this work, we present a self-supervised multi-frame method, based on deep image priors, for denoising, deblurring, and coadding ground-based exposures. Central to our approach is a carefully designed convolutional neural network that integrates information across multiple observations and enforces physically motivated constraints. We demonstrate the method's potential by processing Hyper Suprime-Cam exposures, yielding promising preliminary results with sharper restored images.

Sky coverage is one of the most important pieces of information about astronomical observations. We discuss possible representations and present algorithms to create and manipulate shapes consisting of generalized spherical polygons with arbitrary complexity and size on the celestial sphere. This shape specification integrates well with our Hierarchical Triangular Mesh indexing toolbox, whose performance and capabilities are enhanced by the novel advanced features presented here. Our portable implementation of the relevant spherical geometry routines comes with wrapper functions for database queries, which are currently being used within several scientific catalog archives, including that of the Sloan Digital Sky Survey, the Galaxy Evolution Explorer, the UKIRT Infrared Deep Sky Survey, SuperCOSMOS, VISTA, Hubble Legacy Archive, and the Footprint Service of the Virtual Observatory.

A key processing step in ground-based astronomy involves combining multiple noisy and blurry exposures to produce an image of the night sky with an improved signal-to-noise ratio. Typically, this is achieved via image coaddition, and can be undertaken such that the resulting night sky image has enhanced spatial resolution. Yet, this task remains a formidable challenge despite decades of advancements. In this paper, we introduce ImageMM: a new framework based on the majorization-minimization (MM) algorithm for joint multi-frame astronomical image restoration and super-resolution. ImageMM uses multiple registered astronomical exposures to produce a nonparametric latent image of the night sky, prior to the atmosphere's impact on the observed exposures. Our framework also features a novel variational approach to compute refined point-spread functions of arbitrary resolution for the restoration and super-resolution procedure. Our algorithms, implemented in TensorFlow, leverage graphics processing unit acceleration to produce latent images in near real time, even when processing high-resolution exposures. We tested ImageMM on Hyper Suprime-Cam (HSC) exposures, which are a precursor of the upcoming imaging data from the Rubin Observatory. The results are encouraging: ImageMM produces sharp latent images, in which spatial features of bright sources are revealed in unprecedented detail (e.g., showing the structure of spiral galaxies), and where faint sources that are usually indistinguishable from the noisy sky background also become discernible, thus pushing the detection limits. Moreover, aperture photometry performed on the HSC pipeline coadd and ImageMM's latent images yielded consistent source detection and flux measurements, thereby demonstrating ImageMM's suitability for cutting-edge photometric studies with state-of-the-art astronomical imaging data.

ABSTRACT Not only source catalogs can be extracted from astronomy observations; their sky coverage is also always carefully recorded and used in statistical analyses, such as correlation and luminosity function studies. Here we present a novel method for catalog matching, which inherently builds on coverage information for better performance and completeness. A modified version of the zones algorithm is introduced for matching partially overlapping observations, where irrelevant parts of the data are excluded up front for efficiency. Our design enables searches to focus on specific areas on the sky to further speed up the process. Another important advantage of the new method over traditional techniques is its ability to quickly detect dropouts, i.e., the missing components that are in the observed regions of the celestial sphere but did not reach the detection limit in some observations. These often provide invaluable insight into the spectral energy distribution of the matched sources but are rarely available in traditional associations.