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Register a new userCatalog Matching with Astrometric Correction and its Application to the Hubble Legacy Archive
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Authors
Tamas Budavari
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Object cross-identification in multiple observations is often complicated by the uncertainties in their astrometric calibration. Due to the lack of standard reference objects, an image with a small field of view can have significantly larger errors in its absolute positioning than the relative precision of the detected sources within. We present a new general solution for the relative astrometry that quickly refines the World Coordinate System of overlapping fields. The efficiency is obtained through the use of infinitesimal 3-D rotations on the celestial sphere, which do not involve trigonometric functions. They also enable an analytic solution to an important step in making the astrometric corrections. In cases with many overlapping images, the correct identification of detections that match together across different images is difficult to determine. We describe a new greedy Bayesian approach for selecting the best object matches across a large number of overlapping images. The methods are developed and demonstrated on the Hubble Legacy Archive, one of the most challenging data sets today. We describe a novel catalog compiled from many Hubble Space Telescope observations, where the detections are combined into a searchable collection of matches that link the individual detections. The matches provide descriptions of astronomical objects involving multiple wavelengths and epochs. High relative positional accuracy of objects is achieved across the Hubble images, often sub-pixel precision in the order of just a few milli-arcseconds. The result is a reliable set of high-quality associations that are publicly available online.
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A public release of slitless spectra, obtained with ACS/WFC and the G800L grism, is presented. Spectra were automatically extracted in a uniform way from 153 archival fields (or associations) distributed across the two Galactic caps, covering all observations to 2008. The ACS G800L grism provides a wavelength range of 0.55-1.00 mu$m, with a dispersion of $40 AA / pixel$ and a resolution of $sim 80 AA$ for point-like sources. The ACS G800L images and matched direct images were reduced with an automatic pipeline that handles all steps from archive retrieval, alignment and astrometric calibration, direct image combination, catalogue generation, spectral extraction and collection of metadata. The large number of extracted spectra (73,581) demanded automatic methods for quality control and an automated classification algorithm was trained on the visual inspection of several thousand spectra. The final sample of quality controlled spectra includes 47,919 datasets (65% of the total number of extracted spectra) for $32,149$ unique objects, with a median $i_{rm AB}$-band magnitude of 23.7, reaching 26.5 AB for the faintest objects. Each released dataset contains science-ready 1D and 2D spectra, as well as multi-band image cutouts of corresponding sources and a useful preview page summarising the direct and slitless data, astrometric and photometric parameters. In order to characterize the slitless spectra, emission-line flux and equivalent width sensitivity of the ACS data were compared with public ground-based spectra in the GOODS-South field. An example list of emission line galaxies with two or more identified lines is also included, covering the redshift range $0.2-4.6$.
This manuscript describes the public release of the Hubble Legacy Fields (HLF) project photometric catalog for the extended GOODS-South region from the Hubble Space Telescope (HST) archival program AR-13252. The analysis is based on the version 2.0 HLF data release that now includes all ultraviolet (UV) imaging, combining three major UV surveys. The HLF data combines over a decade worth of 7475 exposures taken in 2635 orbits totaling 6.3 Msec with the HST Advanced Camera for Surveys Wide Field Channel (ACS/WFC) and the Wide Field Camera 3 (WFC3) UVIS/IR Channels in the greater GOODS-S extragalactic field, covering all major observational efforts (e.g., GOODS, GEMS, CANDELS, ERS, UVUDF and many other programs; see Illingworth et al 2019, in prep). The HLF GOODS-S catalogs include photometry in 13 bandpasses from the UV (WFC3/UVIS F225W, F275W and F336W filters), optical (ACS/WFC F435W, F606W, F775W, F814W and F850LP filters), to near-infrared (WFC3/IR F098M, F105W, F125W, F140W and F160W filters). Such a data set makes it possible to construct the spectral energy distributions (SEDs) of objects over a wide wavelength range from high resolution mosaics that are largely contiguous. Here, we describe a photometric analysis of 186,474 objects in the HST imaging at wavelengths 0.2--1.6$mu$m. We detect objects from an ultra-deep image combining the PSF-homogenized and noise-equalized F850LP, F125W, F140W and F160W images, including Gaia astrometric corrections. SEDs were determined by carefully taking the effects of the point-spread function in each observation into account. All of the data presented herein are available through the HLF website (https://archive.stsci.edu/prepds/hlf/).
Not only source catalogs are extracted from astronomy observations. Their sky coverage is 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 the 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 rarely available in traditional associations.
This work emphasizes that heterogeneity, diversity, discontinuity, and discreteness in data is to be exploited in classification and regression problems. A global a priori model may not be desirable. For data analytics in cosmology, this is motivated by the variety of cosmological objects such as elliptical, spiral, active, and merging galaxies at a wide range of redshifts. Our aim is matching and similarity-based analytics that takes account of discrete relationships in the data. The information structure of the data is represented by a hierarchy or tree where the branch structure, rather than just the proximity, is important. The representation is related to p-adic number theory. The clustering or binning of the data values, related to the precision of the measurements, has a central role in this methodology. If used for regression, our approach is a method of cluster-wise regression, generalizing nearest neighbour regression. Both to exemplify this analytics approach, and to demonstrate computational benefits, we address the well-known photometric redshift or `photo-z problem, seeking to match Sloan Digital Sky Survey (SDSS) spectroscopic and photometric redshifts.
The Hubble Catalog of Variables (HCV) is a 3 year ESA funded project that aims to develop a set of algorithms to identify variables among the sources included in the Hubble Source Catalog (HSC) and produce the HCV. We will process all HSC sources with more than a predefined number of measurements in a single filter/instrument combination and compute a range of lightcurve features to determine the variability status of each source. At the end of the project, the first release of the Hubble Catalog of Variables will be made available at the Mikulski Archive for Space Telescopes (MAST) and the ESA Science Archives. The variability detection pipeline will be implemented at the Space Telescope Science Institute (STScI) so that updat
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