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Register a new userEfficient Catalog Matching with Dropout Detection
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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.
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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.
Dropout is a popular technique for regularizing artificial neural networks. Dropout networks are generally trained by minibatch gradient descent with a dropout mask turning off some of the units---a different pattern of dropout is applied to every sample in the minibatch. We explore a very simple alternative to the dropout mask. Instead of masking dropped out units by setting them to zero, we perform matrix multiplication using a submatrix of the weight matrix---unneeded hidden units are never calculated. Performing dropout batchwise, so that one pattern of dropout is used for each sample in a minibatch, we can substantially reduce training times. Batchwise dropout can be used with fully-connected and convolutional neural networks.
Observational astronomy in the time-domain era faces several new challenges. One of them is the efficient use of observations obtained at multiple epochs. The work presented here addresses faint object detection with multi-epoch data, and describes an incremental strategy for separating real objects from artifacts in ongoing surveys, in situations where the single-epoch data are summaries of the full image data, such as single-epoch catalogs of flux and direction estimates for candidate sources. The basic idea is to produce low-threshold single-epoch catalogs, and use a probabilistic approach to accumulate catalog information across epochs; this is in contrast to more conventional strategies based on co-added or stacked image data across all epochs. We adopt a Bayesian approach, addressing object detection by calculating the marginal likelihoods for hypotheses asserting there is no object, or one object, in a small image patch containing at most one cataloged source at each epoch. The object-present hypothesis interprets the sources in a patch at different epochs as arising from a genuine object; the no-object (noise) hypothesis interprets candidate sources as spurious, arising from noise peaks. We study the detection probability for constant-flux objects in a simplified Gaussian noise setting, comparing results based on single exposures and stacked exposures to results based on a series of single-epoch catalog summaries. Computing the detection probability based on catalog data amounts to generalized cross-matching: it is the product of a factor accounting for matching of the estimated fluxes of candidate sources, and a factor accounting for matching of their estimated directions. We find that probabilistic fusion of multi-epoch catalog information can detect sources with only modest sacrifice in sensitivity and selectivity compared to stacking.
The CatWISE2020 Catalog consists of 1,890,715,640 sources over the entire sky selected from WISE and NEOWISE survey data at 3.4 and 4.6 $mu$m (W1 and W2) collected from 2010 Jan. 7 to 2018 Dec. 13. This dataset adds two years to that used for the CatWISE Preliminary Catalog (Eisenhardt et al., 2020), bringing the total to six times as many exposures spanning over sixteen times as large a time baseline as the AllWISE catalog. The other major change from the CatWISE Preliminary Catalog is that the detection list for the CatWISE2020 Catalog was generated using ${it crowdsource}$ (Schlafly et al. 2019), while the CatWISE Preliminary Catalog used the detection software used for AllWISE. These two factors result in roughly twice as many sources in the CatWISE2020 Catalog. The scatter with respect to ${it Spitzer}$ photometry at faint magnitudes in the COSMOS field, which is out of the Galactic plane and at low ecliptic latitude (corresponding to lower WISE coverage depth) is similar to that for the CatWISE Preliminary Catalog. The 90% completeness depth for the CatWISE2020 Catalog is at W1=17.7 mag and W2=17.5 mag, 1.7 mag deeper than in the CatWISE Preliminary Catalog. From comparison to ${it Gaia}$, CatWISE2020 motions are accurate at the 20 mas yr$^{-1}$ level for W1$sim$15 mag sources, and at the $sim100$ mas yr$^{-1}$ level for W1$sim$17 mag sources. This level of precision represents a 12$times$ improvement over AllWISE. The CatWISE catalogs are available in the WISE/NEOWISE Enhanced and Contributed Products area of the NASA/IPAC Infrared Science Archive.
The URAT Parallax Catalog (UPC) consists of 112,177 parallaxes. The catalog utilizes all Northern Hemisphere exposures from the United States Naval Observatory (USNO) Robotic Astrometric Telescope (URAT) obtained between April 2012 and June 2015. Relative parallaxes are converted to absolute using photometric distance estimates of UCAC4 reference stars. There are 2 groups of stars in this catalog: 1) 58,677 stars with prior published trigonometric parallax (Hipparcos, Yale Parallax Catalog, MEarth project and SIMBAD), and 2) 53,500 stars with first time trigonometric parallaxes as obtained from URAT data. More stringent selection criteria have been applied for group 2 then for group 1 in order to keep the rate of false detections low. The mean error in UPC parallaxes is 10.8 and 4.3 mas for groups 1 and 2, respectively. All stars in UPC are north of -13 deg Dec and between 6.5 and 17 mag. The UPC is published by CDS as catalog I/333 and the acronym has been registered with the IAU. The Finch & Zacharias (2016, in press with AJ) paper describes the data, reductions, and results of an about 1000 star subset (stars within 40 pc of the Sun) of the entire UPC. The UPC also provides accurate positions and proper motions on the ICRS. This is the largest parallax catalog published since the Hipparcos Catalog.
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