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Register a new userA Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources II: Update to the PS1 Point Source Catalog
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A. A. Miller
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We present an update to the PanSTARRS-1 Point Source Catalog (PS1 PSC), which provides morphological classifications of PS1 sources. The original PS1 PSC adopted stringent detection criteria that excluded hundreds of millions of PS1 sources from the PSC. Here, we adapt the supervised machine learning methods used to create the PS1 PSC and apply them to different photometric measurements that are more widely available, allowing us to add $sim$144 million new classifications while expanding the the total number of sources in PS1 PSC by $sim$10%. We find that the new methodology, which utilizes PS1 forced photometry, performs $sim$6-8% worse than the original method. This slight degradation in performance is offset by the overall increase in the size of the catalog. The PS1 PSC is used by time-domain surveys to filter transient alert streams by removing candidates coincident with point sources that are likely to be Galactic in origin. The addition of $sim$144 million new classifications to the PS1 PSC will improve the efficiency with which transients are discovered.
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In the era of large photometric surveys, the importance of automated and accurate classification is rapidly increasing. Specifically, the separation of resolved and unresolved sources in astronomical imaging is a critical initial step for a wide array of studies, ranging from Galactic science to large scale structure and cosmology. Here, we present our method to construct a large, deep catalog of point sources utilizing Pan-STARRS1 (PS1) 3$pi$ survey data, which consists of $sim$3$times10^9$ sources with $mlesssim23.5,$mag. We develop a supervised machine-learning methodology, using the random forest (RF) algorithm, to construct the PS1 morphology model. We train the model using $sim$5$times10^4$ PS1 sources with HST COSMOS morphological classifications and assess its performance using $sim$4$times10^6$ sources with Sloan Digital Sky Survey (SDSS) spectra and $sim$2$times10^8$ textit{Gaia} sources. We construct 11 white flux features, which combine PS1 flux and shape measurements across 5 filters, to increase the signal-to-noise ratio relative to any individual filter. The RF model is compared to 3 alternative models, including the SDSS and PS1 photometric classification models, and we find that the RF model performs best. By number the PS1 catalog is dominated by faint sources ($mgtrsim21,$mag), and in this regime the RF model significantly outperforms the SDSS and PS1 models. For time-domain surveys, identifying unresolved sources is crucial for inferring the Galactic or extragalactic origin of new transients. We have classified $sim$1.5$times10^9$ sources using the RF model, and these results are used within the Zwicky Transient Facility real-time pipeline to automatically reject stellar sources from the extragalactic alert stream.
The Spectral and Photometric Imaging Receiver (SPIRE) was launched as one of the scientific instruments on board of the space observatory Herschel. The SPIRE photometer opened up an entirely new window in the Submillimeter domain for large scale mapping, that up to then was very difficult to observe. There are already several catalogs that were produced by individual Herschel science projects. Yet, we estimate that the objects of only a fraction of these maps will ever be systematically extracted and published by the science teams that originally proposed the observations. The SPIRE instrument performed its standard photometric observations in an optically very stable configuration, only moving the telescope across the sky, with variations in its configuration parameters limited to scan speed and sampling rate. This and the scarcity of features in the data that require special processing steps made this dataset very attractive for producing an expert reduced catalog of point sources that is being described in this document. The Catalog was extracted from a total of 6878 unmodified SPIRE scan map observations. The photometry was obtained by a systematic and homogeneous source extraction procedure, followed by a rigorous quality check that emphasized reliability over completeness. Having to exclude regions affected by strong Galactic emission, that pushed the limits of the four source extraction methods that were used, this catalog is aimed primarily at the extragalactic community. The result can serve as a pathfinder for ALMA and other Submillimeter and Far-Infrared facilities. 1,693,718 sources are included in the final catalog, splitting into 950688, 524734, 218296 objects for the 250mu m, 350mu m, and 500mu m bands, respectively. The catalog comes with well characterized environments, reliability, completeness, and accuracies, that single programs typically cannot provide.
With growing data volumes from synoptic surveys, astronomers must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities, and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All Sky Automated Survey (ASAS), and unveil the Machine-learned ASAS Classification Catalog (MACC), which is a 28-class probabilistic classification catalog of 50,124 ASAS sources. We estimate that MACC achieves a sub-20% classification error rate, and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes. The MACC is publicly available at http://www.bigmacc.info.
Precision measurement of the scalar perturbation spectral index, n_s, from the Wilkinson Microwave Anisotropy Probe temperature angular power spectrum requires the subtraction of unresolved point source power. Here we reconsider this issue. First, we note a peculiarity in the WMAP temperature likelihoods response to the source correction: Cosmological parameters do not respond to increased source errors. An alternative and more direct method for treating this error term acts more sensibly, and also shifts n_s by ~0.3 sigma closer to unity. Second, we re-examine the source fit used to correct the power spectrum. This fit depends strongly on the galactic cut and the weighting of the map, indicating that either the source population or masking procedure is not isotropic. Jackknife tests appear inconsistent, causing us to assign large uncertainties to account for possible systematics. Third, we note that the WMAP teams spectrum was computed with two different weighting schemes: uniform weights transition to inverse noise variance weights at l = 500. The fit depends on such weighting schemes, so different corrections apply to each multipole range. For the Kp2 mask used in cosmological analysis, we prefer source corrections A = 0.012 +/- 0.005 muK^2 for uniform weighting and A = 0.015 +/- 0.005 muK^2 for N_obs weighting. Correcting WMAPs spectrum correspondingly, we compute cosmological parameters with our alternative likelihood, finding n_s = 0.970 +/- 0.017 and sigma_8 = 0.778 +/- 0.045 . This n_s is only 1.8 sigma from unity, compared to the ~2.6 sigma WMAP 3-year result. Finally, an anomalous feature in the source spectrum at l<200 remains, most strongly associated with W-band.
Detection of point sources in images is a fundamental operation in astrophysics, and is crucial for constraining population models of the underlying point sources or characterizing the background emission. Standard techniques fall short in the crowded-field limit, losing sensitivity to faint sources and failing to track their covariance with close neighbors. We construct a Bayesian framework to perform inference of faint or overlapping point sources. The method involves probabilistic cataloging, where samples are taken from the posterior probability distribution of catalogs consistent with an observed photon count map. In order to validate our method we sample random catalogs of the gamma-ray sky in the direction of the North Galactic Pole (NGP) by binning the data in energy and Point Spread Function (PSF) classes. Using three energy bins spanning $0.3 - 1$, $1 - 3$ and $3 - 10$ GeV, we identify $270substack{+30 -10}$ point sources inside a $40^circ times 40^circ$ region around the NGP above our point-source inclusion limit of $3 times 10^{-11}$/cm$^2$/s/sr/GeV at the $1-3$ GeV energy bin. Modeling the flux distribution as a power law, we infer the slope to be $-1.92substack{+0.07 -0.05}$ and estimate the contribution of point sources to the total emission as $18substack{+2 -2}$%. These uncertainties in the flux distribution are fully marginalized over the number as well as the spatial and spectral properties of the unresolved point sources. This marginalization allows a robust test of whether the apparently isotropic emission in an image is due to unresolved point sources or of truly diffuse origin.
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