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This paper describes the organization of the database and the catalog data products from the Pan-STARRS1 $3pi$ Steradian Survey. The catalog data products are available in the form of an SQL-based relational database from MAST, the Mikulski Archive for Space Telescopes at STScI. The database is described in detail, including the construction of the database, the provenance of the data, the schema, and how the database tables are related. Examples of queries for a range of science goals are included. The catalog data products are available in the form of an SQL-based relational database from MAST, the Mikulski Archive for Space Telescopes at STScI.
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Pan-STARRS1 has carried out a set of distinct synoptic imaging sky surveys including the $3pi$ Steradian Survey and the Medium Deep Survey in 5 bands ($grizy_{P1}$). The mean 5$sigma$ point source limiting sensitivities in the stacked 3$pi$ Steradian Survey in $grizy_{P1}$ are (23.3, 23.2, 23.1, 22.3, 21.4) respectively. The upper bound on the systematic uncertainty in the photometric calibration across the sky is 7-12 millimag depending on the bandpass. The systematic uncertainty of the astrometric calibration using the Gaia frame comes from a comparison of the results with Gaia: the standard deviation of the mean and median residuals ($ Delta ra, Delta dec $) are (2.3, 1.7) milliarcsec, and (3.1, 4.8) milliarcsec respectively. The Pan-STARRS system and the design of the PS1 surveys are described and an overview of the resulting image and catalog data products and their basic characteristics are described together with a summary of important results. The images, reduced data products, and derived data products from the Pan-STARRS1 surveys are available to the community from the Mikulski Archive for Space Telescopes (MAST) at STScI.
The Pan-STARRS1 survey is collecting multi-epoch, multi-color observations of the sky north of declination -30 deg to unprecedented depths. These data are being photometrically and astrometrically calibrated and will serve as a reference for many other purposes. In this paper we present our determination of the Pan-STARRS photometric system: gp1, rp1, ip1, zp1, yp1, and wp1. The Pan-STARRS photometric system is fundamentally based on the HST Calspec spectrophotometric observations, which in turn are fundamentally based on models of white dwarf atmospheres. We define the Pan-STARRS magnitude system, and describe in detail our measurement of the system passbands, including both the instrumental sensitivity and atmospheric transmission functions. Byproducts, including transformations to other photometric systems, galactic extinction, and stellar locus are also provided. We close with a discussion of remaining systematic errors.
Centaurs are small bodies orbiting in the giant planet region which were scattered inwards from their source populations beyond Neptune. Some members of the population display comet-like activity during their transition through the solar system, the source of which is not well understood. The range of heliocentric distances where the active Centaurs have been observed, and their median lifetime in the region suggest this activity is neither driven by water-ice sublimation, nor entirely by super-volatiles. Here we present an observational and thermo-dynamical study of 13 Centaurs discovered in the Pan-STARRS1 detection database aimed at identifying and characterizing active objects beyond the orbit of Jupiter. We find no evidence of activity associated with any of our targets at the time of their observations with the Gemini North telescope in 2017 and 2018, or in archival data from 2013 to 2019. Upper limits on the possible volatile and dust production rates from our targets are 1-2 orders of magnitude lower than production rates in some known comets, and are in agreement with values measured for other inactive Centaurs. Our numerical integrations show that the orbits of six of our targets evolved interior to r$sim$15 AU over the past 100,000 years where several possible processes could trigger sublimation and outgassing, but their apparent inactivity indicates their dust production is either below our detection limit or that the objects are dormant. Only one Centaur in our sample -- 2014 PQ$_{70}$ experienced a sudden decrease in semi-major axis and perihelion distance attributed to the onset of activity for some previously known inactive Centaurs, and therefore is a likely candidate for future outburst. This object should be a target of interest for further observational monitoring.
Using shape measurement techniques developed for weak lensing surveys we have identified three new ultracool binaries in the Pan-STARRS1 survey. Binary companions which are not completely resolved can still alter the shapes of stellar images. These shape distortions can be measured if PSF anisotropy caused by the telescope is properly accounted for. We show using both a sample of known binary stars and simulated binaries that we can reliably recover binaries wider than around 0.3 and with flux ratios greater than around 0.1. We then applied our method to a sample of ultracool dwarfs within 30pc with 293 objects having sufficient Pan-STARRS1 data for our method. In total we recovered all but one of the 11 binaries wider than 0.3 in this sample. Our one failure was a true binary detected with a significant but erroneously high ellipticity which led it to be rejected in our analysis. We identify three new binaries, one a simultaneous discovery, with primary spectral types M6.5, L1 and T0.5. These latter two were confirmed with Keck/NIRC2 follow-up imaging. This technique will be useful for identifying large numbers of stellar and substellar binaries in the upcoming LSST and DES sky surveys.
Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper we explore a simple machine learning approach to real-bogus classification by constructing a training set from the image data of ~32000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20x20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25% of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1%, the classifier initially suggests a missed detection rate of around 10%. However we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6%.
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