Pan-STARRS Research Articles

Bayesian Cross-matching of High Proper-motion Stars in Gaia DR2 and Photometric Metallicities for ∼1.7 million K and M Dwarfs

Abstract We present a Bayesian method to cross-match 5,827,988 high proper-motion Gaia sources (μ > 40 mas yr−1) to various photometric surveys: Two Micron All Sky Survey, AllWISE data release from the Wide-field Infrared Explorer (WISE) mission, Galaxy Evolution Explorer, Radial Velocity Experiment, Sloan Digital Sky Survey, and Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). To efficiently associate these objects across catalogs, we develop a technique that compares the multidimensional distribution of all sources in the vicinity of each Gaia star to a reference distribution of random field stars obtained by extracting all sources in a region on the sky displaced 2′. This offset preserves the local field stellar density and magnitude distribution, allowing us to characterize the frequency of chance alignments. The resulting catalog with Bayesian probabilities >95% has a marginally higher match rate than current internal Gaia data release 2 (DR2) matches for most catalogs. However, a significant improvement is found with Pan-STARRS, where ∼99.8% of the sample within the Pan-STARRS footprint is recovered, as compared to a low ∼20.8% in Gaia DR2. Using these results, we train a Gaussian process regressor to calibrate two photometric metallicity relationships. For dwarfs of 3500 < T eff < 5280 K, we use metallicity values of 4378 stars from the Apache Point Observatory Galactic Evolution Experiment and Hejazi et al. to calibrate the relationship, producing results with a 1σ precision of 0.12 dex and few systematic errors. We then indirectly infer the metallicity of 4018 stars with 2850 < T eff < 3500 K, which are wide companions of primaries whose metallicities are estimated with our first regressor, to produce a relationship with a 1σ precision of 0.21 dex and significant systematic errors. Additional work is needed to better remove unresolved binaries from this second sample to reduce these systematic errors.

Finding Quasars behind the Galactic Plane. I. Candidate Selections with Transfer Learning

Abstract Quasars behind the Galactic plane (GPQs) are important astrometric references and useful probes of Milky Way gas. However, the search for GPQs is difficult due to large extinctions and high source densities in the Galactic plane. Existing selection methods for quasars developed using high Galactic latitude (high-b) data cannot be applied to the Galactic plane directly because the photometric data obtained from high-b regions and the Galactic plane follow different probability distributions. To alleviate this data set shift problem for quasar candidate selection, we adopt a transfer-learning framework at both the data and algorithm levels. At the data level, to make a training set in which a data set shift is modeled, we synthesize quasars and galaxies behind the Galactic plane based on SDSS sources and the Galactic dust map. At the algorithm level, to reduce the effect of class imbalance, we transform the three-class classification problem for stars, galaxies, and quasars into two binary classification tasks. We apply the XGBoost algorithm to Pan-STARRS1 (PS1) and AllWISE photometry for classification and an additional cut on Gaia proper motion to remove stellar contaminants. We obtain a reliable GPQ candidate catalog with 160,946 sources located at ∣b∣ ≤ 20° in the PS1-AllWISE footprint. Photometric redshifts of GPQ candidates achieved with the XGBoost regression algorithm show that our selection method can identify quasars in a wide redshift range (0 < z ≲ 5). This study extends the systematic searches for quasars to the dense stellar fields and shows the feasibility of using astronomical knowledge to improve data mining under complex conditions in the big-data era.

Supernovae Detection with Fully Convolutional One-Stage Framework.

A series of sky surveys were launched in search of supernovae and generated a tremendous amount of data, which pushed astronomy into a new era of big data. However, it can be a disastrous burden to manually identify and report supernovae, because such data have huge quantity and sparse positives. While the traditional machine learning methods can be used to deal with such data, deep learning methods such as Convolutional Neural Networks demonstrate more powerful adaptability in this area. However, most data in the existing works are either simulated or without generality. How do the state-of-the-art object detection algorithms work on real supernova data is largely unknown, which greatly hinders the development of this field. Furthermore, the existing works of supernovae classification usually assume the input images are properly cropped with a single candidate located in the center, which is not true for our dataset. Besides, the performance of existing detection algorithms can still be improved for the supernovae detection task. To address these problems, we collected and organized all the known objectives of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) and the Popular Supernova Project (PSP), resulting in two datasets, and then compared several detection algorithms on them. After that, the selected Fully Convolutional One-Stage (FCOS) method is used as the baseline and further improved with data augmentation, attention mechanism, and small object detection technique. Extensive experiments demonstrate the great performance enhancement of our detection algorithm with the new datasets.

Classification of Planetary Nebulae through Deep Transfer Learning

This study investigate the effectiveness of using Deep Learning (DL) for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted the deep transfer learning approach using three ImageNet pre-trained algorithms. This study was conducted using images from the Hong Kong/Australian Astronomical Observatory/Strasbourg Observatory H-alpha Planetary Nebula research platform database (HASH DB) and the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). We found that the algorithm has high success in distinguishing True PNe from other types of objects even without any parameter tuning. The Matthews correlation coefficient is 0.9. Our analysis shows that DenseNet201 is the most effective DL algorithm. For the morphological classification, we found for three classes, Bipolar, Elliptical and Round, half of objects are correctly classified. Further improvement may require more data and/or training. We discuss the trade-offs and potential avenues for future work and conclude that deep transfer learning can be utilized to classify wide-field astronomical images.

PS15cey and PS17cke: prospective candidates from the Pan-STARRS Search for kilonovae

ABSTRACT Time domain astronomy was revolutionized with the discovery of the first kilonova, AT2017gfo, in August 2017, which was associated with the gravitational wave signal GW170817. Since this event, numerous wide-field surveys have been optimizing search strategies to maximize their efficiency of detecting these fast and faint transients. With the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS), we have been conducting a volume-limited survey for intrinsically faint and fast-fading events to a distance of D ≃ 200 Mpc. Two promising candidates have been identified from this archival search, with sparse data – PS15cey and PS17cke. Here, we present more detailed analysis and discussion of their nature. We observe that PS15cey was a luminous, fast-declining transient at 320 Mpc. Models of BH–NS mergers with a very stiff equation of state could possibly reproduce the luminosity and decline but the physical parameters are extreme. A more likely scenario is that this was an AT2018kzr-like merger event. PS17cke was a faint and fast-declining event at 15 Mpc. We explore several explosion scenarios of this transient including models of it as a NS–NS and BH–NS merger, the outburst of a massive luminous star, and compare it against other known fast-fading transients. Although there is uncertainty in the explosion scenario due to difficulty in measuring the explosion epoch, we find PS17cke to be a plausible kilonova candidate from the model comparisons.

Pan-STARRS Pixel Processing: Detrending, Warping, Stacking

Abstract The Pan-STARRS1 (PS1) Science Consortium has carried out a set of imaging surveys using the 1.4 gigapixel GPC1 camera on the PS1 telescope. As this camera is composed of many individual electronic readouts and covers a very large field of view, great care was taken to ensure that the many instrumental effects were corrected to produce the most uniform detector response possible. We present the image-detrending steps used as part of the processing of the data contained within the public release of Pan-STARRS1 Data Release 1 (DR1). In addition to the single image processing, the methods used to transform the 375,573 individual exposures into a common sky-oriented grid are discussed, as well as those used to produce both the image stack and difference combination products.

Star Formation and Morphological Properties of Galaxies in the Pan-STARRS 3π Survey. I. A Machine-learning Approach to Galaxy and Supernova Classification

Abstract We present a classification of galaxies in the Pan-STARRS1 (PS1) 3π survey based on their recent star formation history and morphology. Specifically, we train and test two Random Forest (RF) classifiers using photometric features (colors and moments) from the PS1 data release 2. The labels for the morphological classification are taken from Huertas-Company et al., while labels for the star formation fraction (SFF) are from the Blanton et al. catalog. We find that colors provide more predictive accuracy than photometric moments. We morphologically classify galaxies as either early- or late-type, and our RF model achieves a 78% classification accuracy. Our second model classifies galaxies as having either a low-to-moderate or high SFF. This model achieves an 89% classification accuracy. We apply both RF classifiers to the entire PS1 3π dataset, which allows us to assign two scores to each PS1 source: P HSFF, which quantifies the probability of having a high SFF; and P spiral, which quantifies the probability of having a late-type morphology. Finally, as a proof of concept, we apply our classification framework to supernova (SN) host galaxies from the Zwicky Transient Factory and the Lick Observatory Supernova Search samples. We show that by selecting P HSFF or P spiral, it is possible to significantly enhance or suppress the fraction of core-collapse SNe (or thermonuclear SNe) in the sample with respect to random guessing. This result demonstrates how contextual information can aid transient classifications at the time of first detection. In the current era of spectroscopically starved time-domain astronomy, prompt automated classification is paramount. Our table is available at 10.5281/zenodo.3990545.

PS1-STRM: neural network source classification and photometric redshift catalogue for PS1 3π DR1

ABSTRACT The Pan-STARRS1 (PS1) 3π survey is a comprehensive optical imaging survey of three quarters of the sky in the grizy broad-band photometric filters. We present the methodology used in assembling the source classification and photometric redshift (photo-z) catalogue for PS1 3π Data Release 1, titled Pan-STARRS1 Source Types and Redshifts with Machine learning (PS1-STRM). For both main data products, we use neural network architectures, trained on a compilation of public spectroscopic measurements that has been cross-matched with PS1 sources. We quantify the parameter space coverage of our training data set, and flag extrapolation using self-organizing maps. We perform a Monte Carlo sampling of the photometry to estimate photo-z uncertainty. The final catalogue contains 2902 054 648 objects. On our validation data set, for non-extrapolated sources, we achieve an overall classification accuracy of $98.1{{\ \rm per\ cent}}$ for galaxies, $97.8{{\ \rm per\ cent}}$ for stars, and $96.6{{\ \rm per\ cent}}$ for quasars. Regarding the galaxy photo-z estimation, we attain an overall bias of 〈Δznorm〉 = 0.0005, a standard deviation of σ(Δznorm) = 0.0322, a median absolute deviation of MAD(Δznorm) = 0.0161, and an outlier fraction of $P\left(|\Delta z_{\mathrm{norm}}|\gt 0.15\right)=1.89{{\ \rm per\ cent}}$. The catalogue will be made available as a high-level science product via the Mikulski Archive for Space Telescopes.

Search for the sub-stellar lithium depletion boundary in the open star cluster Coma Berenices

Aims. We mainly aim to search for the lithium depletion boundary (LDB) among the sub-stellar population of the open star cluster Coma Berenices. Methods. Since the number of brown dwarf candidates in Coma Ber available in the literature is scarce, we carried out a search for additional candidates photometrically using colour–magnitude diagrams combining optical and infrared photometry from the latest public releases of the following large-scale surveys: the United Kingdom InfraRed Telescope Infrared Deep Sky Survey (UKIRT/UKIDSS), the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS), the Sloan Digital Sky Survey (SDSS), and AllWISE. We checked astrometric consistency with cluster membership using Gaia DR2. A search for Li in three new and five previously known brown dwarf candidate cluster members was performed via spectroscopic observations using the OSIRIS instrument at the 10.4 m Gran Telescopio de Canarias (GTC). Results. A couple dozen new photometric candidate brown dwarfs located inside the tidal radius of Coma Ber are reported, but none of these are significantly fainter and cooler than previously known members. No LiI resonance doublet at 6707.8 Å was detected in any of eight Coma Ber targets in the magnitude range J = 15–19 and G = 20–23 observed with the GTC. Spectral types and radial velocities were derived from the GTC spectra. These values confirm the cluster membership of four L2–L2.5 dwarfs, two of which are new in the literature. Conclusions. The large Li depletion factors found among the four bona fide sub-stellar members in Coma Ber implies that the LDB must be located at spectral type later than L2.5 in this cluster. Using the latest evolutionary models for brown dwarfs, a lower limit of 550 Myr on the cluster age is set. This constraint has been combined with other dating methods to obtain an updated age estimate of 780 ± 230 Myr for the Coma Ber open cluster. Identification of significantly cooler sub-stellar cluster members in Coma Ber awaits the advent of the Euclid wide survey, which should reach a depth of about J = 23; this superb sensitivity will make it possible to determine the precise location of the sub-stellar LDB in this cluster and to carry out a complete census of its sub-stellar population.

Orphan GRB Afterglow Searches with the Pan-STARRS1 COSMOS Survey

Abstract We present the result of a search for orphan gamma-ray burst (GRB) afterglows in the Pan-STARRS1 (PS1) COSMOS survey. There is extensive theoretical and observational evidence suggesting that GRBs are collimated jets; the direct observation of orphan GRB afterglows would further support this model. An optimal survey strategy is designed by coupling the PS1 survey with the Subaru/Hyper-Suprime-Cam (HSC) survey. The PS1 COSMOS survey, one of the survey fields in the PS1 Medium Deep Survey (PS1/MDS), searches a field of 7 deg2 from 2011 December to 2014 January, reaching a limiting magnitude R ∼ 23. The dense cadence of PS1/MDS is crucial for identifying transients, and the deep magnitude reached by the HSC survey (R ∼ 26) is important for evaluating potential GRB hosts. A transient classification method is employed to select potential orphan GRB afterglow candidates. After a thorough analysis of the transient and host galaxy properties, we conclude that there are no candidates in this survey field. The null result implies that the consideration of jet structures is essential for further orphan GRB afterglow surveys.

The Extended Gaia–PS1–SDSS (GPS1+) Proper Motion Catalog

Abstract The Gaia + PanSTARRS1 (PS1) + Sloan Digital Sky Survey (SDSS) + Two Micron All-Sky Survey (2MASS) (GPS1) catalog was released in 2017. It delivered precise proper motions for around 350 million sources down to a magnitude of mag. In this study, we present GPS1+, the extension GPS1 catalog down to mag, based on Gaia data release 2 (DR2), PS1, SDSS, and 2MASS astrometry. GPS1+ totally provides proper motions for ∼400 million sources with a characteristic systematic error of less than 0.1 mas yr−1. This catalog is divided into two subsamples, i.e., the primary and secondary parts. The primary ∼264 million sources have either or both Gaia and SDSS astrometry, with a typical precision of 2.0–5.0 mas yr−1. In this part, ∼160 million sources have Gaia proper motions, and we provide another new proper motion for each of them by building a Bayesian model. Relative to Gaia's values, the precision is improved by ∼0.1 dex on average; ∼50 million sources are the objects whose proper motions are missing in Gaia DR2, and we provide their proper motions with a precision of ∼4.5 mas yr−1. The remaining ∼54 million faint sources are beyond Gaia detecting capability, and we provide their proper motions for the first time with a precision of 7.0 mas yr−1. However, the secondary ∼136 million sources only have PS1 astrometry, where the average precision is worse than 15.0 mas yr−1. The large uncertainty probably limits it to qualitative applications. All the proper motions have been validated using QSOs and the existing Gaia proper motions. The catalog will be available via the TAP Service in the German Astrophysical Virtual Observatory.

Milky Way Satellite Census. I. The Observational Selection Function for Milky Way Satellites in DES Y3 and Pan-STARRS DR1

Abstract We report the results of a systematic search for ultra-faint Milky Way satellite galaxies using data from the Dark Energy Survey (DES) and Pan-STARRS1 (PS1). Together, DES and PS1 provide multi-band photometry in optical/near-infrared wavelengths over ∼80% of the sky. Our search for satellite galaxies targets ∼25,000 deg2 of the high-Galactic-latitude sky reaching a 10σ point-source depth of ≳22.5 mag in the g and r bands. While satellite galaxy searches have been performed independently on DES and PS1 before, this is the first time that a self-consistent search is performed across both data sets. We do not detect any new high-significance satellite galaxy candidates, recovering the majority of satellites previously detected in surveys of comparable depth. We characterize the sensitivity of our search using a large set of simulated satellites injected into the survey data. We use these simulations to derive both analytic and machine-learning models that accurately predict the detectability of Milky Way satellites as a function of their distance, size, luminosity, and location on the sky. To demonstrate the utility of this observational selection function, we calculate the luminosity function of Milky Way satellite galaxies, assuming that the known population of satellite galaxies is representative of the underlying distribution. We provide access to our observational selection function to facilitate comparisons with cosmological models of galaxy formation and evolution.

Asteroid Discovery and Light Curve Extraction Using the Hough Transform: A Rotation Period Study for Subkilometer Main-belt Asteroids

Abstract The intranight trajectories of asteroids can be approximated by straight lines, and so are their intranight detections. Therefore, the Hough transform, a line detecting algorithm, can be used to connect the line-up detections to find asteroids. We applied this algorithm to a high-cadence Pan-STARRS 1 (PS1) observation, which was originally designed to collect asteroid light curves for rotation period measurements. The algorithm recovered most of the known asteroids in the observing fields and, moreover, discovered 3574 new asteroids with magnitude mainly of 21.5 < w p1 < 22.5 mag. This magnitude range is equivalent to subkilometer main-belt asteroids (MBAs), which usually lack rotation period measurements due to their faintness. Using the light curves of the 3574 new asteroids, we obtained 122 reliable rotation periods, of which 13 are super-fast rotators (SRFs; i.e., rotation period of <2 hr). The required cohesion to survive these SFRs range from tens to thousands of Pa, a value consistent with the known SFRs and the regolith on the Moon and Mars. The higher chance of discovering SFRs here suggests that subkilometer MBAs probably harbor more SFRs.

Exploring Reionization-era Quasars. III. Discovery of 16 Quasars at 6.4 ≲ z ≲ 6.9 with DESI Legacy Imaging Surveys and the UKIRT Hemisphere Survey and Quasar Luminosity Function at z ∼ 6.7

Abstract This is the third paper in a series aimed at finding reionization-era quasars with the combination of DESI Legacy imaging Surveys (DELS), the Pan-STARRS1 (PS1) Survey, and near-infrared imaging surveys, such as the UKIRT Hemisphere Survey (UHS), as well as the Wide-field Infrared Survey Explorer (WISE) mid-infrared survey. In this paper, we describe the updated quasar candidate selection procedure, report the discovery of 16 quasars at 6.4 ≲ z ≲ 6.9 from an area of ∼13,020 deg2, and present the quasar luminosity function (QLF) at z ∼ 6.7. The measured QLF follows in the magnitude range −27.6 < M 1450 < −25.5. We determine the quasar comoving spatial density at = 6.7 and M 1450 < −26.0 to be 0.39 ± 0.11 Gpc−3 and find the exponential density evolution parameter to be k = −0.78 ± 0.18 from z ∼ 6 to z ∼ 6.7, corresponding to a rapid decline by a factor of ∼6 per unit redshift toward earlier epochs. This indicates that the rapid decline of quasar spatial density at z > 5 that was found by previous works continues to z > 6, at a rate significantly faster than the average decline rate between z ∼ 3 and 5. We measured quasar comoving emissivity at z ∼ 6.7, which indicates that high-redshift quasars are highly unlikely to make a significant contribution to hydrogen reionization. The broad absorption line quasar fraction at z ≳ 6.5 is measured to be ≳22%. In addition, we also report the discovery of six additional quasars at z ∼ 6 in the Appendix.

A luminous stellar outburst during a long-lasting eruptive phase first, and then SN IIn 2018cnf

We present the results of the monitoring campaign of the Type IIn supernova (SN) 2018cnf (a.k.a. ASASSN-18mr). It was discovered about ten days before the maximum light (on MJD = 58 293.4 ± 5.7 in the V band, with MV = −18.13 ± 0.15 mag). The multiband light curves show an immediate post-peak decline with some minor luminosity fluctuations, followed by a flattening starting about 40 days after maximum. The early spectra are relatively blue and show narrow Balmer lines with P Cygni profiles. Additionally, Fe II, O I, He I, and Ca II are detected. The spectra show little evolution with time and with intermediate-width features becoming progressively more prominent, indicating stronger interaction of the SN ejecta with the circumstellar medium. The inspection of archival images from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) survey has revealed a variable source at the SN position with a brightest detection in December 2015 at Mr = −14.66 ± 0.17 mag. This was likely an eruptive phase from the massive progenitor star that started from at least mid-2011, and that produced the circumstellar environment within which the star exploded as a Type IIn SN. The overall properties of SN 2018cnf closely resemble those of transients such as SN 2009ip. This similarity favours a massive hypergiant, perhaps a luminous blue variable, as progenitor for SN 2018cnf.

Constraining the Shape Distribution of Near-Earth Objects from Partial Light Curves

Abstract In the absence of dense photometry for a large population of near-Earth objects (NEOs), the best method of obtaining a shape distribution comes from sparse photometry and partial light curves. We have used 867 partial light curves obtained by Spitzer to determine a shape distribution for subkilometer NEOs. From this data we find a best-fit average elongation of . We compare this result with a shape distribution obtained from 1869 NEOs in the same size range observed by Pan-STARRS 1 (PS1) and find the Spitzer-obtained elongation to be in excellent agreement with this PS1 value of . These values are also in agreement with literature values for 1 < D < 10 km objects in the main asteroid belt, however, there is a size discrepancy between the two data sets. Using a smaller sample of NEOs in the size range of 1 < D < 5 km from PS1 data, we obtain an average axis ratio of b/a = 0.70 ± 0.12. This is more elongated than the shape distribution for main belt objects in the same size regime, although the current uncertainties are sizeable and this should be verified using a larger data set. As future large surveys come online it will be possible to observe smaller main belt asteroids to allow for better comparisons of different subkilometer populations.

AREAcat: A Tool to Compute Area Coverage of GALEX UV GUVcat and BCScat, SDSS, PanSTARRS, and Gaia Source Catalogs in Chosen Sky Regions

Abstract We present AREAcat, an interactive online tool to compute the area mapped by recent catalogs that cover most of the sky, but not contiguously, such as the Galaxy Evolution Explorer (GALEX) ultraviolet (UV) source catalogs GUVcat_AIS. Constructed from ≲600 million measurements by removing duplicate measurements of the same source in repeated observations, and sources in field rims, GUVcat_AIS contains photometry in FUV (λ eff ∼ 1528 Å) and NUV (λ eff ∼ 2310 Å) of ∼83 million UV sources. When the surface density of a given class of objects is of interest, the actual area covered by the catalogs used in the source selection must be calculated, taking into account overlap among repeated observations and gaps between pointings. We developed a tool to calculate the area covered in any chosen region of the sky by GUVcat, as well as by the Sloan Digital Sky Survey data release 14 (SDSS DR14), the Panoramic Survey Telescope and Rapid Response System (PanSTARRS) PS1 3π, Gaia DR2, and their overlap with GUVcat. The desired sky region can be specified in either Galactic (l, b) or equatorial (α, δ) coordinates. GUVcat flags sources within the footprint of extended objects, such as nearby large galaxies or Galactic stellar clusters, where the photometry of individual point-like sources is often compromised by crowding or by the underlying galaxy light. For statistical studies of clean samples over large areas, the sources within such extended objects can be excluded using the appropriate GUVcat flags; the corresponding regions can then be excised from the area estimate in AREAcat.

Searching for Super-fast Rotators Using the Pan-STARRS 1

Abstract A class of asteroids, called large super-fast rotators (large SFRs), have rotation periods shorter than 2 hr and diameters larger than ∼0.3 km. They pose challenges to the usual interior rubble-pile structure unless a relatively high bulk density is assumed. So far, only six large SFRs have been found. Therefore, we present a survey of asteroid rotation periods using the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) 1 telescope during 2016 October 26–31 to search for more large SFRs and to study their properties. A total of 876 reliable rotation periods are measured, among which seven are large SFRs, thereby increasing the inventory of known large SFRs. These seven newly discovered large SFRs have diverse colors and locations in the main asteroid belt, suggesting that the taxonomic tendency and the location preference in the inner main belt of the six previously known large SFRs could be a bias due to various observational limits. Interestingly, five out of the seven newly discovered large SFRs are mid main-belt asteroids (MBAs). Considering the rare discovery rates of large SFR in the previously similar surveys and the survey condition in this work, the chance of detecting a large SFR in the inner main belt seems to be relatively low. This probably suggests that the inner main belt harbors fewer large SFRs than the mid main belt. From our survey, we also found a drop in the number appearing at f > 5 rev day−1 on the spin-rate distribution for the outer MBAs of D < 3 km, which was reported for the inner and mid main belt by Chang et al.

The LOFAR Two-metre Sky Survey

The LOFAR Two-metre Sky Survey (LoTSS) is a sensitive, high-resolution 120–168 MHz survey of the Northern sky. The LoTSS First Data Release (DR1) presents 424 square degrees of radio continuum observations over the HETDEX Spring Field (10h45m00s < right ascension < 15h30m00s and 45°00′00″ < declination < 57°00′00″) with a median sensitivity of 71 μJy beam−1 and a resolution of 6″. In this paper we present photometric redshifts (photo-z) for 94.4% of optical sources over this region that are detected in the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) 3π steradian survey. Combining the Pan-STARRS optical data with mid-infrared photometry from the Wide-field Infrared Survey Explorer, we estimate photo-zs using a novel hybrid photometric redshift methodology optimised to produce the best possible performance for the diverse sample of radio continuum selected sources. For the radio-continuum detected population, we find an overall scatter in the photo-z of 3.9% and an outlier fraction (|zphot−zspec|/(1 + zspec) > 0.15) of 7.9%. We also find that, at a given redshift, there is no strong trend in photo-z quality as a function of radio luminosity. However there are strong trends as a function of redshift for a given radio luminosity, a result of selection effects in the spectroscopic sample and/or intrinsic evolution within the radio source population. Additionally, for the sample of sources in the LoTSS First Data Release with optical counterparts, we present rest-frame optical and mid-infrared magnitudes based on template fits to the consensus photometric (or spectroscopic when available) redshift.

Precision Distances to Dwarf Galaxies and Globular Clusters from Pan-STARRS1 3π RR Lyrae

Abstract We present new spatial models and distance estimates for globular clusters and dwarf spheroidals orbiting our Galaxy based on RR Lyrae (RRab) stars in the Pan-STARRS1 (PS1) 3π survey. Using the PS1 sample of RRab stars from Sesar et al. in 16 globular clusters and 5 dwarf galaxies, we fit structural models in (l, b, D) space; for 13 globular clusters and 6 dwarf galaxies, we give only their mean heliocentric distance D. We verify the accuracy of the period–luminosity relations used in Sesar et al. to constrain the distance to those stars, and compare them to period–luminosity–metallicity relations using metallicities from Carretta et al. We compare our Sesar et al. distances to the parallax-based Gaia DR2 distance estimates from Bailer-Jones et al. and find our distances to be consistent and considerably more precise.