No abstract

The INT Galactic Plane Survey (IGAPS) is the merger of the optical photometric surveys, IPHAS and UVEX, based on data from the Isaac Newton Telescope (INT) obtained between 2003 and 2018. Here, we present the IGAPS point source catalogue. It contains 295.4 million rows providing photometry in the filters, i, r, narrow-band Hα, g and U RGO . The IGAPS footprint fills the Galactic coordinate range, |b| < 5 • and 30 • < < 215 • . A uniform calibration, referred to the Pan-STARRS system, is applied to g, r and i, while the Hα calibration is linked to r and then is reconciled via field overlaps. The astrometry in all 5 bands has been recalculated on the Gaia DR2 frame. Down to i ∼ 20 mag. (Vega system), most stars are also detected in g, r and Hα. As exposures in the r band were obtained within the IPHAS and UVEX surveys a few years apart, typically, the catalogue includes two distinct r measures, r I and r U . The r 10σ limiting magnitude is ∼21, with median seeing 1.1 arcsec. Between ∼13th and ∼19th magnitudes in all bands, the photometry is internally reproducible to within 0.02 magnitudes. Stars brighter than r = 19.5 have been tested for narrow-band Hα excess signalling line emission, and for variation exceeding |r I − r U | = 0.2 mag. We find and flag 8292 candidate emission line stars and over 53000 variables (both at > 5σ confidence). The 174-column catalogue will be available via CDS Strasbourg.Article number, page 3 of 28 A&A proofs: manuscript no. main filters at each pointing should be observed consecutivelyusually within an elapsed time of ∼5 min. All included exposure sets meet this criterion. Article number, page 20 of 28 M. Monguió et al.: IGAPS

The classification and identification of quasars is fundamental to many astronomical research areas. Given the large volume of photometric survey data available in the near future, automated methods for doing so are required. In this article, we present a new quasar candidate catalog from the Red-Sequence Cluster Survey 2 (RCS-2), identified solely from photometric information using an automated algorithm suitable for large surveys. The algorithm performance is tested using a well-defined SDSS spectroscopic sample of quasars and stars. The Random Forest algorithm constructs the catalog from RCS-2 point sources using SDSS spectroscopicallyconfirmed stars and quasars. The algorithm identifies putative quasars from broadband magnitudes (g, r, i, z) and colors. Exploiting NUV GALEX measurements for a subset of the objects, we refine the classifier by adding new information. An additional subset of the data with WISE W1 and W2 bands is also studied. Upon analyzing 542 897 RCS-2 point sources, the algorithm identified 21 501 quasar candidates with a training-set-derived precision (the fraction of true positives within the group assigned quasar status) of 89.5% and recall (the fraction of true positives relative to all sources that actually are quasars) of 88.4%. These performance metrics improve for the GALEX subset: 6529 quasar candidates are identified from 16 898 sources, with a precision and recall of 97.0% and 97.5%, respectively. Algorithm performance is further improved when WISE data are included, with precision and recall increasing to 99.3% and 99.1%, respectively, for 21 834 quasar candidates from 242 902 sources. We compiled our final catalog (38 257) by merging these samples and removing duplicates. An observational follow up of 17 bright (r < 19) candidates with long-slit spectroscopy at DuPont telescope (LCO) yields 14 confirmed quasars. The results signal encouraging progress in the classification of point sources with Random Forest algorithms to search for quasars within current and future large-area photometric surveys.

We present a catalogue of 4098 photometrically selected galaxy clusters with a median redshift 〈z〉= 0.32 in the 270 deg2‘Stripe 82’ region of the Sloan Digital Sky Survey (SDSS), covering the celestial equator in the Southern Galactic Cap (−50° < α < 59°, |δ| ≤ 125). Owing to the multi‐epoch SDSS coverage of this region, the ugriz photometry is ∼2 mag deeper than single scans within the main SDSS footprint. We exploit this to detect clusters of galaxies using an algorithm that searches for statistically significant overdensities of galaxies in a Voronoi tessellation of the projected sky. 32 per cent of the clusters have at least one member with a spectroscopic redshift from existing public data (SDSS Data Release 7, 2SLAQ and WiggleZ), and the remainder have a robust photometric redshift (accurate to ∼5–9 per cent at the median redshift of the sample). The weighted average of the member galaxies’ redshifts provides a reasonably accurate estimate of the cluster redshift. The cluster catalogue is publicly available for exploitation by the community to pursue a range of science objectives. In addition to the cluster catalogue, we provide a linked catalogue of 18 295 V≤ 21‐mag quasar sightlines with impact parameters within ≤3 Mpc of the cluster cores selected from the catalogue of Veron‐Cetty & Veron (2010). The background quasars cover 0.25 < z < 2, where Mg ii absorption‐line systems associated with the clusters are detectable in optical spectra.

We present a new cluster‐detection algorithm designed for the Panoramic Survey Telescope and Rapid Response System (Pan‐STARRS) survey but with generic application to any multiband data. The method makes no prior assumptions about the properties of clusters other than (i) the similarity in colour of cluster galaxies (the ‘red sequence’); and (ii) an enhanced projected surface density. The detector has three main steps: (i) it identifies cluster members by photometrically filtering the input catalogue to isolate galaxies in colour–magnitude space; (ii) a Voronoi diagram identifies regions of high surface density; and (iii) galaxies are grouped into clusters with a Friends‐of‐Friends technique. Where multiple colours are available, we require systems to exhibit sequences in two colours. In this paper, we present the algorithm and demonstrate it on two data sets. The first is a 7‐deg2 sample of the deep Sloan Digital Sky Survey (SDSS) equatorial stripe (Stripe 82), from which we detect 97 clusters with z≤ 0.6. Benefitting from deeper data, we are 100 per cent complete in the maxBCG optically selected cluster catalogue (based on shallower single‐epoch SDSS data) and find an additional 78 previously unidentified clusters. The second data set is a mock Medium Deep Survey Pan‐STARRS catalogue, based on the Λ cold dark matter (ΛCDM) model and a semi‐analytic galaxy formation recipe. Knowledge of galaxy–halo memberships in the mock catalogue allows for the quantification of algorithm performance. We detect 305 mock clusters in haloes with mass >1013 h−1 M⊙ at z≲ 0.6 and determine a spurious detection rate of <1 per cent, consistent with tests on the Stripe 82 catalogue. The detector performs well in the recovery of model ΛCDM clusters. At the median redshift of the catalogue, the algorithm achieves >75 per cent completeness down to halo masses of 1013.4 h−1 M⊙ and recovers >75 per cent of the total stellar mass of clusters in haloes down to 1013.8 h−1 M⊙. A companion paper presents the complete cluster catalogue over the full 270‐deg2 Stripe 82 catalogue.