We present optical light curves, redshifts, and classifications for spectroscopically confirmed Type Ia supernovae (SNe Ia) discovered by the Pan-STARRS1 (PS1) Medium Deep Survey. We detail ...improvements to the PS1 SN photometry, astrometry, and calibration that reduce the systematic uncertainties in the PS1 SN Ia distances. We combine the subset of PS1 SNe Ia (0.03 < z < 0.68) with useful distance estimates of SNe Ia from the Sloan Digital Sky Survey (SDSS), SNLS, and various low-z and Hubble Space Telescope samples to form the largest combined sample of SNe Ia, consisting of a total of SNe Ia in the range of 0.01 < z < 2.3, which we call the "Pantheon Sample." When combining Planck 2015 cosmic microwave background (CMB) measurements with the Pantheon SN sample, we find and for the wCDM model. When the SN and CMB constraints are combined with constraints from BAO and local H0 measurements, the analysis yields the most precise measurement of dark energy to date: and for the CDM model. Tension with a cosmological constant previously seen in an analysis of PS1 and low-z SNe has diminished after an increase of 2× in the statistics of the PS1 sample, improved calibration and photometry, and stricter light-curve quality cuts. We find that the systematic uncertainties in our measurements of dark energy are almost as large as the statistical uncertainties, primarily due to limitations of modeling the low-redshift sample. This must be addressed for future progress in using SNe Ia to measure dark energy.
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 ∼32 000 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 20 × 20 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 per cent 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 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. 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 per cent.
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.
ABSTRACT The dust extinction curve is a critical component of many observational programs and an important diagnostic of the physics of the interstellar medium. Here we present new measurements of ...the dust extinction curve and its variation toward tens of thousands of stars, a hundred-fold larger sample than in existing detailed studies. We use data from the APOGEE spectroscopic survey in combination with ten-band photometry from Pan-STARRS1, the Two Micron All-Sky Survey, and Wide-field Infrared Survey Explorer. We find that the extinction curve in the optical through infrared is well characterized by a one-parameter family of curves described by R(V). The extinction curve is more uniform than suggested in past works, with ( R ( V ) ) = 0.18 , and with less than one percent of sight lines having R ( V ) > 4 . Our data and analysis have revealed two new aspects of Galactic extinction: first, we find significant, wide-area variations in R(V) throughout the Galactic plane. These variations are on scales much larger than individual molecular clouds, indicating that R(V) variations must trace much more than just grain growth in dense molecular environments. Indeed, we find no correlation between R(V) and dust column density up to E ( B − V ) 2 . Second, we discover a strong relationship between R(V) and the far-infrared dust emissivity.
We have discovered using Pan-STARRS1 an extremely red late-L dwarf, which has (J - K) sub(MKO) = 2.78 and (J - K) sub(2MASS) = 2.84, making it the reddest known field dwarf and second only to 2MASS ...J1207-39b among substellar companions. Near-IR spectroscopy shows a spectral type of L7 + or - 1 and reveals a triangular H-band continuum and weak alkali (K I and Na I) lines, hallmarks of low surface gravity. Near-IR astrometry from the Hawaii Infrared Parallax Program gives a distance of 24.6 + or - 1.4 pc and indicates a much fainter J-band absolute magnitude than field L dwarfs. The position and kinematics of PSO J318.5-22 point to membership in the beta Pic moving group. Evolutionary models give a temperature of (ProQuest: Formulae and/or non-USASCII text omitted) K and a mass of (ProQuest: Formulae and/or non-USASCII text omitted) M sub(Jup), making PSO J318.5-22 one of the lowest mass free-floating objects in the solar neighborhood. This object adds to the growing list of low-gravity field L dwarfs and is the first to be strongly deficient in methane relative to its estimated temperature. Comparing their spectra suggests that young L dwarfs with similar ages and temperatures can have different spectral signatures of youth. For the two objects with well constrained ages (PSO J318.5-22 and 2MASS J0355+11), we find their temperatures are approximate400 K cooler than field objects of similar spectral type but their luminosities are similar, i.e., these young L dwarfs are very red and unusually cool but not "underluminous." Altogether, PSO J318.5-22 is the first free-floating object with the colors, magnitudes, spectrum, luminosity, and mass that overlap the young dusty planets around HR 8799 and 2MASS J1207-39.
We study the time lags between the continuum emission of quasars at different wavelengths, based on more than four years of multi-band (g, r, i, z) light curves in the Pan-STARRS Medium Deep Fields. ...As photons from different bands emerge from different radial ranges in the accretion disk, the lags constrain the sizes of the accretion disks. We select 240 quasars with redshifts of z 1 or z 0.3 that are relatively emission-line free. The light curves are sampled from day to month timescales, which makes it possible to detect lags on the scale of the light crossing time of the accretion disks. With the code JAVELIN, we detect typical lags of several days in the rest frame between the g band and the riz bands. The detected lags are ∼2-3 times larger than the light crossing time estimated from the standard thin disk model, consistent with the recently measured lag in NGC 5548 and microlensing measurements of quasars. The lags in our sample are found to increase with increasing luminosity. Furthermore, the increase in lags going from g − r to g − i and then to g − z is slower than predicted in the thin disk model, particularly for high-luminosity quasars. The radial temperature profile in the disk must be different from what is assumed. We also find evidence that the lags decrease with increasing line ratios between ultraviolet Fe ii lines and Mg ii, which may point to changes in the accretion disk structure at higher metallicity.
ABSTRACT Luminous quasars at can be studied in detail with the current generation of telescopes and provide us with unique information on the first gigayear of the universe. Thus far, these studies ...have been statistically limited by the number of quasars known at these redshifts. Such quasars are rare, and therefore, wide-field surveys are required to identify them, and multiwavelength data are required to separate them efficiently from their main contaminants, the far more numerous cool dwarfs. In this paper, we update and extend the selection for the quasars presented in Bañados et al. (2014) using the Pan-STARRS1 (PS1) survey. We present the PS1 distant quasar sample, which currently consists of 124 quasars in the redshift range that satisfy our selection criteria. Of these quasars, 77 have been discovered with PS1, and 63 of them are newly identified in this paper. We present the composite spectra of the PS1 distant quasar sample. This sample spans a factor of ∼20 in luminosity and shows a variety of emission line properties. The number of quasars at presented in this work almost doubles the previously known quasars at these redshifts, marking a transition phase from studies of individual sources to statistical studies of the high-redshift quasar population, which was impossible with earlier, smaller samples.
THE Pan-STARRS1 PHOTOMETRIC SYSTEM TONRY, J. L; STUBBS, C. W; MAGNIER, E. A ...
The Astrophysical journal,
05/2012, Volume:
750, Issue:
2
Journal Article
Peer reviewed
Open access
The Pan-STARRS1 survey is collecting multi-epoch, multi-color observations of the sky north of declination -30 Degree-Sign 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-STARRS1 photometric system: g{sub P1}, r{sub P1}, i{sub P1}, z{sub P1}, y{sub P1}, and w{sub P1}. The Pan-STARRS1 photometric system is fundamentally based on the Hubble Space Telescope Calspec spectrophotometric observations, which in turn are fundamentally based on models of white dwarf atmospheres. We define the Pan-STARRS1 magnitude system and describe in detail our measurement of the system passbands, including both the instrumental sensitivity and atmospheric transmission functions. By-products, including transformations to other photometric systems, Galactic extinction, and stellar locus, are also provided. We close with a discussion of remaining systematic errors.
As of 2012 January 21, the Pan-STARRS 1 3pi Survey has observed the 3/4 of the sky visible from Hawaii with a minimum of 2 and mean of 7.6 observations in five filters, g sub(P1), r sub(P1), i ...sub(P1), z sub(P1), y sub(P1). Now at the end of the second year of the mission, we are in a position to make an initial public release of a portion of this unprecedented data set. This article describes the PS1 Photometric Ladder, Release 12.01. This is the first of a series of data releases to be generated as the survey coverage increases and the data analysis improves. The Photometric Ladder has rungs every hour in right ascension and at four intervals in declination. We will release updates with increased area coverage (more rungs) from the latest data set until the PS1 survey and the final re-reduction are completed. The currently released catalog presents photometry of ~ 1000 objects per square degree in the rungs of the ladder. Saturation occurs at g sub(P1), r sub(P1), i sub(P1) ~ 13.5; z sub(P1) ~ 13.0; and y sub(P1) ~ 12.0. Photometry is provided for stars down to g sub(P1), r sub(P1), z sub(P1) ~ 19.1 in the AB system. This data release depends on the rigid "Ubercal" photometric calibration using only the photometric nights, with systematic uncertainties of (8.0, 7.0, 9.0, 10.7, 12.4) mmag in (g sub(P1), r sub(P1), i sub(p1), z sub(P1), y sub(P1)). Areas covered only with lower quality nights are also included, and have been tied to the Ubercal solution via relative photometry; photometric accuracy of the non-photometric regions is lower and should be used with caution.
We use 1169 Pan-STARRS supernovae (SNe) and 195 low-z (z < 0.1) SNe Ia to measure cosmological parameters. Though most Pan-STARRS SNe lack spectroscopic classifications, in a previous paper we ...demonstrated that photometrically classified SNe can be used to infer unbiased cosmological parameters by using a Bayesian methodology that marginalizes over core-collapse (CC) SN contamination. Our sample contains nearly twice as many SNe as the largest previous SN Ia compilation. Combining SNe with cosmic microwave background (CMB) constraints from Planck, we measure the dark energy equation-of-state parameter w to be −0.989 0.057 (stat+sys). If w evolves with redshift as w(a) = w0 + wa(1 − a), we find w0 = −0.912 0.149 and wa = −0.513 0.826. These results are consistent with cosmological parameters from the Joint Light-curve Analysis and the Pantheon sample. We try four different photometric classification priors for Pan-STARRS SNe and two alternate ways of modeling CC SN contamination, finding that no variant gives a w differing by more than 2% from the baseline measurement. The systematic uncertainty on w due to marginalizing over CC SN contamination, , is the third-smallest source of systematic uncertainty in this work. We find limited (1.6 ) evidence for evolution of the SN color-luminosity relation with redshift, a possible systematic that could constitute a significant uncertainty in future high-z analyses. Our data provide one of the best current constraints on w, demonstrating that samples with ∼5% CC SN contamination can give competitive cosmological constraints when the contaminating distribution is marginalized over in a Bayesian framework.
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