arXiv subjects

N. Metcalfe

Publications and source records attributed to N. Metcalfe.

At least 55 records · Page 3Linked to original sources

arXiv2016

PS1-14bj: A Hydrogen-Poor Superluminous Supernova With a Long Rise and Slow Decay

We present photometry and spectroscopy of PS1-14bj, a hydrogen-poor superluminous supernova (SLSN) at redshift $z=0.5215$ discovered in the last months of the Pan-STARRS1 Medium Deep Survey. PS1-14bj stands out by its extremely slow evolution, with an observed rise of $\gtrsim 125$ rest-frame days, and exponential decline out to $\sim 250$ days past peak at a measured rate of $0.01~{\rm mag~day}^{-1}$, consistent with fully-trapped $^{56}$Co decay. This is the longest rise time measured in a SLSN to date, and the first SLSN to show a rise time consistent with pair-instability supernova (PISN) models. Compared to other slowly-evolving SLSNe, it is spectroscopically similar to the prototype SN2007bi at maximum light, though lower in luminosity ($L_{\rm peak} \simeq 4.6 \times 10^{43} {\rm erg s}^{-1}$) and with a flatter peak than previous events. PS1-14bj shows a number of peculiar properties, including a near-constant color temperature for $>200$ days past peak, and strong emission lines from [O III] $λ$5007 and [O III] $λ$4363 with a velocity width of $\sim$3400 km/s, in its late-time spectra. These both suggest there is a sustained source of heating over very long timescales, and are incompatible with a simple $^{56}$Ni-powered/PISN interpretation. A modified magnetar model including emission leakage at late times can reproduce the light curve, in which case the blue continuum and [O III] features are interpreted as material heated and ionized by the inner pulsar wind nebula becoming visible at late times. Alternatively, the late-time heating could be due to interaction with a shell of H-poor circumstellar material.

astro-ph.HE↗

arXiv2016

The Pan-STARRS1 distant z>5.6 quasar survey: more than 100 quasars within the first Gyr of the universe

Luminous quasars at z>5.6 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 needed to separate them efficiently from their main contaminants, the far more numerous cool dwarfs. In this paper, we update and extend the selection for z~6 quasars presented in Banados 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 5.6 5.6 presented in this work almost double the quasars previously known 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.

astro-ph.GA↗

arXiv2016

Slow blue nuclear hypervariables in PanSTARRS-1

We discuss 76 large amplitude transients (Delta-m>1.5) occurring in the nuclei of galaxies, nearly all with no previously known Active Galactic Nucleus (AGN). They have been discovered as part of the Pan-STARRS1 (PS1) 3pi survey, by comparison with SDSS photometry a decade earlier, and then monitored with the Liverpool Telescope, and studied spectroscopically with the William Herschel Telescope (WHT). Based on colours, light curve shape, and spectra, these transients fall into four groups. A few are misclassified stars or objects of unknown type. Some are red/fast transients and are known or likely nuclear supernovae. A few are either radio sources or erratic variables and so likely blazars. However the majority (~66%) are blue and evolve slowly, on a timescale of years. Spectroscopy shows them to be AGN at z~ 0.3 - 1.4, which must have brightened since the SDSS photometry by around an order of magnitude. It is likely that these objects were in fact AGN a decade ago, but too weak to be recognised by SDSS; they could then be classed as "hypervariable" AGN. By searching the SDSS Stripe 82 quasar database, we find 15 similar objects. We discuss several possible explanations for these slow blue hypervariables - (i) unusually luminous tidal disruption events; (ii) extinction events; (iii) changes in accretion state; and (iv) large amplitude microlensing by stars in foreground galaxies. A mixture of explanations (iii) and (iv) seems most likely. Both hold promise of considerable new insight into the AGN phenomenon.

astro-ph.HE↗

arXiv2016

A Multi-Wavelength Photometric Census of AGN and Star Formation Activity in the Brightest Cluster Galaxies of X-ray Selected Clusters

Despite their reputation as being "red and dead", the unique environment inhabited by Brightest Cluster Galaxies (BCGs) can often lead to a self-regulated feedback cycle between radiatively cooling intracluster gas and star formation and AGN activity in the BCG. However the prevalence of "active" BCGs, and details of the feedback involved, are still uncertain. We have performed an optical, UV and Mid-IR photometric analysis of the BCGs in 981 clusters at 0.03 < z < 0.5, selected from the ROSAT All Sky Survey. Using Pan-STARRS PS1 3pi, GALEX and WISE survey data we look for BCGs with photometric colours which deviate from that of the bulk population of passive BCGs - indicative of AGN and/or star formation activity within the BCG. We find that whilst the majority of BCGs are consistent with being passive, at least 14% of our BCGs show a significant colour offset from passivity in at least one colour index. And, where available, supplementary spectroscopy reveals the majority of these particular BCGs show strong optical emission lines. On comparing BCG "activity" with the X-ray luminosity of the host cluster, we find that BCGs showing a colour offset are preferentially found in the more X-ray luminous clusters, indicative of the connection between BCG "activity" and the intracluster medium.

astro-ph.GA↗

arXiv2016

The 2QDES Pilot : The luminosity and redshift dependence of quasar clustering

We present a new redshift survey, the 2dF Quasar Dark Energy Survey pilot (2QDESp), which consists of ${\approx}10000$ quasars from ${\approx}150$ deg$^2$ of the southern sky, based on VST-ATLAS imaging and 2dF/AAOmega spectroscopy. Combining our optical photometry with the WISE (W1,W2) bands we can select essentially contamination free quasar samples with $0.8{<}z{<}2.5$ and $g{<}20.5$. At fainter magnitudes, optical UVX selection is still required to reach our $g{\approx}22.5$ limit. Using both these techniques we observed quasar redshifts at sky densities up to $90$ deg$^{-2}$. By comparing 2QDESp with other surveys (SDSS, 2QZ and 2SLAQ) we find that quasar clustering is approximately luminosity independent, with results for all four surveys consistent with a correlation scale of $r_{0}{=}6.1{\pm}0.1 \: h^{-1}$Mpc, despite their decade range in luminosity. We find a significant redshift dependence of clustering, particularly when BOSS data with $r_{0}{=}7.3{\pm}0.1 \: h^{-1}$Mpc are included at $z{\approx}2.4$. All quasars remain consistent with having a single host halo mass of ${\approx}2{\pm}1{\times}10^{12} \: h^{-1}M_\odot$. This result implies that either quasars do not radiate at a fixed fraction of the Eddington luminosity or AGN black hole and dark matter halo masses are weakly correlated. No significant evidence is found to support fainter, X-ray selected quasars at low redshift having larger halo masses as predicted by the `hot halo' mode AGN model of Fanidakis et al. 2013. Finally, although the combined quasar sample reaches an effective volume as large as that of the original SDSS LRG sample, we do not detect the BAO feature in these data.

astro-ph.GA↗

arXiv2016

Of Genes and Machines: application of a combination of machine learning tools to astronomy datasets

We apply a combination of a Genetic Algorithms (GA) and Support Vector Machines (SVM) machine learning algorithm to solve two important problems faced by the astronomical community: star/galaxy separation, and photometric redshift estimation of galaxies in survey catalogs. We use the GA to select the relevant features in the first step, followed by optimization of SVM parameters in the second step to obtain an optimal set of parameters to classify or regress, in process of which we avoid over-fitting. We apply our method to star/galaxy separation in Pan-STARRS1 data. We show that our method correctly classifies 98% of objects down to i_P1= 24.5, with a completeness (or true positive rate) of 99% for galaxies, and 88% for stars. By combining colors with morphology, our star/classification method yields better results than the new SExtractor classifier spread_model in particular at the faint end (i_P1>22). We also use our method to derive photometric redshifts for galaxies in the COSMOS bright multi-wavelength dataset down to an error in (1+z) of sigma=0.013, which compares well with estimates from SED fitting on the same data (sigma=0.007) while making a significantly smaller number of assumptions.

astro-ph.IM↗

arXiv2016

The Optical-Infrared Extinction Curve and its Variation in the Milky Way

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 towards 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, 2MASS, and WISE. 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 sigma(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.

astro-ph.GA↗

arXiv2015

Pan-Planets: Searching for hot Jupiters around cool dwarfs

The Pan-Planets survey observed an area of 42 sq deg. in the galactic disk for about 165 hours. The main scientific goal of the project is the detection of transiting planets around M dwarfs. We establish an efficient procedure for determining the stellar parameters $T_{eff}$ and log$g$ of all sources using a method based on SED fitting, utilizing a three-dimensional dust map and proper motion information. In this way we identify more than 60000 M dwarfs, which is by far the largest sample of low-mass stars observed in a transit survey to date. We present several planet candidates around M dwarfs and hotter stars that are currently being followed up. Using Monte-Carlo simulations we calculate the detection efficiency of the Pan-Planets survey for different stellar and planetary populations. We expect to find $3.0^{+3.3}_{-1.6}$ hot Jupiters around F, G, and K dwarfs with periods lower than 10 days based on the planet occurrence rates derived in previous surveys. For M dwarfs, the percentage of stars with a hot Jupiter is under debate. Theoretical models expect a lower occurrence rate than for larger main sequence stars. However, radial velocity surveys find upper limits of about 1\% due to their small sample, while the Kepler survey finds a occurrence rate that we estimate to be at least $0.17(^{+0.67}_{-0.04})$%, making it even higher than the determined fraction from OGLE-III for F, G and K stellar types, $0.14(^{+0.15}_{-0.076})\%$. With the large sample size of Pan-Planets, we are able to determine an occurrence rate of $0.11(^{+0.37}_{-0.02})$% in case one of our candidates turns out to be a real detection. If, however, none of our candidates turn out to be true planets, we are able to put an upper limit of 0.34% with a 95% confidence on the hot Jupiter occurrence rate of M dwarfs. Therefore we cannot yet confirm the theoretical prediction of a lower occurrence rate for cool stars.

astro-ph.EP↗

arXiv2015

Hypercalibration: A Pan-STARRS1-based recalibration of the Sloan Digital Sky Survey

We present a recalibration of the Sloan Digital Sky Survey (SDSS) photometry with new flat fields and zero points derived from Pan-STARRS1 (PS1). Using PSF photometry of 60 million stars with $16 < r < 20$, we derive a model of amplifier gain and flat-field corrections with per-run RMS residuals of 3 millimagnitudes (mmag) in $griz$ bands and 15 mmag in $u$ band. The new photometric zero points are adjusted to leave the median in the Galactic North unchanged for compatibility with previous SDSS work. We also identify transient non-photometric periods in SDSS ("contrails") based on photometric deviations co-temporal in SDSS bands. The recalibrated stellar PSF photometry of SDSS and PS1 has an RMS difference of {9,7,7,8} mmag in $griz$, respectively, when averaged over $15'$ regions.

astro-ph.IM↗

arXiv2015

Finding, characterizing and classifying variable sources in multi-epoch sky surveys: QSOs and RR Lyrae in PS1 3$π$ data

In area and depth, the Pan-STARRS1 (PS1) 3$π$ survey is unique among many-epoch, multi-band surveys and has enormous potential for all-sky identification of variable sources. PS1 has observed the sky typically seven times in each of its five bands ($grizy$) over 3.5 years, but unlike SDSS not simultaneously across the bands. Here we develop a new approach for quantifying statistical properties of non-simultaneous, sparse, multi-color lightcurves through light-curve structure functions, effectively turning PS1 into a $\sim 35$-epoch survey. We use this approach to estimate variability amplitudes and timescales $(ω_r, τ)$ for all point-sources brighter than $r_{\mathrm{P1}}=21.5$ mag in the survey. With PS1 data on SDSS Stripe 82 as ``ground truth", we use a Random Forest Classifier to identify QSOs and RR Lyrae based on their variability and their mean PS1 and WISE colors. We find that, aside from the Galactic plane, QSO and RR Lyrae samples of purity $\sim$75\% and completeness $\sim$92\% can be selected. On this basis we have identified a sample of $\sim 1,000,000$ QSO candidates, as well as an unprecedentedly large and deep sample of $\sim$150,000 RR Lyrae candidates with distances from $\sim$10 kpc to $\sim$120 kpc. Within the Draco dwarf spheroidal, we demonstrate a distance precision of 6\% for RR Lyrae candidates. We provide a catalog of all likely variable point sources and likely QSOs in PS1, a total of $25.8\times 10^6$ sources.

astro-ph.GA↗

arXiv2015

Supercal: Cross-Calibration of Multiple Photometric Systems to Improve Cosmological Measurements with Type Ia Supernovae

Current cosmological analyses which use Type Ia supernova (SN Ia) observations combine SN samples to expand the redshift range beyond that of a single sample and increase the overall sample size. The inhomogeneous photometric calibration between different SN samples is one of the largest systematic uncertainties of the cosmological parameter estimation. To place these different samples on a single system, analyses currently use observations of a small sample of very bright flux standards on the $HST$ system. We propose a complementary method, called `Supercal', in which we use measurements of secondary standards in each system, compare these to measurements of the same stars in the Pan-STARRS1 (PS1) system, and determine offsets for each system relative to PS1, placing all SN observations on a single, consistent photometric system. PS1 has observed $3π$ of the sky and has a relative calibration of better than 5 mmag (for $\sim15<griz<21$ mag), making it an ideal reference system. We use this process to recalibrate optical observations taken by the following SN samples: PS1, SNLS, SDSS, CSP, and CfA1-4. We measure discrepancies on average of 10 mmag, but up to 35 mmag, in various optical passbands. We find that correcting for these differences changes recovered values for the dark energy equation-of-state parameter, $w$, by on average $2.6\%$. This change is roughly half the size of current statistical constraints on $w$. The size of this effect strongly depends on the error in the $B-V$ calibration of the low-$z$ surveys. The Supercal method will allow future analyses to tie past samples to the best calibrated sample.

astro-ph.IM↗

arXiv2015

Pan-STARRS1 variability of XMM-COSMOS AGN. I. Impact on photometric redshifts

[Abbreviated] Upcoming large area sky surveys like EUCLID and eROSITA crucially depend on accurate photometric redshifts (photo-z). The identification of variable sources, such as AGNs, and the achievable redshift accuracy for varying objects are important in view of the science goals of the EUCLID and eROSITA missions. We probe AGN optical variability for a large sample of X-ray-selected AGNs in the XMM-COSMOS field, using the light curves provided by the Pan-STARRS1 (PS1) 3pi and MDF04 surveys. Utilizing two different variability parameters, we defined a sample of varying AGNs for every PS1 band. We investigated the influence of variability on the calculation of photo-z by applying three different input photometry sets for our fitting procedure. For each of the five PS1 bands, we chose either the epochs minimizing the interval in observing time, the median magnitude values, or randomly drawn light curve points to compute the redshift. In addition, we derived photo-z using PS1 photometry extended by GALEX/IRAC bands. We find that the photometry produced by the 3pi survey is sufficient to reliably detect variable sources provided that the fractional variability amplitude is at least 3%. Considering the photo-z of variable AGNs, we observe that minimizing the time spacing of the chosen points yields superior photo-z in terms of the percentage of outliers (33%) and accuracy (0.07), outperforming the other two approaches. Drawing random points from the light curve gives rise to typically 57% of outliers and an accuracy of 0.4. Adding GALEX/IRAC bands for the redshift determination weakens the influence of variability. Although the redshift quality generally improves when adding these bands, we still obtain not less than 26% of outliers and an accuracy of 0.05 at best, therefore variable sources should receive a flag stating that their photo-z may be low quality.

astro-ph.GA↗

arXiv2015

Galaxy and Mass Assembly (GAMA): Projected Galaxy Clustering

We measure the projected 2-point correlation function of galaxies in the 180 deg$^2$ equatorial regions of the GAMA II survey, for four different redshift slices between z = 0.0 and z=0.5. To do this we further develop the Cole (2011) method of producing suitable random catalogues for the calculation of correlation functions. We find that more r-band luminous, more massive and redder galaxies are more clustered. We also find that red galaxies have stronger clustering on scales less than ~3 $h^{-1}$ Mpc. We compare to two different versions of the GALFORM galaxy formation model, Lacey et al (in prep.) and Gonzalez-Perez et al. (2014), and find that the models reproduce the trend of stronger clustering for more massive galaxies. However, the models under predict the clustering of blue galaxies, can incorrectly predict the correlation function on small scales and under predict the clustering in our sample of galaxies with ~3$L_r$ . We suggest possible avenues to explore to improve these cluster- ing predictions. The measurements presented in this paper can be used to test other galaxy formation models, and we make the measurements available online to facilitate this.

astro-ph.GA↗

arXiv2015

The shell game: a panoramic view of Fornax

We present a panoramic study of the Fornax dwarf spheroidal galaxy, using data obtained as part of the VLT Survey Telescope (VST) ATLAS Survey. The data presented here -- a subset of the full survey -- uniformly cover a region of 25 square degrees centred on the galaxy, in $g$, $r$ and $i$-bands. This large area coverage reveals two key differences to previous studies of Fornax. First, data extending beyond the nominal tidal radius of the dwarf highlight the presence of a second distinct red giant branch population. This bluer red giant branch appears to be coeval with the horizontal branch population. Second, a shell structure located approximately 1.4 degrees from the centre of Fornax is shown to be a mis-identified background overdensity of galaxies. This last result casts further doubt on the hypothesis that Fornax underwent a gas-rich merger in its relatively recent past.

astro-ph.GA↗

arXiv2015

Two bright z > 6 quasars from VST ATLAS and a new method of optical plus mid-infra-red colour selection

We present the discovery of two z > 6 quasars, selected as i band dropouts in the VST ATLAS survey. Our first quasar has redshift, z = 6.31 \pm 0.03, z band magnitude, z_AB = 19.63 \pm 0.08 and rest frame 1450A absolute magnitude, M_1450 = -27.8 \pm 0.2, making it the joint second most luminous quasar known at z > 6. The second quasar has z = 6.02 \pm 0.03, z_AB = 19.54 \pm 0.08 and M_1450 = -27.0 \pm 0.1. We also recover a z = 5.86 quasar discovered by Venemans et al. (2015, in prep.). To select our quasars we use a new 3D colour space, combining the ATLAS optical colours with mid-infra-red data from the Wide-field Infrared Survey Explorer (WISE). We use i_AB - z_AB colour to exclude main sequence stars, galaxies and lower redshift quasars, W1 - W2 to exclude L dwarfs and z_AB - W2 to exclude T dwarfs. A restrictive set of colour cuts returns only our three high redshift quasars and no contaminants, albeit with a sample completeness of ~50%. We discuss how our 3D colour space can be used to reject the majority of contaminants from samples of bright 5.7 < z < 6.3 quasars, replacing follow-up near-infra-red photometry, whilst retaining high completeness.

astro-ph.GA↗

arXiv2015

Optical Confirmation and Redshift Estimation of the Planck Cluster Candidates overlapping the Pan-STARRS Survey

We report results of a study of Planck Sunyaev-Zel'dovich effect (SZE) selected galaxy cluster candidates using the Panoramic Survey Telescope & Rapid Response System (Pan-STARRS) imaging data. We first examine 150 Planck confirmed galaxy clusters with spectroscopic redshifts to test our algorithm for identifying optical counterparts and measuring their redshifts; our redshifts have a typical accuracy of $σ_{z/(1+z)} \sim 0.022$ for this sample. Using 60 random sky locations, we estimate that our chance of contamination through a random superposition is ~ 3 per cent. We then examine an additional 237 Planck galaxy cluster candidates that have no redshift in the source catalogue. Of these 237 unconfirmed cluster candidates we are able to confirm 60 galaxy clusters and measure their redshifts. A further 83 candidates are so heavily contaminated by stars due to their location near the Galactic plane that we do not attempt to identify counterparts. For the remaining 94 candidates we find no optical counterpart but use the depth of the Pan-STARRS1 data to estimate a redshift lower limit $z_{\text{lim}(10^{15})}$ beyond which we would not have expected to detect enough galaxies for confirmation. Scaling from the already published Planck sample, we expect that $\sim$12 of these unconfirmed candidates may be real clusters.

astro-ph.CO↗

arXiv2015

Constraining the radio-loud fraction of quasars at z>5.5

Radio-loud Active Galactic Nuclei at z~2-4 are typically located in dense environments and their host galaxies are among the most massive systems at those redshifts, providing key insights for galaxy evolution. Finding radio-loud quasars at the highest accessible redshifts (z~6) is important to study their properties and environments at even earlier cosmic time. They would also serve as background sources for radio surveys intended to study the intergalactic medium beyond the epoch of reionization in HI 21 cm absorption. Currently, only five radio-loud ($R=f_{ν,5{\rm GHz}}/f_{ν,4400Å}>10$) quasars are known at z~6. In this paper we search for 5.5 < z < 7.2 quasars by cross-matching the optical Pan-STARRS1 and radio FIRST surveys. The radio information allows identification of quasars missed by typical color-based selections. While we find no good 6.4 < z <7.2 quasar candidates at the sensitivities of these surveys, we discover two new radio-loud quasars at z~6. Furthermore, we identify two additional z~6 radio-loud quasars which were not previously known to be radio-loud, nearly doubling the current z~6 sample. We show the importance of having infrared photometry for z>5.5 quasars to robustly classify them as radio-quiet or radio-loud. Based on this, we reclassify the quasar J0203+0012 (z=5.72), previously considered radio-loud, to be radio-quiet. Using the available data in the literature, we constrain the radio-loud fraction of quasars at z~6, using the Kaplan--Meier estimator, to be $8.1^{+5.0}_{-3.2}\%$. This result is consistent with there being no evolution of the radio-loud fraction with redshift, in contrast to what has been suggested by some studies at lower redshifts.

astro-ph.GA↗

arXiv2015

Machine learning for transient discovery in Pan-STARRS1 difference imaging

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%.

astro-ph.IM↗