arXiv subjects

R. J. Brunner

Publications and source records attributed to R. J. Brunner.

At least 19 recordsLinked to original sources

arXiv2017

Vizic: A Jupyter-based Interactive Visualization Tool for Astronomical Catalogs

The ever-growing datasets in observational astronomy have challenged scientists in many aspects, including an efficient and interactive data exploration and visualization. Many tools have been developed to confront this challenge. However, they usually focus on displaying the actual images or focus on visualizing patterns within catalogs in a predefined way. In this paper we introduce Vizic, a Python visualization library that builds the connection between images and catalogs through an interactive map of the sky region. Vizic visualizes catalog data over a custom background canvas using the shape, size and orientation of each object in the catalog. The displayed objects in the map are highly interactive and customizable comparing to those in the images. These objects can be filtered by or colored by their properties, such as redshift and magnitude. They also can be sub-selected using a lasso-like tool for further analysis using standard Python functions from inside a Jupyter notebook. Furthermore, Vizic allows custom overlays to be appended dynamically on top of the sky map. We have initially implemented several overlays, namely, Voronoi, Delaunay, Minimum Spanning Tree and HEALPix grid layers, which are helpful for visualizing large-scale structure. All these overlays can be generated, added or removed interactively with one line of code. The catalog data is stored in a non-relational database, and the interfaces were developed in JavaScript and Python to work within Jupyter Notebook, which allows to create custom widgets, user generated scripts to analyze and plot the data selected/displayed in the interactive map. This unique design makes Vizic a very powerful and flexible interactive analysis tool. Vizic can be adopted in variety of exercises, for example, data inspection, clustering analysis, galaxy alignment studies, outlier identification or simply large-scale visualizations.

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arXiv2016

The Dark Energy Survey: more than dark energy - an overview

This overview article describes the legacy prospect and discovery potential of the Dark Energy Survey (DES) beyond cosmological studies, illustrating it with examples from the DES early data. DES is using a wide-field camera (DECam) on the 4m Blanco Telescope in Chile to image 5000 sq deg of the sky in five filters (grizY). By its completion the survey is expected to have generated a catalogue of 300 million galaxies with photometric redshifts and 100 million stars. In addition, a time-domain survey search over 27 sq deg is expected to yield a sample of thousands of Type Ia supernovae and other transients. The main goals of DES are to characterise dark energy and dark matter, and to test alternative models of gravity; these goals will be pursued by studying large scale structure, cluster counts, weak gravitational lensing and Type Ia supernovae. However, DES also provides a rich data set which allows us to study many other aspects of astrophysics. In this paper we focus on additional science with DES, emphasizing areas where the survey makes a difference with respect to other current surveys. The paper illustrates, using early data (from `Science Verification', and from the first, second and third seasons of observations), what DES can tell us about the solar system, the Milky Way, galaxy evolution, quasars, and other topics. In addition, we show that if the cosmological model is assumed to be Lambda+ Cold Dark Matter (LCDM) then important astrophysics can be deduced from the primary DES probes. Highlights from DES early data include the discovery of 34 Trans Neptunian Objects, 17 dwarf satellites of the Milky Way, one published z > 6 quasar (and more confirmed) and two published superluminous supernovae (and more confirmed).

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arXiv2016

Galaxy clustering with photometric surveys using PDF redshift information

Photometric surveys produce large-area maps of the galaxy distribution, but with less accurate redshift information than is obtained from spectroscopic methods. Modern photometric redshift (photo-z) algorithms use galaxy magnitudes, or colors, that are obtained through multi-band imaging to produce a probability density function (PDF) for each galaxy in the map. We used simulated data to study the effect of using different photo-z estimators to assign galaxies to redshift bins in order to compare their effects on angular clustering and galaxy bias measurements. We found that if we use the entire PDF, rather than a single-point (mean or mode) estimate, the deviations are less biased, especially when using narrow redshift bins. When the redshift bin widths are $Δz=0.1$, the use of the entire PDF reduces the typical measurement bias from 5%, when using single point estimates, to 3%.

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arXiv2015

Galaxy clustering, photometric redshifts and diagnosis of systematics in the DES Science Verification data

We study the clustering of galaxies detected at $i<22.5$ in the Science Verification observations of the Dark Energy Survey (DES). Two-point correlation functions are measured using $2.3\times 10^6$ galaxies over a contiguous 116 deg$^2$ region in five bins of photometric redshift width $Δz = 0.2$ in the range $0.2 < z < 1.2.$ The impact of photometric redshift errors are assessed by comparing results using a template-based photo-$z$ algorithm (BPZ) to a machine-learning algorithm (TPZ). A companion paper (Leistedt et al 2015) presents maps of several observational variables (e.g. seeing, sky brightness) which could modulate the galaxy density. Here we characterize and mitigate systematic errors on the measured clustering which arise from these observational variables, in addition to others such as Galactic dust and stellar contamination. After correcting for systematic effects we measure galaxy bias over a broad range of linear scales relative to mass clustering predicted from the Planck $Λ$CDM model, finding agreement with CFHTLS measurements with $χ^2$ of 4.0 (8.7) with 5 degrees of freedom for the TPZ (BPZ) redshifts. We test a "linear bias" model, in which the galaxy clustering is a fixed multiple of the predicted non-linear dark-matter clustering. The precision of the data allow us to determine that the linear bias model describes the observed galaxy clustering to $2.5\%$ accuracy down to scales at least $4$ to $10$ times smaller than those on which linear theory is expected to be sufficient.

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arXiv2015

Observation and Confirmation of Six Strong Lensing Systems in The Dark Energy Survey Science Verification Data

We report the observation and confirmation of the first group- and cluster-scale strong gravitational lensing systems found in Dark Energy Survey (DES) data. Through visual inspection of data from the Science Verification (SV) season, we identified 53 candidate systems. We then obtained spectroscopic follow-up of 21 candidates using the Gemini Multi-Object Spectrograph (GMOS) at the Gemini South telescope and the Inamori-Magellan Areal Camera and Spectrograph (IMACS) at the Magellan/Baade telescope. With this follow-up, we confirmed six candidates as gravitational lenses: Three of the systems are newly discovered, and the remaining three were previously known. Of the 21 observed candidates, the remaining 15 were either not detected in spectroscopic observations, were observed and did not exhibit continuum emission (or spectral features), or were ruled out as lensing systems. The confirmed sample consists of one group-scale and five galaxy cluster-scale lenses. The lensed sources range in redshift z ~ 0.80-3.2, and in i-band surface brightness i_{SB} ~ 23-25 mag/sq.-arcsec. (2" aperture). For each of the six systems, we estimate the Einstein radius and the enclosed mass, which have ranges ~ 5.0 - 8.6" and ~ 7.5 x 10^{12} - 6.4 x 10^{13} solar masses, respectively.

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arXiv2014

On the Clustering of Compact Galaxy Pairs in Dark Matter Haloes

We analyze the clustering of photometrically selected galaxy pairs by using the halo-occupation distribution (HOD) model. We measure the angular two-point auto-correlation function, $ω(θ)$, for galaxies and galaxy pairs in three volume-limited samples and develop an HOD to model their clustering. Our results are successfully fit by these HOD models, and we see the separation of "1-halo" and "2-halo" clustering terms for both single galaxies and galaxy pairs. Our clustering measurements and HOD model fits for the single galaxy samples are consistent with previous results. We find that the galaxy pairs generally have larger clustering amplitudes than single galaxies, and the quantities computed during the HOD fitting, e.g., effective halo mass, $M_{eff}$, and linear bias, $b_{g}$, are also larger for galaxy pairs. We find that the central fractions for galaxy pairs are significantly higher than single galaxies, which confirms that galaxy pairs are formed at the center of more massive dark matter haloes. We also model the clustering dependence of the galaxy pair correlation function on redshift, galaxy type, and luminosity. We find early-early pairs (bright galaxy pairs) cluster more strongly than late-late pairs (dim galaxy pairs), and that the clustering does not depend on the luminosity contrast between the two galaxies in the compact group.

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arXiv2014

Exhausting the Information: Novel Bayesian Combination of Photometric Redshift PDFs

The estimation and utilization of photometric redshift probability density functions (photo-$z$ PDFs) has become increasingly important over the last few years and currently there exist a wide variety of algorithms to compute photo-$z$'s, each with their own strengths and weaknesses. In this paper, we present a novel and efficient Bayesian framework that combines the results from different photo-$z$ techniques into a more powerful and robust estimate by maximizing the information from the photometric data. To demonstrate this we use a supervised machine learning technique based on random forest, an unsupervised method based on self-organizing maps, and a standard template fitting method but can be easily extend to other existing techniques. We use data from the DEEP2 and the SDSS surveys to explore different methods for combining the predictions from these techniques. By using different performance metrics, we demonstrate that we can improve the accuracy of our final photo-$z$ estimate over the best input technique, that the fraction of outliers is reduced, and that the identification of outliers is significantly improved when we apply a Na\"ıve Bayes Classifier to this combined information. Our more robust and accurate photo-$z$ PDFs will allow even more precise cosmological constraints to be made by using current and future photometric surveys. These improvements are crucial as we move to analyze photometric data that push to or even past the limits of the available training data, which will be the case with the Large Synoptic Survey Telescope.

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arXiv2014

Sparse Representation of Photometric Redshift PDFs: Preparing for Petascale Astronomy

One of the consequences of entering the era of precision cosmology is the widespread adoption of photometric redshift probability density functions (PDFs). Both current and future photometric surveys are expected to obtain images of billions of distinct galaxies. As a result, storing and analyzing all of these PDFs will be non-trivial and even more severe if a survey plans to compute and store multiple different PDFs. In this paper we propose the use of a sparse basis representation to fully represent individual photo-$z$ PDFs. By using an Orthogonal Matching Pursuit algorithm and a combination of Gaussian and Voigt basis functions, we demonstrate how our approach is superior to a multi-Gaussian fitting, as we require approximately half of the parameters for the same fitting accuracy with the additional advantage that an entire PDF can be stored by using a 4-byte integer per basis function, and we can achieve better accuracy by increasing the number of bases. By using data from the CFHTLenS, we demonstrate that only ten to twenty points per galaxy are sufficient to reconstruct both the individual PDFs and the ensemble redshift distribution, $N(z)$, to an accuracy of 99.9% when compared to the one built using the original PDFs computed with a resolution of $δz = 0.01$, reducing the required storage of two hundred original values by a factor of ten to twenty. Finally, we demonstrate how this basis representation can be directly extended to a cosmological analysis, thereby increasing computational performance without losing resolution nor accuracy.

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arXiv2013

SOMz: photometric redshift PDFs with self organizing maps and random atlas

In this paper we explore the applicability of the unsupervised machine learning technique of Self Organizing Maps (SOM) to estimate galaxy photometric redshift probability density functions (PDFs). This technique takes a spectroscopic training set, and maps the photometric attributes, but not the redshifts, to a two dimensional surface by using a process of competitive learning where neurons compete to more closely resemble the training data multidimensional space. The key feature of a SOM is that it retains the topology of the input set, revealing correlations between the attributes that are not easily identified. We test three different 2D topological mapping: rectangular, hexagonal, and spherical, by using data from the DEEP2 survey. We also explore different implementations and boundary conditions on the map and also introduce the idea of a random atlas where a large number of different maps are created and their individual predictions are aggregated to produce a more robust photometric redshift PDF. We also introduced a new metric, the $I$-score, which efficiently incorporates different metrics, making it easier to compare different results (from different parameters or different photometric redshift codes). We find that by using a spherical topology mapping we obtain a better representation of the underlying multidimensional topology, which provides more accurate results that are comparable to other, state-of-the-art machine learning algorithms. Our results illustrate that unsupervised approaches have great potential for many astronomical problems, and in particular for the computation of photometric redshifts.

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arXiv2013

Dark energy with gravitational lens time delays

Strong lensing gravitational time delays are a powerful and cost effective probe of dark energy. Recent studies have shown that a single lens can provide a distance measurement with 6-7 % accuracy (including random and systematic uncertainties), provided sufficient data are available to determine the time delay and reconstruct the gravitational potential of the deflector. Gravitational-time delays are a low redshift (z~0-2) probe and thus allow one to break degeneracies in the interpretation of data from higher-redshift probes like the cosmic microwave background in terms of the dark energy equation of state. Current studies are limited by the size of the sample of known lensed quasars, but this situation is about to change. Even in this decade, wide field imaging surveys are likely to discover thousands of lensed quasars, enabling the targeted study of ~100 of these systems and resulting in substantial gains in the dark energy figure of merit. In the next decade, a further order of magnitude improvement will be possible with the 10000 systems expected to be detected and measured with LSST and Euclid. To fully exploit these gains, we identify three priorities. First, support for the development of software required for the analysis of the data. Second, in this decade, small robotic telescopes (1-4m in diameter) dedicated to monitoring of lensed quasars will transform the field by delivering accurate time delays for ~100 systems. Third, in the 2020's, LSST will deliver 1000's of time delays; the bottleneck will instead be the aquisition and analysis of high resolution imaging follow-up. Thus, the top priority for the next decade is to support fast high resolution imaging capabilities, such as those enabled by the James Webb Space Telescope and next generation adaptive optics systems on large ground based telescopes.

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arXiv2013

TPZ : Photometric redshift PDFs and ancillary information by using prediction trees and random forests

With the growth of large photometric surveys, accurately estimating photometric redshifts, preferably as a probability density function (PDF), and fully understanding the implicit systematic uncertainties in this process has become increasingly important. In this paper, we present a new, publicly available, parallel, machine learning algorithm that generates photometric redshift PDFs by using prediction trees and random forest techniques, which we have named TPZ. This new algorithm incorporates measurement errors into the calculation while also dealing efficiently with missing values in the data. In addition, our implementation of this algorithm provides supplementary information regarding the data being analyzed, including unbiased estimates of the accuracy of the technique without resorting to a validation data set, identification of poor photometric redshift areas within the parameter space occupied by the spectroscopic training data, a quantification of the relative importance of the variables used to construct the PDF, and a robust identification of outliers. This extra information can be used to optimally target new spectroscopic observations and to improve the overall efficacy of the redshift estimation. We have tested TPZ on galaxy samples drawn from the SDSS main galaxy sample and from the DEEP2 survey, obtaining excellent results in each case. We also have tested our implementation by participating in the PHAT1 project, which is a blind photometric redshift contest, finding that TPZ performs comparable to if not better than other empirical photometric redshift algorithms. Finally, we discuss the various parameters that control the operation of TPZ, the specific limitations of this approach and an application of photometric redshift PDFs.

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arXiv2013

The SDSS Galaxy Angular Two-Point Correlation Function

We present the galaxy two-point angular correlation function for galaxies selected from the seventh data release of the Sloan Digital Sky Survey. The galaxy sample was selected with $r$-band apparent magnitudes between 17 and 21; and we measure the correlation function for the full sample as well as for the four magnitude ranges: 17-18, 18-19, 19-20, and 20-21. We update the flag criteria to select a clean galaxy catalog and detail specific tests that we perform to characterize systematic effects, including the effects of seeing, Galactic extinction, and the overall survey uniformity. Notably, we find that optimally we can use observed regions with seeing $< 1\farcs5$, and $r$-band extinction < 0.13 magnitudes, smaller than previously published results. Furthermore, we confirm that the uniformity of the SDSS photometry is minimally affected by the stripe geometry. We find that, overall, the two-point angular correlation function can be described by a power law, $ω(θ) = A_ωθ^{(1-γ)}$ with $γ\simeq 1.72$, over the range $0\fdg005$--$10\degr$. We also find similar relationships for the four magnitude subsamples, but the amplitude within the same angular interval for the four subsamples is found to decrease with fainter magnitudes, in agreement with previous results. We find that the systematic signals are well below the galaxy angular correlation function for angles less than approximately $5\degr$, which limits the modeling of galaxy angular correlations on larger scales. Finally, we present our custom, highly parallelized two-point correlation code that we used in this analysis.

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arXiv2007

On the variability of quasars: a link between Eddington ratio and optical variability?

Repeat scans by the Sloan Digital Sky Survey (SDSS) of a 278 square degree stripe along the Celestial equator have yielded an average of over 10 observations each for nearly 8,000 spectroscopically confirmed quasars. Over 2500 of these quasars are in the redshift range such that the CIV emission line is visible in the SDSS spectrum. Utilising the width of these CIV lines and the luminosity of the nearby continuum, we estimate black hole masses for these objects. In an effort to isolate the effects of black hole mass and luminosity on the photometric variability of our dataset, we create several subsamples by binning in these two physical parameters. By comparing the ensemble structure functions of the quasars in these bins, we are able to reproduce the well-known anticorrelation between luminosity and variability, now showing that this anticorrelation is independent of the black hole mass. In addition, we find a correlation between variability and the mass of the central black hole. By combining these two relations, we identify the Eddington ratio as a possible driver of quasar variability, most likely due to differences in accretion efficiency.

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arXiv2007

The Effect of Variability on the Estimation of Quasar Black Hole Masses

We investigate the time-dependent variations of ultraviolet (UV) black hole mass estimates of quasars in the Sloan Digital Sky Survey (SDSS). From SDSS spectra of 615 high-redshift (1.69 < z < 4.75) quasars with spectra from two epochs, we estimate black hole masses, using a single-epoch technique which employs an additional, automated night-sky-line removal, and relies on UV continuum luminosity and CIV (1549A) emission line dispersion. Mass estimates show variations between epochs at about the 30% level for the sample as a whole. We determine that, for our full sample, measurement error in the line dispersion likely plays a larger role than the inherent variability, in terms of contributing to variations in mass estimates between epochs. However, we use the variations in quasars with r-band spectral signal-to-noise ratio greater than 15 to estimate that the contribution to these variations from inherent variability is roughly 20%. We conclude that these differences in black hole mass estimates between epochs indicate variability is not a large contributer to the current factor of two scatter between mass estimates derived from low- and high-ionization emission lines.

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arXiv2006

The 2dF-SDSS LRG and QSO Survey: QSO clustering and the L-z degeneracy

We combine the QSO samples from the 2dF QSO Redshift Survey (2QZ) and the 2dF-SDSS LRG and QSO Survey (2SLAQ) in order to investigate the clustering of z~1.4 QSOs and measure the correlation function. The clustering signal in z-space, projected along the sky direction, is similar to that previously obtained from 2QZ alone. By fitting the z-space correlation function and lifting the degeneracy between beta and Omega_m_0 by using linear theory predictions, we obtain beta(z=1.4) = 0.60+-0.12 and Omega_m_0=0.25+-0.08, implying a value for the QSO bias, b(z=1.4)=1.5+-0.2. We further find that QSO clustering does not depend strongly on luminosity at fixed redshift. This result is inconsistent with the expectation of simple `high peaks' biasing models where more luminous, rare QSOs are assumed to inhabit higher mass haloes. The data are more consistent with models which predict that QSOs of different luminosities reside in haloes of similar mass. We find that halo mass does not evolve strongly with redshift nor depend on QSO luminosity. We finally investigate how black hole mass correlates with luminosity and redshift and ascertain the relation between Eddington efficiency and black hole mass. Our results suggest that QSOs of different luminosities may contain black holes of similar mass.

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arXiv2006

X-ray Galaxy Clusters in NoSOCS: Substructure and the Correlation of Optical and X-ray Properties

We present a comparison of optical and X-ray properties of galaxy clusters in the northern sky. We determine the recovery rate of X-ray detected clusters in the optical as a function of richness, redshift and X-ray luminosity, showing that the missed clusters are typically low contrast systems when observed optically. We employ four different statistical tests to test for the presence of substructure using optical two-dimensional data, finding that approximately 35% of the clusters show strong signs of substructure. However, the results are test-dependent, with variations also due to the magnitude range and radius utilized.We have also performed a comparison of X-ray luminosity and temperature with optical galaxy counts (richness). We find that the slope and scatter of the relations between richness and the X-ray properties are heavily dependent on the density contrast of the clusters. The selection of substructure-free systems does not improve the correlation between X-ray luminosity and richness, but this comparison also shows much larger scatter than one obtained using the X-ray temperature. In the latter case, the sample is significantly reduced because temperature measurements are available only for the most massive (and thus high contrast) systems. However, the comparison between temperature and richness is very sensitive to the exclusion of clusters showing signs of substructure. The correlation of X-ray luminosity and richness is based on the largest sample to date ($\sim$ 750 clusters), while tests involving temperature use a similar number of objects as previous works ($\lsim$100). The results presented here are in good agreement with existing literature.

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arXiv2006

The 2dF-SDSS LRG and QSO survey: Evolution of the Luminosity Function of Luminous Red Galaxies to z=0.6

We present new measurements of the luminosity function (LF) of Luminous Red Galaxies (LRGs) from the Sloan Digital Sky Survey (SDSS) and the 2dF-SDSS LRG and Quasar (2SLAQ) survey. We have carefully quantified, and corrected for, uncertainties in the K and evolutionary corrections, differences in the colour selection methods, and the effects of photometric errors, thus ensuring we are studying the same galaxy population in both surveys. Using a limited subset of 6326 SDSS LRGs (with 0.17<z<0.24) and 1725 2SLAQ LRGs (with 0.5 <z<0.6), for which the matching colour selection is most reliable, we find no evidence for any additional evolution in the LRG LF, over this redshift range, beyond that expected from a simple passive evolution model. This lack of additional evolution is quantified using the comoving luminosity density of SDSS and 2SLAQ LRGs, brighter than M_r - 5logh = -22.5, which are 2.51+/-0.03 x 10^-7 L_sun Mpc^-3 and 2.44+/-0.15 x 10^-7 L_sun Mpc^-3 respectively (<10% uncertainty). We compare our LFs to the COMBO-17 data and find excellent agreement over the same redshift range. Together, these surveys show no evidence for additional evolution (beyond passive) in the LF of LRGs brighter than M_r - 5logh = -21 (or brighter than L*). We test our SDSS and 2SLAQ LFs against a simple ``dry merger'' model for the evolution of massive red galaxies and find that at least half of the LRGs at z=0.2 must already have been well-assembled (with more than half their stellar mass) by z=0.6. This limit is barely consistent with recent results from semi-analytical models of galaxy evolution.

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arXiv2006

The 2dF-SDSS LRG and QSO (2SLAQ) Luminous Red Galaxy Survey

We present a spectroscopic survey of almost 15,000 candidate intermediate-redshift Luminous Red Galaxies (LRGs) brighter than i=19.8, observed with 2dF on the Anglo-Australian Telescope. The targets were selected photometrically from the Sloan Digital Sky Survey (SDSS) and lie along two narrow equatorial strips covering 180 sq deg. Reliable redshifts were obtained for 92% of the targets and the selection is very efficient: over 90% have redshifts between 0.45 and 0.8. More than 80% of the ~11,000 red galaxies have pure absorption-line spectra consistent with a passively-evolving old stellar population. The redshift, photometric and spatial distributions of the LRGs are described. The 2SLAQ data will be released publicly from mid-2006, providing a powerful resource for observational cosmology and the study of galaxy evolution.

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