arXiv subjects

Adam D. Myers

Publications and source records attributed to Adam D. Myers.

At least 181 records · Page 10Linked to original sources

arXiv2008

Efficient Photometric Selection of Quasars from the Sloan Digital Sky Survey: II. ~1,000,000 Quasars from Data Release Six

We present a catalog of 1,172,157 quasar candidates selected from the photometric imaging data of the Sloan Digital Sky Survey (SDSS). The objects are all point sources to a limiting magnitude of i=21.3 from 8417 sq. deg. of imaging from SDSS Data Release 6 (DR6). This sample extends our previous catalog by using the latest SDSS public release data and probing both UV-excess and high-redshift quasars. While the addition of high-redshift candidates reduces the overall efficiency (quasars:quasar candidates) of the catalog to ~80%, it is expected to contain no fewer than 850,000 bona fide quasars -- ~8 times the number of our previous sample, and ~10 times the size of the largest spectroscopic quasar catalog. Cross-matching between our photometric catalog and spectroscopic quasar catalogs from both the SDSS and 2dF Surveys, yields 88,879 spectroscopically confirmed quasars. For judicious selection of the most robust UV-excess sources (~500,000 objects in all), the efficiency is nearly 97% -- more than sufficient for detailed statistical analyses. The catalog's completeness to type 1 (broad-line) quasars is expected to be no worse than 70%, with most missing objects occurring at z<0.7 and 2.5<z<3.0. In addition to classification information, we provide photometric redshift estimates (typically good to Delta z +/- 0.3 [2 sigma]) and cross-matching with radio, X-ray, and proper motion catalogs. Finally, we consider the catalog's utility for determining the optical luminosity function of quasars and are able to confirm the flattening of the bright-end slope of the quasar luminosity function at z~4 as compared to z~2.

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arXiv2008

AGN Environments in the Sloan Digital Sky Survey I: Dependence on Type, Redshift, and Luminosity

We explore how the local environment is related to the redshift, type, and luminosity of active galactic nuclei (AGN). Recent simulations and observations are converging on the view that the extreme luminosity of quasars is fueled in major mergers of gas-rich galaxies. In such a picture, quasars are expected to be located in regions with a higher density of galaxies on small scales where mergers are more likely to take place. However, in this picture, the activity observed in low-luminosity AGN is due to secular processes that are less dependent on the local galaxy density. To test this hypothesis, we compare the local photometric galaxy density on kiloparsec scales around spectroscopic Type I and Type II quasars to the local density around lower luminosity spectroscopic Type I and Type II AGN. To minimize projection effects and evolution in the photometric galaxy sample we use to characterize AGN environments, we place our random control sample at the same redshift as our AGN and impose a narrow redshift window around both the AGN and control targets. We find that higher luminosity AGN have more overdense environments compared to lower luminosity AGN on all scales out to our $2\Mpchseventy$ limit. Additionally, in the range $0.3\leqslant z\leqslant 0.6$, Type II quasars have similarly overdense environments to those of bright Type I quasars on all scales out to our $2\Mpchseventy$ limit, while the environment of dimmer Type I quasars appears to be less overdense than the environment of Type II quasars. We see increased overdensity for Type II AGN compared to Type I AGN on scales out to our limit of $2\Mpchseventy$ in overlapping redshift ranges. We also detect marginal evidence for evolution in the number of galaxies within $2\Mpchseventy$ of a quasar with redshift.

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arXiv2008

Mitrion-C Application Development on SGI Altix 350/RC100

This paper provides an evaluation of SGI RASCTM RC100 technology from a computational science software developer's perspective. A brute force implementation of a two-point angular correlation function is used as a test case application. The computational kernel of this test case algorithm is ported to the Mitrion-C programming language and compiled, targeting the RC100 hardware. We explore several code optimization techniques and report performance results for different designs. We conclude the paper with an analysis of this system based on our observations while implementing the test case. Overall, the hardware platform and software development tools were found to be satisfactory for accelerating computationally intensive applications, however, several system improvements are desirable.

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arXiv2008

Combined analysis of the integrated Sachs-Wolfe effect and cosmological implications

We present a global measurement of the integrated Sachs-Wolfe (ISW) effect obtained by cross-correlating all relevant large scale galaxy data sets with the cosmic microwave background radiation map provided by the Wilkinson Microwave Anisotropy Probe. With these measurements, the overall ISW signal is detected at the ~ 4.5 sigma level. We also examine the cosmological implications of these measurements, particularly the dark energy equation of state w, its sound speed, and the overall curvature of the Universe. The flat LCDM model is a good fit to the data and, assuming this model, we find that the ISW data constrain Omega_m = 0.20 +0.19 -0.11 at the 95% confidence level. When we combine our ISW results with the latest baryon oscillation and supernovae measurements, we find that the result is still consistent with a flat LCDM model with w = -1 out to redshifts z > 1.

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arXiv2008

Robust Machine Learning Applied to Terascale Astronomical Datasets

We present recent results from the LCDM (Laboratory for Cosmological Data Mining; http://lcdm.astro.uiuc.edu) collaboration between UIUC Astronomy and NCSA to deploy supercomputing cluster resources and machine learning algorithms for the mining of terascale astronomical datasets. This is a novel application in the field of astronomy, because we are using such resources for data mining, and not just performing simulations. Via a modified implementation of the NCSA cyberenvironment Data-to-Knowledge, we are able to provide improved classifications for over 100 million stars and galaxies in the Sloan Digital Sky Survey, improved distance measures, and a full exploitation of the simple but powerful k-nearest neighbor algorithm. A driving principle of this work is that our methods should be extensible from current terascale datasets to upcoming petascale datasets and beyond. We discuss issues encountered to-date, and further issues for the transition to petascale. In particular, disk I/O will become a major limiting factor unless the necessary infrastructure is implemented.

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arXiv2008

Normalization of the Matter Power Spectrum via Higher-Order Angular Correlations of Luminous Red Galaxies

We present a novel technique to measure $σ_8$, by measuring the dependence of the second-order bias of a density field on $σ_8$ using two separate techniques. Each technique employs area-averaged angular correlation functions ($\barω_N$), one relying on the shape of $\barω_2$, the other relying on the amplitude of $s_3$ ($s_3 =\barω_3/\barω_2^2$). We confirm the validity of the method by testing it on a mock catalog drawn from Millennium Simulation data and finding $σ_8^{measured}- σ_8^{true} = -0.002 \pm 0.062$. We create a catalog of photometrically selected LRGs from SDSS DR5 and separate it into three distinct data sets by photometric redshift, with median redshifts of 0.47, 0.53, and 0.61. Measurements of $c_2$, and $σ_8$ are made for each data set, assuming flat geometry and WMAP3 best-fit priors on $Ω_m$, $h$, and $Γ$. We find, with increasing redshfit, $c_2 = 0.09 \pm 0.04$, $0.09 \pm 0.05$, and $0.09 \pm 0.03$ and $σ_8 = 0.78 \pm 0.08$, $0.80 \pm 0.09$, and $0.80 \pm 0.09$. We combine these three consistent $σ_8$ measurements to produce the result $σ_8 = 0.79 \pm 0.05$. Allowing the parameters $Ω_m$, $h$, and $Γ$ to vary within their WMAP3 1$σ$ error, we find that the best-fit $σ_8$ does not change by more than 8% and we are thus confident our measurement is accurate to within 10%. We anticipate that future surveys, such as Pan-STARRS, DES, and LSST, will be able to employ this method to measure $σ_8$ to great precision, and will serve as an important check, complementary, on the values determined via more established methods.

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arXiv2008

Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX

We apply machine learning in the form of a nearest neighbor instance-based algorithm (NN) to generate full photometric redshift probability density functions (PDFs) for objects in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS DR5). We use a conceptually simple but novel application of NN to generate the PDFs - perturbing the object colors by their measurement error - and using the resulting instances of nearest neighbor distributions to generate numerous individual redshifts. When the redshifts are compared to existing SDSS spectroscopic data, we find that the mean value of each PDF has a dispersion between the photometric and spectroscopic redshift consistent with other machine learning techniques, being sigma = 0.0207 +/- 0.0001 for main sample galaxies to r < 17.77 mag, sigma = 0.0243 +/- 0.0002 for luminous red galaxies to r < ~19.2 mag, and sigma = 0.343 +/- 0.005 for quasars to i < 20.3 mag. The PDFs allow the selection of subsets with improved statistics. For quasars, the improvement is dramatic: for those with a single peak in their probability distribution, the dispersion is reduced from 0.343 to sigma = 0.117 +/- 0.010, and the photometric redshift is within 0.3 of the spectroscopic redshift for 99.3 +/- 0.1% of the objects. Thus, for this optical quasar sample, we can virtually eliminate 'catastrophic' photometric redshift estimates. In addition to the SDSS sample, we incorporate ultraviolet photometry from the Third Data Release of the Galaxy Evolution Explorer All-Sky Imaging Survey (GALEX AIS GR3) to create PDFs for objects seen in both surveys. For quasars, the increased coverage of the observed frame UV of the SED results in significant improvement over the full SDSS sample, with sigma = 0.234 +/- 0.010. We demonstrate that this improvement is genuine. [Abridged]

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arXiv2007

Developing and Deploying Advanced Algorithms to Novel Supercomputing Hardware

The objective of our research is to demonstrate the practical usage and orders of magnitude speedup of real-world applications by using alternative technologies to support high performance computing. Currently, the main barrier to the widespread adoption of this technology is the lack of development tools and case studies that typically impede non-specialists that might otherwise develop applications that could leverage these technologies. By partnering with the Innovative Systems Laboratory at the National Center for Supercomputing, we have obtained access to several novel technologies, including several Field-Programmable Gate Array (FPGA) systems, NVidia Graphics Processing Units (GPUs), and the STI Cell BE platform. Our goal is to not only demonstrate the capabilities of these systems, but to also serve as guides for others to follow in our path. To date, we have explored the efficacy of the SRC-6 MAP-C and MAP-E and SGI RASC Athena and RC100 reconfigurable computing platforms in supporting a two-point correlation function which is used in a number of different scientific domains. In a brute force test, the FPGA based single-processor system has achieved an almost two orders of magnitude speedup over a single-processor CPU system. We are now developing implementations of this algorithm on other platforms, including one using a GPU. Given the considerable efforts of the cosmology community in optimizing these classes of algorithms, we are currently working to implement an optimized version of the basic family of correlation functions by using tree-based data structures. Finally, we are also exploring other algorithms, such as instance-based classifiers, power spectrum estimators, and higher-order correlation functions that are also commonly used in a wide range of scientific disciplines.

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arXiv2007

Dynamic load-balancing on multi-FPGA systems: a case study

In this case study, we investigate the impact of workload balance on the performance of multi-FPGA codes. We start with an application in which two distinct kernels run in parallel on two SRC-6 MAP processors. We observe that one of the MAP processors is idle 18% of the time while the other processor is fully utilized. We investigate a task redistribution schema which serializes the execution of the two kernels, yet parallelizes execution of each individual kernel by spreading the workload between two MAP processors. This implementation results in a near 100% utilization of both MAP processors and the overall application performance is improved by 9%.

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arXiv2007

Robust Machine Learning Applied to Terascale Astronomical Datasets

We present recent results from the Laboratory for Cosmological Data Mining (http://lcdm.astro.uiuc.edu) at the National Center for Supercomputing Applications (NCSA) to provide robust classifications and photometric redshifts for objects in the terascale-class Sloan Digital Sky Survey (SDSS). Through a combination of machine learning in the form of decision trees, k-nearest neighbor, and genetic algorithms, the use of supercomputing resources at NCSA, and the cyberenvironment Data-to-Knowledge, we are able to provide improved classifications for over 100 million objects in the SDSS, improved photometric redshifts, and a full exploitation of the powerful k-nearest neighbor algorithm. This work is the first to apply the full power of these algorithms to contemporary terascale astronomical datasets, and the improvement over existing results is demonstrable. We discuss issues that we have encountered in dealing with data on the terascale, and possible solutions that can be implemented to deal with upcoming petascale datasets.

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arXiv2007

Quasar Clustering at $25\kpch$ from a Complete Sample of Binaries

We present spectroscopy of binary quasar candidates selected from Data Release 4 of the Sloan Digital Sky Survey (SDSS DR4) using Kernel Density Estimation (KDE). We present 27 new sets of observations, 10 of which are binary quasars, roughly doubling the number of known $g < 21$ binaries with component separations of 3 to 6". Only 3 of 49 spectroscopically identified objects are non-quasars, confirming that the quasar selection efficiency of the KDE technique is $\sim95$%. Several of our observed binaries are wide-separation lens candidates that merit additional higher-resolution observations. One interesting pair may be an M star binary, or an M star-binary quasar superposition. Our candidates are initially selected by UV-excess ($u-g < 1$), but are otherwise selected irrespective of the relative colors of the quasar pair, and we thus use them to suggest optimal color similarity and photometric redshift approaches for targeting binary quasars, or projected quasar pairs. From a sample that is complete on proper scales of $23.7 < R_{prop} < 29.7\kpch$, we determine the projected quasar correlation function to be $W_p=24.0 \pm^{16.9}_{10.8}$, which is $2σ$ lower than recent estimates. We argue that our low $W_p$ estimates may indicate redshift evolution in the quasar correlation function from $z\sim1.9$ to $z\sim1.4$ on scales of $R_{prop} \sim25\kpch$. The size of this evolution broadly tracks quasar clustering on larger scales, consistent with merger-driven models of quasar origin. Although our sample alone is insufficient to detect evolution in quasar clustering on small scales, an $i$-selected DR6 KDE quasar catalog, which will contain several hundred $z \leqsim 5$ binary quasars, could easily constrain any clustering evolution at $R_{prop} \sim25\kpch$.

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arXiv2007

The Co-Formation of Spheroids and Quasars Traced in their Clustering

We compare observed clustering of quasars and galaxies as a function of redshift, mass, luminosity, & color/morphology, to constrain models of quasar fueling and spheroid-BH co-evolution. High redshift quasars are shown to be drawn from progenitors of local early-type galaxies, with the characteristic quasar luminosity L* reflecting a characteristic mass of 'active' BH/host populations at each redshift. Evolving observed high-z quasar clustering to z=0 predicts a trend of clustering in 'quasar remnants' as a function of stellar mass identical to that observed for early-types. However, quasar clustering does not simply reflect observed early (or late)-type populations; at each redshift, quasars cluster as an 'intermediate' population. Comparing with the age of elliptical stellar populations reveals that this 'intermediate' population represents those ellipticals undergoing or terminating their final significant star formation at each epoch. Assuming that quasar triggering is associated with the formation/termination epoch of ellipticals predicts quasar clustering at all observed redshifts without any model dependence or assumptions about quasar light curves, lifetimes, or accretion rates. This is not true for disks or quasar halos: i.e. quasars do not generically trace star formation, disks, or halo assembly. Quasar clustering at all z is consistent with a constant halo mass ~4x10^{12} M_sun, similar to local 'group scales.' The observations support a scenario in which major mergers trigger quasar activity and dominate bright, high-z quasar populations. We show that measurements of quasar clustering versus luminosity at z~1 can be used to constrain different lower-luminosity AGN fueling mechanisms, and that high-z clustering is sensitive to whether or not systems 'shut down' growth at z>3.

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arXiv2007

Higher-Order Angular Galaxy Correlations in the SDSS: Redshift and Color Dependence of non-Linear Bias

We present estimates of the N-point galaxy, area-averaged, angular correlation functions $\barω_{N}$($θ$) for $N$ = 2,...,7 for galaxies from the fifth data release of the Sloan Digital Sky Survey. Our parent sample is selected from galaxies with $18 \leq r < 21$, and is the largest ever used to study higher-order correlations. We subdivide this parent sample into two volume limited samples using photometric redshifts, and these two samples are further subdivided by magnitude, redshift, and color (producing early- and late-type galaxy samples) to determine the dependence of $\barω_{N}$($θ$) on luminosity, redshift, and galaxy-type. We measure $\barω_{N}$($θ$) using oversampling techniques and use them to calculate the projected, $s_{N}$. Using models derived from theoretical power-spectra and perturbation theory, we measure the bias parameters $b_1$ and $c_2$, finding that the large differences in both bias parameters ($b_1$ and $c_2$) between early- and late-type galaxies are robust against changes in redshift, luminosity, and $σ_8$, and that both terms are consistently smaller for late-type galaxies. By directly comparing their higher-order correlation measurements, we find large differences in the clustering of late-type galaxies at redshifts lower than 0.3 and those at redshifts higher than 0.3, both at large scales ($c_2$ is larger by $\sim0.5$ at $z > 0.3$) and small scales (large amplitudes are measured at small scales only for $z > 0.3$, suggesting much more merger driven star formation at $z > 0.3$). Finally, our measurements of $c_2$ suggest both that $σ_8 < 0.8$ and $c_2$ is negative.

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arXiv2007

Robust Machine Learning Applied to Astronomical Datasets II: Quantifying Photometric Redshifts for Quasars Using Instance-Based Learning

We apply instance-based machine learning in the form of a k-nearest neighbor algorithm to the task of estimating photometric redshifts for 55,746 objects spectroscopically classified as quasars in the Fifth Data Release of the Sloan Digital Sky Survey. We compare the results obtained to those from an empirical color-redshift relation (CZR). In contrast to previously published results using CZRs, we find that the instance-based photometric redshifts are assigned with no regions of catastrophic failure. Remaining outliers are simply scattered about the ideal relation, in a similar manner to the pattern seen in the optical for normal galaxies at redshifts z < ~1. The instance-based algorithm is trained on a representative sample of the data and pseudo-blind-tested on the remaining unseen data. The variance between the photometric and spectroscopic redshifts is sigma^2 = 0.123 +/- 0.002 (compared to sigma^2 = 0.265 +/- 0.006 for the CZR), and 54.9 +/- 0.7%, 73.3 +/- 0.6%, and 80.7 +/- 0.3% of the objects are within delta z < 0.1, 0.2, and 0.3 respectively. We also match our sample to the Second Data Release of the Galaxy Evolution Explorer legacy data and the resulting 7,642 objects show a further improvement, giving a variance of sigma^2 = 0.054 +/- 0.005, and 70.8 +/- 1.2%, 85.8 +/- 1.0%, and 90.8 +/- 0.7% of objects within delta z < 0.1, 0.2, and 0.3. We show that the improvement is indeed due to the extra information provided by GALEX, by training on the same dataset using purely SDSS photometry, which has a variance of sigma^2 = 0.090 +/- 0.007. Each set of results represents a realistic standard for application to further datasets for which the spectra are representative.

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arXiv2006

Clustering Analyses of 300,000 Photometrically Classified Quasars--I. Luminosity and Redshift Evolution in Quasar Bias

Using ~300,000 photometrically classified quasars, by far the largest quasar sample ever used for such analyses, we study the redshift and luminosity evolution of quasar clustering on scales of ~50 kpc/h to ~20 Mpc/h from redshifts of z~0.75 to z~2.28. We parameterize our clustering amplitudes using realistic dark matter models, and find that a LCDM power spectrum provides a superb fit to our data with a redshift-averaged quasar bias of b_Q = 2.41+/-0.08 ($P_{<χ^2}=0.847$) for $σ_8=0.9$. This represents a better fit than the best-fit power-law model ($ω= 0.0493\pm0.0064θ^ {-0.928\pm0.055}$; $P_{<χ^2}=0.482$). We find b_Q increases with redshift. This evolution is significant at >99.6% using our data set alone, increasing to >99.9999% if stellar contamination is not explicitly parameterized. We measure the quasar classification efficiency across our full sample as a = 95.6 +/- ^{4.4}_{1.9}%, a star-quasar separation comparable with the star-galaxy separation in many photometric studies of galaxy clustering. We derive the mean mass of the dark matter halos hosting quasars as MDMH=(5.2+/-0.6)x10^{12} M_solar/h. At z~1.9 we find a $1.5σ$ deviation from luminosity-independent quasar clustering; this suggests that increasing our sample size by a factor of 1.8 could begin to constrain any luminosity dependence in quasar bias at z~2. Our results agree with recent studies of quasar environments at z < 0.4, which detected little luminosity dependence to quasar clustering on proper scales >50 kpc/h. At z < 1.6, our analysis suggests that b_Q is constant with luminosity to within ~0.6, and that, for g < 21, angular quasar autocorrelation measurements are unlikely to have sufficient statistical power at z < 1.6 to detect any luminosity dependence in quasars' clustering.

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arXiv2006

Clustering Analyses of 300,000 Photometrically Classified Quasars--II. The Excess on Very Small Scales

We study quasar clustering on small scales, modeling clustering amplitudes using halo-driven dark matter descriptions. From 91 pairs on scales <35 kpc/h, we detect only a slight excess in quasar clustering over our best-fit large-scale model. Integrated across all redshifts, the implied quasar bias is b_Q = 4.21+/-0.98 (b_Q = 3.93+/-0.71) at ~18 kpc/h (~28 kpc/h). Our best-fit (real-space) power index is ~-2 (i.e., $ξ(r) \propto r^{-2}$), implying steeper halo profiles than currently found in simulations. Alternatively, quasar binaries with separation <35 kpc/h may trace merging galaxies, with typical dynamical merger times t_d~(610+/-260)m^{-1/2} Myr/h, for quasars of host halo mass m x 10^{12} Msolar/h. We find UVX quasars at ~28 kpc/h cluster >5 times higher at z > 2, than at z < 2, at the $2.0σ$ level. However, as the space density of quasars declines as z increases, an excess of quasar binaries (over expectation) at z > 2 could be consistent with reduced merger rates at z > 2 for the galaxies forming UVX quasars. Comparing our clustering at ~28 kpc/h to a $ξ(r)=(r/4.8\Mpch)^{-1.53}$ power-law, we find an upper limit on any excess of a factor of 4.3+/-1.3, which, noting some caveats, differs from large excesses recently measured for binary quasars, at $2.2σ$. We speculate that binary quasar surveys that are biased to z > 2 may find inflated clustering excesses when compared to models fit at z < 2. We provide details of 111 photometrically classified quasar pairs with separations <0.1'. Spectroscopy of these pairs could significantly constrain quasar dynamics in merging galaxies.

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arXiv2006

A high redshift detection of the integrated Sachs-Wolfe effect

We present evidence of a large angle correlation between the cosmic microwave background measured by WMAP and a catalog of photometrically detected quasars from the SDSS. The observed cross correlation is (0.30 +- 0.14) microK at zero lag, with a shape consistent with that expected for correlations arising from the integrated Sachs-Wolfe effect. The photometric redshifts of the quasars are centered at z ~ 1.5, making this the deepest survey in which such a correlation has been observed. Assuming this correlation is due to the ISW effect, this constitutes the earliest evidence yet for dark energy and it can be used to constrain exotic dark energy models.

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arXiv2006

Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that these star/galaxy classifications are expected to be reliable for approximately 22 million objects with r < ~20. The general machine learning environment Data-to-Knowledge and supercomputing resources enabled extensive investigation of the decision tree parameter space. This work presents the first public release of objects classified in this way for an entire SDSS data release. The objects are classified as either galaxy, star or nsng (neither star nor galaxy), with an associated probability for each class. To demonstrate how to effectively make use of these classifications, we perform several important tests. First, we detail selection criteria within the probability space defined by the three classes to extract samples of stars and galaxies to a given completeness and efficiency. Second, we investigate the efficacy of the classifications and the effect of extrapolating from the spectroscopic regime by performing blind tests on objects in the SDSS, 2dF Galaxy Redshift and 2dF QSO Redshift (2QZ) surveys. Given the photometric limits of our spectroscopic training data, we effectively begin to extrapolate past our star-galaxy training set at r ~ 18. By comparing the number counts of our training sample with the classified sources, however, we find that our efficiencies appear to remain robust to r ~ 20. As a result, we expect our classifications to be accurate for 900,000 galaxies and 6.7 million stars, and remain robust via extrapolation for a total of 8.0 million galaxies and 13.9 million stars. [Abridged]

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