Fast Calculation of the Lomb-Scargle Periodogram Using Graphics Processing Units

I introduce a new code for fast calculation of the Lomb-Scargle periodogram, that leverages the computing power of graphics processing units (GPUs). After establishing a background to the newly emergent field of GPU computing, I discuss the code design and narrate key parts of its source. Benchmarking calculations indicate no significant differences in accuracy compared to an equivalent CPU-based code. However, the differences in performance are pronounced; running on a low-end GPU, the code can match 8 CPU cores, and on a high-end GPU it is faster by a factor approaching thirty. Applications of the code include analysis of long photometric time series obtained by ongoing satellite missions and upcoming ground-based monitoring facilities; and Monte-Carlo simulation of periodogram statistical properties.Comment: Accepted by ApJ. Accompanying program source (updated since acceptance) can be downloaded from http://www.astro.wisc.edu/~townsend/resource/download/code/culsp.tar.g

3D Reconstruction of the Density Field: An SVD Approach to Weak Lensing Tomography

We present a new method for constructing three-dimensional mass maps from gravitational lensing shear data. We solve the lensing inversion problem using truncation of singular values (within the context of generalized least squares estimation) without a priori assumptions about the statistical nature of the signal. This singular value framework allows a quantitative comparison between different filtering methods: we evaluate our method beside the previously explored Wiener filter approaches. Our method yields near-optimal angular resolution of the lensing reconstruction and allows cluster sized halos to be de-blended robustly. It allows for mass reconstructions which are 2-3 orders-of-magnitude faster than the Wiener filter approach; in particular, we estimate that an all-sky reconstruction with arcminute resolution could be performed on a time-scale of hours. We find however that linear, non-parametric reconstructions have a fundamental limitation in the resolution achieved in the redshift direction.Comment: 11 pages, 6 figures. Accepted for publication in Ap

Dynamical Capture Binary Neutron Star Mergers

We study dynamical capture binary neutron star mergers as may arise in dense stellar regions such as globular clusters. Using general-relativistic hydrodynamics, we find that these mergers can result in the prompt collapse to a black hole or in the formation of a hypermassive neutron star, depending not only on the neutron star equation of state but also on impact parameter. We also find that these mergers can produce accretion disks of up to a tenth of a solar mass and unbound ejected material of up to a few percent of a solar mass. We comment on the gravitational radiation and electromagnetic transients that these sources may produce.Comment: 6 pages, 4 figures; revised to match published versio

Transit surveys for Earths in the habitable zones of white dwarfs

To date the search for habitable Earth-like planets has primarily focused on nuclear burning stars. I propose that this search should be expanded to cool white dwarf stars that have expended their nuclear fuel. I define the continuously habitable zone of white dwarfs, and show that it extends from ~0.005 to 0.02 AU for white dwarfs with masses from 0.4 to 0.9 solar masses, temperatures less than 10,000 K, and habitable durations of at least 3 Gyr. As they are similar in size to Earth, white dwarfs may be deeply eclipsed by terrestrial planets that orbit edge-on, which can easily be detected with ground-based telescopes. If planets can migrate inward or reform near white dwarfs, I show that a global robotic telescope network could carry out a transit survey of nearby white dwarfs placing interesting constraints on the presence of habitable Earths. If planets were detected, I show that the survey would favor detection of planets similar to Earth: similar size, temperature, rotation period, and host star temperatures similar to the Sun. The Large Synoptic Survey Telescope could place even tighter constraints on the frequency of habitable Earths around white dwarfs. The confirmation and characterization of these planets might be carried out with large ground and space telescopes.Comment: 5 pages, 4 figures, published in ApJL; Figure 4 corrected, other minor change

Expected Large Synoptic Survey Telescope (LSST) Yield of Eclipsing Binary Stars

In this paper we estimate the Large Synoptic Survey Telescope (LSST) yield of eclipsing binary stars, which will survey ~20,000 square degrees of the southern sky during the period of 10 years in 6 photometric passbands to r ~ 24.5. We generate a set of 10,000 eclipsing binary light curves sampled to the LSST time cadence across the whole sky, with added noise as a function of apparent magnitude. This set is passed to the Analysis of Variance (AoV) period finder to assess the recoverability rate for the periods, and the successfully phased light curves are passed to the artificial intelligence-based pipeline EBAI to assess the recoverability rate in terms of the eclipsing binaries' physical and geometric parameters. We find that, out of ~24 million eclipsing binaries observed by LSST with S/N>10 in mission life-time, ~28% or 6.7 million can be fully characterized by the pipeline. Of those, ~25% or 1.7 million will be double-lined binaries, a true treasure trove for stellar astrophysics.Comment: 19 pages, 7 figures. Accepted to AJ, to appear in issue 142:2 (Aug 2011

Cosmological parameter constraints from galaxy–galaxy lensing and galaxy clustering with the SDSS DR7

Recent studies have shown that the cross-correlation coefficient between galaxies and dark matter is very close to unity on scales outside a few virial radii of galaxy haloes, independent of the details of how galaxies populate dark matter haloes. This finding makes it possible to determine the dark matter clustering from measurements of galaxy–galaxy weak lensing and galaxy clustering. We present new cosmological parameter constraints based on large-scale measurements of spectroscopic galaxy samples from the Sloan Digital Sky Survey (SDSS) data release 7. We generalize the approach of Baldauf et al. to remove small-scale information (below 2 and 4 h^(−1) Mpc for lensing and clustering measurements, respectively), where the cross-correlation coefficient differs from unity. We derive constraints for three galaxy samples covering 7131 deg^2, containing 69 150, 62 150 and 35 088 galaxies with mean redshifts of 0.11, 0.28 and 0.40. We clearly detect scale-dependent galaxy bias for the more luminous galaxy samples, at a level consistent with theoretical expectations. When we vary both σ_8 and Ω_m (and marginalize over non-linear galaxy bias) in a flat Λ cold dark matter model, the best-constrained quantity is σ_8(Ω_m/0.25)^(0.57) = 0.80 ± 0.05 (1σ, stat. + sys.), where statistical and systematic errors (photometric redshift and shear calibration) have comparable contributions, and we have fixed n_s = 0.96 and h = 0.7. These strong constraints on the matter clustering suggest that this method is competitive with cosmic shear in current data, while having very complementary and in some ways less serious systematics. We therefore expect that this method will play a prominent role in future weak lensing surveys. When we combine these data with Wilkinson Microwave Anisotropy Probe 7-year (WMAP7) cosmic microwave background (CMB) data, constraints on σ_8, Ω_m, H_0, w_(de) and ∑m_ν become 30–80 per cent tighter than with CMB data alone, since our data break several parameter degeneracies

Characterizing and Propagating Modeling Uncertainties in Photometrically-Derived Redshift Distributions

The uncertainty in the redshift distributions of galaxies has a significant potential impact on the cosmological parameter values inferred from multi-band imaging surveys. The accuracy of the photometric redshifts measured in these surveys depends not only on the quality of the flux data, but also on a number of modeling assumptions that enter into both the training set and SED fitting methods of photometric redshift estimation. In this work we focus on the latter, considering two types of modeling uncertainties: uncertainties in the SED template set and uncertainties in the magnitude and type priors used in a Bayesian photometric redshift estimation method. We find that SED template selection effects dominate over magnitude prior errors. We introduce a method for parameterizing the resulting ignorance of the redshift distributions, and for propagating these uncertainties to uncertainties in cosmological parameters.Comment: 13 pages, 12 figures, version published in Ap

The reliability of the AIC method in Cosmological Model Selection

The Akaike information criterion (AIC) has been used as a statistical criterion to compare the appropriateness of different dark energy candidate models underlying a particular data set. Under suitable conditions, the AIC is an indirect estimate of the Kullback-Leibler divergence D(T//A) of a candidate model A with respect to the truth T. Thus, a dark energy model with a smaller AIC is ranked as a better model, since it has a smaller Kullback-Leibler discrepancy with T. In this paper, we explore the impact of statistical errors in estimating the AIC during model comparison. Using a parametric bootstrap technique, we study the distribution of AIC differences between a set of candidate models due to different realizations of noise in the data and show that the shape and spread of this distribution can be quite varied. We also study the rate of success of the AIC procedure for different values of a threshold parameter popularly used in the literature. For plausible choices of true dark energy models, our studies suggest that investigating such distributions of AIC differences in addition to the threshold is useful in correctly interpreting comparisons of dark energy models using the AIC technique.Comment: Figures and further discussions of the results were added, and the version matches the version published in MNRA

Collisional Evolution of Ultra-Wide Trans-Neptunian Binaries

The widely-separated, near-equal mass binaries hosted by the cold Classical Kuiper Belt are delicately bound and subject to disruption by many perturbing processes. We use analytical arguments and numerical simulations to determine their collisional lifetimes given various impactor size distributions, and include the effects of mass-loss and multiple impacts over the lifetime of each system. These collisional lifetimes constrain the population of small (R > ~1 km) objects currently residing in the Kuiper Belt, and confirm that the size distribution slope at small size cannot be excessively steep - likely q < ~3.5. We track mutual semi-major axis, inclination, and eccentricity evolution through our simulations, and show that it is unlikely that the wide binary population represents an evolved tail of the primordially-tight binary population. We find that if the wide binaries are a collisionally-eroded population, their primordial mutual orbit planes must have preferred to lie in the plane of the solar system. Finally, we find that current limits on the size distribution at small radii remain high enough that the prospect of detecting dust-producing collisions in real-time in the Kuiper Belt with future optical surveys is feasible.Comment: Accepted for publication in the Astrophysical Journa

On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics ("feature"), detail methods to robustly estimate periodic light-curve features, introduce tree-ensemble methods for accurate variable star classification, and show how to rigorously evaluate the classification results using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% overall classification error using the random forest classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is superior to other machine-learned methods in terms of accuracy, speed, and relative immunity to features with no useful class information; the RF classifier can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which further reduces the catastrophic error rate to 7.8%. Excluding low-amplitude sources, our overall error rate improves to 14%, with a catastrophic error rate of 3.5%.Comment: 23 pages, 9 figure