Search

The invariance of the speed of light implies a series of consequences related to our perception of simultaneity and of time itself. Whilst these consequences are experimentally well studied for subluminal speeds, the kinematics of superluminal motion lack direct evidence. Using high temporal resolution imaging techniques, we demonstrate that if a source approaches an observer at superluminal speeds, the temporal ordering of events is inverted and its image appears to propagate backwards. If the source changes its speed, crossing the interface between sub- and super-luminal propagation, we observe image pair annihilation and creation. These results show that it is not possible to unambiguously determine the kinematics of an event from imaging and time-resolved measurements alone.

Flat-spectrum radio-loud Narrow-Line Seyfert 1 galaxies (NLS1s) are a recently discovered class of $\gamma$-ray emitting Active Galactic Nuclei (AGN), that exhibit some blazar-like properties which are explained with the presence of a relativistic jet viewed at small angles. When blazars are observed at larger angles they appear as radio-galaxies, and we expect to observe an analogue parent population for beamed NLS1s. However, the number of known NLS1s with the jet viewed at large angles is not enough. Therefore, we tried to understand the origin of this deficit. Current hypotheses about the nature of parent sources are steep-spectrum radio-loud NLS1s, radio-quiet NLS1s and disk-hosted radio-galaxies. To test these hypotheses we built three samples of candidate sources plus a control sample, and calculated their black hole mass and Eddington ratio using their optical spectra. We then performed a Kolmogorov-Smirnov statistical test to investigate the compatibility of our different samples with a beamed population. Our results indicate that, when the inclination angle increases, a beamed source appears as a steep-spectrum radio-loud NLS1, or possibly even as a disk-hosted radio-galaxy with low black hole mass and high Eddington ratio. Further investigations, involving larger complete samples and observations at radio frequency, are needed to understand the incidence of disk-hosted radio-galaxies in the parent population, and to assess whether radio-quiet NLS1s can play a role, as well., Comment: 12 pages, 6 figures, accepted for publication by Astronomy and Astrophysics

We have discovered kiloparsec-scale extended radio emission in three narrow-line Seyfert 1 galaxies (NLS1s) in sub-arcsecond resolution 9 GHz images from the Karl G. Jansky Very Large Array (VLA). We find all sources show two-sided, mildly core-dominated jet structures with diffuse lobes dominated by termination hotspots. These span 20-70 kpc with morphologies reminiscent of FR II radio galaxies, while the extended radio luminosities are intermediate between FR I and FR II sources. In two cases the structure is linear, while a $45^{\circ}$ bend is apparent in the third. Very Long Baseline Array images at 7.6 GHz reveal parsec-scale jet structures, in two cases with extended structure aligned with the inner regions of the kiloparsec-scale jets. Based on this alignment, the ratio of the radio core luminosity to the optical luminosity, the jet/counter-jet intensity and extension length ratios, and moderate core brightness temperatures ($\lesssim10^{10}$ K), we conclude these jets are mildly relativistic ($\beta\lesssim0.3$, $\delta\sim1$-$1.5$) and aligned at moderately small angles to the line of sight (10-15$^{\circ}$). The derived kinematic ages of $\sim10^6$-$10^7$ y are much younger than radio galaxies but comparable to other NLS1s. Our results increase the number of radio-loud NLS1s with known kiloparsec-scale extensions from seven to ten and suggest that such extended emission may be common, at least among the brightest of these sources., Comment: 5 pages, 3 figures, 2 tables, accepted to ApJ Letters

To study the effect of supermassive black holes (SMBHs) on their host galaxies it is important to study the hosts when the SMBH is near its peak activity. A method to investigate the host galaxies of high luminosity quasars is to obtain optical spectra at positions offset from the nucleus where the relative contribution of the quasar and host are comparable. However, at these extended radii the galaxy surface brightness is often low (20-22 mag per arcsec$^{2}$) and the resulting spectrum might have such low S/N that it hinders analysis with standard stellar population modeling techniques. To address this problem we have developed a method that can recover galaxy star formation histories (SFHs) from rest frame optical spectra with S/N $\sim$ 5~\AA$^{-1}$. This method uses the statistical technique diffusion k-means to tailor the stellar population modeling basis set. Our diffusion k-means minimal basis set, composed of 4 broad age bins, is successful in recovering a range of galaxy SFHs. Additionally, using an analytic prescription for seeing conditions, we are able to simultaneously model scattered quasar light and the SFH of quasar host galaxies (QHGs). We use synthetic data to compare results of our novel method with previous techniques. We also present the modeling results on a previously published QHG and show that galaxy properties recovered from a diffusion k-means basis set are less sensitive to noise added to this quasar host galaxy spectrum. Our new method has a clear advantage in recovering information from QHGs and could also be applied to the analysis of other low S/N galaxy spectra such as those typically obtained for high redshift objects or integral field spectroscopic surveys., Comment: 26 pages, 14 figures, accepted to MNRAS 2014 November 28

Next-generation synoptic photometric surveys will yield unprecedented (for the astronomical community) volumes of data and the processes of discovery and rare-object identification are, by necessity, becoming more autonomous. Such autonomous searches can be used to find objects of interest applicable to a wide range of outstanding problems in astronomy, and in this paper we present the methods and results of a largely autonomous search for highly eccentric detached eclipsing binary systems in the Machine-learned ASAS Classification Catalog. 106 detached eclipsing binaries with eccentricities greater than 0.1 are presented, most of which are identified here for the first time. We also present new radial-velocity curves and absolute parameters for 6 of those systems with the long-term goal of increasing the number of highly eccentric systems with orbital solutions, thereby facilitating further studies of the tidal circularization process in binary stars., Comment: 13 pages, 4 tables, 6 figures; Accepted for publication in MNRAS; References updated

We use photometry from the recent AllWISE Data Release of the Wide-field In-frared Survey Explorer (WISE) of 129 calibration stars, combined with prior distances obtained from the established $M_V-$[Fe/H] relation and Hubble Space Telescope trigonometric parallax, to derive mid-infrared period--luminosity relations for RR Lyrae pulsating variable stars. We derive relations in the W1, W2, and W3 wavebands (3.4, 4.6, and 12 {\mu}m, respectively), and for each of the two main RR Lyrae sub-types (RRab and RRc). We report an error on the period--luminosity relation slope for RRab stars of 0.2. We also fit posterior distances for the calibration catalog and find a median fractional distance error of 0.8 per cent., Comment: 5 pages, 2 figures, 1 table

We present a group of 3740 previously identified RR Lyrae variables well-observed with the Wide-field Infrared Survey Explorer (WISE). We explore how the shape of the generic RR Lyrae mid-infrared light curve evolves in period-space, comparing light curves in mid-infrared and optical bands. We find that optical light curves exhibit high amplitudes and a large spectrum of light curve shapes, while mid-infrared light curves have low amplitudes and uniform light curve shapes. From the period-space analysis, we hope to improve the classification methods of RR Lyrae variables and enable reliable discovery of these pulsators in the WISE catalog and future mid-infrared surveys such as the James Webb Space Telescope (JWST). We provide mid-infrared templates for typical RR Lyrae stars and demonstrate how these templates can be applied to improve estimates of mid-infrared RR Lyrae mean magnitude, which is used for distance measurement. This method of template fitting is particularly beneficial for improving observational efficiency. For example, using light curves with observational noise of 0.05 mag, we obtain the same level of accuracy in mean magnitude estimates for light curves randomly sampled at 12 data points with template fitting as with light curves randomly sampled at 20 data points with harmonic modelling., Comment: Submitted to MNRAS, 24 pages, 10 figures

We present results from four years of twice-weekly 15 GHz radio monitoring of about 1500 blazars with the Owens Valley Radio Observatory 40 m telescope. Using the intrinsic modulation index to measure variability amplitude, we find that, with $>\!6\sigma$ significance, the radio variability of radio-selected gamma-ray-loud blazars is stronger than that of gamma-ray-quiet blazars. Our extended data set also includes at least 21 months of data for all AGN with `clean' associations in the Fermi Large Area Telescope First AGN catalogue, 1LAC. With these additional data we examine the radio variability properties of a gamma-ray-selected blazar sample. Within this sample, we find no evidence for a connection between radio variability amplitude and optical classification. In contrast, for our radio-selected sample we find that the BL Lac object subpopulation is more variable than the flat spectrum radio quasar (FSRQ) subpopulation. Radio variability is found to correlate with the synchrotron peak frequency, with low- and intermediate-synchrotron-peaked blazars varying less than high-synchrotron-peaked ones. We find evidence for a significant negative correlation between redshift and radio variability among bright FSRQs., Comment: 12 pages, 14 figures. Accepted by MNRAS

In September 2012, the high-synchrotron-peaked (HSP) blazar Markarian 421 underwent a rapid wideband radio flare, reaching nearly twice the brightest level observed in the centimeter band in over three decades of monitoring. In response to this event we carried out a five epoch centimeter- to millimeter-band multifrequency Very Long Baseline Array (VLBA) campaign to investigate the aftermath of this emission event. Rapid radio variations are unprecedented in this object and are surprising in an HSP BL Lac object. In this flare, the 15 GHz flux density increased with an exponential doubling time of about 9 days, then faded to its prior level at a similar rate. This is comparable with the fastest large-amplitude centimeter-band radio variability observed in any blazar. Similar flux density increases were detected up to millimeter bands. This radio flare followed about two months after a similarly unprecedented GeV gamma-ray flare (reaching a daily E>100 MeV flux of (1.2 +/- 0.7)x10^(-6) ph cm^(-2) s^(-1)) reported by the Fermi Large Area Telescope (LAT) collaboration, with a simultaneous tentative TeV detection by ARGO-YBJ. A cross-correlation analysis of long-term 15 GHz and LAT gamma-ray light curves finds a statistically significant correlation with the radio lagging ~40 days behind, suggesting that the gamma-ray emission originates upstream of the radio emission. Preliminary results from our VLBA observations show brightening in the unresolved core region and no evidence for apparent superluminal motions or substantial flux variations downstream., Comment: 5 pages, 8 figures. Contributed talk at the meeting "The Innermost Regions of Relativistic Jets and Their Magnetic Fields", Granada, Spain. Updated to correct author list and references

We report on spectroscopic observations covering most of the 475 BL Lacs in the 2nd Fermi LAT catalog of AGN. Including archival measurements (correcting several erroneous literature values) we now have spectroscopic redshifts for 44% of the BL Lacs. We establish firm lower redshift limits via intervening absorption systems and statistical lower limits via searches for host galaxies for an additional 51% of the sample leaving only 5% of the BL Lacs unconstrained. The new redshifts raise the median spectroscopic z from 0.23 to 0.33 and include redshifts as large as z=2.471. Spectroscopic redshift minima from intervening absorbers have ~ z= 0.70, showing a substantial fraction at large z and arguing against strong negative evolution. We find that detected BL Lac hosts are bright ellipticals with black hole masses M_\bullet ~ 10^{8.5-9}, substantially larger than the mean of optical AGN and LAT Flat Spectrum Radio Quasar samples. A slow increase in M_\bullet with z may be due to selection bias. We find that the power-law dominance of the optical spectrum extends to extreme values, but this does not strongly correlate with the gamma-ray properties, suggesting that strong beaming is the primary cause of the range in continuum dominance., Comment: 14 pages; Accepted for publication in The Astrophysical Journal

Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability. Astrophysical discovery in such data sets is complicated by the fact that detections of real transient and variable sources are highly outnumbered by bogus detections caused by imperfect subtractions, atmospheric effects and detector artefacts. In this work we present a machine learning (ML) framework for discovery of variability in time-domain imaging surveys. Our ML methods provide probabilistic statements, in near real time, about the degree to which each newly observed source is astrophysically relevant source of variable brightness. We provide details about each of the analysis steps involved, including compilation of the training and testing sets, construction of descriptive image-based and contextual features, and optimization of the feature subset and model tuning parameters. Using a validation set of nearly 30,000 objects from the Palomar Transient Factory, we demonstrate a missed detection rate of at most 7.7% at our chosen false-positive rate of 1% for an optimized ML classifier of 23 features, selected to avoid feature correlation and over-fitting from an initial library of 42 attributes. Importantly, we show that our classification methodology is insensitive to mis-labelled training data up to a contamination of nearly 10%, making it easier to compile sufficient training sets for accurate performance in future surveys. This ML framework, if so adopted, should enable the maximization of scientific gain from future synoptic survey and enable fast follow-up decisions on the vast amounts of streaming data produced by such experiments., Comment: 16 pages, 14 figures

We present the discovery and extensive early-time observations of the Type Ic supernova (SN) PTF12gzk. Our finely sampled light curves show a rise of 0.8mag within 2.5hr. Power-law fits [f(t)\sim(t-t_0)^n] to these data constrain the explosion date to within one day. We cannot rule out the expected quadratic fireball model, but higher values of n are possible as well for larger areas in the fit parameter space. Our bolometric light curve and a dense spectral sequence are used to estimate the physical parameters of the exploding star and of the explosion. We show that the photometric evolution of PTF12gzk is slower than that of most SNe Ic, and its high ejecta velocities (~30,000km/s four days after explosion) are closer to the observed velocities of broad-lined SNe Ic associated with gamma-ray bursts (GRBs) than to the observed velocities in normal Type Ic SNe. The high velocities are sustained through the SN early evolution, and are similar to those of GRB-SNe when the SN reach peak magnitude. By comparison with the spectroscopically similar SN 2004aw, we suggest that the observed properties of PTF12gzk indicate an initial progenitor mass of 25-35 solar mass and a large (5-10E51 erg) kinetic energy, close to the regime of GRB-SN properties. The host-galaxy characteristics are consistent with GRB-SN hosts, and not with normal SN Ic hosts as well, yet this SN does not show the broad lines over extended periods of time that are typical of broad-line Type Ic SNe.

With growing data volumes from synoptic surveys, astronomers must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities, and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All Sky Automated Survey (ASAS), and unveil the Machine-learned ASAS Classification Catalog (MACC), which is a 28-class probabilistic classification catalog of 50,124 ASAS sources. We estimate that MACC achieves a sub-20% classification error rate, and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes. The MACC is publicly available at http://www.bigmacc.info., Comment: 56 pages, 15 figures, 8 tables, submitted. The Machine-learned ASAS Classification Catalog is available at http://www.bigmacc.info

This paper presents both the result of a search for fossil systems (FSs) within the XMM Cluster Survey and the Sloan Digital Sky Survey and the results of a study of the stellar mass assembly and stellar populations of their fossil galaxies. In total, 17 groups and clusters are identified at z < 0.25 with large magnitude gaps between the first and fourth brightest galaxies. All the information necessary to classify these systems as fossils is provided. For both groups and clusters, the total and fractional luminosity of the brightest galaxy is positively correlated with the magnitude gap. The brightest galaxies in FSs (called fossil galaxies) have stellar populations and star formation histories which are similar to normal brightest cluster galaxies (BCGs). However, at fixed group/cluster mass, the stellar masses of the fossil galaxies are larger compared to normal BCGs, a fact that holds true over a wide range of group/cluster masses. Moreover, the fossil galaxies are found to contain a significant fraction of the total optical luminosity of the group/cluster within 0.5R200, as much as 85%, compared to the non-fossils, which can have as little as 10%. Our results suggest that FSs formed early and in the highest density regions of the universe and that fossil galaxies represent the end products of galaxy mergers in groups and clusters. The online FS catalog can be found at http://www.astro.ljmu.ac.uk/~xcs/Harrison2012/XCSFSCat.html., Comment: 30 pages, 50 figures. ApJ published version, online FS catalog added: http://www.astro.ljmu.ac.uk/~xcs/Harrison2012/XCSFSCat.html

A Bayesian approach to calibrating period-luminosity (PL) relations has substantial benefits over generic least-squares fits. In particular, the Bayesian approach takes into account the full prior distribution of the model parameters, such as the a priori distances, and refits these parameters as part of the process of settling on the most highly-constrained final fit. Additionally, the Bayesian approach can naturally ingest data from multiple wavebands and simultaneously fit the parameters of PL relations for each waveband in a procedure that constrains the parameter posterior distributions so as to minimize the scatter of the final fits appropriately in all wavebands. Here we describe the generalized approach to Bayesian model fitting and then specialize to a detailed description of applying Bayesian linear model fitting to the mid-infrared PL relations of RR Lyrae variable stars. For this example application we quantify the improvement afforded by using a Bayesian model fit. We also compare distances previously predicted in our example application to recently published parallax distances measured with the Hubble Space Telescope and find their agreement to be a vindication of our methodology. Our intent with this article is to spread awareness of the benefits and applicability of this Bayesian approach and encourage future PL relation investigations to consider employing this powerful analysis method., Comment: 6 pages, 1 figure. Accepted for publication in Astrophysics & Space Science. Following a presentation at the conference The Fundamental Cosmic Distance Scale: State of the Art and the Gaia Perspective, Naples, May 2011

Efficient and automated classification of periodic variable stars is becoming increasingly important as the scale of astronomical surveys grows. Several recent papers have used methods from machine learning and statistics to construct classifiers on databases of labeled, multi--epoch sources with the intention of using these classifiers to automatically infer the classes of unlabeled sources from new surveys. However, the same source observed with two different synoptic surveys will generally yield different derived metrics (features) from the light curve. Since such features are used in classifiers, this survey-dependent mismatch in feature space will typically lead to degraded classifier performance. In this paper we show how and why feature distributions change using OGLE and \textit{Hipparcos} light curves. To overcome survey systematics, we apply a method, \textit{noisification}, which attempts to empirically match distributions of features between the labeled sources used to construct the classifier and the unlabeled sources we wish to classify. Results from simulated and real--world light curves show that noisification can significantly improve classifier performance. In a three--class problem using light curves from \textit{Hipparcos} and OGLE, noisification reduces the classifier error rate from 27.0% to 7.0%. We recommend that noisification be used for upcoming surveys such as Gaia and LSST and describe some of the promises and challenges of applying noisification to these surveys., Comment: 30 pages, 25 figures

We report on optical spectroscopy of 165 Flat Spectrum Radio Quasars (FSRQs) in the Fermi 1LAC sample, which have helped allow a nearly complete study of this population. Fermi FSRQ show significant evidence for non-thermal emission even in the optical; the degree depends on the gamma-ray hardness. They also have smaller virial estimates of hole mass than the optical quasar sample. This appears to be largely due to a preferred (axial) view of the gamma-ray FSRQ and non-isotropic (H/R ~ 0.4) distribution of broad-line velocities. Even after correction for this bias, the Fermi FSRQ show higher mean Eddington ratios than the optical population. A comparison of optical spectral properties with Owens Valley Radio Observatory radio flare activity shows no strong correlation., Comment: Accepted for publication in ApJ

As the number of observed Gamma-Ray Bursts (GRBs) continues to grow, follow-up resources need to be used more efficiently in order to maximize science output from limited telescope time. As such, it is becoming increasingly important to rapidly identify bursts of interest as soon as possible after the event, before the afterglows fade beyond detectability. Studying the most distant (highest redshift) events, for instance, remains a primary goal for many in the field. Here we present our Random forest Automated Triage Estimator for GRB redshifts (RATE GRB-z) for rapid identification of high-redshift candidates using early-time metrics from the three telescopes onboard Swift. While the basic RATE methodology is generalizable to a number of resource allocation problems, here we demonstrate its utility for telescope-constrained follow-up efforts with the primary goal to identify and study high-z GRBs. For each new GRB, RATE GRB-z provides a recommendation - based on the available telescope time - of whether the event warrants additional follow-up resources. We train RATE GRB-z using a set consisting of 135 Swift bursts with known redshifts, only 18 of which are z > 4. Cross-validated performance metrics on this training data suggest that ~56% of high-z bursts can be captured from following up the top 20% of the ranked candidates, and ~84% of high-z bursts are identified after following up the top ~40% of candidates. We further use the method to rank 200+ Swift bursts with unknown redshifts according to their likelihood of being high-z., Comment: 44 pages, 11 figures, 6 tables, Accepted to ApJ

The radio properties of blazars detected by the Large Area Telescope (LAT) on board the Fermi Gamma-ray Space Telescope have been observed contemporaneously by the Very Long Baseline Array (VLBA). In total, 232 sources were observed with the VLBA. Ninety sources that were previously observed as part of the VLBA Imaging and Polarimetry Survey (VIPS) have been included in the sample, as well as 142 sources not found in VIPS. This very large, flux-limited sample of active galactic nuclei (AGN) provides insights into the mechanism that produces strong gamma-ray emission. In particular, we see that gamma-ray emission is related to strong, uniform magnetic fields in the cores of the host AGN. Included in this sample are non-blazar AGN such as 3C84, M82, and NGC 6251. For the blazars, the total VLBA radio flux density at 5 GHz correlates strongly with gamma-ray flux. The LAT BL Lac objects tend to be similar to the non-LAT BL Lac objects, but the LAT flat-spectrum radio quasars (FSRQs) are significantly different from the non-LAT FSRQs. Strong core polarization is significantly more common among the LAT sources, and core fractional polarization appears to increase during LAT detection., Comment: 47 pages, 12 figures, accepted for publication in The Astrophysical Journal

Since late 2007, we have been monitoring a large sample of known and likely gamma-ray-loud blazars at 15 GHz twice per week with the Owens Valley Radio Observatory (OVRO) 40 m Telescope. Our initial sample included the 1158 sources above declination -20 degrees from the Candidate Gamma-Ray Blazar Survey (CGRaBS), and we have since added nearly 400 more sources, including all blazars associated with Fermi Large Area Telescope (LAT) detections in the First AGN Catalog (1LAC). Here, we describe the new sample and present results for 2008 through early 2011. Using statistical likelihood analyses, we compare the variability amplitude for various sub-populations within our sample. These include comparisons of gamma-ray-loud versus gamma-ray-quiet objects, BL Lac objects versus flat-spectrum radio quasars, and a study of the variability amplitude trend with redshift. We also describe KuPol, the new digital Ku-band receiver being constructed for the 40 m telescope. This new receiver will provide total intensity and linear polarization measurements over the 12-18 GHz band, with 16 MHz spectral resolution., Comment: 4 pages, 5 figures; 2011 Fermi Symposium proceedings - eConf C110509

Type Ia supernovae (SNe) serve as a fundamental pillar of modern cosmology, owing to their large luminosity and a well-defined relationship between light-curve shape and peak brightness. The precision distance measurements enabled by SNe Ia first revealed the accelerating expansion of the universe, now widely believed (though hardly understood) to require the presence of a mysterious "dark" energy. General consensus holds that Type Ia SNe result from thermonuclear explosions of a white dwarf (WD) in a binary system; however, little is known of the precise nature of the companion star and the physical properties of the progenitor system. Here we make use of extensive historical imaging obtained at the location of SN 2011fe/PTF11kly, the closest SN Ia discovered in the digital imaging era, to constrain the visible-light luminosity of the progenitor to be 10-100 times fainter than previous limits on other SN Ia progenitors. This directly rules out luminous red giants and the vast majority of helium stars as the mass-donating companion to the exploding white dwarf. Any evolved red companion must have been born with mass less than 3.5 times the mass of the Sun. These observations favour a scenario where the exploding WD of SN 2011fe/PTF11kly, accreted matter either from another WD, or by Roche-lobe overflow from a subgiant or main-sequence companion star., Comment: 22 pages, 6 figures, submitted

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because a) standard assumptions for machine-learned model selection procedures break down and b) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting (IW), co-training (CT), and active learning (AL). We argue that AL---where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up---is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and OGLE, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply active learning to classify variable stars in the ASAS survey, finding dramatic improvement in our agreement with the ACVS catalog, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations., Comment: 43 pages, 11 figures, submitted to ApJ

Parameter estimation in astrophysics often requires the use of complex physical models. In this paper we study the problem of estimating the parameters that describe star formation history (SFH) in galaxies. Here, high-dimensional spectral data from galaxies are appropriately modeled as linear combinations of physical components, called simple stellar populations (SSPs), plus some nonlinear distortions. Theoretical data for each SSP is produced for a fixed parameter vector via computer modeling. Though the parameters that define each SSP are continuous, optimizing the signal model over a large set of SSPs on a fine parameter grid is computationally infeasible and inefficient. The goal of this study is to estimate the set of parameters that describes the SFH of each galaxy. These target parameters, such as the average ages and chemical compositions of the galaxy's stellar populations, are derived from the SSP parameters and the component weights in the signal model. Here, we introduce a principled approach of choosing a small basis of SSP prototypes for SFH parameter estimation. The basic idea is to quantize the vector space and effective support of the model components. In addition to greater computational efficiency, we achieve better estimates of the SFH target parameters. In simulations, our proposed quantization method obtains a substantial improvement in estimating the target parameters over the common method of employing a parameter grid. Sparse coding techniques are not appropriate for this problem without proper constraints, while constrained sparse coding methods perform poorly for parameter estimation because their objective is signal reconstruction, not estimation of the target parameters., Comment: Published in at http://dx.doi.org/10.1214/11-AOAS500 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Interstellar dust presents a significant challenge to extending parallax-determined distances of optically observed pulsational variables to larger volumes. Distance ladder work at mid-infrared wavebands, where dust effects are negligible and metallicity correlations are minimized, have been largely focused on few-epoch Cepheid studies. Here we present the first determination of mid-infrared period-luminosity (PL) relations of RR Lyrae stars from phase-resolved imaging using the preliminary data release of the Wide-Field Infrared Survey Explorer (WISE). We present a novel statistical framework to predict posterior distances of 76 well-observed RR Lyrae that uses the optically constructed prior distance moduli while simultaneously imposing a power-law PL relation to WISE-determined mean magnitudes. We find that the absolute magnitude in the bluest WISE filter is M_W1 = (-0.421+-0.014) - (1.681+-0.147)*log(P/0.50118 day), with no evidence for a correlation with metallicity. Combining the results from the three bluest WISE filters, we find that a typical star in our sample has a distance measurement uncertainty of 0.97% (statistical) plus 1.17% (systematic). We do not fundamentalize the periods of RRc stars to improve their fit to the relations. Taking the Hipparcos-derived mean V-band magnitudes, we use the distance posteriors to determine a new optical metallicity-luminosity relation which we present in Section 5. The results of this analysis will soon be tested by HST parallax measurements and, eventually, with the Gaia astrometric mission., Comment: 33 pages, 12 figures, 2 tables. Accepted for publication in ApJ, June 27th, 2011

The changing heavens have played a central role in the scientific effort of astronomers for centuries. Galileo's synoptic observations of the moons of Jupiter and the phases of Venus starting in 1610, provided strong refutation of Ptolemaic cosmology. In more modern times, the discovery of a relationship between period and luminosity in some pulsational variable stars led to the inference of the size of the Milky Way, the distance scale to the nearest galaxies, and the expansion of the Universe. Distant explosions of supernovae were used to uncover the existence of dark energy and provide a precise numerical account of dark matter. Indeed, time-domain observations of transient events and variable stars, as a technique, influences a broad diversity of pursuits in the entire astronomy endeavor. While, at a fundamental level, the nature of the scientific pursuit remains unchanged, the advent of astronomy as a data-driven discipline presents fundamental challenges to the way in which the scientific process must now be conducted. Digital images (and data cubes) are not only getting larger, there are more of them. On logistical grounds, this taxes storage and transport systems. But it also implies that the intimate connection that astronomers have always enjoyed with their data---from collection to processing to analysis to inference---necessarily must evolve. The pathway to scientific inference is now influenced (if not driven by) modern automation processes, computing, data-mining and machine learning. The emerging reliance on computation and machine learning is a general one, but the time-domain aspect of the data and the objects of interest presents some unique challenges, which we describe and explore in this chapter., Comment: 37 pages, 11 figures. Chapter in the forthcoming book "Advances in Machine Learning and Data Mining for Astronomy"

We present a semi-supervised method for photometric supernova typing. Our approach is to first use the nonlinear dimension reduction technique diffusion map to detect structure in a database of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template based methods. Applied to supernova data simulated by Kessler et al. (2010b) to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 95% Type Ia purity and 87% Type Ia efficiency on the spectroscopic sample, but only 50% Type Ia purity and 50% efficiency on the photometric sample due to their spectroscopic follow-up strategy. To improve the performance on the photometric sample, we search for better spectroscopic follow-up procedures by studying the sensitivity of our machine learned supernova classification on the specific strategy used to obtain training sets. With a fixed amount of spectroscopic follow-up time, we find that deeper magnitude-limited spectroscopic surveys are better for producing training sets. For supernova Ia (II-P) typing, we obtain a 44% (1%) increase in purity to 72% (87%) and 30% (162%) increase in efficiency to 65% (84%) of the sample using a 25th (24.5th) magnitude-limited survey instead of the shallower spectroscopic sample used in the original simulations. When redshift information is available, we incorporate it into our analysis using a novel method of altering the diffusion map representation of the supernovae. Incorporating host redshifts leads to a 5% improvement in Type Ia purity and 13% improvement in Type Ia efficiency., Comment: 16 pages, 11 figures, accepted for publication in MNRAS

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 figures

The Large Area Telescope (LAT) aboard the Fermi Gamma-ray Space Telescope provides an unprecedented opportunity to study gamma-ray blazars. To capitalize on this opportunity, beginning in late 2007, about a year before the start of LAT science operations, we began a large-scale, fast-cadence 15 GHz radio monitoring program with the 40-m telescope at the Owens Valley Radio Observatory (OVRO). This program began with the 1158 northern (declination>-20 deg) sources from the Candidate Gamma-ray Blazar Survey (CGRaBS) and now encompasses over 1500 sources, each observed twice per week with a ~4 mJy (minimum) and 3% (typical) uncertainty. Here, we describe this monitoring program and our methods, and present radio light curves from the first two years (2008 and 2009). As a first application, we combine these data with a novel measure of light curve variability amplitude, the intrinsic modulation index, through a likelihood analysis to examine the variability properties of subpopulations of our sample. We demonstrate that, with high significance (7-sigma), gamma-ray-loud blazars detected by the LAT during its first 11 months of operation vary with about a factor of two greater amplitude than do the gamma-ray-quiet blazars in our sample. We also find a significant (3-sigma) difference between variability amplitude in BL Lacertae objects and flat-spectrum radio quasars (FSRQs), with the former exhibiting larger variability amplitudes. Finally, low-redshift (z<1) FSRQs are found to vary more strongly than high-redshift FSRQs, with 3-sigma significance. These findings represent an important step toward understanding why some blazars emit gamma-rays while others, with apparently similar properties, remain silent., Comment: 23 pages, 24 figures. Submitted to ApJS

We report results from the Supernova Photometric Classification Challenge (SNPCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rate. The simulation was realized in the griz filters of the Dark Energy Survey (DES) with realistic observing conditions (sky noise, point-spread function and atmospheric transparency) based on years of recorded conditions at the DES site. Simulations of non-Ia type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). A spectroscopically confirmed subset was provided for training. We challenged scientists to run their classification algorithms and report a type and photo-z for each SN. Participants from 10 groups contributed 13 entries for the sample that included a host-galaxy photo-z for each SN, and 9 entries for the sample that had no redshift information. Several different classification strategies resulted in similar performance, and for all entries the performance was significantly better for the training subset than for the unconfirmed sample. For the spectroscopically unconfirmed subset, the entry with the highest average figure of merit for classifying SNe~Ia has an efficiency of 0.96 and an SN~Ia purity of 0.79. As a public resource for the future development of photometric SN classification and photo-z estimators, we have released updated simulations with improvements based on our experience from the SNPCC, added samples corresponding to the Large Synoptic Survey Telescope (LSST) and the SDSS, and provided the answer keys so that developers can evaluate their own analysis., Comment: accepted by PASP

To further our knowledge of the complex physical process of galaxy formation, it is essential that we characterize the formation and evolution of large databases of galaxies. The spectral synthesis STARLIGHT code of Cid Fernandes et al. (2004) was designed for this purpose. Results of STARLIGHT are highly dependent on the choice of input basis of simple stellar population (SSP) spectra. Speed of the code, which uses random walks through the parameter space, scales as the square of the number of basis spectra, making it computationally necessary to choose a small number of SSPs that are coarsely sampled in age and metallicity. In this paper, we develop methods based on diffusion map (Lafon & Lee, 2006) that, for the first time, choose appropriate bases of prototype SSP spectra from a large set of SSP spectra designed to approximate the continuous grid of age and metallicity of SSPs of which galaxies are truly composed. We show that our techniques achieve better accuracy of physical parameter estimation for simulated galaxies. Specifically, we show that our methods significantly decrease the age-metallicity degeneracy that is common in galaxy population synthesis methods. We analyze a sample of 3046 galaxies in SDSS DR6 and compare the parameter estimates obtained from different basis choices., Comment: Resubmitted to MNRAS; 16 pages, 15 figures

Dimension-reduction techniques can greatly improve statistical inference in astronomy. A standard approach is to use Principal Components Analysis (PCA). In this work we apply a recently-developed technique, diffusion maps, to astronomical spectra for data parameterization and dimensionality reduction, and develop a robust, eigenmode-based framework for regression. We show how our framework provides a computationally efficient means by which to predict redshifts of galaxies, and thus could inform more expensive redshift estimators such as template cross-correlation. It also provides a natural means by which to identify outliers (e.g., misclassified spectra, spectra with anomalous features). We analyze 3835 SDSS spectra and show how our framework yields a more than 95% reduction in dimensionality. Finally, we show that the prediction error of the diffusion map-based regression approach is markedly smaller than that of a similar approach based on PCA, clearly demonstrating the superiority of diffusion maps over PCA for this regression task., Comment: 24 pages, 8 figures

We present optical UBVRI sky brightness measures from 1992 through 2006. The data are based on CCD imagery obtained with the CTIO 0.9-m, 1.3-m, and 1.5-m telescopes. The B- and V-band data are in reasonable agreement with measurements previously made at Mauna Kea, though on the basis of a small number of images per year there are discrepancies for the years 1992 through 1994. Our CCD-based data are not significantly different than values obtained at Cerro Paranal. We find that the yearly averages of V-band sky brightness are best correlated with the 10.7-cm solar flux taken 5 days prior to the sky brightness measures. This implies an average speed of 350 km/sec for the solar wind. While we can measure an enhancement of the night sky levels over La Serena 10 degrees above the horizon, at elevation angles above 45 degrees we find no evidence that the night sky brightness at Cerro Tololo is affected by artificial light of nearby towns and cities., Comment: 24 pages, 5 figures, to be published in the June, 2007, issue of the Publications of the Astron. Society of the Pacific