Charles Alcock

ARE THE MAGELLANIC CLOUDS ON THEIR FIRST PASSAGE ABOUT THE MILKY WAY

Recent proper-motion measurements of the Large and Small Magellanic Clouds (LMC and SMC, respectively) by Kallivayalil and coworkers suggest that the 3D velocities of the Clouds are substantially higher (~100 km s-1) than previously estimated and now approach the escape velocity of the Milky Way (MW). Previous studies have also assumed that the Milky Way can be adequately modeled as an isothermal sphere to large distances. Here we reexamine the orbital history of the Clouds using the new velocities and a ΛCDM-motivated MW model with virial mass Mvir = 1012 M☉ (e.g., Klypin and coworkers). We conclude that the LMC and SMC are either currently on their first passage about the MW or, if the MW can be accurately modeled by an isothermal sphere to distances 200 kpc (i.e., Mvir > 2 × 1012 M☉), that their orbital period and apogalacticon distance must be a factor of 2 larger than previously estimated, increasing to 3 Gyr and 200 kpc, respectively. A first passage scenario is consistent with the fact that the LMC and SMC appear to be outliers when compared to other satellite galaxies of the MW: they are irregular in appearance and are moving faster. We discuss the implications of this orbital analysis for our understanding of the star formation history, the nature of the warp in the MW disk and the origin of the Magellanic Stream (MS), a band of H I gas trailing the LMC and SMC that extends ~100° across the sky. Specifically, as a consequence of the new orbital history of the Clouds, the origin of the MS may not be explainable by current tidal and ram pressure stripping models.

The proper motion of the large magellanic cloud using HST

The authors present a measurement of the systemic proper motion of the Large Magellanic Cloud (LMC) from astrometry with the High Resolution Camera (HRC) of the Advanced Camera for Surveys (ACS) on the Hubble Space Telescope (HST). They observed LMC fields centered on 21 background QSOs that were discovered from their optical variability in the MACHO database. The QSOs are distributed homogeneously behind the central few degrees of the LMC. With 2 epochs of HRC data and a {approx} 2 year baseline they determine the proper motion of the LMC to better than 5% accuracy: {mu}{sub W} = -2.03 {+-} 0.08 mas yr{sup -1}, {mu}{sub N} = 0.44 {+-} 0.05 mas yr{sup -1}. This is the most accurate proper motion measurement for any Milky Way satellite thus far. When combined with HI data from the Magellanic Stream this should provide new constraints on both the mass distribution of the Galactic Halo and models of the Stream.

Is the SMC bound to the LMC? The Hubble space telescope proper motion of the SMC

We present a measurement of the systemic proper motion of the Small Magellanic Cloud (SMC) made using the Advanced Camera for Surveys (ACS) on the Hubble Space Telescope (HST). We tracked the SMCs motion relative to four background QSOs over a baseline of approximately 2 yr. The measured proper motion is μW = -1.16 ± 0.18 mas yr-1, μN = -1.17 ± 0.18 mas yr-1. This is the best measurement yet of the SMCs proper motion. We combine this new result with our prior estimate of the proper motion of the Large Magellanic Cloud (LMC) from the same observing program to investigate the orbital evolution of both Clouds over the past 9 Gyr. The current relative velocity between the Clouds is 105 ± 42 km s-1. Our investigations of the past orbital motions of the Clouds in a simple model for the dark halo of the Milky Way imply that the Clouds could be unbound from each other. However, our data are also consistent with orbits in which the Clouds have been bound to each other for approximately a Hubble time. Smaller proper-motion errors and better understanding of the LMC and SMC masses would be required to constrain their past orbital history and their bound versus unbound nature unambiguously. The new proper-motion measurements should be sufficient to allow the construction of improved models for the origin and properties of the Magellanic Stream. In turn, this will provide new constraints on the properties of the Milky Way dark halo.

Finding anomalous periodic time series

Catalogs of periodic variable stars contain large numbers of periodic light-curves (photometric time series data from the astrophysics domain). Separating anomalous objects from well-known classes is an important step towards the discovery of new classes of astronomical objects. Most anomaly detection methods for time series data assume either a single continuous time series or a set of time series whose periods are aligned. Light-curve data precludes the use of these methods as the periods of any given pair of light-curves may be out of sync. One may use an existing anomaly detection method if, prior to similarity calculation, one performs the costly act of aligning two light-curves, an operation that scales poorly to massive data sets. This paper presents PCAD, an unsupervised anomaly detection method for large sets of unsynchronized periodic time-series data, that outputs a ranked list of both global and local anomalies. It calculates its anomaly score for each light-curve in relation to a set of centroids produced by a modified k-means clustering algorithm. Our method is able to scale to large data sets through the use of sampling. We validate our method on both light-curve data and other time series data sets. We demonstrate its effectiveness at finding known anomalies, and discuss the effect of sample size and number of centroids on our results. We compare our method to naive solutions and existing time series anomaly detection methods for unphased data, and show that PCAD’s reported anomalies are comparable to or better than all other methods. Finally, astrophysicists on our team have verified that PCAD finds true anomalies that might be indicative of novel astrophysical phenomena.

Finding outlier light curves in catalogues of periodic variable stars

We present a methodology to discover outliers in catalogues of periodic light curves. We use a cross-correlation as the measure of ‘similarity’ between two individual light curves, and then classify light curves with lowest average ‘similarity’ as outliers. We performed the analysis on catalogues of periodic variable stars of known type from the MACHO and OGLE projects. This analysis was carried out in Fourier space and we established that our method correctly identifies light curves that do not belong to those catalogues as outliers. We show how an approximation to this method, carried out in real space, can scale to large data sets that will be available in the near future such as those anticipated from the Panoramic Survey Telescope & Rapid Response System (Pan-STARRS) and Large Synoptic Survey Telescope (LSST).

Artificial Intelligence Approach to the Determination of Physical Properties of Eclipsing Binaries. I. The EBAI Project

Achieving maximum scientific results from the overwhelming volume of astronomical data to be acquired over the next few decades demands novel, fully automatic methods of data analysis. Here we concentrate on eclipsing binary (EB) stars, a prime source of astrophysical information, of which only some hundreds have been rigorously analyzed, but whose numbers will reach millions in a decade. We describe the artificial neural network (ANN) approach which is able to surmount the human bottleneck and permit EB-based scientific yield to keep pace with future data rates. The ANN, following training on a sample of 33,235 model light curves, outputs a set of approximate model parameters [T2/T1, (R1 + R2)/a, esin ω , ecos ω , and sin i] for each input light curve data set. The obtained parameters can then be readily passed to sophisticated modeling engines. We also describe a novel method polyfit for preprocessing observational light curves before inputting their data to the ANN and present the results and analysis of testing the approach on synthetic data and on real data including 50 binaries from the Catalog and Atlas of Eclipsing Binaries (CALEB) database and 2580 light curves from OGLE survey data. The success rate, defined by less than a 10% error in the network output parameter values, is approximately 90% for the OGLE sample and close to 100% for the CALEB sample—sufficient for a reliable statistical analysis. The code is made available to the public. Our approach is applicable to EB light curves of all classes; this first paper in the eclipsing binaries via artificial intelligence (EBAI) series focuses on detached EBs, which is the class most challenging for this approach.

QUASI-STELLAR OBJECT SELECTION ALGORITHM USING TIME VARIABILITY AND MACHINE LEARNING: SELECTION OF 1620 QUASI-STELLAR OBJECT CANDIDATES FROM MACHO LARGE MAGELLANIC CLOUD DATABASE

We present a new QSO selection algorithm using a Support Vector Machine (SVM), a supervised classification method, on a set of extracted time series features including period, amplitude, color, and autocorrelation value. We train a model that separates QSOs from variable stars, non-variable stars and microlensing events using 58 known QSOs, 1,629 variable stars and 4,288 non-variables using the MAssive Compact Halo Object (MACHO) database as a training set. To estimate the efficiency and the accuracy of the model, we perform a cross-validation test using the training set. The test shows that the model correctly identifies ∼80% of known QSOs with a 25% false positive rate. The majority of the false positives are Be stars. We applied the trained model to the MACHO Large Magellanic Cloud (LMC) dataset, which consists of 40 million lightcurves, and found 1,620 QSO candidates. During the selection none of the 33,242 known MACHO variables were misclassified as QSO candidates. In order to estimate the true false positive rate, we crossmatched the candidates with astronomical catalogs including the Spitzer Surveying the Agents of a Galaxy’s Evolution (SAGE) LMC catalog and a few X-ray catalogs. The results further suggest that the majority of the candidates, more than 70%, are QSOs. Subject headings: Magellanic Clouds methods: data analysis quasars: generalWe present a new quasi-stellar object (QSO) selection algorithm using a Support Vector Machine, a supervised classification method, on a set of extracted time series features including period, amplitude, color, and autocorrelation value. We train a model that separates QSOs from variable stars, non-variable stars, and microlensing events using 58 known QSOs, 1629 variable stars, and 4288 non-variables in the MAssive Compact Halo Object (MACHO) database as a training set. To estimate the efficiency and the accuracy of the model, we perform a cross-validation test using the training set. The test shows that the model correctly identifies ~80% of known QSOs with a 25% false-positive rate. The majority of the false positives are Be stars. We applied the trained model to the MACHO Large Magellanic Cloud (LMC) data set, which consists of 40 million light curves, and found 1620 QSO candidates. During the selection none of the 33,242 known MACHO variables were misclassified as QSO candidates. In order to estimate the true false-positive rate, we crossmatched the candidates with astronomical catalogs including the Spitzer Surveying the Agents of a Galaxys Evolution LMC catalog and a few X-ray catalogs. The results further suggest that the majority of the candidates, more than 70%, are QSOs.

Repetitive patterns in rapid optical variations in the nearby black-hole binary V404 Cygni.

How black holes accrete surrounding matter is a fundamental yet unsolved question in astrophysics. It is generally believed that matter is absorbed into black holes via accretion disks, the state of which depends primarily on the mass-accretion rate. When this rate approaches the critical rate (the Eddington limit), thermal instability is supposed to occur in the inner disk, causing repetitive patterns of large-amplitude X-ray variability (oscillations) on timescales of minutes to hours. In fact, such oscillations have been observed only in sources with a high mass-accretion rate, such as GRS 1915+105 (refs 2, 3). These large-amplitude, relatively slow timescale, phenomena are thought to have physical origins distinct from those of X-ray or optical variations with small amplitudes and fast timescales (less than about 10 seconds) often observed in other black-hole binaries—for example, XTE J1118+480 (ref. 4) and GX 339−4 (ref. 5). Here we report an extensive multi-colour optical photometric data set of V404 Cygni, an X-ray transient source containing a black hole of nine solar masses (and a companion star) at a distance of 2.4 kiloparsecs (ref. 8). Our data show that optical oscillations on timescales of 100 seconds to 2.5 hours can occur at mass-accretion rates more than ten times lower than previously thought. This suggests that the accretion rate is not the critical parameter for inducing inner-disk instabilities. Instead, we propose that a long orbital period is a key condition for these large-amplitude oscillations, because the outer part of the large disk in binaries with long orbital periods will have surface densities too low to maintain sustained mass accretion to the inner part of the disk. The lack of sustained accretion—not the actual rate—would then be the critical factor causing large-amplitude oscillations in long-period systems.

The Taos Project:upper bounds on the population of small kuiper belt objects and tests of models of formation and evolution of the outer solar system

We have analyzed the first 3.75 years of data from the Taiwanese American Occultation Survey (TAOS). TAOS monitors bright stars to search for occultations by Kuiper Belt objects (KBOs). This data set comprises 5 × 10^5 star hours of multi-telescope photometric data taken at 4 or 5 Hz. No events consistent with KBO occultations were found in this data set. We compute the number of events expected for the Kuiper Belt formation and evolution models of Pan & Sari, Kenyon & Bromley, Benavidez & Campo Bagatin, and Fraser. A comparison with the upper limits we derive from our data constrains the parameter space of these models. This is the first detailed comparison of models of the KBO size distribution with data from an occultation survey. Our results suggest that the KBO population is composed of objects with low internal strength and that planetary migration played a role in the shaping of the size distribution.

ECLIPSING BINARY STARS IN THE LARGE AND SMALL MAGELLANIC CLOUDS FROM THE MACHO PROJECT: THE SAMPLE

We present a new sample of 4634 eclipsing binary stars in the Large Magellanic Cloud (LMC), expanding on a previous sample of 611 objects and a new sample of 1509 eclipsing binary stars in the Small Magellanic Cloud (SMC), that were identified in the light curve database of the MACHO project. We perform a cross correlation with the OGLE-II LMC sample, finding 1236 matches. A cross correlation with the OGLE-II SMC sample finds 698 matches. We then compare the LMC subsamples corresponding to center and the periphery of the LMC and find only minor differences between the two populations. These samples are sufficiently large and complete that statistical studies of the binary star populations are possible.