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Register a new userA classification algorithm for time-domain novelties in preparation for LSST alerts: Application to variable stars and transients detected with DECam in the Galactic Bulge
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With the advent of the Large Synoptic Survey Telescope (LSST), time-domain astronomy will be faced with an unprecedented volume and rate of data. Real-time processing of variables and transients detected by such large-scale surveys is critical to identifying the more unusual events and allocating scarce follow-up resources efficiently. We develop an algorithm to identify these novel events within a given population of variable sources. We determine the distributions of magnitude changes (dm) over time intervals (dt) for a given passband f, pf(dm|dt), and use these distributions to compute the likelihood of a test source being consistent with the population, or an outlier. We demonstrate our algorithm by applying it to the DECam multi-band time-series data of more than 2000 variable stars identified by Saha et al. (2019) in the Galactic Bulge that are largely dominated by long-period variables and pulsating stars. Our algorithm discovers 18 outlier sources in the sample, including a microlensing event, a dwarf nova, and two chromospherically active RS CVn stars, as well as sources in the Blue Horizontal Branch region of the color-magnitude diagram without any known counterparts. We compare the performance of our algorithm for novelty detection with multivariate KDE and Isolation Forest on the simulated PLAsTiCC dataset. We find that our algorithm yields comparable results despite its simplicity. Our method provides an efficient way for flagging the most unusual events in a real-time alert-broker system.
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The expert system for time series analysis of irregularly spaced signals is reviewed. It consists of a number of complementary algorithms and programs, which may be effective for different types of variability. Obviously, for a pure sine signal, all the methods should produce the same results. However, for irregularly spaced signals with a complicated structure, e.g. a sum of different components, different methods may produce significantly different results. The basic approach is based on classical method of the least squares (1994OAP.....7...49A). However, contrary to common step-by-step methods of removal important components (e.g. mean, trend (detrending), sine wave (prewhitening), where covariations between different components are ignored, i.e. erroneously assumed to be zero, we use complete mathematical models. Some of the methods are illustrated on the observations of the semi-regular pulsating variable RY UMa. The star shows a drastic cyclic change of semi-amplitude of pulsations between 0.01 to 0.37mag, which is interpreted as a bias between the waves with close periods and a beat period of 4000d (11yr). The dominating period has changed from 307.35(8)d before 1993 to 285.26(6)d after 1993. The initial epoch of the maximum brightness for the recent interval is 2454008.8(5). It is suggested that the apparent period switch is due to variability of amplitudes of these two waves and an occasional swap of the dominating wave.
We report the detection of 1143 variable stars towards the Galactic bulge, including 320 previously uncatalogued variables, using time-series photometry extracted from data obtained with the VIMOS imager at the Very Large Telescope. Observations of the Sagittarius Window Eclipsing Extrasolar Planet Search (SWEEPS) field in the Galactic Bulge were taken over 2 years between March and October at a cadence of $sim$ 4 days, enabling the detection of variables with periods up to $sim$100 days. Many of these were already known, but we detected a significant number of new variables, including 26 Cepheids, a further 18 Cepheid candidates, and many contact binaries. Here we publish the catalog of the new variables, containing coordinates, mean magnitudes as well as periods and classification; full light curves for these variables are also made available electronically.
Stars exhibit a bewildering variety of variable behaviors ranging from explosive magnetic flares to stochastically changing accretion to periodic pulsations or rotations. The principal LSST surveys will have cadences too sparse and irregular to capture most of these phenomena. A novel idea is proposed here to observe a single Galactic field, rich in unobscured stars, in a continuous sequence of $sim 15$ second exposures for one long winter night in a single photometric band. The result will be a unique dataset of $sim 1$ million regularly spaced stellar lightcurves. The lightcurves will gives a particularly comprehensive collection of dM star variability. A powerful array of statistical procedures can be applied to the ensemble of lightcurves from the long-standing fields of time series analysis, signal processing and econometrics. Dozens of `features describing the variability can be extracted and subject to machine learning classification, giving a unique authoritative objective classification of rapidly variable stars. The most effective features can then inform the wider LSST community on the best approaches to variable star identification and classification from the sparse, irregular cadences that dominate the LSST project.
The software MAVKA is described, which was elaborated for statistically optimal determination of the characteristics of the extrema of 1000+ variable stars of different types, mainly eclipsing and pulsating. The approximations are phenomenological, but not physical. As often, the discovery of a new variable star is made on time series of a single-filter (single-channel) data, and there is no possibility to determine parameters needed for physical modelling (e.g. temperature, radial velocities, mass ratio of binaries). Besides classical polynomial approximation AP (we limited the degree of the polynomial from 2 to 9), there are realized symmetrical approximations (symmetrical polynomials SP, wall-supported horizontal line WSL and parabola WSP, restricted polynomials of non-integer order based on approximations of the functions proposed by Andronov (2012) and Mikulasek (2015) and generally asymmetric functions (asymptotic parabola AP, parabolic spline PS, generalized hyperbolic secant function SECH and log-normal-like BSK). This software is a successor of the Observation Obscurer with some features for the variable star research, including a block for running parabola RP scalegram and approximation. Whereas the RP is oriented on approximation of the complete data set. MAVKA is pointed to parts of the light curve close to extrema (including total eclipses and transits of stars and exoplanets). The functions for wider intervals, covering the eclipse totally, were discussed in 2017Ap.....60...57A . Global and local approximations are reviewed in 2020kdbd.book..191A . The software is available at http://uavso.org.ua/mavka and https://katerynaandrych.wixsite.com/mavka. We have analyzed the data from own observations, as well as from monitoring obtained at ground-based and space (currently, mainly, TESS) observatories. It may be used for signals of any nature.
We obtained high-resolution near-IR spectra of 45 AGB stars located in the Galactic bulge. The aim of the project is to determine key elemental abundances in these stars to help constrain the formation history of the bulge. A further aim is to link the photospheric abundances to the dust species found in the winds of the stars. Here we present a progress report of the analysis of the spectra.
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