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Register a new userA microlensing search of 700 million VVV light curves
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The VISTA Variables in the Via Lactea (VVV) survey and its extension, have been monitoring about 560 square degrees of sky centred on the Galactic bulge and inner disc for nearly a decade. The photometric catalogue contains of order 10$^9$ sources monitored in the K$_s$ band down to 18 mag over hundreds of epochs from 2010-2019. Using these data we develop a decision tree classifier to identify microlensing events. As inputs to the tree, we extract a few physically motivated features as well as simple statistics ensuring a good fit to a microlensing model both on and off the event amplification. This produces a fast and efficient classifier trained on a set of simulated microlensing events and catacylsmic variables, together with flat baseline light curves randomly chosen from the VVV data. The classifier achieves 97 per cent accuracy in identifying simulated microlensing events in a validation set. We run the classifier over the VVV data set and then visually inspect the results, which produces a catalogue of 1,959 microlensing events. For these events, we provide the Einstein radius crossing time via a Bayesian analysis. The spatial dependence on recovery efficiency of our classifier is well characterised, and this allows us to compute spatially resolved completeness maps as a function of Einstein crossing time over the VVV footprint. We compare our approach to previous microlensing searches of the VVV. We highlight the importance of Bayesian fitting to determine the microlensing parameters for events with surveys like VVV with sparse data.
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The creation of a 3D map of the bulge using RRLyrae (RRL) is one of the main goals of the VVV(X) surveys. The overwhelming number of sources under analysis request the use of automatic procedures. In this context, previous works introduced the use of Machine Learning (ML) methods for the variable star classification. Our goal is the development and analysis of an automatic procedure, based on ML, for the identification of RRLs in the VVV Survey. This procedure will be use to generate reliable catalogs integrated over several tiles in the survey. After the reconstruction of light-curves, we extract a set of period and intensity-based features. We use for the first time a new subset of pseudo color features. We discuss all the appropriate steps needed to define our automatic pipeline: selection of quality measures; sampling procedures; classifier setup and model selection. As final result, we construct an ensemble classifier with an average Recall of 0.48 and average Precision of 0.86 over 15 tiles. We also make available our processed datasets and a catalog of candidate RRLs. Perhaps most interestingly, from a classification perspective based on photometric broad-band data, is that our results indicate that Color is an informative feature type of the RRL that should be considered for automatic classification methods via ML. We also argue that Recall and Precision in both tables and curves are high quality metrics for this highly imbalanced problem. Furthermore, we show for our VVV data-set that to have good estimates it is important to use the original distribution more than reduced samples with an artificial balance. Finally, we show that the use of ensemble classifiers helps resolve the crucial model selection step, and that most errors in the identification of RRLs are related to low quality observations of some sources or to the difficulty to resolve the RRL-C type given the date.
Owing to the advent of large area photometric surveys, the possibility to use broad band photometric data, instead of spectra, to measure the size of the broad line region of active galactic nuclei, has raised a large interest. We describe here a new method using time-delay lensed quasars where one or several images are affected by microlensing due to stars in the lensing galaxy. Because microlensing decreases (or increases) the flux of the continuum compared to the broad line region, it changes the contrast between these two emission components. We show that this effect can be used to effectively disentangle the intrinsic variability of those two regions, offering the opportunity to perform reverberation mapping based on single band photometric data. Based on simulated light curves generated using a damped random walk model of quasar variability, we show that measurement of the size of the broad line region can be achieved using this method, provided one spectrum has been obtained independently during the monitoring. This method is complementary to photometric reverberation mapping and could also be extended to multi-band data. Because the effect described above produces a variability pattern in difference light curves between pairs of lensed images which is correlated with the time-lagged continuum variability, it can potentially produce systematic errors in measurement of time delays between pairs of lensed images. Simple simulations indicate that time-delay measurement techniques which use a sufficiently flexible model for the extrinsic variability are not affected by this effect and produce accurate time delays.
Microlensing is a powerful tool for discovering cold exoplanets, and the The Roman Space Telescope microlensing survey will discover over 1000 such planets. Rapid, automated classification of Romans microlensing events can be used to prioritize follow-up observations of the most interesting events. Machine learning is now often used for classification problems in astronomy, but the success of such algorithms can rely on the definition of appropriate features that capture essential elements of the observations that can map to parameters of interest. In this paper, we introduce tools that we have developed to capture features in simulated Roman light curves of different types of microlensing events, and evaluate their effectiveness in classifying microlensing light curves. These features are quantified as parameters that can be used to decide the likelihood that a given light curve is due to a specific type of microlensing event. This method leaves us with a list of parameters that describe features like the smoothness of the peak, symmetry, the number of peaks, and width and height of small deviations from the main peak. This will allow us to quickly analyze a set of microlensing light curves and later use the resulting parameters as input to machine learning algorithms to classify the events.
Context. The Vista Variables in the Via Lactea (VVV) ESO Public Survey is a variability survey of the Milky Way bulge and an adjacent section of the disk carried out from 2010 on ESO Visible and Infrared Survey Telescope for Astronomy (VISTA). VVV will eventually deliver a deep near-IR atlas with photometry and positions in five passbands (ZYJHK_S) and a catalogue of 1-10 million variable point sources - mostly unknown - which require classifications. Aims. The main goal of the VVV Templates Project, that we introduce in this work, is to develop and test the machine-learning algorithms for the automated classification of the VVV light-curves. As VVV is the first massive, multi-epoch survey of stellar variability in the near-infrared, the template light-curves that are required for training the classification algorithms are not available. In the first paper of the series we describe the construction of this comprehensive database of infrared stellar variability. Methods. First we performed a systematic search in the literature and public data archives, second, we coordinated a worldwide observational campaign, and third we exploited the VVV variability database itself on (optically) well-known stars to gather high-quality infrared light-curves of several hundreds of variable stars. Results. We have now collected a significant (and still increasing) number of infrared template light-curves. This database will be used as a training-set for the machine-learning algorithms that will automatically classify the light-curves produced by VVV. The results of such an automated classification will be covered in forthcoming papers of the series.
Time-delay cosmography in strongly lensed quasars offer an independent way of measuring the Hubble constant, $H_0$. However, it has been proposed that the combination of microlensing and source-size effects, also known as microlensing time delay can potentially increase the uncertainty in time-delay measurements as well as lead to a biased time delay. In this work, we first investigate how microlensing time delay changes with assumptions on the initial mass function (IMF) and find that the more massive microlenses produce the sharper distributions of microlensing time delays. We also find that the IMF has modest effect on the the magnification probability distributions. Second, we present a new method to measure the color-dependent source size in lensed quasars using the microlensing time delays inferred from multi-band light curves. In practice the relevant observable is the differential microlensing time delays between different bands. We show from simulation using the facility as Vera C. Rubin Observatory that if this differential time delay between bands can be measured with a precision of $0.1$ days in any given lensed image, the disk size can be recovered to within a factor of $2$. If four lensed images are used, our method is able to achieve an unbiased source measurement within error of the order of $20%$, which is comparable with other techniques.
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