Predicted microlensing events from analysis of Gaia Data Release 2

Astrometric microlensing can be used to make precise measurements of the masses of lens stars that are independent of their assumed internal physics. Such direct mass measurements, obtained purely by observing the gravitational effects of the stars on external objects, are crucial for validating theoretical stellar models. Specifically, astrometric microlensing provides a channel to direct mass measurements of single stars for which so few measurements exist. To use the astrometric solutions and photometric measurements of ~1.7 billion stars from Gaia Data Release 2 to predict microlensing events during the nominal Gaia mission and beyond. This will enable astronomers to observe the entirety of each event with appropriate observing resources. The data will allow precise lens mass measurements for white dwarfs and low-mass main sequence stars helping to constrain stellar evolutionary models. I search for source-lens pairs in GDR2 that could lead to events between 25/07/2014 and 25/07/2026. I estimate lens masses using GDR2 photometry and parallaxes, and appropriate model isochrones. Combined with source and lens parallax measurements from GDR2, this allows the Einstein radius to be computed for each pair. By considering the paths on the sky, I calculate the microlensing signals that are to be expected. I present a list of 76 predicted microlensing events. 9 and 5 astrometric events will be caused by LAWD37 and Stein2051B. 9 events will exhibit detectable photometric and astrometric signatures. Of the remaining events, ten will exhibit astrometric signals with amplitudes above 0.5 mas, while the rest are low-amplitude astrometric events with amplitudes between 0.131 and 0.5 mas. 5 and 2 events will reach their peaks during 2018 and 2019. 5 of the photometric events have the potential to evolve into high-magnification events, which may also probe for planetary companions to the lenses.Comment: Accepted A&

A New Algorithm For Difference Image Analysis

In the context of difference image analysis (DIA), we present a new method for determining the convolution kernel matching a pair of images of the same field. Unlike the standard DIA technique which involves modelling the kernel as a linear combination of basis functions, we consider the kernel as a discrete pixel array and solve for the kernel pixel values directly using linear least-squares. The removal of basis functions from the kernel model is advantageous for a number of compelling reasons. Firstly, it removes the need for the user to specify such functions, which makes for a much simpler user application and avoids the risk of an inappropriate choice. Secondly, basis functions are constructed around the origin of the kernel coordinate system, which requires that the two images are perfectly aligned for an optimal result. The pixel kernel model is sufficiently flexible to correct for image misalignments, and in the case of a simple translation between images, image resampling becomes unnecessary. Our new algorithm can be extended to spatially varying kernels by solving for individual pixel kernels in a grid of image sub-regions and interpolating the solutions to obtain the kernel at any one pixel.Comment: MNRAS Letters Accepte

SIDRA: a blind algorithm for signal detection in photometric surveys

We present the Signal Detection using Random-Forest Algorithm (SIDRA). SIDRA is a detection and classification algorithm based on the Machine Learning technique (Random Forest). The goal of this paper is to show the power of SIDRA for quick and accurate signal detection and classification. We first diagnose the power of the method with simulated light curves and try it on a subset of the Kepler space mission catalogue. We use five classes of simulated light curves (CONSTANT, TRANSIT, VARIABLE, MLENS and EB for constant light curves, transiting exoplanet, variable, microlensing events and eclipsing binaries, respectively) to analyse the power of the method. The algorithm uses four features in order to classify the light curves. The training sample contains 5000 light curves (1000 from each class) and 50000 random light curves for testing. The total SIDRA success ratio is $\geq 90\%$. Furthermore, the success ratio reaches 95 - 100$\%$ for the CONSTANT, VARIABLE, EB, and MLENS classes and 92$\%$ for the TRANSIT class with a decision probability of 60$\%$. Because the TRANSIT class is the one which fails the most, we run a simultaneous fit using SIDRA and a Box Least Square (BLS) based algorithm for searching for transiting exoplanets. As a result, our algorithm detects 7.5$\%$ more planets than a classic BLS algorithm, with better results for lower signal-to-noise light curves. SIDRA succeeds to catch 98$\%$ of the planet candidates in the Kepler sample and fails for 7$\%$ of the false alarms subset. SIDRA promises to be useful for developing a detection algorithm and/or classifier for large photometric surveys such as TESS and PLATO exoplanet future space missions.Comment: 8 pages, 9 figures, 2 Table

Variable stars in the globular cluster NGC 7492. New discoveries and physical parameters determination

We have performed a photometric V, R, I CCD time-series analysis with a baseline of ~8 years of the outer-halo globular cluster NGC 7492 with the aim of searching for new variables and using these (and the previously known variables) to determine the physical parameters of interest for the cluster (e.g. metallicity, absolute magnitude of the horizontal branch, distance, etc.). We use difference image analysis (DIA) to extract precise light curves in the relatively crowded star field, especially towards the densely populated central region. Several approaches are used for variability detection that recover the known variables and lead to new discoveries. We determine the physical parameters of the only RR0 star using light curve Fourier decomposition analysis. We find one new long period variable and two SX Phe stars in the blue straggler region. We also present one candidate SX Phe star which requires follow-up observations. Assuming that the SX Phe stars are cluster members and using the period-luminosity relation for these stars, we estimate their distances as ~25.2+-1.8 and 26.8+-1.8 kpc, and identify their possible modes of oscillation. We refine the periods of the two RR Lyrae stars in our field of view. We find that the RR1 star V2 is undergoing a period change and possibly exhibits the Blazhko effect. Fourier decomposition of the light curve of the RR0 star V1 allows us to estimate the metallicity [Fe/H]_ZW-1.68+-0.10 or [Fe/H]_UVES-1.64+-0.13, log-luminosity ~1.76+-0.02, absolute magnitude ~0.38+-0.04 mag, and true distance modulus of ~16.93+-0.04 mag, which is equivalent to a distance of ~24.3+-0.5 kpc. All of these values are consistent with previous estimates in the literature.Comment: 12 pages, 13 figures, 6 tables, accepted for publication in A&

Difference image analysis: The interplay between the photometric scale factor and systematic photometric errors

Context: Understanding the source of systematic errors in photometry is essential for their calibration. Aims: We investigate how photometry performed on difference images can be influenced by errors in the photometric scale factor. Methods: We explore the equations for difference image analysis (DIA) and we derive an expression describing how errors in the difference flux, the photometric scale factor and the reference flux are propagated to the object photometry. Results: We find that the error in the photometric scale factor is important, and while a few studies have shown that it can be at a significant level, it is currently neglected by the vast majority of photometric surveys employing DIA. Conclusions: Minimising the error in the photometric scale factor, or compensating for it in a post-calibration model, is crucial for reducing the systematic errors in DIA photometry.Comment: Accepted A&