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Register a new userShort timescale variables in the Gaia era: detection and characterization by structure function analysis
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We investigate the capabilities of the ESA Gaia mission for detecting and character- izing short timescale variability, from tens of seconds to a dozen hours. We assess the efficiency of the variogram analysis, for both detecting short timescale variability and estimating the underlying characteristic timescales from Gaia photometry, through extensive light-curve simulations for various periodic and transient short timescale variable types. We show that, with this approach, we can detect fast periodic variabil- ity, with amplitudes down to a few millimagnitudes, as well as some M dwarf flares and supernovae explosions, with limited contamination from longer timescale variables or constant sources. Timescale estimates from the variogram give valuable informa- tion on the rapidity of the underlying variation, which could complement timescale estimates from other methods, like Fourier-based periodograms, and be reinvested in preparation of ground-based photometric follow-up of short timescale candidates evi- denced by Gaia. The next step will be to find new short timescale variable candidates from real Gaia data, and to further characterize them using all the Gaia information, including color and spectrum.
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Combined studies of variable stars and stellar clusters open great horizons, and they allow us to improve our understanding of stellar cluster formation and stellar evolution. In that prospect, the Gaia mission will provide astrometric, photometric, and spectroscopic data for about one billion stars of the Milky Way. This will represent a major census of stellar clusters, and it will drastically increase the number of known variable stars. In particular, the peculiar Gaia scanning law offers the opportunity to investigate the rather unexplored domain of short timescale variability (from tens of seconds to a dozen of hours), bringing invaluable clues to the fields of stellar physics and stellar aggregates. We assess the Gaia capabilities in terms of short timescale variability detection, using extensive light-curve simulations for various variable object types. We show that Gaia can detect periodic variability phenomena with amplitude variations larger than a few millimagnitudes. Additionally, we plan to perform subsequent follow-up of variables stars detected in clusters by Gaia to better characterize them. Hence, we develop a pipeline for the analysis of high cadence photometry from ground-based telescopes such as the 1.2m Euler telescope (La Silla, Chile) and the 1.2m Mercator telescope (La Palma, Canary Islands).
The Gaia DR2 sample of short-timescale variable candidates results from the investigation of the first 22 months of Gaia photometry for a subsample of sources at the Gaia faint end. For this exercise, we limited ourselves to the case of suspected rapid periodic variability. Our study combines fast-variability detection through variogram analysis, high-frequency search by means of least-squares periodograms, and empirical selection based on the investigation of specific sources seen through the Gaia eyes (e.g. known variables or visually identified objects with peculiar features in their light curves). The progressive definition and validation of this selection criterion also benefited from supplementary ground-based photometric monitoring of a few preliminary candidates, performed at the Flemish Mercator telescope (Canary Islands, Spain) between August and November 2017. We publish a list of 3,018 short-timescale variable candidates, spread throughout the sky, with a false-positive rate up to 10-20% in the Magellanic Clouds, and a more significant but justifiable contamination from longer-period variables between 19% and 50%, depending on the area of the sky. Although its completeness is limited to about 0.05%, this first sample of Gaia short-timescale variables recovers some very interesting known short-period variables, such as post-common envelope binaries or cataclysmic variables, and brings to light some fascinating, newly discovered variable sources. In the perspective of future Gaia data releases, several improvements of the short-timescale variability processing are considered, by enhancing the existing variogram and period-search algorithms or by classifying the identified candidates. Nonetheless, the encouraging outcome of our Gaia DR2 analysis demonstrates the power of this mission for such fast-variability studies, and opens great perspectives for this domain of astrophysics.
The second Gaia data release (DR2, spring 2018) included a unique all-sky catalogue of large-amplitude long-period variables (LPVs) containing Miras and semi-regular variables. These stars are on the Asymptotic Giant Branch (AGB), and are characterized by high luminosity, changing surface composition, and intense mass loss, that make them of paramount importance for stellar, galactic, and extra-galactic studies. An initial investigation of LPVs in the Large Magellanic Cloud (LMC) from the DR2 catalog of LPVs has revealed the possibility to disentangle O-rich and C-rich stars using a combination of optical Gaia and infrared 2MASS photometry. The so-called Gaia-2MASS diagram constructed to achieve this has further been shown to enable the identification of sub-groups of AGB stars among the O-rich and C-rich LPVs. Here, we extend this initial study of the Gaia-2MASS diagram to the Small Magellanic Cloud and the Galaxy, and use a variability amplitude proxy to identify LPVs from the full Gaia DR2 archive. We show that the remarkable properties found in the LMC also apply to these other stellar systems. Interesting features, moreover, emerge as a result of the different metallicities between the three stellar environments, which we highlight in this exploratory presentation of Gaias potential to study stellar populations harboring LPVs. Finally, we look ahead to the future, and highlight the power of the exploitation of Gaia RP spectra for the identification of carbon stars using solely Gaia data in forthcoming data releases, as revealed in an Image of the Week published by the Gaia consortium on the European Space Agencys web site. These proceedings include three animated images that can be used as outreach material.
We use methods of differential astrometry to construct a small field inertial reference frame stable at the micro-arcsecond level. Such a high level of astrometric precision can be expected with the end-of-mission standard errors to be achieved with the Gaia space satellite using global astrometry. We harness Gaia measurements of field angles and look at the influence of the number of reference stars and the stars magnitude as well as astrometric systematics on the total error budget with the help of Gaia-like simulations around the Ecliptic Pole in a differential astrometric scenario. We find that the systematic errors are modeled and reliably estimated to the $mu$as level even in fields with a modest number of 37 stars with G $<$13 mag over a 0.24 sq.degs. field of view for short time scales of the order of a day with high-cadence observations such as those around the North Ecliptic Pole during the EPSL scanning mode of Gaia for a perfect instrument. The inclusion of the geometric instrument model over such short time scales accounting for large-scale calibrations requires fainter stars down to G = 14 mag without diminishing the accuracy of the reference frame. We discuss several future perspectives of utilizing this methodology over different and longer timescales.
Over 3 billion astronomical objects have been detected in the more than 22 million orthogonal transfer CCD images obtained as part of the Pan-STARRS1 $3pi$ survey. Over 85 billion instances of those objects have been automatically detected and characterized by the Pan-STARRS Image Processing Pipeline photometry software, psphot. This fast, automatic, and reliable software was developed for the Pan-STARRS project, but is easily adaptable to images from other telescopes. We describe the analysis of the astronomical objects by psphot in general as well as for the specific case of the 3rd processing version used for the first two public releases of the Pan-STARRS $3pi$ survey data, DR1 & DR2.
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