No Arabic abstract
In astronomy, we are witnessing an enormous increase in the number of source detections, precision, and diversity of measurements. Additionally, multi-epoch data is becoming the norm, making time-series analyses an important aspect of current astronomy. The Gaia mission is an outstanding example of a multi-epoch survey that provides measurements in a large diversity of domains, with its broad-band photometry; spectrophotometry in blue and red (used to derive astrophysical parameters); spectroscopy (employed to infer radial velocities, v sin(i), and other astrophysical parameters); and its extremely precise astrometry. Most of all that information is provided for sources covering the entire sky. Here, we present several properties related to the Gaia time series, such as the time sampling; the different types of measurements; the Gaia G, G BP and G RP-band photometry; and Gaia-inspired studies using the CORrelation-RAdial-VELocities data to assess the potential of the information on the radial velocity, the FWHM, and the contrast of the cross-correlation function. We also present techniques (which are used or are under development) that optimize the extraction of astrophysical information from the different instruments of Gaia, such as the principal component analysis and the multi-response regression. The detailed understanding of the behavior of the observed phenomena in the various measurement domains can lead to richer and more precise characterization of the Gaia data, including the definition of more informative attributes that serve as input to (our) machine-learning algorithms.
Gaia is a very ambitious mission of the European Space Agency. At the heart of Gaia lie the measurements of the positions, distances, space motions, brightnesses and astrophysical parameters of stars, which represent fundamental pillars of modern astronomical knowledge. We provide a brief description of the Gaia mission with an emphasis on binary stars. In particular, we summarize results of simulations, which estimate the number of binary stars to be processed to several tens of millions. We also report on the catalogue release scenarios. In the current proposal, the first results for binary stars will be available in 2017 (for a launch in 2013).
I provide a summary of the ESA space astrometry mission Gaia regarding its main objectives and current status following the 2nd data release (Gaia DR2) in April 2018. The Gaia achievements in astrometry are assessed with a historical perspective by comparing the DR2 content to sky surveys or parallax searches over the last two centuries. One shows that Gaia sounds more like a big leap into a new world than an incremental progress in this field.
Tens of millions of new variable objects are expected to be identified in over a billion time series from the Gaia mission. Crossmatching known variable sources with those from Gaia is crucial to incorporate current knowledge, understand how these objects appear in the Gaia data, train supervised classifiers to recognise known classes, and validate the results of the Variability Processing and Analysis Coordination Unit (CU7) within the Gaia Data Analysis and Processing Consortium (DPAC). The method employed by CU7 to crossmatch variables for the first Gaia data release includes a binary classifier to take into account positional uncertainties, proper motion, targeted variability signals, and artefacts present in the early calibration of the Gaia data. Crossmatching with a classifier makes it possible to automate all those decisions which are typically made during visual inspection. The classifier can be trained with objects characterized by a variety of attributes to ensure similarity in multiple dimensions (astrometry, photometry, time-series features), with no need for a-priori transformations to compare different photometric bands, or of predictive models of the motion of objects to compare positions. Other advantages as well as some disadvantages of the method are discussed. Implementation steps from the training to the assessment of the crossmatch classifier and selection of results are described.
On the 19th of December 2013, the Gaia spacecraft was successfully launched by a Soyuz rocket from French Guiana and started its amazing journey to map and characterise one billion celestial objects with its one billion pixel camera. In this presentation, we briefly review the general aims of the mission and describe what has happened since launch, including the Ecliptic Pole scanning mode. We also focus especially on binary stars, starting with some basic observational aspects, and then turning to the remarkable harvest that Gaia is expected to yield for these objects.
Supernovae mark the explosive deaths of stars and enrich the cosmos with heavy elements. Future telescopes will discover thousands of new supernovae nightly, creating a need to flag astrophysically interesting events rapidly for followup study. Ideally, such an anomaly detection pipeline would be independent of our current knowledge and be sensitive to unexpected phenomena. Here we present an unsupervised method to search for anomalous time series in real time for transient, multivariate, and aperiodic signals. We use a RNN-based variational autoencoder to encode supernova time series and an isolation forest to search for anomalous events in the learned encoded space. We apply this method to a simulated dataset of 12,159 supernovae, successfully discovering anomalous supernovae and objects with catastrophically incorrect redshift measurements. This work is the first anomaly detection pipeline for supernovae which works with online datastreams.