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Register a new userAstrometric signal profile fitting for Gaia
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A tool for representation of the one-dimensional astrometric signal of Gaia is described and investigated in terms of fit discrepancy and astrometric performance with respect to number of parameters required. The proposed basis function is based on the aberration free response of the ideal telescope and its derivatives, weighted by the source spectral distribution. The influence of relative position of the detector pixel array with respect to the optical image is analysed, as well as the variation induced by the source spectral emission. The number of parameters required for micro-arcsec level consistency of the reconstructed function with the detected signal is found to be 11. Some considerations are devoted to the issue of calibration of the instrument response representation, taking into account the relevant aspects of source spectrum and focal plane sampling. Additional investigations and other applications are also suggested.
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A general purpose fitting model for one-dimensional astrometric signals is developed, building on a maximum likelihood framework, and its performance is evaluated by simulation over a set of realistic image instances. The fit quality is analysed as a function of the number of terms used for signal expansion, and of astrometric error, rather than RMS discrepancy with respect to the input signal. The tuning of the function basis to the statistical characteristics of the signal ensemble is discussed. The fit sensitivity to a priori knowledge of the source spectra is addressed. Some implications of the current results on calibration and data reduction aspects are discussed, in particular with respect to Gaia.
We discuss the prospects of high precision pointing of our transmitter to habitable planets around Galactic main sequence stars. For an efficient signal delivery, the future sky positions of the host stars should be appropriately extrapolated with accuracy better than the beam opening angle $Theta$ of the transmitter. Using the latest data release (DR2) of Gaia, we estimate the accuracy of the extrapolations individually for $4.7times 10^7$ FGK stars, and find that the total number of targets could be $sim 10^7$ for the accuracy goal better than 1. Considering the pairwise nature of communication, our study would be instructive also for SETI (Search for Extraterrestrial Intelligence), not only for sending signals outward.
The Gaia mission is delivering exquisite astrometric data for 1.47 billion sources, which are revolutionizing many fields in astronomy. For a small fraction of these sources the astrometric solutions are poor, and the reported values and uncertainties may not apply. For many analyses it is important to recognize and excise these spurious results, commonly done by means of quality flags in the Gaia catalog. Here we devise and apply a path to separating good from bad astrometric solutions that is an order-of-magnitude cleaner than any single flag: we achieve a purity of 99.7% and a completeness of 97.6% as validated on our test data. We devise an extensive sample of manifestly bad astrometric solutions: sources whose inferred parallax is negative at >= 4.5 sigma; and a corresponding sample of presumably good solutions: the sources in HEALPix patches of the sky that do not contain extremely negative parallaxes. We then train a neural net that uses 14 pertinent Gaia catalog entries to discriminate these two samples, captured in a single astrometric fidelity parameter. An extensive and diverse set of verification tests show that our approach to assessing astrometric fidelity works very cleanly also in the regime where no negative parallaxes are involved; its main limitations are in the very low S/N regime. Our astrometric fidelities for all EDR3 can be queried via the Virtual Observatory. In the spirit of open science, we make our code and training/validation data public, so that our results can be easily reproduced.
The standard errors of the end-of-mission Gaia astrometry have been re-assessed after conclusion of the in-orbit commissioning phase of the mission. An analytical relation is provided for the parallax standard error as function of Gaia G magnitude (and V-I colour) which supersedes the pre-launch relation provided in de Bruijne (2012).
Gaia Data Release 2 (Gaia DR2) contains results for 1693 million sources in the magnitude range 3 to 21 based on observations collected by the European Space Agency Gaia satellite during the first 22 months of its operational phase. We describe the input data, models, and processing used for the astrometric content of Gaia DR2, and the validation of these results performed within the astrometry task. Some 320 billion centroid positions from the pre-processed astrometric CCD observations were used to estimate the five astrometric parameters (positions, parallaxes, and proper motions) for 1332 million sources, and approximate positions at the reference epoch J2015.5 for an additional 361 million mostly faint sources. Special validation solutions were used to characterise the random and systematic errors in parallax and proper motion. For the sources with five-parameter astrometric solutions, the median uncertainty in parallax and position at the reference epoch J2015.5 is about 0.04 mas for bright (G<14 mag) sources, 0.1 mas at G=17 mag, and 0.7 mas at G=20 mag. In the proper motion components the corresponding uncertainties are 0.05, 0.2, and 1.2 mas/yr, respectively. The optical reference frame defined by Gaia DR2 is aligned with ICRS and is non-rotating with respect to the quasars to within 0.15 mas/yr. From the quasars and validation solutions we estimate that systematics in the parallaxes depending on position, magnitude, and colour are generally below 0.1 mas, but the parallaxes are on the whole too small by about 0.03 mas. Significant spatial correlations of up to 0.04 mas in parallax and 0.07 mas/yr in proper motion are seen on small (<1 deg) and intermediate (20 deg) angular scales. Important statistics and information for the users of the Gaia DR2 astrometry are given in the appendices.
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