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Register a new userCompleteness of the Gaia-verse IV: The Astrometry Spread Function of Gaia DR2
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Gaia DR2 published positions, parallaxes and proper motions for an unprecedented 1,331,909,727 sources, revolutionising the field of Galactic dynamics. We complement this data with the Astrometry Spread Function (ASF), the expected uncertainty in the measured positions, proper motions and parallax for a non-accelerating point source. The ASF is a Gaussian function for which we construct the 5D astrometric covariance matrix as a function of position on the sky and apparent magnitude using the Gaia DR2 scanning law and demonstrate excellent agreement with the observed data. This can be used to answer the question `What astrometric covariance would Gaia have published if my star was a non-accelerating point source?. The ASF will enable characterisation of binary systems, exoplanet orbits, astrometric microlensing events and extended sources which add an excess astrometric noise to the expected astrometry uncertainty. By using the ASF to estimate the unit weight error (UWE) of Gaia DR2 sources, we demonstrate that the ASF indeed provides a direct probe of the excess source noise. We use the ASF to estimate the contribution to the selection function of the Gaia astrometric sample from a cut on astrometric_sigma5d_max showing high completeness for $G<20$ dropping to $<1%$ in underscanned regions of the sky for $G=21$. We have added an ASF module to the Python package SCANNINGLAW (https://github.com/gaiaverse/scanninglaw) through which users can access the ASF.
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Gaias Early Third Data Release (EDR3) does not contain new radial velocities because these will be published in Gaias full third data release (DR3), expected in the first half of 2022. To maximise the usefulness of EDR3, Gaias second data release (DR2) sources (with radial velocities) are matched to EDR3 sources to allow their DR2 radial velocities to also be included in EDR3. This presents two considerations: (i) arXiv:1901.10460 (hereafter B19) published a list of 70,365 sources with potentially contaminated DR2 radial velocities; and (ii) EDR3 is based on a new astrometric solution and a new source list, which means sources in DR2 may not be in EDR3. EDR3 contains 7,209,831 sources with a DR2 radial velocity, which is 99.8% of sources with a radial velocity in DR2. 14,800 radial velocities from DR2 are not propagated to any EDR3 sources because (i) 3871 from the B19 list are found to either not have an unpublished, preliminary DR3 radial velocity or it differs significantly from its DR2 value, and 5 high-velocity stars not in the B19 list are confirmed to have contaminated radial velocities; and (ii) 10,924 DR2 sources could not be satisfactorily matched to any EDR3 sources, so their DR2 radial velocities are also missing from EDR3. The reliability of radial velocities in EDR3 has improved compared to DR2 because the update removes a small fraction of erroneous radial velocities (0.05% of DR2 radial velocities and 5.5% of the B19 list). Lessons learnt from EDR3 (e.g. bright star contamination) will improve the radial velocities in future Gaia data releases. The main reason for radial velocities from DR2 not propagating to EDR3 is not related to DR2 radial velocity quality. It is because the DR2 astrometry is based on one component of close binary pairs, while EDR3 astrometry is based on the other component, which prevents these sources from being unambiguously matched. (Abridged)
RR Lyrae stars are an important and widely used tracer of the most ancient populations of our Galaxy, mainly due to their standard candle nature. The availability of large scale surveys of variable stars is allowing us to trace the structure of our entire Galaxy, even in previously inaccessible areas like the Galactic disc. In this work we aim to provide an empirical assessment of the completeness of the three largest RR Lyrae catalogues available: Gaia DR2, PanSTARRS-1 and ASAS-SN-II. Using a joint probabilistic analysis of the three surveys we compute 2D and 3D completeness maps in each surveys full magnitude range. At the bright end (G<13) we find ASAS-SN-II and Gaia are near 100% complete in RRab at high latitude (|b|>20deg); ASAS-SN-II has the best completeness at low latitude for RRab and at all latitudes for RRc. At the faint end (G>13), Gaia DR2 is the most complete catalogue for both RR Lyrae types, at any latitude, with median completeness rates of 95% (RRab) and >85% (RRc) outside the ecliptic plane (|beta|>25deg). We confirm a high and uniform completeness of PanSTARRS-1 RR Lyrae at 91% (RRab) and 82% (RRc) down to G~18, and provide the first estimate of its completeness at low galactic latitude (|b|<20deg) at an estimated median 65% (RRab) and 50-60% (RRc). Our results are publicly available as 2D and 3D completeness maps, and as functions to evaluate each surveys completeness versus distance or per line-of sight.
The Gaia Data Release 2 contains the 1st release of radial velocities complementing the kinematic data of a sample of about 7 million relatively bright, late-type stars. Aims: This paper provides a detailed description of the Gaia spectroscopic data processing pipeline, and of the approach adopted to derive the radial velocities presented in DR2. Methods: The pipeline must perform four main tasks: (i) clean and reduce the spectra observed with the Radial Velocity Spectrometer (RVS); (ii) calibrate the RVS instrument, including wavelength, straylight, line-spread function, bias non-uniformity, and photometric zeropoint; (iii) extract the radial velocities; and (iv) verify the accuracy and precision of the results. The radial velocity of a star is obtained through a fit of the RVS spectrum relative to an appropriate synthetic template spectrum. An additional task of the spectroscopic pipeline was to provide 1st-order estimates of the stellar atmospheric parameters required to select such template spectra. We describe the pipeline features and present the detailed calibration algorithms and software solutions we used to produce the radial velocities published in DR2. Results: The spectroscopic processing pipeline produced median radial velocities for Gaia stars with narrow-band near-IR magnitude Grvs < 12 (i.e. brighter than V~13). Stars identified as double-lined spectroscopic binaries were removed from the pipeline, while variable stars, single-lined, and non-detected double-lined spectroscopic binaries were treated as single stars. The scatter in radial velocity among different observations of a same star, also published in DR2, provides information about radial velocity variability. For the hottest (Teff > 7000 K) and coolest (Teff < 3500 K) stars, the accuracy and precision of the stellar parameter estimates are not sufficient to allow selection of appropriate templates. [Abridged]
We find that the combined LF of N- and SC-type stars are consistent with a Gaussian distribution peaking at M_bol~ -5.2 mag. The resulting LF however shows two tails at lower and higher luminosities more extended than those previously found, indicating that AGB carbon stars with Solar metallicity may reach M_bol~-6.0 mag. We find that J-type stars are about half a magnitude fainter on average than N- and SC-type stars, while R-hot stars are half a magnitude brighter than previously found. The Galactic spatial distribution and velocity components of the N-, SC- and J-type stars are very similar, while about 30 % of the R-hot stars in the sample are located at distances larger than ~ 500 pc from the Galactic Plane, and show a significant drift with respect to the local standard of rest. The LF derived for N- and SC-type in the Solar neighbourhood fully agrees with the expected luminosity of stars of 1.5-3 M_o on the AGB. On a theoretical basis, the existence of an extended low luminosity tail would require a contribution of extrinsic low mass carbon stars, while the high luminosity one would imply that stars with mass up to ~5 Mo may become carbon star on the AGB. J-type stars not only differ significantly in their chemical composition with respect to the N- and SC-types but also in their LF, which reinforces the idea that these carbon stars belong to a dvifferent type whose origin is still unknown. The derived luminosities of R-hot stars make these stars unlikely to be in the red-clump as previously claimed. On the other hand, the derived spatial distribution and kinematic properties, together with their metallicity, indicate that most of the N-, SC- and J-type stars belong to the thin disc population, while a significant fraction of R-hot stars show characteristics compatible with the thick disc.
The Ophiuchus cloud complex is one of the best laboratories to study the earlier stages of the stellar and protoplanetary disc evolution. The wealth of accurate astrometric measurements contained in the Gaia Data Release 2 can be used to update the census of Ophiuchus member candidates. We seek to find potential new members of Ophiuchus and identify those surrounded by a circumstellar disc. We constructed a control sample composed of 188 bona fide Ophiuchus members. Using this sample as a reference we applied three different density-based machine learning clustering algorithms (DBSCAN, OPTICS, and HDBSCAN) to a sample drawn from the Gaia catalogue centred on the Ophiuchus cloud. The clustering analysis was applied in the five astrometric dimensions defined by the three-dimensional Cartesian space and the proper motions in right ascension and declination. The three clustering algorithms systematically identify a similar set of candidate members in a main cluster with astrometric properties consistent with those of the control sample. The increased flexibility of the OPTICS and HDBSCAN algorithms enable these methods to identify a secondary cluster. We constructed a common sample containing 391 member candidates including 166 new objects, which have not yet been discussed in the literature. By combining the Gaia data with 2MASS and WISE photometry, we built the spectral energy distributions from 0.5 to $22microm$ for a subset of 48 objects and found a total of 41 discs, including 11 Class II and 1 Class III new discs. Density-based clustering algorithms are a promising tool to identify candidate members of star forming regions in large astrometric databases. If confirmed, the candidate members discussed in this work would represent an increment of roughly 40% of the current census of Ophiuchus.
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