No Arabic abstract
We take advantage of the availability of precision parallax data from Gaia Data Release 2 together with machine learning to develop a set of equations for transforming Tycho-2 (VT, BT) magnitudes into the Johnson-Cousins (J-C) system. Starting with data for 558 standard stars with apparent magnitudes brighter than 11.0, we employed one step supervised learning with weight decay regularization and 10-fold cross validation to produce a set of transformation equations from Tycho-2 into J-C, which in turn were used to derive transformations of the Tycho-2 standard deviations into the J-C system. Both the aggregated cross validation data sets and the in-sample results from the final training were essentially unbiased (average errors << 1 mmag in both B and V) and had error standard deviations comparable to those of the input data. Comparison of errors in- and out-of-sample indicate modest generalization error growth. Moreover, testing of the distributions of the normalized errors indicated that the predicted standard deviations are accurate, enabling them to be reliably employed in the suitability ranking of comparison star candidates. These results thus enable utilization of a substantial portion of the 2.5 million star Tycho-2 data set as comparison stars for two-color bright star ensemble photometry.
In this letter, we have carried out an independent validation of the Gaia EDR3 photometry using about 10,000 Landolt standard stars from Clem & Landolt (2013). Using a machine learning technique, the UBVRI magnitudes are converted into the Gaia magnitudes and colors and then compared to those in the EDR3, with the effect of metallicity incorporated. Our result confirms the significant improvements in the calibration process of the Gaia EDR3. Yet modest trends up to 10 mmag with G magnitude are found for all the magnitudes and colors for the 10 < G < 19 mag range, particularly for the bright and faint ends. With the aid of synthetic magnitudes computed on the CALSPEC spectra with the Gaia EDR3 passbands, absolute corrections are further obtained, paving the way for optimal usage of the Gaia EDR3 photometry in high accuracy investigations.
Stellar clusters are important for astrophysics in many ways, for instance as optimal tracers of the Galactic populations to which they belong or as one of the best test bench for stellar evolutionary models. Gaia DR1, with TGAS, is just skimming the wealth of exquisite information we are expecting from the more advanced catalogues, but already offers good opportunities and indicates the vast potentialities. Gaia results can be efficiently complemented by ground-based data, in particular by large spectroscopic and photometric surveys. Examples of some scientific results of the Gaia-ESO survey are presented, as a teaser for what will be possible once advanced Gaia releases and ground-based data will be combined.
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 $<$13 mag over a 0.24 sq.degs. field of view for short time scales of the order of a day with high-cadence observations such as those around the North Ecliptic Pole during the EPSL scanning mode of Gaia for a perfect instrument. The inclusion of the geometric instrument model over such short time scales accounting for large-scale calibrations requires fainter stars down to G = 14 mag without diminishing the accuracy of the reference frame. We discuss several future perspectives of utilizing this methodology over different and longer timescales.
Effective temperatures and luminosities are calculated for 1,475,921 Tycho-2 and 107,145 Hipparcos stars, based on distances from Gaia Data Release 1. Parameters are derived by comparing multi-wavelength archival photometry to BT-Settl model atmospheres. The 1-sigma uncertainties for the Tycho-2 and Hipparcos stars are +/-137 K and +/-125 K in temperature and +/-35 per cent and +/-19 per cent in luminosity. The luminosity uncertainty is dominated by that of the Gaia parallax. Evidence for infrared excess between 4.6 and 25 microns is found for 4256 stars, of which 1883 are strong candidates. These include asymptotic giant branch (AGB) stars, Cepheids, Herbig Ae/Be stars, young stellar objects, and other sources. We briefly demonstrate the capabilities of this dataset by exploring local interstellar extinction, the onset of dust production in AGB stars, the age and metallicity gradients of the solar neighbourhood and structure within the Gould Belt. We close by discussing the potential impact of future Gaia data releases.
We present results from the analysis of 401 RR Lyrae stars (RRLs) belonging to the field of the Milky Way (MW). For a fraction of them multi-band ($V$, $K_{rm s}$, $W1$) photometry, metal abundances, extinction values and pulsation periods are available in the literature and accurate trigonometric parallaxes measured by the Gaia mission alongside Gaia $G$-band time-series photometry have become available with the Gaia second data release (DR2) on 2018 April 25. Using a Bayesian fitting approach we derive new near-, mid-infrared period-absolute magnitude-metallicity ($PMZ$) relations and new absolute magnitude-metallicity relations in the visual ($M_V - {rm [Fe/H]}$) and $G$ bands ($M_G - {rm [Fe/H]}$), based on the Gaia DR2 parallaxes. We find the dependence of luminosity on metallicity to be higher than usually found in the literature, irrespective of the passband considered. Running the adopted Bayesian model on a simulated dataset we show that the high metallicity dependence is not caused by the method, but likely arises from the actual distribution of the data and the presence of a zero-point offset in the Gaia parallaxes. We infer a zero-point offset of $-0.057$ mas, with the Gaia DR2 parallaxes being systematically smaller. We find the RR Lyrae absolute magnitude in the $V$, $G$, $K_{rm s}$ and $W1$ bands at metallicity of [Fe/H]=$-1.5$ dex and period of P = 0.5238 days, based on Gaia DR2 parallaxes to be $M_V = 0.66pm0.06$ mag, $M_G = 0.63pm0.08$ mag, $M_{K_{rm s}} = -0.37pm0.11$ mag and $M_{W1} = -0.41pm0.11$ mag, respectively.