arXiv subjects

R. Cancelliere

Publications and source records attributed to R. Cancelliere.

7 recordsLinked to original sources

arXiv2020

Gaia Early Data Release 3: The astrometric solution

Gaia Early Data Release 3 (Gaia EDR3) contains results for 1.812 billion sources in the magnitude range G = 3 to 21 based on observations collected by the European Space Agency Gaia satellite during the first 34 months of its operational phase. We describe the input data, the models, and the processing used for the astrometric content of Gaia EDR3, as well as the validation of these results performed within the astrometry task. The processing broadly followed the same procedures as for Gaia DR2, but with significant improvements to the modelling of observations. For the first time in the Gaia data processing, colour-dependent calibrations of the line- and point-spread functions have been used for sources with well-determined colours from DR2. In the astrometric processing these sources obtained five-parameter solutions, whereas other sources were processed using a special calibration that allowed a pseudocolour to be estimated as the sixth astrometric parameter. Compared with DR2, the astrometric calibration models have been extended, and the spin-related distortion model includes a self-consistent determination of basic-angle variations, improving the global parallax zero point. Gaia EDR3 gives full astrometric data (positions at epoch J2016.0, parallaxes, and proper motions) for 1.468 billion sources (585 million with five-parameter solutions, 882 million with six parameters), and mean positions at J2016.0 for an additional 344 million. Solutions with five parameters are generally more accurate than six-parameter solutions, and are available for 93% of the sources brighter than G = 17 mag. The median uncertainty in parallax and annual proper motion is 0.02-0.03 mas at magnitude G = 9 to 14, and around 0.5 mas at G = 20. Extensive characterisation of the statistical properties of the solutions is provided, including the estimated angular power spectrum of parallax bias from the quasars.

astro-ph.IM↗

arXiv2018

Gaia Data Release 2: The astrometric solution

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.

astro-ph.IM↗

arXiv2015

An analysis of numerical issues in neural training by pseudoinversion

Some novel strategies have recently been proposed for single hidden layer neural network training that set randomly the weights from input to hidden layer, while weights from hidden to output layer are analytically determined by pseudoinversion. These techniques are gaining popularity in spite of their known numerical issues when singular and/or almost singular matrices are involved. In this paper we discuss a critical use of Singular Value Analysis for identification of these drawbacks and we propose an original use of regularisation to determine the output weights, based on the concept of critical hidden layer size. This approach also allows to limit the training computational effort. Besides, we introduce a novel technique which relies an effective determination of input weights to the hidden layer dimension. This approach is tested for both regression and classification tasks, resulting in a significant performance improvement with respect to alternative methods.

cs.LG↗

arXiv2013

Statistically optimal fitting of astrometric signals

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.

astro-ph.IM↗

arXiv2008

Astrometric effects of non-uniform telescope throughput

In real telescopes, the optical parameters evolve with time, and the degradation is often not uniform. This introduces variations in the image profile and therefore photo-centre displacements which, unless corrected, may result in astrometric errors. The effects induced on individual telescopes and interferometric arrays are derived by numerical implementation of a range of cases. The results are evaluated with respect to the potential impact on the most relevant experiments for high precision astrometry in the near future, i.e. Gaia, PRIMA and SIM, and to mitigation techniques applicable from design stage to calibrations.

astro-ph↗

arXiv2007

An efficient PSF construction method

Image computation is a fundamental tool for performance assessment of astronomical instrumentation, usually implemented by Fourier transform techniques. We review the numerical implementation, evaluating a direct implementation of the discrete Fourier transform (DFT) algorithm, compared with fast Fourier transform (FFT) tools. Simulations show that the precision is quite comparable, but in the case investigated the computing performance is considerably higher for DFT than FFT. The application to image simulation for the mission Gaia and for Extremely Large Telescopes is discussed.

astro-ph↗

arXiv2005

Neural network correction of astrometric chromaticity

In this paper we deal with the problem of chromaticity, i.e. apparent position variation of stellar images with their spectral distribution, using neural networks to analyse and process astronomical images. The goal is to remove this relevant source of systematic error in the data reduction of high precision astrometric experiments, like Gaia. This task can be accomplished thanks to the capability of neural networks to solve a nonlinear approximation problem, i.e. to construct an hypersurface that approximates a given set of scattered data couples. Images are encoded associating each of them with conveniently chosen moments, evaluated along the y axis. The technique proposed, in the current framework, reduces the initial chromaticity of few milliarcseconds to values of few microarcseconds.

astro-ph↗