The analysis of our reconstructed images shows that the 2014 near-IR flux extends to ~1.7–2.3 equatorial radii. Our results from interferometric imaging are also supported by several H α line profiles showing that Achernar started an emission-line phase sometime in the beginning of 2013. In 2014, on the other hand, a disk was already formed and our reconstructed image reveals an extended H -band continuum excess flux. The observations taken in 20, during the quiescent phase of Achernar, do not exhibit a disk at the detection limit of the instrument. To study the disk formation, we compared the observations and reconstructed images to previously computed models of both the stellar photosphere alone (normal B phase) and the star presenting a circumstellar disk (Be phase). We used infrared long-baseline interferometry with the PIONIER instrument at the Very Large Telescope Interferometer (VLTI) to produce reconstructed images of the photosphere and close environment of the star over four years of observations.
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Therefore we seek to produce an image of the photosphere and close environment of the star. Despite all these previous studies, the disk around Achernar was never directly imaged. The star Achernar is a privileged laboratory to probe these additional mechanisms because it is close, presents B ⇌ Be phase variations on timescales ranging from ~6 yr to ~15 yr, a companion star was discovered around it, and probably presents a polar wind or jet.Īims. In particular, it is not clear which mechanisms operate, in addition to fast rotation, to produce the observed variable ejection of matter. The mechanism of disk formation around fast-rotating Be stars is not well understood. The software, available online, is developed in MATLAB as part of an astronomical image processing environment and it can be run also as a stand-alone code.Ĭontext. I discuss the effects of seeing, airmass, and the order of the transformation on the astrometric accuracy. Running on PTF 60-s integration images, and using the GAIA-DR2 as a reference catalog, the typical two-axes-combined astrometric root-mean square (rms) is 14 mas at the bright end, presumably due to astrometric scintillation noise and systematic errors. The tested images equally represent low and high Galactic latitude fields and exhibit failure/bad-solution rate of 2 × 10⁻⁵.
#Astrometry for astrophysics pdf code
The code was tested on over 5 × 10⁴ images from various sources, including the Palomar Transient Factory (PTF) and the Zwicky Transient Facility (ZTF). The code puts emphasis on robustness against failures for correctly matching the sources in the image to a reference catalog, and on the stability of the solutions over the field of view (e.g., using orthogonal polynomials for the fitted transformation).
#Astrometry for astrophysics pdf software
I present a software tool for solving the astrometry of astronomical images. Where intra-pixel response changes are neglected, and where flat-fielding is Results are valid for an idealized linear (one-dimensional) array detector Performance of the least-squares method under a wide range of conditions. Theorem) can be used as a benchmark indicator of the expected statistical We show that the nominal valueįor the mean-square-error of the least-squares estimator (obtained from our Observations under typical observing conditions. We validate our theoretical analysis through simulated digital-detector However, we also demonstrate that, in general, there is no unbiasedĮstimator for the astrometric position that can precisely reach the Cramer-Raoīound. Is near optimal, as its performance asymptotically approaches the Cramer-Raoīound. On the positive side, we show that for theĬhallenging low signal-to-noise regime (attributed to either a weakĪstronomical signal or a noise-dominated condition) the least-squares estimator High signal-to-noise ratio regime, the performance of the least-squaresĮstimator is significantly poorer than the Cramer-Rao bound, and weĬharacterize this gap analytically. Based on our results, we show that, for the From the predicted nominal value weĪnalyze how efficient is the least-squares estimator in comparison with the Where both the bias and the mean-square-error of the least-squares estimatorĪre bounded and approximated analytically, in the latter case in terms of a Not offer a closed-form expression, but a new result is presented (Theorem 1) In this inference context the performance of the least-squares estimator does
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We characterize the performance of the widely-used least-squares estimator inĪstrometry in terms of a comparison with the Cramer-Rao lower variance bound.