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New estimate of the distances of 36 nearby galaxies is presented. It is based on the calibration of the V- and I-band Period-Lumi- nosity relations for galactic Cepheids measured by the HIPPARCOS mission. The distance moduli are obtained in a classical way. The statistical bias due to the incompleteness of the sample is corrected according to the precepts introduced by Teerikorpi (1987). We adopt a constant slope (the one obtained with LMC Cepheids). The correction for incompleteness bias introduce an uncertainty which depends on each galaxy. On the mean, this uncertainty is small (0.04 mag) but it may reach 0.3 mag. We show that the un- certainty due to the correction of the extinction is small (propably less than 0.05 mag.). The correlation between the metallicity and the morphological type of the host galaxy sug- gests us to reduce the application to spiral galaxies in order to bypass the problem of metallicity. We suspect that the adopted PL slopes are not valid for all morphological types of galaxies. This may induce a mean systematic shift of 0.1 mag on distance moduli. A comparison with the distance moduli recently published by Freedman et al. (2001) shows there is a reasonably good agreement with our distance moduli.
New estimates of the distances of 36 nearby galaxies are presented based on accurate distances of galactic Cepheids obtained by Gieren, Fouque and Gomez (1998) from the geometrical Barnes-Evans method. The concept of sosie is applied to extend the
Hipparcos, the first ever experiment of global astrometry, was launched by ESA in 1989 and its results published in 1997 (Perryman et al., Astron. Astrophys. 323, L49, 1997; Perryman & ESA (eds), The Hipparcos and Tycho catalogues, ESA SP-1200, 1997)
In Yoshii et al. (2014), we described a new method for measuring extragalactic distances based on dust reverberation in active galactic nuclei (AGNs), and we validated our new method with Cepheid variable stars. In this paper, we validate our new met
We present the modeling tool we developed to incorporate multi-technique observations of Cepheids in a single pulsation model: the Spectro-Photo-Interferometry of Pulsating Stars (SPIPS). The combination of angular diameters from optical interferomet
Deep neural networks (DNNs) are poorly calibrated when trained in conventional ways. To improve confidence calibration of DNNs, we propose a novel training method, distance-based learning from errors (DBLE). DBLE bases its confidence estimation on di