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We present a method for the in-flight relative flux self-calibration of a spectro-photometer instrument, general enough to be applied to any upcoming galaxy survey on satellite. The instrument response function, that accounts for a smooth continuous variation due to telescope optics, on top of a discontinuous effect due to the segmentation of the detector, is inferred with a $chi^2$ statistics. The method provides unbiased inference of the sources count rates and of the reconstructed relative response function, in the limit of high count rates. We simulate a simplified sequence of observations following a spatial random pattern and realistic distributions of sources and count rates, with the purpose of quantifying the relative importance of the number of sources and exposures for correctly reconstructing the instrument response. We present a validation of the method, with the definition of figures of merit to quantify the expected performance, in plausible scenarios.
We consider the application of relative self-calibration using overlap regions to spectroscopic galaxy surveys that use slit-less spectroscopy. This method is based on that developed for the SDSS by Padmanabhan at al. (2008) in that we consider joint
The answers to fundamental science questions in astrophysics, ranging from the history of the expansion of the universe to the sizes of nearby stars, hinge on our ability to make precise measurements of diverse astronomical objects. As our knowledge
Future radio surveys will generate catalogues of tens of millions of radio sources, for which redshift estimates will be essential to achieve many of the science goals. However, spectroscopic data will be available for only a small fraction of these
Recent large-scale galaxy spectroscopic surveys, such as the Sloan Digital Sky Survey (SDSS), enable us to execute a systematic, relatively-unbiased search for galaxy clusters. Such surveys make it possible to measure the 3-d distribution of galaxies
We developed a code that estimates distances to stars using measured spectroscopic and photometric quantities. We employ a Bayesian approach to build the probability distribution function over stellar evolutionary models given these data, delivering