Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA proposal for relative in-flight flux self-calibrations for spectro-photometric surveys
118
0
0.0
(
0
)
Authors
S. Davini
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
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 jointly fitting and marginalising over calibrator brightness, rather than treating these as free parameters. However, we separate the calibration of the detector-to-detector from the full-focal-plane exposure-to-exposure calibration. To demonstrate how the calibration procedure will work, we simulate the procedure for a potential implementation of the spectroscopic component of the wide Euclid survey. We study the change of coverage and the determination of relative multiplicative errors in flux measurements for different dithering configurations. We use the new method to study the case where the flat-field across each exposure or detector is measured precisely and only exposure-to-exposure or detector-to-detector variation in the flux error remains. We consider several base dither patterns and find that they strongly influence the ability to calibrate, using this methodology. To enable self-calibration, it is important that the survey strategy connects different observations with at least a minimum amount of overlap, and we propose an S-pattern for dithering that fulfills this requirement. The final survey strategy adopted by Euclid will have to optimise for a number of different science goals and requirements. The large-scale calibration of the spectroscopic galaxy survey is clearly cosmologically crucial, but is not the only one.
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 of the underlying physics of objects improves along with advances in detectors and instrumentation, the limits on our capability to extract science from measurements is set, not by our lack of understanding of the nature of these objects, but rather by the most mundane of all issues: the precision with which we can calibrate observations in physical units. We stress the need for a program to improve upon and expand the current networks of spectrophotometrically calibrated stars to provide precise calibration with an accuracy of equal to and better than 1% in the ultraviolet, visible and near-infrared portions of the spectrum, with excellent sky coverage and large dynamic range.
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 sources, and in most cases even the optical and infrared photometry will be of limited quality. Furthermore, radio sources tend to be at higher redshift than most optical sources and so a significant fraction of radio sources hosts differ from those for which most photometric redshift templates are designed. We therefore need to develop new techniques for estimating the redshifts of radio sources. As a starting point in this process, we evaluate a number of machine-learning techniques for estimating redshift, together with a conventional template-fitting technique. We pay special attention to how the performance is affected by the incompleteness of the training sample and by sparseness of the parameter space or by limited availability of ancillary multi-wavelength data. As expected, we find that the quality of the photometric-redshift degrades as the quality of the photometry decreases, but that even with the limited quality of photometry available for all sky-surveys, useful redshift information is available for the majority of sources, particularly at low redshift. We find that a template-fitting technique performs best with high-quality and almost complete multi-band photometry, especially if radio sources that are also X-ray emitting are treated separately. When we reduced the quality of photometry to match that available for the EMU all-sky radio survey, the quality of the template-fitting degraded and became comparable to some of the machine learning methods. Machine learning techniques currently perform better at low redshift than at high redshift, because of incompleteness of the currently available training data at high redshifts.
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 but are hampered by the incompleteness problem due to fiber collisions. In this study we aim to develop a density measuring technique that alleviates the problem and derives densities more accurately by adding additional cluster member galaxies that follow optical color-magnitude relations for the given redshift. The new density measured with both spectroscopic and photometric data shows a good agreement with apparent information on cluster images and is supported by follow-up observations. By adopting this new method, a total of 924 $robust$ galaxy clusters are found from the SDSS DR5 database in the redshift range $0.05<z<0.1$, of which 212 are new. Local maximum-density galaxies successfully represent cluster centers. We provide the cluster catalogue including a number of cluster parameters.
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 estimates of model parameters for each star individually. The code was first tested on simulations, successfully recovering input distances to mock stars with <1% bias.The method-intrinsic random distance uncertainties for typical spectroscopic survey measurements amount to around 10% for dwarf stars and 20% for giants, and are most sensitive to the quality of $log g$ measurements. The code was validated by comparing our distance estimates to parallax measurements from the Hipparcos mission for nearby stars (< 300 pc), to asteroseismic distances of CoRoT red giant stars, and to known distances of well-studied open and globular clusters. The external comparisons confirm that our distances are subject to very small systematic biases with respect to the fundamental Hipparcos scale (+0.4 % for dwarfs, and +1.6% for giants). The typical random distance scatter is 18% for dwarfs, and 26% for giants. For the CoRoT-APOGEE sample, the typical random distance scatter is ~15%, both for the nearby and farther data. Our distances are systematically larger than the CoRoT ones by about +9%, which can mostly be attributed to the different choice of priors. The comparison to known distances of star clusters from SEGUE and APOGEE has led to significant systematic differences for many cluster stars, but with opposite signs, and with substantial scatter. Finally, we tested our distances against those previously determined for a high-quality sample of giant stars from the RAVE survey, again finding a small systematic trend of +5% and an rms scatter of 30%.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
