The flux distributions of spectrophotometric standard stars were initially derived from the comparison of stars to laboratory sources of known flux but are now based on calculated model atmospheres. For example, pure hydrogen white dwarf (WD) models provide the basis for the HST CALSPEC archive of flux standards. There is good evidence that relative fluxes from the visible to the near-IR wavelength of ∼2.5 μm are currently accurate to ∼1% for the primary reference standards.

Optical large-scale medium-resolution spectroscopic surveys such as SDSS, LAMOST, DESI, WEAVE or 4MOST are subject to constraints that limit the choice of flux calibrators, and the attained precision. The use of optical fibers, a large but limited field of view, the tiling strategies and tight schedules, are all factors that call for a careful evaluation of the flux calibration procedures.

The density of stars with well-known spectral energy distributions is so low that makes them unsuitable for flux calibration of large scale spectroscopic surveys. The alternative is to use stars with relatively simple spectra, which can be approximated well by synthetic spectra based on model atmospheres. One example are white dwarfs (Bohlin 1996), but their density is also too low for practical purposes: a few per square degree down to 19th magnitude. An alternative choice, exploited by the SDSS, are halo turn-off F-type stars (Stoughton et al.2002). A-type stars offer another option, albeit with lower densities at high Galactic latitudes (Allende Prieto del Burgo 2016). Ideally, one would use stars of various spectral types. The most common type, halo turn-off stars, can be used for the actual calibration, and the others for quality assessment.

The spectral typing needs to be performed before spectra are flux calibrated. Our group has explored various strategies for continuum normalization (the removal of the instrument response), finding good results using a running mean filter (Aguado et al.2017; Allende Prieto et al.2014). Interpolation in the models speeds up the model fitting process, but it is important to ensure that interpolations are sufficiently accurate (see, e.g. Mészáros Allende Prieto 2013).

Fiber-fed spectrographs are particularly challenging, since errors in positioning fibers, guiding errors, or differential atmospheric refraction, add up. In our tests with data from the Baryonic Oscillations Spectroscopic Survey (BOSS; Dawson et al.2016), we conclude that while the flux calibration is statistically accurate (<5%), individual spectra can exhibit much larger excursions, in excess of 20%.

The Ultra-Violet Imaging Telescope (UVIT) is one of the payloads in Astrosat, the first Indian Space Observatory. The UVIT instrument has two 375 mm telescopes: one for the far-ultraviolet (FUV) channel (1300-1800 Å), and the other for the near-ultraviolet (NUV) channel (2000-3000 Å) and the visible (VIS) channel (3200-5500 Å). We shall discuss the issues with standardization in the UV with reference to Astrosat Observations (Cycle A04). I shall discuss the problems faced in data-analysis and how these in turn lead to serious issues dealing with the color-magnitude diagarms, membership and age of the young embedded clusters studied.

High-precision spectroscopy of large stellar samples plays a crucial role for several topical issues in astrophysics, such as studying the chemical evolution of the Milky Way Galaxy. Data are accumulating from instruments that obtain high-quality spectra of stars in the ultraviolet, optical and infrared wavelength regions on a routine basis. The interpretation of these spectra is often based on synthetic stellar spectra, either calculated on the fly or taken from a spectral library. One of the most important ingredients of these spectra is a set of high-quality transition data for numerous species, in particular neutral and singly ionized atoms. We rely heavily on the continuous activities of laboratory astrophysics groups that produce and improve the relevant experimental and theoretical atomic data. As an example, we briefly describe the efforts done in the context of the Gaia-ESO Public Spectroscopic Survey to compile and assess the best available data in a standard way, providing a list of recommended lines for analysis of optical spectra of FGK stars. The line data, together with specialised analysis methods, allow different surveys to obtain abundances with typical precisions of ∼0.1 dex on an industrial scale for ∼10 chemical elements. Several elements with urgent need for better atomic data have been identified.

I use SkyMapper DR1.1 to explore the quality of its uvgriz photometry, and zero-points. I introduce a formalism to derive photometric zero-points across the sky by benchmarking against stars with well known effective temperatures, bypassing the need for absolute spectrophotometry.

Gaia DR2 was released in April 2018 and contains a photometric catalogue of more than 1 billion sources. This release contains colour information in the form of integrated BP and RP photometry in addition to the latest G-band photometry. The level of uncertainty can be as good as 2 mmag with some residual systematics at the 10 mmag level. The addition of colour information greatly enhances the value of the photometric data for the scientific community. A high level overview of the photometric processing, with a focus on the improvements with respect to Gaia DR1, was given. The definition of the Gaia photometric system, a crucial part of the calibration of the photometry, was also explained. Finally, some of the photometric improvements expected for the next data release were described.

I discuss the photometric calibration of the SkyMapper Southern Survey, our adopted methods and what we learned from comparisons with external catalogues.

Photometric passbands are usually characterised through laboratory measurements and once in operations they are refined with true observations of reference sources with known spectral energy distribution. This paper revises the methods to determine those passbands and discusses the limitations encountered. The passbands are not fully constrained by the reference sources used and the method presented here allows to evaluate which is the constrained and the unconstrained component of the passband.