Attaining Spectral Energy Distributions With Sub-Percent Uncertainties: All-Sky DA White Dwarf Spectrophotometric Standard Stars For Large Telescopes And Surveys

TL;DR

This paper introduces a new set of 32 faint DA white dwarfs as highly accurate, all-sky spectrophotometric standards for large telescopes and surveys, validated to sub-percent precision.

Contribution

It establishes a comprehensive, validated catalog of faint white dwarf standards with sub-percent flux accuracy, expanding the existing flux scale for astronomical calibration.

Findings

Validated the use of pure hydrogen models for flux prediction with a few parts per thousand accuracy.

Achieved residuals of about 0.4% after accounting for interstellar reddening.

Provided an all-sky, brightness-matched standard star ensemble for ground and space telescopes.

Abstract

We present a synopsis of the project to establish thirty-two new faint ($ 16.5 \leq V \leq 19.8 $) DA white dwarfs as spectrophotometric standards distributed over the whole sky. Our results validate the use of fully radiative pure hydrogen model fluxes for hot DA white dwarfs to predict the observed broadband fluxes from near ultraviolet through the near infrared to accuracies of a few parts per thousand. After fitting the line of sight reddenings simultaneously with the model spectral energy distributions of these stars against spectroscopic and multi-band photometric observations, we have shown that residuals have an rms of typically 0.4 percent. This indicates that the complications from interstellar dust extinction have been adequately mitigated. Our stars supplement the three brighter DA white dwarfs that define the flux scale of CALSPEC. The consequent photometric accuracy, their all sky coverage, and their brightness range that matches the dynamic range of large telescopes, constitutes an unprecedented ensemble of standard stars for both ground as well as space based use. This paper targets readers who may wish to use these as standard stars, and provides for them the essential content to understand their strengths and limitations, without traversing the technical details of analysis that are already captured in a series of papers since 2016. The narrative here describes the motivation, justification, and evolution of the analysis methods; the input data that constrain the modeling; as well as the stability of our results in the face of future improvements in models.