The survey of DA double white dwarf candidates based on DESI EDR

Mergers of double white dwarfs (DWDs) are considered significant potential progenitors of type Ia supernovae (SNe Ia), which serve as “standard candles” in cosmology to measure the expansion rate of the Universe and explore the nature of dark energy. Although there is no direct observational evidence to definitively determine the formation pathways of SNe Ia, studying the physical properties of DWDs provides valuable insights into their evolutionary processes, interaction modes, and merger mechanisms, which are essential for understanding the explosion mechanisms of SNe Ia. This study aims to identify DWD candidates through spectroscopic radial velocity (RV) measurements and analyze their physical properties based on DESI EDR. We crossmatched DESI EDR with the Gaia EDR3 white dwarf (WD) catalog to select DA spectra. We measured the spectroscopic RV using the cross-correlation function (CCF) and assessed the significance of RV variability using a chi-squared-based variability method. Spectroscopic T_ and log g were derived by fitting the hydrogen Balmer lines, with 3D convection corrections applied. Orbital periods and semi-amplitudes were obtained through a Lomb-Scargle analysis of the RV time series. We interpolated WD cooling models and applied Monte Carlo simulations to calculate masses, cooling ages, radii, and their associated uncertainties. Additionally, we analyzed their photometric and spectral energy distribution properties to derive photometric temperatures and radii, which were then compared with the corresponding spectroscopic parameters. We identified 33 DA DWD candidates with significant RV variability, including 28 new discoveries. Among them, we found an extremely low-mass DWD candidate and a potential triple system. For these candidates, we measured key physical parameters including T_ log g, mass, and radius, and estimated the orbital periods based on the available data. Of these, 17 candidates exhibit relatively clear periodic RV variability in the current data, and we report their best-fitting periods and RV semi-amplitudes.