Constraining the evolution of cataclysmic variables via the masses and accretion rates of their underlying white dwarfs

We report on the masses ($M_\mathrm{WD}$), effective temperatures ($T_\mathrm{eff}$) and secular mean accretion rates ($\langle \dot{M} \rangle$) of 43 cataclysmic variable (CV) white dwarfs, 42 of which were obtained from the combined analysis of their $\mathit{Hubble~Space~Telescope}$ ultraviolet data with the parallaxes provided by the Early Third Data Release of the $\mathit{Gaia}$ space mission, and one from the white dwarf gravitational redshift. Our results double the number of CV white dwarfs with an accurate mass measurement, bringing the total census to 89 systems. From the study of the mass distribution, we derive $\langle M_\mathrm{WD} \rangle = 0.81^{+0.16}_{-0.20}\,\mathrm{M_\odot}$, in perfect agreement with previous results, and find no evidence of any evolution of the mass with orbital period. Moreover, we identify five systems with $M_\mathrm{WD} < 0.5\mathrm{M_\odot}$, which are most likely representative of helium-core white dwarfs, showing that these CVs are present in the overall population. We reveal the presence of an anti-correlation between the average accretion rates and the white dwarf masses for the systems below the $2-3\,$h period gap. Since $\langle \dot{M} \rangle$ reflects the rate of system angular momentum loss, this correlation suggests the presence of an additional mechanism of angular momentum loss that is more efficient at low white dwarf masses. This is the fundamental concept of the recently proposed empirical prescription of consequential angular momentum loss (eCAML) and our results provide observational support for it, although we also highlight how its current recipe needs to be refined to better reproduce the observed scatter in $T_\mathrm{eff}$ and $\langle \dot{M} \rangle$, and the presence of helium-core white dwarfs.
Accepted for publication in MNRAS. 22 pages of main body (6 tables and 14 figures) and 54 pages of appendices. Appendix B includes the best-fitting parameters and models to the HST data