This work combines spectroscopic and photometric data of the polluted white dwarf WD0141-675 which has a now retracted astrometric super-Jupiter candidate and investigates the most promising ways to confirm Gaia astrometric planetary candidates and obtain follow-up data. Obtaining precise radial velocity measurement for white dwarfs is challenging due to their intrinsic faint magnitudes, lack of spectral absorption lines, and broad spectral features. However, dedicated radial velocity campaigns are capable of confirming close in giant exoplanets (a few M$_{\textrm{Jup}}$) around polluted white dwarfs, where additional metal lines aid radial velocity measurements. Infrared emission from these giant exoplanets is shown to be detectable with JWST MIRI and will provide constraints on the formation of the planet. Using the initial Gaia astrometric solution for WD0141-675 as a case study, if there were a planet with a 33.65 d period or less with a nearly edge on orbit, 1) ground-based radial velocity monitoring limits the mass to $<$ 15.4 M$_{\textrm{Jup}}$, and 2) space-based infrared photometry shows a lack of infrared excess and in a cloud-free planetary cooling scenario, a sub-stellar companion would have to be $<$ 16 M$_{\textrm{Jup}}$ and be older than 3.7 Gyr. These results demonstrate how radial velocities and infrared photometry can probe the mass of the objects producing some of the astrometric signals, and rule out parts of the brown dwarf and planet mass parameter space. Therefore, combining astrometric data with spectroscopic and photometric data is crucial to both confirm, and characterise astrometric planet candidates around white dwarfs.
We present new results on PHL 5038AB, a widely separated binary system composed of a white dwarf and a brown dwarf, refining the white and brown dwarf parameters and determining the binary separation to be $66^{+12}_{-24}$~AU. New spectra of the white dwarf show calcium absorption lines suggesting the hydrogen-rich atmosphere is weakly polluted, inferring the presence of planetesimals in the system, which we determine are in an S-type orbit around the white dwarf in orbits closer than 17-32 AU. We do not detect any infrared excess that would indicate the presence of a disc, suggesting all dust present has either been totally accreted or is optically thin. In this system, we suggest the metal pollution in the white dwarf atmosphere can be directly attributed to the presence of the brown dwarf companion disrupting the orbits of planetesimals within the system.
We present a detailed analysis of a large spectroscopic and photometric sample of DZ white dwarfs based on our latest model atmosphere calculations. We revise the atmospheric parameters of the trigonometric parallax sample of Bergeron, Leggett, & Ruiz (12 stars) and analyze 147 new DZ white dwarfs discovered in the Sloan Digital Sky Survey. The inclusion of metals and hydrogen in our model atmosphere calculations leads to different atmospheric parameters than those derived from pure helium models. Calcium abundances are found in the range from log (Ca/He) = -12 to -8. We also find that fits of the coolest objects show peculiarities, suggesting that our physical models may not correctly describe the conditions of high atmospheric pressure encountered in the coolest DZ stars. We find that the mean mass of the 11 DZ stars with trigonometric parallaxes, = 0.63 Mo, is significantly lower than that obtained from pure helium models, = 0.78 Mo, and in much better agreement with the mean mass of other types of white dwarfs. We determine hydrogen abundances for 27% of the DZ stars in our sample, while only upper limits are obtained for objects with low signal-to-noise ratio spectroscopic data. We confirm with a high level of confidence that the accretion rate of hydrogen is at least two orders of magnitude smaller than that of metals (and up to five in some cases) to be compatible with the observations. We find a correlation between the hydrogen abundance and the effective temperature, suggesting for the first time empirical evidence of a lower temperature boundary for the hydrogen screening mechanism. Finally, we speculate on the possibility that the DZA white dwarfs could be the result of the convective mixing of thin hydrogen-rich atmospheres with the underlying helium convection zone.
Observations of atmospheric metals and dust discs around white dwarfs provide important clues to the fate of terrestrial planetary systems around intermediate mass stars. We present Spitzer IRAC observations of 15 metal polluted white dwarfs to investigate the occurrence and physical properties of circumstellar dust created by the disruption of planetary bodies. We find subtle infrared excess emission consistent with warm dust around KUV 15519+1730 and HS 2132+0941, and weaker excess around the DZ white dwarf G245-58, which, if real, makes it the coolest white dwarf known to exhibit a 3.6 micron excess and the first DZ star with a bright disc. All together our data corroborate a picture where 1) discs at metal-enriched white dwarfs are commonplace and most escape detection in the infrared (possibly as narrow rings), 2) the discs are long lived, having lifetimes on the order of 10^6 yr or longer, and 3) the frequency of bright, infrared detectable discs decreases with age, on a timescale of roughly 500 Myr, suggesting large planetesimal disruptions decline on this same timescale.
We present improved atmospheric parameters of nearby white dwarfs lying within 20 pc of the Sun. The aim of the current study is to obtain the best statistical model of the least-biased sample of the white dwarf population. A homogeneous analysis of the local population is performed combining detailed spectroscopic and photometric analyses based on improved model atmosphere calculations for various spectral types including DA, DB, DC, DQ, and DZ stars. The spectroscopic technique is applied to all stars in our sample for which optical spectra are available. Photometric energy distributions, when available, are also combined to trigonometric parallax measurements to derive effective temperatures, stellar radii, as well as atmospheric compositions. A revised catalog of white dwarfs in the solar neighborhood is presented. We provide, for the first time, a comprehensive analysis of the mass distribution and the chemical distribution of white dwarf stars in a volume-limited sample.
Over the past several decades, conventional spectral analysis techniques of polluted white dwarfs have become powerful tools to learn about the geology and chemistry of extrasolar bodies. Despite their proven capabilities and extensive legacy of scientific discoveries, these techniques are however still limited by their manual, time-intensive, and iterative nature. As a result, they are susceptible to human errors and are difficult to scale up to population-wide studies of metal pollution. This paper seeks to address this problem by presenting cecilia, the first Machine Learning (ML)-powered spectral modeling code designed to measure the metal abundances of intermediate-temperature (10,000$\leq T_{\rm eff} \leq$20,000 K), Helium-rich polluted white dwarfs. Trained with more than 22,000 randomly drawn atmosphere models and stellar parameters, our pipeline aims to overcome the limitations of classical methods by replacing the generation of synthetic spectra from computationally expensive codes and uniformly spaced model grids, with a fast, automated, and efficient neural-network-based interpolator. More specifically, cecilia combines state-of-the-art atmosphere models, powerful artificial intelligence tools, and robust statistical techniques to rapidly generate synthetic spectra of polluted white dwarfs in high-dimensional space, and enable accurate ($\lesssim$0.1 dex) and simultaneous measurements of 14 stellar parameters -- including 11 elemental abundances -- from real spectroscopic observations. As massively multiplexed astronomical surveys begin scientific operations, cecilia's performance has the potential to unlock large-scale studies of extrasolar geochemistry and propel the field of white dwarf science into the era of Big Data. In doing so, we aspire to uncover new statistical insights that were previously impractical with traditional white dwarf characterisation techniques.
