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Register a new userKnow thy star, know thy planet: Chemo-kinematically characterizing TESS targets
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The Transiting Exoplanet Survey Satellite (TESS) has already begun to discover what will ultimately be thousands of exoplanets around nearby cool bright stars. These potential host stars must be well-understood to accurately characterize exoplanets at the individual and population levels. We present a catalogue of the chemo-kinematic properties of 2,218,434 stars in the TESS Candidate Target List using survey data from Gaia DR2, APOGEE, GALAH, RAVE, LAMOST, and photometrically-derived stellar properties from SkyMapper. We compute kinematic thin disc, thick disc, and halo membership probabilities for these stars and find that though the majority of TESS targets are in the thin disc, 4% of them reside in the thick disc and <1% of them are in the halo. The TESS Objects of Interest in our sample also display similar contributions from the thin disc, thick disc, and halo with a majority of them being in the thin disc. We also explore metallicity and [alpha/Fe] distributions for each Galactic component and show that each cross-matched survey exhibits metallicity and [alpha/Fe] distribution functions that peak from higher to lower metallicity and lower to higher [alpha/Fe] from the thin disc to the halo. This catalogue will be useful to explore planet occurrence rates, among other things, with respect to kinematics, component-membership, metallicity, or [alpha/Fe].
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HD 8673 hosts a massive exoplanet in a highly eccentric orbit (e=0.723). Based on two epochs of speckle interferometry a previous publication identified a candidate stellar companion. We observed HD 8673 multiple times with the 10 m Keck II telescope, the 5 m Hale telescope, the 3.63 m AEOS telescope and the 1.5m Palomar telescope in a variety of filters with the aim of confirming and characterizing the stellar companion. We did not detect the candidate companion, which we now conclude was a false detection, but we did detect a fainter companion. We collected astrometry and photometry of the companion on six epochs in a variety of filters. The measured differential photometry enabled us to determine that the companion is an early M dwarf with a mass estimate of 0.33-0.45 M?. The companion has a projected separation of 10 AU, which is one of the smallest projected separations of an exoplanet host binary system. Based on the limited astrometry collected, we are able to constrain the orbit of the stellar companion to a semi-major axis of 35{60 AU, an eccentricity ? 0.5 and an inclination of 75{85?. The stellar companion has likely strongly in uenced the orbit of the exoplanet and quite possibly explains its high eccentricity.
The Transiting Exoplanet Survey Satellite (TESS) is observing bright known planet-host stars across almost the entire sky. These stars have been subject to extensive ground-based observations, providing a large number of radial velocity (RV) measurements. In this work we use the new TESS photometric observations to characterize the star $lambda^2$ Fornacis, and following this to update the parameters of the orbiting planet $lambda^2$ For b. We measure the p-mode oscillation frequencies in $lambda^2$ For, and in combination with non-seismic parameters estimate the stellar fundamental properties using stellar models. Using the revised stellar properties and a time series of archival RV data from the UCLES, HIRES and HARPS instruments spanning almost 20 years, we refit the orbit of $lambda^2$ For b and search the RV residuals for remaining variability. We find that $lambda^2$ For has a mass of $1.16pm0.03$M$_odot$ and a radius of $1.63pm0.04$R$_odot$, with an age of $6.3pm0.9$Gyr. This and the updated RV measurements suggest a mass of $lambda^2$ For b of $16.8^{+1.2}_{-1.3}$M$_oplus$, which is $sim5$M$_oplus$ less than literature estimates. We also detect a periodicity at 33 days in the RV measurements, which is likely due to the rotation of the host star. While previous literature estimates of the properties of $lambda^2$ are ambiguous, the asteroseismic measurements place the star firmly at the early stage of its subgiant evolutionary phase. Typically only short time series of photometric data are available from TESS, but by using asteroseismology it is still possible to provide tight constraints on the properties of bright stars that until now have only been observed from the ground. This prompts a reexamination of archival RV data from the past few decades to update the characteristics of the planet hosting systems observed by TESS for which asteroseismology is possible.
Novae have been reported as transients for more than two thousand years. Their bright optical outbursts are the result of explosive nuclear burning of gas accreted from a binary companion onto a white dwarf. Novae containing a white dwarf close to the Chandrasekhar mass limit and accreting at a high rate are potentially the unknown progenitors of the type Ia supernovae used to measure the acceleration of the Universe. Swift X-ray observations have radically transformed our view of novae by providing dense monitoring throughout the outburst, revealing new phenomena in the super-soft X-rays from the still-burning white dwarf such as early extreme variability and half- to one-minute timescale quasi-periodic oscillations. The distinct evolution of this emission from the harder X-ray emission due to ejecta shocks has been clearly delineated. Soft X-ray observations allow the mass of the white dwarf, the mass burned and the mass ejected to be estimated. In combination with observations at other wavelengths, including the high spectral resolution observations of the large X-ray observatories, high resolution optical and radio imaging, radio monitoring, optical spectroscopy, and the detection of GeV gamma-ray emission from recent novae, models of the explosion have been tested and developed. I review nine novae for which Swift has made a significant impact; these have shown the signature of the components in the interacting binary system in addition to the white dwarf: the re-formed accretion disk, the companion star and its stellar wind.
Deep generative models have been demonstrated as state-of-the-art density estimators. Yet, recent work has found that they often assign a higher likelihood to data from outside the training distribution. This seemingly paradoxical behavior has caused concerns over the quality of the attained density estimates. In the context of hierarchical variational autoencoders, we provide evidence to explain this behavior by out-of-distribution data having in-distribution low-level features. We argue that this is both expected and desirable behavior. With this insight in hand, we develop a fast, scalable and fully unsupervised likelihood-ratio score for OOD detection that requires data to be in-distribution across all feature-levels. We benchmark the method on a vast set of data and model combinations and achieve state-of-the-art results on out-of-distribution detection.
Lipreading has a lot of potential applications such as in the domain of surveillance and video conferencing. Despite this, most of the work in building lipreading systems has been limited to classifying silent videos into classes representing text phrases. However, there are multiple problems associated with making lipreading a text-based classification task like its dependence on a particular language and vocabulary mapping. Thus, in this paper we propose a multi-view lipreading to audio system, namely Lipper, which models it as a regression task. The model takes silent videos as input and produces speech as the output. With multi-view silent videos, we observe an improvement over single-view speech reconstruction results. We show this by presenting an exhaustive set of experiments for speaker-dependent, out-of-vocabulary and speaker-independent settings. Further, we compare the delay values of Lipper with other speechreading systems in order to show the real-time nature of audio produced. We also perform a user study for the audios produced in order to understand the level of comprehensibility of audios produced using Lipper.
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