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We examine the relationship between circumnebular extinction and core mass for sets of [O III]-bright planetary nebulae (PNe) in the Large Magellanic Cloud and M31. We confirm that for PNe within 1 magnitude of the planetary nebula luminosity function’s (PNLF’s) bright-end cutoff magnitude (M*), higher core-mass PNe are disproportionally affected by greater circumnebular extinction. We show that this result can explain why the PNLF cutoff is so insensitive to population age. In younger populations, the higher-mass, higher-luminosity cores experience greater circumnebular extinction from the dust created by their asymptotic giant branch (AGB) progenitors compared to the lower-mass cores. We further show that when our core-mass–nebular extinction law is combined with post-AGB stellar evolutionary models, the result is a large range of population ages where the brightest PNe all have nearly identical [O III] luminosities. Finally, we note that while there is some uncertainty about whether the oldest stellar populations can produce PNe as bright as M*, this issue is resolved if the initial–final mass relation (IFMR) for the lowest-mass stars results in slightly more massive cores, as observed in some clusters. Alternatively, introducing a small amount of intrinsic scatter (0.022 Msun) into the IFMR also addresses this uncertainty. 

Abstract We present a provisory scattered-light detection of the Vega debris disk using deep Hubble Space Telescope (HST) coronagraphy (PID 16666). At only 7.7 pc, Vega is immensely important in debris disk studies both for its prominence and also because it allows the highest physical resolution among all debris systems relative to temperature zones around the star. We employ the STIS coronagraph’s widest wedge position and classical reference differential imaging to achieve among the lowest surface-brightness sensitivities to date ( $\sim 4\mu {\mathrm{Jy}\mathrm{arcsec}}^{-2}$) at wide separations using 32 orbits in Cycle 29. We detect a halo extending from the inner edge of our effective inner working angle at 10.″5 out to the photon noise floor at 30″ (80–230 au). The face-on orientation of the system and the lack of a perfectly color-matched point-spread function star have posed significant challenges to the reductions, particularly regarding artifacts from the imperfect color matching. However, we find that a halo of small dust grains provides the best explanation for the observed signal. Unlike Fomalhaut (a close twin to Vega in luminosity, distance, and age), there is no clear distinction in scattered light between the parent planetesimal belt observed with the Atacama Large Millimeter/submillimeter Array and the extended dust halo. These HST observations complement JWST GTO Cycle 1 observations of the system with NIRCam and MIRI. 

Context. Parker Solar Probe (PSP) performs Venus gravity assists (VGAs) in order to lower its perihelion. PSP takes high-cadence electric and magnetic field observations during these VGAs, providing the opportunity to study plasma waves in Venus’s induced magnetosphere. Aims. We summarize the plasma environment during these VGAs, including the regions of near-Venus space that PSP traversed and the key boundary crossings. We comprehensively identify Langmuir, ion acoustic, whistler-mode, and ion cyclotron waves during these VGAs and map the location of these waves throughout near-Venus space. Methods. This study analyzes different data products from the PSP FIELDS instrument suite from throughout the first five VGAs. Results. We compare the FIELDS instrumentation capabilities to the capabilities of the plasma wave instruments on board the Pioneer Venus Orbiter (PVO) and the Venus Express (VEX). We find that the PVO electric field instrument was well suited to observe Langmuir waves, especially near the bow shock and in the foreshock. However, evaluation of the other plasma waves detected by PSP FIELDS reveals that PVO and VEX would have often been unable to observe key features of these waves modes, including maximum power, bandwidth, and propagation direction. These wave characteristics provide critical information on the wave generation mechanisms and wave-particle interactions, so provide fundamental information on the nature of Venus’s induced magnetosphere. Conclusions. These results highlight the advances in plasma wave instrumentation capabilities that have been made in the decades since the PVO and VEX eras, and illustrate the value of a plasma wave instrument on a new Venus mission. 

Kernel survival analysis models estimate individual survival distributions with the help of a kernel function, which measures the similarity between any two data points. Such a kernel function can be learned using deep kernel survival models. In this paper, we present a new deep kernel survival model called a survival kernet, which scales to large datasets in a manner that is amenable to model interpretation and also theoretical analysis. Specifically, the training data are partitioned into clusters based on a recently developed training set compression scheme for classification and regression called kernel netting that we extend to the survival analysis setting. At test time, each data point is represented as a weighted combination of these clusters, and each such cluster can be visualized. For a special case of survival kernets, we establish a finite-sample error bound on predicted survival distributions that is, up to a log factor, optimal. Whereas scalability at test time is achieved using the aforementioned kernel netting compression strategy, scalability during training is achieved by a warm-start procedure based on tree ensembles such as XGBoost and a heuristic approach to accelerating neural architecture search. On four standard survival analysis datasets of varying sizes (up to roughly 3 million data points), we show that survival kernets are highly competitive compared to various baselines tested in terms of time-dependent concordance index. Our code is available at: https://github.com/georgehc/survival-kernets