We examine the ``puffed-up inner disk'' model (Dullemond, Dominik & Natta 2001), proposed for explaining the near-IR excess radiation from Herbig Ae/Be stars. Detailed model computations show that the observed near-IR excess requires more hot dust than is contained in the puffed-up disk rim. The rim can produce the observed near-IR excess only if its dust has perfectly gray opacity, but such dust is in conflict with the observed 10$\mu$m spectral feature. We find that a compact ($\sim$ 10 AU) tenuous ($\tau_V \la 0.4$) dusty halo around the disk inner regions contains enough dust to readily explain the observations. Furthermore, this model also resolves the puzzling relationship noted by Monnier and Millan-Gabet (2002) between luminosity and the interferometric inner radii of disks.Comment: 14 pages, 10 figures, accepted in Ap

We present a new model-independent method of comparison of NIR visibility data of YSOs. The method is based on scaling the measured baseline with the YSO's distance and luminosity, which removes the dependence of visibility on these two variables. We use this method to compare all available NIR visibility data and demonstrate that it distinguishes YSOs of luminosity >1000L_sun (low-L) from YSOs of <1000L_sun (high-L). This confirms earlier suggestions, based on fits of image models to the visibility data, for the difference between the NIR sizes of these two luminosity groups. When plotted against the ``scaled'' baseline, the visibility creates the following data clusters: low-L Herbig Ae/Be stars, T Tauri stars, and high-L Herbig Be stars. The T Tau cluster is similar to the low-L Herbig Ae/Be cluster, which has ~7 times smaller ``scaled'' baselines than the high-L Herbig Be cluster. We model the shape and size of clusters with different image models and find that low-L Herbig stars are the best explained by the uniform brightness ring and the halo model, T Tauri stars with the halo model, and high-L Herbig stars with the accretion disk model. However, the plausibility of each model is not well established. Therefore, we try to build a descriptive model of the circumstellar environment consistent with various observed properties of YSOs. We argue that low-L YSOs have optically thick disks with the optically thin inner dust sublimation cavity and an optically thin dusty outflow above the inner disk regions. High-L YSOs have optically thick accretion disks with high accretion rates enabling gas to dominate the NIR emission over dust. Although observations would favor such a description of YSOs, the required dust distribution is not supported by our current understanding of dust dynamics.Comment: 20 pages, 12 figures, Accepted for publication in the Astrophysical Journa

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D type symbiotic binaries are an excellent astrophysical laboratory for investigation of the dust properties and dust formation under the influence of theMira stellar wind and nova activity and of...

As demonstrated with the Sloan Digital Sky Survey (SDSS), Pan-STARRS, and most recently with Gaia data, broadband near-UV to near-IR stellar photometry can be used to estimate distance, metallicity, and interstellar dust extinction along the line of sight for stars in the Galaxy. Anticipating photometric catalogs with tens of billions of stars from Rubin's Legacy Survey of Space and Time (LSST), we present a Bayesian model and pipeline that build on previous work and can handle LSST-sized datasets. Likelihood computations utilize MIST/Dartmouth isochrones and priors are derived from TRILEGAL-based simulated LSST catalogs from P. Dal Tio et al. The computation speed is about 10 ms per star on a single core for both optimized grid search and Markov Chain Monte Carlo methods; we show in a companion paper by K. Mrakovčić et al. how to utilize neural networks to accelerate this performance by up to an order of magnitude. We validate our pipeline, named PhotoD (in analogy with photo-z, photometric redshifts of galaxies) using both simulated catalogs and SDSS, DECam, and Gaia photometry. We intend to make LSST-based value-added PhotoD catalogs publicly available via the Rubin Science Platform with every LSST data release.