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Abstract We present a sample of 305 QSO candidates having ∣b∣ < 30°, the majority with GALEX magnitudes near-UV < 18.75. To generate this sample, we apply UV–IR color selection criteria to photometric data from the Ultraviolet Galactic Plane Survey as part of GALEX-CAUSE, the Million Quasars Catalog, Gaia DR2, and Pan-STARRS DR1. 165 of these 305 candidate UV-bright active galactic nuclei (AGN; 54%) have published spectroscopic redshifts from 45 different surveys, confirming them as AGN. We further obtained low-dispersion, optical, long-slit spectra with the Apache Point Observatory 3.5 m, MDM 2.4 m, and MDM 1.3 m telescopes for 84 of the candidates, and confirm 86% (N= 72) as AGN, generally withz< 0.6. Of these 72 confirmed AGN, 25 are newly discovered low-latitude QSOs without any previous spectroscopy. These sources fill a gap in the Galactic latitude coverage of the available samples of known UV-bright QSO background probes. Along with a description of the confirmed QSO properties, we provide the fully reduced, flux- and wavelength-calibrated spectra of 72 low-latitude QSOs through the Mikulski Archive for Space Telescopes. Future Hubble Space Telescope/Cosmic Origins Spectrograph spectroscopy of these low-Galactic-latitude QSOs has the potential to transform our view of the Milky Way and Local Group circumgalactic medium. 

Abstract The Large Magellanic Cloud (LMC) is home to many Hiiregions, which may lead to significant outflows. We examine the LMC’s multiphase gas (T∼10^4-5K) in Hi, Sii, Siiv, and Civusing 110 stellar sight lines from the Hubble Space Telescope’s Ultraviolet Legacy Library of Young Stars as Essential Standards program. We develop a continuum fitting algorithm based on the concept of Gaussian process regression and identify reliable LMC interstellar absorption overv_helio= 175–375 km s⁻¹. Our analyses show disk-wide ionized outflows in Siivand Civacross the LMC with bulk velocities of ∣v_{out, bulk}∣ ∼ 20–60 km s⁻¹, which indicates that most of the outflowing mass is gravitationally bound. The outflows’ column densities correlate with the LMC’s star formation rate surface densities (Σ_SFR), and the outflows with higher Σ_SFRtend to be more ionized. Considering outflows from both sides of the LMC as traced by Civ, we conservatively estimate a total outflow rate of ${\dot{M}}_{\mathrm{out}}\gtrsim 0.03M_{\odot }{\mathrm{yr}}^{-1}$and a mass-loading factor ofη≳ 0.15. We compare the LMC’s outflows with those detected in starburst galaxies and simulation predictions, and find a universal scaling relation of $\mid v_{\mathrm{out},\mathrm{bulk}}\mid \propto {\mathrm{\Sigma }}_{\mathrm{SFR}}^{0.23}$over a wide range of star-forming conditions (Σ_SFR∼ 10^−4.5–10²M_⊙yr⁻¹kpc⁻²). Lastly, we find that the outflows are corotating with the LMC’s young stellar disk and the velocity field does not seem to be significantly impacted by external forces; we thus speculate on the existence of a bow shock leading the LMC, which may have shielded the outflows from ram pressure as the LMC orbits the Milky Way. 

Abstract Discoveries of gaps in data have been important in astrophysics. For example, there are kinematic gaps opened by resonances in dynamical systems, or exoplanets of a certain radius that are empirically rare. A gap in a data set is a kind of anomaly, but in an unusual sense: instead of being a single outlier data point, situated far from other data points, it is a region of the space, or a set of points, that is anomalous compared to its surroundings. Gaps are both interesting and hard to find and characterize, especially when they have nontrivial shapes. We present in this paper a statistic that can be used to estimate the (local) “gappiness” of a point in the data space. It uses the gradient and Hessian of the density estimate (and thus requires a twice-differentiable density estimator). This statistic can be computed at (almost) any point in the space and does not rely on optimization; it allows us to highlight underdense regions of any dimensionality and shape in a general and efficient way. We illustrate our method on the velocity distribution of nearby stars in the Milky Way disk plane, which exhibits gaps that could originate from different processes. Identifying and characterizing those gaps could help determine their origins. We provide in an appendix implementation notes and additional considerations for finding underdensities in data, using critical points and the properties of the Hessian of the density. 7 7 A Python implementation of t methods presented here is available at https://github.com/contardog/FindTheGap .