We present 160 new trigonometric parallaxes for 151 M dwarf systems from the REsearch Consortium On Nearby Stars (RECONS) group's long-term astrometry/photometry program at the CTIO/SMARTS 0.9 m telescope. Most systems (124 or 82%) are found to lie within 25 pc. The stars have 119 mas yr -1 μ 828 mas yr −1 and 3.85. Among these are 58 systems from the SuperCOSMOS-RECONS search, discovered via our proper motion trawls of the SuperCOSMOS digitized archival photographic plates, while the remaining stars were suspected via photometric distance estimates to lie nearby. Sixteen systems were newly discovered via astrometric perturbations to be binaries, many of which are ideal for accurate mass determinations due to their proximity and orbital periods on the order of a decade. A variability analysis of the stars presented, two-thirds of which are new results, shows six of the stars to vary by more than 20 mmag. This effort brings the total number of parallaxes for M dwarf systems measured by RECONS to nearly 500 and increases by 26% the number of southern M dwarf systems with accurate trigonometric parallaxes placing them within 25 pc.
We present the results of a recent reverberation mapping campaign for UGC 06728, a nearby low-luminosity Seyfert 1 in a late-type galaxy. Nightly monitoring in the spring of 2015 allowed us to determine an Hβ time delay of τ = 1.4 ± 0.8 days. Combined with the width of the variable Hβ line profile, we determine a black hole mass of M BH = (7.1 ± 4.0) × 10 5 M ⊙ . We also constrain the bulge stellar velocity dispersion from higher-resolution long slit spectroscopy along the galaxy minor axis and find σ ⋆ = 51.6 ± 4.9 km s −1 . The measurements presented here are in good agreement with both the R BLR − L relationship and the M BH − σ ⋆ relationship for AGNs. Combined with a previously published spin measurement, our mass determination for UGC 06728 makes it the lowest-mass black hole that has been fully characterized, and thus an important object to help anchor the low-mass end of black hole evolutionary models.
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