Improved dynamical constraints on the mass of the central black hole in NGC 404
Astrophysical Journal Institute of Physics 836:2 (2017) 237
Abstract:
We explore the nucleus of the nearby 109 M⊙ early-type galaxy, NGC 404, using Hubble Space Telescope (HST)/STIS spectroscopy and WFC3 imaging. We first present evidence for nuclear variability in UV, optical, and infrared filters over a time period of 15 years. This variability adds to the already substantial evidence for an accreting black hole at the center of NGC 404. We then redetermine the dynamical black hole mass in NGC 404 including modeling of the nuclear stellar populations. We combine HST/STIS spectroscopy with WFC3 images to create a local color-M/L relation derived from stellar population modeling of the STIS data. We then use this to create a mass model for the nuclear region. We use Jeans modeling to fit this mass model to adaptive optics stellar kinematic observations from Gemini/NIFS. From our stellar dynamical modeling, we find a 3σ upper limit on the black hole mass of 1.5 × 105 M⊙. Given the accretion evidence for a black hole, this upper limit makes NGC 404 the lowest mass central black hole with dynamical mass constraints. We find that the kinematics of H2 emission line gas show evidence for non-gravitational motions preventing the use of gas dynamical modeling to constrain the black hole mass. Our stellar population modeling also reveals that the central, counter-rotating region of the nuclear cluster is dominated by ∼1 Gyr old populations.Discovery of water at high spectral resolution in the atmosphere of 51 Peg b
(2017)
THE SAMI GALAXY SURVEY: REVISITING GALAXY CLASSIFICATION THROUGH HIGH-ORDER STELLAR KINEMATICS
ASTROPHYSICAL JOURNAL 835:1 (2017) ARTN 104
A fast machine learning based algorithm for MKID readout power tuning
ISSTT 2017 - 28th International Symposium on Space Terahertz Technology 2017-March (2017)
Abstract:
As high pixel count Microwave Kinetic Inductance Detector (MKID) arrays become widely adopted, there is a growing demand for automated device readout calibration. These calibrations include ascertaining the optimal driving power for best pixel sensitivity, which, because of large variations in MKID behavior, is typically performed by manual inspection. This process takes roughly 1 hour per 1000 MKIDs, making the manual characterization of ten-kilopixel scale arrays unfeasible. We propose the concept of using a machine-learning algorithm, based on a convolution neural network (CNN) architecture, which should reliably tune ten-kilopixel scale MKID arrays on the order of several minutes.ERIS, first generation becoming second generation, or re-vitalizing an AO instrument
Adaptive Optics for Extremely Large Telescopes, 2017 AO4ELT5 2017-June (2017)