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1. Direct measurement of stellar angular diameters by the VERITAS Cherenkov telescopes

   https://doi.org/10.1038/s41550-019-0741-z  

   Benbow, W. ; Bird, R. ; Brill, A. ; Brose, R. ; Chromey, A. J. ; Daniel, M. K. ; Feng, Q. ; Finley, J. P. ; Fortson, L. ; Furniss, A. ; et al ( April 2019 , Nature Astronomy)

   The angular size of a star is a critical factor in determining its basic properties. Direct measurement of stellar angular diameters is difficult: at interstellar distances stars are generally too small to resolve by any individual imaging telescope. This fundamental limitation can be overcome by studying the diffraction pattern in the shadow cast when an asteroid occults a star, but only when the photometric uncertainty is smaller than the noise added by atmospheric scintillation. Atmospheric Cherenkov telescopes used for particle astrophysics observations have not generally been exploited for optical astronomy due to the modest optical quality of the mirror surface. However, their large mirror area makes them well suited for such high-time-resolution precision photometry measurements. Here we report two occultations of stars observed by the Very Energetic Radiation Imaging Telescope Array System (VERITAS) Cherenkov telescopes with millisecond sampling, from which we are able to provide a direct measurement of the occulted stars’ angular diameter at the ≤0.1 mas scale. This is a resolution never achieved before with optical measurements and represents an order of magnitude improvement over the equivalent lunar occultation method. We compare the resulting stellar radius with empirically derived estimates from temperature and brightness measurements, confirming the latter can be biased for stars with ambiguous stellar classifications. 

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2. Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning

   Colin J Burke, Patrick D ( October 2019 , Monthly notices of the Royal Astronomical Society)

   e apply a new deep learning technique to detect, classify, and deblend sources in multi-band astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask R-CNN image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92% at 80% recall for stars and a precision of 98% at 80% recall for galaxies in a typical field with ∼30 galaxies/arcmin2. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as LSST and WFIRST. Our code, Astro R-CNN, is publicly available at https://github.com/burke86/astro_rcnn 

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3. Deblending and classifying astronomical sources with Mask R-CNN deep learning

   https://doi.org/10.1093/mnras/stz2845  

   Burke, Colin J. ; Aleo, Patrick D. ; Chen, Yu-Ching ; Liu, Xin ; Peterson, John R. ; Sembroski, Glenn H. ; Lin, Joshua Yao-Yu ( October 2019 , Monthly Notices of the Royal Astronomical Society)

   We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent recall for galaxies in a typical field with ∼30 galaxies arcmin−2. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as Large Synoptic Survey Telescope and Wide-Field Infrared Survey Telescope. Our code, astro r-cnn, is publicly available at https://github.com/burke86/astro_rcnn.

    

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4. Global‐Scale Observations of the Limb and Disk Mission Implementation: 2. Observations, Data Pipeline, and Level 1 Data Products

   https://doi.org/10.1029/2020JA027809  

   McClintock, William E. ; Eastes, Richard W. ; Beland, Stephane ; Bryant, Karen B. ; Burns, Alan G. ; Correira, John ; Danielll, Robert E. ; Evans, J. Scott ; Harper, Connie S. ; Karan, Deepak K. ; et al ( May 2020 , Journal of Geophysical Research: Space Physics)

   The Global‐scale Observations of the Limb and Disk (GOLD) is a National Aeronautics and Space Administration mission of opportunity designed to study how the Earth's ionosphere‐thermosphere system responds to geomagnetic storms, solar radiation, and upward propagating atmospheric tides and waves. GOLD employs two identical ultraviolet spectrographs that make observations of the Earth's thermosphere and ionosphere from a commercial communications satellite owned and operated by Société Européenne des Satellites (SES) and located in geostationary orbit at 47.5° west longitude (near the mouth of the Amazon River). They make images of atomic oxygen 135.6 nm and N₂Lyman‐Birge‐Hopfield radiances from the entire disk that is observable from geostationary orbit and on the near‐equatorial limb. They also observe occultations of stars to measure molecular oxygen column densities on the limb. Here we describe the algorithms and science data processing that convert downlinked data to spectral‐spatial image cubes and occultation spectra, calibrated in geophysical units. These Level 1 data products include disk and near‐equatorial limb images of spectra acquired on a 30‐min cadence beginning at 06:10 Coordinated Universal Time and ending at 23:10. Nighttime images of the disk, covering regions east of the terminator, begin at 20:10 Coordinated Universal Time and also continue until 00:40 the next day. During the day, some limb images are replaced by time series of stellar spectra that exhibit absorption by molecular oxygen as the star is occulted by the Earth's atmosphere. Instrumental artifacts that occasionally appear in the released data are discussed.

    

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5. 2016 UU121: An Active Asteroid Discovery via AI-enhanced Citizen Science

   https://doi.org/10.3847/2515-5172/ad2b66  

   Sedaghat, Nima ; Chandler, Colin Orion ; Oldroyd, William J. ; Trujillo, Chadwick A. ; Burris, William A. ; Hsieh, Henry H. ; Kueny, Jay K. ; Farrell, Kennedy A. ; DeSpain, Jarod A. ; Magbanua, Mark Jesus ; et al ( February 2024 , Research Notes of the AAS)

   We report the discovery of an active asteroid, 2016 UU₁₂₁, for the first time via artificial intelligence-enhanced classification, informed by our NASA Partner programActive Asteroids, a Citizen Science project hosted on theZooniverseplatform. The early version of our deep neural network,TailNet, identified potential activity associated with 2016 UU₁₂₁in 40 Dark Energy Camera (DECam) images from UT 2021 September 10 to 11. The discovery was vetted and confirmed by ourActive Asteroidscore science team. In total, 66 DECam images of this object showed clear activity in the form of a tail. 2016 UU₁₂₁has a Tisserand parameter with respect to Jupiter of 3.161, thus we classify the object as an active asteroid. Moreover, the activity occurred near perihelion, so 2016 UU₁₂₁is also a candidate Main-belt comet.

    

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