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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. An Automated Occultation Network for Gravitational Mapping of the Trans-Neptunian Solar System

   https://doi.org/10.3847/PSJ/ad9f5b  

   Gomes, Daniel_C_H; Bernstein, Gary_M (January 2025, The Planetary Science Journal)

   Abstract We explore the potential of an array of $O\left(100\right)$small fixed telescopes, aligned along a meridian and automated to measure millions of occultations of Gaia stars by minor planets, to constrain gravitational signatures from a “Planet X” mass in the outer solar system. The accuracy of center-of-mass tracking of occulters is limited by photon noise, uncertainties in asteroid shapes, and Gaia’s astrometry of the occulted stars. Using both parametric calculations and survey simulations, we assess the total information obtainable from occultation measurements of main-belt asteroids (MBAs), Jovian Trojans, and trans-Neptunian objects (TNOs). We find that MBAs are the optimal target population due to their higher occultation rates and abundance of objects above Legacy Survey of Space and Time detection thresholds. A 10 yr survey of occultations by MBAs and Trojans using an array of 200 40 cm telescopes at 5 km separation would achieve 5σsensitivity to the gravitational tidal field of a 5M_⊕Planet X at 800 au for >90% of potential sky locations. This configuration corresponds to an initial cost of ≈$15 million. While the survey's sensitivity to tidal forces improves rapidly with increasing number of telescopes, sensitivity to a Planet X becomes limited by degeneracy with the uncertain masses of large moonless TNOs. The 200-telescope survey would additionally detect ≈1800 TNO occultations, providing detailed shape, size, and albedo information. It would also measure the Yarkovsky effect on many individual MBAs, measure masses of many asteroids involved in mutual gravitational deflections, and enable better searches for primordial black holes and departures from general relativity. 

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4. 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)

   Abstract 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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5. Real-Time Road Crack Mapping Using an Optimized Convolutional Neural Network

   https://doi.org/10.1155/2019/2470735  

   Naddaf-Sh, M-Mahdi; Hosseini, SeyedSaeid; Zhang, Jing; Brake, Nicholas A.; Zargarzadeh, Hassan (September 2019, Complexity)

   Pavement surveying and distress mapping is completed by roadway authorities to quantify the topical and structural damage levels for strategic preventative or rehabilitative action. The failure to time the preventative or rehabilitative action and control distress propagation can lead to severe structural and financial loss of the asset requiring complete reconstruction. Continuous and computer-aided surveying measures not only can eliminate human error when analyzing, identifying, defining, and mapping pavement surface distresses, but also can provide a database of road damage patterns and their locations. The database can be used for timely road repairs to gain the maximum durability of the asphalt and the minimum cost of maintenance. This paper introduces an autonomous surveying scheme to collect, analyze, and map the image-based distress data in real time. A descriptive approach is considered for identifying cracks from collected images using a convolutional neural network (CNN) that classifies several types of cracks. Typically, CNN-based schemes require a relatively large processing power to detect desired objects in images in real time. However, the portability objective of this work requires to utilize low-weight processing units. To that end, the CNN training was optimized by the Bayesian optimization algorithm (BOA) to achieve the maximum accuracy and minimum processing time with minimum neural network layers. First, a database consisting of a diverse population of crack distress types such as longitudinal, transverse, and alligator cracks, photographed at multiple angles, was prepared. Then, the database was used to train a CNN whose hyperparameters were optimized using BOA. Finally, a heuristic algorithm is introduced to process the CNN’s output and produce the crack map. The performance of the classifier and mapping algorithm is examined against still images and videos captured by a drone from cracked pavement. In both instances, the proposed CNN was able to classify the cracks with 97% accuracy. The mapping algorithm is able to map a diverse population of surface cracks patterns in real time at the speed of 11.1 km per hour. 

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