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Abstract We present an ultraviolet to infrared search for the electromagnetic (EM) counterpart to GW190425, the second ever binary neutron star merger discovered by the LIGO-Virgo-KAGRA Collaboration. GW190425 was more distant and had a larger localization area than GW170817, so we use a new tool,Teglon, to redistribute the GW190425 localization probability in the context of galaxy catalogs within the final localization volume. We derive a 90th percentile area of 6688 deg², a ∼1.5× improvement relative to the LIGO/Virgo map, and show howTeglonprovides an order-of-magnitude boost to the search efficiency of small (≤1 deg²) field-of-view instruments. We combine our data with a large, publicly reported imaging data set, covering 9078.59 deg²of unique area and 48.13% of the LIGO/Virgo-assigned localization probability, to calculate the most comprehensive kilonova (KN), short gamma-ray burst (sGRB) afterglow, and model-independent constraints on the EM emission from a hypothetical counterpart to GW190425 to date under the assumption that no counterpart was found in these data. If the counterpart were similar to AT 2017gfo, there would be a 28.4% chance of it being detected in the combined data set. We are relatively insensitive to an on-axis sGRB, and rule out a generic transient with a similar peak luminosity and decline rate as AT 2017gfo to 30% confidence. Finally, across our new imaging and publicly reported data, we find 28 candidate optical counterparts that we cannot rule out as being associated with GW190425, finding that four such counterparts discovered within the localization volume and within 5 days of merger exhibit luminosities consistent with a KN. 

Astronomical source deblending is the process of separating the contribution of individual stars or galaxies (sources) to an image comprised of multiple, possibly overlapping sources. Astronomical sources display a wide range of sizes and brightnesses and may show substantial overlap in images. Astronomical imaging data can further challenge off-the-shelf computer vision algorithms owing to its high dynamic range, low signal-to-noise ratio, and unconventional image format. These challenges make source deblending an open area of astronomical research, and in this work, we introduce a new approach called Partial-Attribution Instance Segmentation that enables source detection and deblending in a manner tractable for deep learning models. We provide a novel neural network implementation as a demonstration of the method. 

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.