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Reproductive isolation is necessary for population divergence to lead to the formation of separate species. This can occur due to physical isolation of populations, which drives allopatric speciation, or other methods of isolation, such as sympatric speciation where the diverging species are physically in the same range, but structural genomic changes or mutations cause the population to diverge into two different species. Parapatric speciation occurs when populations that are geographically adjacent to each other diverge, which can be driven by adaptations to environmental differences, even with ongoing gene flow. Two desert- adapted brittlebush species, Encelia farinosa and Encelia californica, diverged less than 1 million years ago (Singhal et al., 2020) and have a parapatric distribution, residing in different environments in the Mojave and Sonoran deserts. Encelia farinosa (Brittlebush) has unique silvery leaves that are covered in tiny hairs (leaf pubescence) to better control leaf temperature in the hot and arid conditions of the Sonoran Desert. Encelia californica (California Brittlebush) does not display leaf pubescence and is found in a smaller region of the Mediterranean-like environment of the west coast of North America. Encelia californica is exposed to more precipitation than most other Encelia species. Even with their different morphologies, these two species are still able to hybridize and create fertile offspring (Clark, 1998). Using PacBio sequencing and Hi-C scaffolding, we assembled and annotated reference genomes for both species to investigate the genomic basis of reproductive isolation in these two species. The scaffold N50/L50 are 10 scaffolds and 76.3 Mbp, and 12 scaffolds and 64.5 Mbp for E. farinosa and E. californica, respectively. Using comparative genomic analyses such as tests for differential adaptation and chromosomal translocations will help reveal whether the drivers of speciation in the Encelia radiation were external (e.g., geologic/climatic) or internal (e.g., genomic rearrangement). These analyses will also help answer how accumulated genomic differences can cause speciation in populations that are not geographically isolated. Analyses such as these are new, exciting sources of information for testing geogenomic and other Earth- life hypotheses. 

Abstract The MicroBooNE liquid argon time projection chamber (LArTPC) maintains a high level of liquid argon purity through the use of a filtration system that removes electronegative contaminants in continuously-circulated liquid, recondensed boil off, and externally supplied argon gas. We use the MicroBooNE LArTPC to reconstruct MeV-scale radiological decays. Using this technique we measure the liquid argon filtration system's efficacy at removing radon. This is studied by placing a 500 kBq 222 Rn source upstream of the filters and searching for a time-dependent increase in the number of radiological decays in the LArTPC. In the context of two models for radon mitigation via a liquid argon filtration system, a slowing mechanism and a trapping mechanism, MicroBooNE data supports a radon reduction factor of greater than 97% or 99.999%, respectively. Furthermore, a radiological survey of the filters found that the copper-based filter material was the primary medium that removed the 222 Rn. This is the first observation of radon mitigation in liquid argon with a large-scale copper-based filter and could offer a radon mitigation solution for future large LArTPCs. 

Abstract In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions within the image. These outputs are identified as either cosmic ray muon or electron neutrino interactions. We find that sMask-RCNN has an average pixel clustering efficiency of 85.9% compared to the dense network's average pixel clustering efficiency of 89.1%. We demonstrate the ability of sMask-RCNN used in conjunction with MicroBooNE's state-of-the-art Wire-Cell cosmic tagger to veto events containing only cosmic ray muons. The addition of sMask-RCNN to the Wire-Cell cosmic tagger removes 70% of the remaining cosmic ray muon background events at the same electron neutrino event signal efficiency. This event veto can provide 99.7% rejection of cosmic ray-only background events while maintaining an electron neutrino event-level signal efficiency of 80.1%. In addition to cosmic ray muon identification, sMask-RCNN could be used to extract features and identify different particle interaction types in other 3D-tracking detectors.