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A specialized ground‐based system has been developed for simultaneous observations of pulsating aurora (PsA) and related magnetospheric phenomena with the Arase satellite. The instrument suite is composed of (a) six 100 Hz sampling high‐speed all‐sky imagers (ASIs), (b) two 10 Hz sampling monochromatic ASIs observing 427.8 and 844.6 nm auroral emissions, (c) a 20 Hz sampling fluxgate magnetometer. The 100 Hz ASIs were deployed in four stations in Scandinavia and two stations in Alaska, which have been used for capturing the main pulsations and quasi 3 Hz internal modulations of PsA at the same time. The 10 Hz sampling monochromatic ASIs have been operative in Tromsø, Norway with the 20 Hz sampling magnetometer. Combination of these multiple instruments with the European Incoherent SCATter (EISCAT) radar enables us to detect the low‐altitude ionization due to energetic electron precipitation during PsA and further to reveal the ionospheric electrodynamics behind PsA. Since the launch of the Arase satellite, the data from these instruments have been examined in comparison with the wave and particle data from the satellite in the magnetosphere. In the future, the system can be utilized not only for studies of PsA but also for other classes of aurora in close collaboration with the planned EISCAT_3D project.

 

To enhance equity and diversity in undergraduate biology, recent research in biology education focuses on best practices that reduce learning barriers for all students and improve academic performance. However, the majority of current research into student experiences in introductory biology takes place at large, predominantly White institutions. To foster contextual knowledge in biology education research, we harnessed data from a large research coordination network to examine the extent of academic performance gaps based on demographic status across institutional contexts and how two psychological factors, test anxiety and ethnicity stigma consciousness, may mediate performance in introductory biology. We used data from seven institutions across three institution types: 2-year community colleges, 4-year inclusive institutions (based on admissions selectivity; hereafter, inclusive), and 4-year selective institutions (hereafter, selective). In our sample, we did not observe binary gender gaps across institutional contexts, but found that performance gaps based on underrepresented minority status were evident at inclusive and selective 4-year institutions, but not at community colleges. Differences in social psychological factors and their impacts on academic performance varied substantially across institutional contexts. Our findings demonstrate that institutional context can play an important role in the mechanisms underlying performance gaps. 

Abstract Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network — a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw scintillation waveforms as input. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach and, importantly, generalize from the training sample and discern salient high-level features of the data. In particular, the network correctly deduces position dependency of scintillation light response in the detector and correctly recognizes dead photodetector channels. The network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy distributions comparable to that of real waveforms. Finally, the remaining discrepancies and potential ways to improve the approach further are highlighted. 

Bald cypress trees over 2,000-years old have been discovered in the forested wetlands along Black River using dendrochronology and radiocarbon dating. The oldest bald cypress yet documented is at least 2,624-years old, makingTaxodium distichumthe oldest-known wetland tree species, the oldest living trees in eastern North America, and the fifth oldest known non-clonal tree species on earth. The annual ring-width chronology developed from the ancient Black River bald cypress trees is positively correlated with growing season precipitation totals over the southeastern United States and with atmospheric circulation over the Northern Hemisphere, providing the longest exactly-dated climate proxy yet developed in eastern North America. The Nature Conservancy owns 6,400 ha in their Black River Preserve and the North Carolina legislature is considering establishment of a Black River State Park, but ancient forested wetlands are found along most of this 106 km stream and remain threatened by logging, water pollution, and sea level rise.

 

Abstract The EXO-200 experiment searched for neutrinoless double-beta decay of 136 Xe with a single-phase liquid xenon detector. It used an active mass of 110 kg of 80.6%-enriched liquid xenon in an ultra-low background time projection chamber with ionization and scintillation detection and readout. This paper describes the design and performance of the various support systems necessary for detector operation, including cryogenics, xenon handling, and controls. Novel features of the system were driven by the need to protect the thin-walled detector chamber containing the liquid xenon, to achieve high chemical purity of the Xe, and to maintain thermal uniformity across the detector. 

The nature of dark matter and properties of neutrinos are among the most pressing issues in contemporary particle physics. The dual-phase xenon time-projection chamber is the leading technology to cover the available parameter space for weakly interacting massive particles, while featuring extensive sensitivity to many alternative dark matter candidates. These detectors can also study neutrinos through neutrinoless double-beta decay and through a variety of astrophysical sources. A next-generation xenon-based detector will therefore be a true multi-purpose observatory to significantly advance particle physics, nuclear physics, astrophysics, solar physics, and cosmology. This review article presents the science cases for such a detector.