Seeing Double: ASASSN-18bt Exhibits a Two-component Rise in the Early-time K2 Light

On 2018 February 4.41, the All-Sky Automated Survey for SuperNovae (ASAS-SN) discovered ASASSN-18bt in the K2 Campaign 16 field. With a redshift of z = 0.01098 and a peak apparent magnitude of B max = 14.31, ASASSN-18bt is the nearest and brightest SNe Ia yet observed by the Kepler spacecraft. Here we present the discovery of ASASSN-18bt, the K2 light curve, and prediscovery data from ASAS-SN and the Asteroid Terrestrial-impact Last Alert System. The K2 early-time light curve has an unprecedented 30-minute cadence and photometric precision for an SN Ia light curve, and it unambiguously shows a ~4 day nearly linear phase followed by a steeper rise. Thus, ASASSN-18bt joins a growing list of SNe Ia whose early light curves are not well described by a single power law. We show that a double-power-law model fits the data reasonably well, hinting that two physical processes must be responsible for the observed rise. However, we find that current models of the interaction with a nondegenerate companion predict an abrupt rise and cannot adequately explain the initial, slower linear phase. Instead, we find that existing published models with shallow 56Ni are able to span the observed behavior and, with tuning, may be able to reproduce the ASASSN-18bt light curve. Regardless, more theoretical work is needed to satisfactorily model this and other early-time SNe Ia light curves. Finally, we use Swift X-ray nondetections to constrain the presence of circumstellar material (CSM) at much larger distances and lower densities than possible with the optical light curve. For a constant-density CSM, these nondetections constrain ρ < 4.5 × 105 cm−3 at a radius of 4 × 1015 cm from the progenitor star. Assuming a wind-like environment, we place mass loss limits of for v w = 100 km s−1, ruling out some symbiotic progenitor systems. This work highlights the power of well-sampled early-time data and the need for immediate multiband, high-cadence follow-up for progress in understanding SNe Ia

Safety and efficacy of a novel polyethylene glycol hydrogel sealant for watertight dural repair

OBJECT: The authors prospectively evaluated the safety and efficacy of a novel polyethylene glycol (PEG) hydrogel sealant in patients undergoing elective cranial surgery with documented cerebrospinal fluid (CSF) leakage after sutured dural repair. METHODS: The PEG hydrogel sealant was used at 11 different study sites in 111 patients with documented intraoperative CSF leakage after neurosurgical dural repair for a variety of conditions. Intraoperative CSF leakage was either spontaneous or induced by a Valsalva maneuver. Patients were monitored for 3 months postoperatively with physical examinations, clinical laboratory analyses, and diagnostic imaging. The PEG hydrogel sealant was 100% effective in stopping CSF leakage in all patients. There were no sealant-related adverse events and all clinical outcomes were consistent with expectations for seriously ill patients undergoing prolonged neurosurgical procedures. CONCLUSIONS: The PEG hydrogel sealant provides a safe and effective watertight closure when used as an adjunct to sutured dural repair during cranial surgery

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology

Atmospheric rivers (ARs) are now widely known for their association with high-impact weather events and long-term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR-related metrics based on 20+ different AR identification and tracking methods applied to Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria-based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all-method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR-related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider

Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Project goals and experimental design

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month proof-of-concept trial run designed to illustrate the utility and feasibility of the ARTMIP project