NASA's Human System Risk Assessment Process

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Rapidly quenched galaxies in the Simba cosmological simulation and observations

Funding: YZ acknowledges support of a China Scholarship Council - University of St Andrews Scholarship. FRM is supported by the Wolfson Harrison UK Research Council Physics Scholarship. RD acknowledges support from the Wolfson Research Merit Award Program of the UK Royal Society.A wide range of mechanisms have been put forward to explain the quenching of star formation in galaxies with cosmic time, however, the true balance of responsible mechanisms remains unknown. The identification and study of galaxies that have shut down their star formation on different timescales might elucidate which mechanisms dominate at different epochs and masses. Here we study the population of rapidly quenched galaxies (RQGs) in the SIMBA cosmological hydrodynamic simulation at 0.5<z<2, comparing directly to observational post-starburst galaxies in the UKIDSS Ultra Deep Survey via their colour distributions and mass functions. We find that the fraction of quiescent galaxies that are rapidly quenched in SIMBA is 59% (or 48% in terms of stellarmass), which is higher than observed. A similar "downsizing" of RQGs is observed in both SIMBA and the UDS, with RQGs at higher redshift having a higher average mass. However, SIMBA produces too many RQGs at 1<zq<1.5 and too few low mass RQGs at 0.5<zq<1. The precise colour distribution of SIMBA galaxies compared to the observations also indicates various inconsistencies in star formation and chemical enrichment histories, including an absence of short, intense starbursts. Our results will help inform the next generation of galaxy evolution models, particularly with respect to the quenching mechanisms employed.Publisher PDFPeer reviewe

Design and implementation of a low-cost phasor measurement unit: a comprehensive review

The complexity of the contemporary electrical power systems imposes challenges in aspect of monitoring, protection and control. In order to obtain high speed of response, wide area effect and prices synchronization, the grid control functions can be benefited by the implementation of Phasor Measurement Units (PMU). The paper is aimed to make a review of the commercial implementation of Phasor Measurement Units and then open source based implementations (open architecture hardware and software). This paper focuses on standard implementations; as a consequence the concept of virtual PMU is not discussed here

Seasonal Climate Prediction: A New Source of Information for the Management of Wind Energy Resources

Climate predictions tailored to the wind energy sector represent an innovation in the use of climate information to better manage the future variability of wind energy resources. Wind energy users have traditionally employed a simple approach that is based on an estimate of retrospective climatological information. Instead, climate predictions can better support the balance between energy demand and supply, as well as decisions relative to the scheduling of maintenance work. One limitation for the use of the climate predictions is the bias, which has until now prevented their incorporation in wind energy models because they require variables with statistical properties that are similar to those observed. To overcome this problem, two techniques of probabilistic climate forecast bias adjustment are considered here: a simple bias correction and a calibration method. Both approaches assume that the seasonal distributions are Gaussian. These methods are linear and robust and neither requires parameter estimation—essential features for the small sample sizes of current climate forecast systems. This paper is the first to explore the impact of the necessary bias adjustment on the forecast quality of an operational seasonal forecast system, using the European Centre for Medium-Range Weather Forecasts seasonal predictions of near-surface wind speed to produce useful information for wind energy users. The results reveal to what extent the bias adjustment techniques, in particular the calibration method, are indispensable to produce statistically consistent and reliable predictions. The forecast-quality assessment shows that calibration is a fundamental requirement for high-quality climate service.The authors acknowledge funding support from the RESILIENCE (CGL2013-41055-R) project, funded by the Spanish Ministerio de Economía y Competitividad (MINECO) and the FP7 EUPORIAS (GA 308291) and SPECS (GA 308378) projects. Special thanks to Nube Gonzalez-Reviriego and Albert Soret for helpful comments and discussion. We also acknowledge the COPERNICUS action CLIM4ENERGY-Climate for Energy (C3S 441 Lot 2) and the New European Wind Atlas (NEWA) project funded from ERA-NET Plus, topic FP7-ENERGY.2013.10.1.2. We acknowledge the s2dverification and SpecsVerification R-based packages. Finally we would like to thank Pierre-Antoine Bretonnière, Oriol Mula and Nicolau Manubens for their technical support at different stages of this project.Peer ReviewedPostprint (author's final draft

Progress toward characterization of the atmospheric boundary layer over northern Alabama using observations by a vertically pointing, S-band profiling radar during VORTEX-Southeast

During spring 2016 and spring 2017, a vertically pointing, S-band FMCW radar (UMass FMCW) was deployed in northern Alabama under the auspices of the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX) – Southeast. In total, ~14 weeks’ worth of data were collected, in conditions ranging from quiescent clear skies to severe thunderstorms. The principal objective of these deployments was to characterize the boundary layer evolution near the VORTEX-Southeast domain. In this paper, we describe intermediate results in service of this objective. Specifically, we describe updates to the UMass FMCW system, document its deployments for VORTEX-Southeast, and apply three automated algorithms: (1) an dealiasing algorithm to the Doppler velocities, (2) a fuzzy logic scatterer classification scheme to separate precipitation from non-precipitation observations, (3) a bright band / melting layer identification algorithm for stratiform precipitation, and (4) an extended Kalman filter-based convective boundary layer depth (mixing height) measurement algorithm for non-precipitation observations. Results from the latter two applications are qualitatively verified against retrieved soundings from a collocated thermodynamic profiling system.Peer ReviewedPostprint (author's final draft

Inferring halo masses with graph neural networks

Understanding the halo–galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase space, we use Graph Neural Networks (GNNs), which are designed to work with irregular and sparse data. We train our models on galaxies from more than 2000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations project. Our model, which accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a ∼0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method

Mergers, starbursts, and quenching in the SIMBA simulation

We use the SIMBAcosmological galaxy formation simulation to investigate the relationship between major mergers ( 4:1), starbursts, and galaxy quenching. Mergers are identified via sudden jumps in stellar mass M∗ well above that expected from in situ star formation, while quenching is defined as going from specific star formation rate (sSFR) > t −1 H to < 0.2t −1 H , where tH is the Hubble time. At z ≈ 0–3, mergers show ∼2–3× higher SFR than a massmatched sample of star-forming galaxies, but globally represent 1 per cent of the cosmic SF budget. At low masses, the increase in SFR in mergers is mostly attributed to an increase in the H2 content, but for M∗ 1010.5M mergers also show an elevated star formation efficiency suggesting denser gas within merging galaxies. The merger rate for star-forming galaxies shows a rapid increase with redshift, ∝(1 + z)3.5, but the quenching rate evolves much more slowly, ∝(1 + z)0.9; there are insufficient mergers to explain the quenching rate at z 1.5. SIMBA first quenches galaxies at z 3, with a number density in good agreement with observations. The quenching time-scales τ q are strongly bimodal, with ‘slow’ quenchings (τ q ∼ 0.1tH) dominating overall, but ‘fast’ quenchings (τ q ∼ 0.01tH) dominating in M∗ ∼ 1010– 1010.5M galaxies, likely induced by SIMBA’s jet-mode black hole feedback. The delay time distribution between mergers and quenching events suggests no physical connection to either fast or slow quenching. Hence, SIMBA predicts that major mergers induce starbursts, but are unrelated to quenching in either fast or slow mode

Inferring halo masses with Graph Neural Networks

Understanding the halo-galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase-space, we use Graph Neural Networks (GNNs), that are designed to work with irregular and sparse data. We train our models on galaxies from more than 2,000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. Our model, that accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a $\sim$0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method. The PyTorch Geometric implementation of the GNN is publicly available on Github at https://github.com/PabloVD/HaloGraphNetComment: 20 pages, 8 figures, code publicly available at https://github.com/PabloVD/HaloGraphNe

New serological platform for detecting antibodies against Mycobacterium tuberculosis complex in European badgers

[EN]European badgers (Meles meles) have been identified as wildlife reservoirs for Mycobacterium bovis in the UK and Ireland, and may also have a role in the epidemiology of animal tuberculosis in other European regions. Thus, detection of M. bovis-infected badgers may be required for the purposes of surveillance and monitoring of disease levels in infected populations. Current serological assays to detect M. bovis infection in live badgers, while rapid and inexpensive, show limited diagnostic sensitivity. Here we describe and evaluate new ELISA platforms for the recognition of the P22 multiprotein complex derived from the purified protein derivative (PPD) of M. bovis. The recognition of IgG against P22 multiprotein complex derived from PPD-B was tested by ELISA in the serum of badgers from the UK, Ireland and Spain. TB infection in the badgers was indicated by the presence of M. bovis in tissues by culture and histology at post-mortem examination and TB-free status was established by repeated negativity in the interferon c release assay (IGRA). In experimentally infected badgers, humoral antibody responses against P22 developed within 45 days post-infection. The ELISA tests showed estimated sensitivity levels of 74–82% in experimentally and naturally infected badgers with specifici-ties ranging from 75% to 100% depending on the badger population tested. The P22 multi-antigen based ELI-SAs provide a sensitive and specific test platform for improved tuberculosis surveillance in badgers.SIThis work was supported by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria of Spain (INIA; RTA2015-00043-C02-02) and the TAVS-CM Programme of the Comunidad de Madrid (S2013/ABI-2747), cofinanced by the FEDER fund ‘A way to build Europe’. This work was partially supported by a FEDER co-funded grant from INIA (RTA2014-00002-C02-01). Jose Antonio Infantes-Lorenzo was supported by an FPU contract-fellowship (Formación de Profesorado Universitario) from the Ministerio de Educación, Cultura y Deporte of the Spanish Government (FPU2013/6000)