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President Obama's Big Data Research and Development Initiative seeks to improve our ability to acquire knowledge and discover insights into large and complex collections of digital data. The Big Earth Data Initiative (BEDI) Invests in standardizing and optimizing the collection, management and delivery of U.S. Government's civil Earth observation data.
Quantifying the ability of a transiting planet survey to recover transit signals has commonly been accomplished through Monte-Carlo injection of transit signals into the observed data and subsequent running of the signal search algorithm (Gilliland et al., 2000; Weldrake et al., 2005; Burke et al., 2006). In order to characterize the performance of the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017) on a sample of over 200,000 stars, two complementary injection and recovery tests are utilized:1. Injection of a single transit signal per target into the image or pixel-level data, hereafter referred to as pixel-level transit injection (PLTI), with subsequent processing through the Photometric Analysis (PA), Presearch Data Conditioning (PDC), Transiting Planet Search (TPS), and Data Validation (DV) modules of the Kepler pipeline. The PLTI quantification of the Kepler pipeline's completeness has been described previously by Christiansen et al. (2015, 2016); the completeness of the final SOC 9.3 Kepler pipeline acting on the Data Release 25 (DR25) light curves is described by Christiansen (2017).2. Injection of multiple transit signals per target into the normalized flux time series data with a subsequent transit search using a stream-lined version of the Transiting Planet Search (TPS) module. This test, hereafter referred to as flux-level transit injection (FLTI), is the subject of this document. By running a heavily modified version of TPS, FLTI is able to perform many injections on selected targets and determine in some detail which injected signals are recoverable. Significant numerical efficiency gains are enabled by precomputing the data conditioning steps at the onset of TPS and limiting the search parameter space (i.e., orbital period, transit duration, and ephemeris zero-point) to a small region around each injected transit signal.The PLTI test has the advantage that it follows transit signals through all processing steps of the Kepler pipeline, and the recovered signals can be further classified as planet candidates or false positives in the exact same manner as detections from the nominal (i.e., observed) pipeline run (Twicken et al., 2016, Thompson et al., in preparation). To date, the PLTI test has been the standard means of measuring pipeline completeness averaged over large samples of targets (Christiansen et al., 2015, 2016; Christiansen, 2017). However, since the PLTI test uses only one injection per target, it does not elucidate individual-target variations in pipeline completeness due to differences in stellar properties or astrophysical variability. Thus, we developed the FLTI test to provide a numerically efficient way to fully map individual targets and explore the performance of the pipeline in greater detail. The FLTI tests thereby allow a thorough validation of the pipeline completeness models (such as window function (Burke and Catanzarite, 2017a), detection efficiency (Burke Catanzarite, 2017b), etc.) across the spectrum of Kepler targets (i.e., various astrophysical phenomena and differences in instrumental noise). Tests during development of the FLTI capability revealed that there are significant target-to-target variations in the detection efficiency.
Leading edge erosion (LEE) of wind turbine blades causes decreased aerodynamic performance leading to lower power production and revenue and increased operations and maintenance costs. LEE is caused primarily by materials stresses when hydrometeors (rain and hail) impact on rotating blades. The kinetic energy transferred by these impacts is a function of the precipitation intensity, droplet size distributions (DSD), hydrometeor phase and the wind turbine rotational speed which in turn depends on the wind speed at hub-height. Hence, there is a need to better understand the hydrometeor properties and the joint probability distributions of precipitation and wind speeds at prospective and operating wind farms in order to quantify the potential for LEE and the financial efficacy of LEE mitigation measures. However, there are relatively few observational datasets of hydrometeor DSD available for such locations. Here, we analyze six observational datasets from spatially dispersed locations and compare them with existing literature and assumed DSD used in laboratory experiments of material fatigue. We show that the so-called Best DSD being recommended for use in whirling arm experiments does not represent the observational data. Neither does the Marshall Palmer approximation. We also use these data to derive and compare joint probability distributions of drivers of LEE; precipitation intensity (and phase) and wind speed. We further review and summarize observational metrologies for hydrometeor DSD, provide information regarding measurement uncertainty in the parameters of critical importance to kinetic energy transfer and closure of data sets from different instruments. A series of recommendations are made about research needed to evolve towards the required fidelity for a priori estimates of LEE potential.
We calculate the intensity and photon spectrum of the intergalactic background light (IBL) as a function of redshift using an approach based on observational data obtained in many different wavelength bands from local to deep galaxy surveys. This allows us to obtain an empirical determination of the IBL and to quantify its observationally based uncertainties. Using our results on the IBL, we then place 68% confidence upper and lower limits on the opacity of the universe to gamma-rays, free of the theoretical assumptions that were needed for past calculations. We compare our results with measurements of the extragalactic background light and upper limits obtained from observations made by the Fermi Gamma-ray Space Telescope.
Context. We investigate iPTF13bvn, a core-collapse (CC) supernova (SN) in the nearby spiral galaxy NGC 5806. This object was discovered by the intermediate Palomar Transient Factory (iPTF) very close to the estimated explosion date and was classified as a stripped-envelope CC SN, likely of Type Ib. Furthermore, a possible progenitor detection in pre-explosion Hubble Space Telescope (HST) images was reported, making this the only SN Ib with such an identification. Based on the luminosity and color of the progenitor candidate, as well as on early-time spectra and photometry of the SN, it was argued that the progenitor candidate is consistent with a single, massive Wolf-Rayet (WR) star. Aims. We aim to confirm the progenitor detection, to robustly classify the SN using additional spectroscopy, and to investigate if our follow-up photometric and spectroscopic data on iPTF13bvn are consistent with a single-star WR progenitor scenario. Methods. We present a large set of observational data, consisting of multi-band light curves (UBVRI, g′r′i′z′) and optical spectra. We perform standard spectral line analysis to track the evolution of the SN ejecta. We also construct a bolometric light curve and perform hydrodynamical calculations to model this light curve to constrain the synthesized radioactive nickel mass and the total ejecta mass of the SN. Late-time photometry is analyzed to constrain the amount of oxygen. Furthermore, image registration of pre- and post-explosion HST images is performed. Results. Our HST astrometry confirms the location of the progenitor candidate of iPTF13bvn, and follow-up spectra securely classify this as a SN Ib. We use our hydrodynamical model to fit the observed bolometric light curve, estimating the total ejecta mass to be 1.9 solar mass and the radioactive nickel mass to be 0.05 solar mass. The model fit requires the nickel synthesized in the explosion to be highly mixed out in the ejecta. We also find that the late-time nebular r′-band luminosity is not consistent with predictions based on the expected oxygen nucleosynthesis in very massive stars. Conclusions. We find that our bolometric light curve of iPTF13bvn is not consistent with the previously proposed single massive WR-star progenitor scenario. The total ejecta mass and, in particular, the late-time oxygen emission are both significantly lower than what would be expected from a single WR progenitor with a main-sequence mass of at least 30 solar mass.
The indirect radiative forcing of aerosols is poorly constrained by the observational data underlying the simple cloud parameterizations in GCMs. signal of cloud response to increased aerosol concentrations from meteorological noise. Recent satellite observations indicate a significant decrease of cloud water in ship tracks, in contrast to an ensemble of in situ measurements showing no average change in cloud water relative to the surrounding clouds. Both results contradict the expectation of cloud water increasing in polluted clouds. We find through large-eddy simulations of stratocumulus that the trend in the satellite data is likely an artifact of sampling only overcast clouds. The simulations instead show cloud cover increasing with droplet concentrations. The simulations also show that increases in cloud water from suppressing drizzle by increased droplet concentrations are favored at night or at extremely low droplet concentrations. At typical droplet concentrations we find that the Twomey effect on cloud albedo is amplified very little by the secondary indirect effect of drizzle suppression, largely because the absorption of solar radiation by cloud water reduces boundary-layer mixing in the daytime and thereby restricts any possible increase in cloud water from drizzle suppression. The cloud and boundary layer respond to radiative heating variations on a time scale of hours, and on longer time scales respond to imbalances between large-scale horizontal advection and the entrainment of inversion air. We analyze the co-varying response of cloud water, cloud thickness, width of droplet size distributions, and dispersion of the optical depth, as well as the overall response of cloud albedo, to changes in droplet concentrations. We also dissect the underlying physical mechanisms through sensitivity studies. Ship tracks represent an ideal natural laboratory to extricate the
Although there is an important set of data showing potential genotoxic effects of nanomaterials (NMs) at the DNA (comet assay) and chromosome (micronucleus test) levels, few studies have been conducted to analyze their potential mutagenic effects at gene level. We have determined the ability of multi-walled carbon nanotubes (MWCNT, NM401), to induce mutations in the HPRT gene in Chinese hamster lung (V79) fibroblasts. NM401, characterized in the EU NanoGenotox project, were further studied within the EU Framework Programme Seven (FP7) project NANoREG. From the proliferation assay data we selected a dose-range of 0.12 to 12 µg/cm{sup 2} At these range we have been able to observe significant cellular uptake of MWCNT by using transmission electron microscopy (TEM), as well as a concentration-dependent induction of intracellular reactive oxygen species. In addition, a clear concentration-dependent increase in the induction of HPRT mutations was also observed. Data support a potential genotoxic/ carcinogenic risk associated with MWCNT exposure. - Highlights: • MWCNT were tested in V79 cells. • Cellular uptake of MWCNT was detected using TEM. • Intracellular ROS induction was observed after MWCNT exposure. • MWCNT induced a concentration-dependent increase of HPRT mutations.
Methods are derived for applying the V/Vmax variable when several complete samples are analyzed simultaneously. A new, related, variable, V(e)/V(a), which is superior to V/Vmax when sufficient observational data are available, is introduced. It is shown how statistically independent complete samples can be generated from partially overlapping samples such that each observed object appears only once in the analysis. These methods make it possible to combine the information from different complete samples, and to make an efficient use of all available data. Illustrative examples for quasars are given. It is shown how the generalized V/Vmax and V(e)/V(a) can be used to find the amount of cosmological evolution of quasars and its associated range of uncertainty.
The current generation of stellar isochrone models exhibits non-negligible discrepancies due to variations in the input physics. The success of each model is determined by how well it fits the observations, and this paper aims to disentangle contributions from the various physical inputs. New deep, wide-field optical and near-infrared photometry (UBVRIJHK{sub S} ) of the cluster M35 is presented, against which several isochrone systems are compared: Padova, PARSEC, Dartmouth, and Y {sup 2}. Two different atmosphere models are applied to each isochrone: ATLAS9 and BT-Settl. For any isochrone set and atmosphere model, observed data are accurately reproduced for all stars more massive than 0.7 M {sub ☉}. For stars less massive than 0.7 M {sub ☉}, Padova and PARSEC isochrones consistently produce higher temperatures than observed. Dartmouth and Y{sup 2} isochrones with BT-Settl atmospheres reproduce optical data accurately; however, they appear too blue in IR colors. It is speculated that molecular contributions to stellar spectra in the near-infrared may not be fully explored, and that future study may reconcile these differences.
During the period included in this report, the expenditure of time and effort, and progress toward performance of the tasks and accomplishing the goals set forth in the two year research grant proposal, consisted primarily of calibration and analysis of selected data sets. The heliographic limits of 30 degrees from central meridian were continued. As previously reported, all analyses are interactive and are performed by the Principal Investigator. It should also be noted that the analysis time involved by the Principal Investigator during this reporting period was limited, partially due to illness and partially resulting from other uncontrollable factors. The calibration technique (as developed by MSFC solar scientists), incorporates sets of constants which vary according to the wave length of the observation data set. One input constant is then varied interactively to correct for observing conditions, etc., to result in a maximum magnetic field strength (in the calibrated data), based on a separate analysis. There is some insecurity in the methodology and the selection of variables to yield the most self-consistent results for variable maximum field strengths and for variable observing/atmospheric conditions. Several data sets were analyzed using differing constant sets, and separate analyses to differing maximum field strength - toward standardizing methodology and technique for the most self-consistent results for the large number of cases. It may be necessary to recalibrate some of the analyses, but the sc analyses are retained on the optical disks and can still be used with recalibration where necessary. Only the extracted parameters will be changed.
The three-cornered hat (3CH) method estimates the uncertainties of three different co-located model or observational data sets (Anthes and Rieckh, 2018; Sjoberg et al., 2021). Rieckh et al. (2021) used the 3CH method to compare the random error statistics of different global forecast and reanalysis models, as well as radio occultation (RO) and radiosonde observations. That study showed that the MERRA-2 reanalysis, while having smaller errors in the stratosphere than its predecessor MERRA, had larger errors in the troposphere than many of the other data sets analyzed. The MERRA-2 errors were particularly large in the tropics. In a collaborative effort between UCAR’s COSMIC (Constellation Observing System for Meteorology, Ionosphere and Meteorology) program and NASA’s Global Modeling and Assimilation Office (GMAO), we carried out further 3CH error diagnostics to help isolate the causes of these larger errors and help guide the development of an improved reanalysis. This presentation summarizes random error statistics associated with MERRA-2, ECMWF’s ERA5 reanalysis, and COSMIC-2 (C2) RO observations. We compute 3CH error variance estimates of refractivity, as well as temperature and specific humidity using UCAR’s COSMIC Data Analysis and Archive Center (CDAAC) improved 1D-variational (1D-Var) retrieval (wetPf2) over 15 latitude bands from 45S to 45N. The 1D-Var retrievals of specific humidity and temperature for C2 use NCEP’s Global Forecast System (GFS) as the background. Anthes et al. (2021) showed that it gives accurate estimates of temperature and specific humidity in the tropics and subtropics, even in the challenging environment of intense Hurricane Dorian (2019). This presentation confirms the previous results that MERRA-2 has significantly larger errors in the tropics and subtropics than either C2 or ERA5. Its errors are larger between 30S and 30N compared to 30-45 N-S latitudes, and are also larger over land compared to oceans. Most of the MERRA-2 refractivity errors come from specific humidity, except over land below 3 km where temperature errors are large. These results suggest that moist convection and atmospheric boundary layer physics in MERRA-2 may be responsible for a significant part of the higher uncertainties. These results are being used to guide GMAO in developing an improved next-generation reanalysis, as shown in a companion presentation submitted to this conference (El Akkraoui et al., 2021), which extends this study and describes improvements to MERRA-2 leading to the next GMAO reanalysis.
Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. Here, this paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system.
We compare the results from several sets of cosmological simulations of cosmic reionization, produced under the Cosmic Reionization On Computers project, with existing observational data on the high-redshift Lyα forest and the abundance of Lyα emitters. We find good consistency with the observational measurements and previous simulation work. By virtue of having several independent realizations for each set of numerical parameters, we are able to explore the effect of cosmic variance on observable quantities. One unexpected conclusion we are forced into is that cosmic variance is unusually large at z > 6, with both our simulations and, most likely, observational measurements still not fully converged for even such basic quantities as the average Gunn-Peterson optical depth or the volume-weighted neutral fraction. We also find that reionization has little effect on the early galaxies or on global cosmic star formation history, because galaxies whose gas content is affected by photoionization contain no molecular (i.e., star-forming) gas in the first place. In particular, measurements of the faint end of the galaxy luminosity function by the James Webb Space Telescope are unlikely to provide a useful constraint on reionization.
The results of a search for stealth supersymmetry in final states with two photons and jets, targeting a phase space region with low missing transverse momentum ( p T miss ), are reported. The study is based on a sample of proton-proton collisions at s = 13 TeV collected by the CMS experiment, corresponding to an integrated luminosity of 138 fb - 1 . As LHC results continue to constrain the parameter space of the minimal supersymmetric standard model, the low p T miss regime is increasingly valuable to explore. To estimate the backgrounds due to standard model processes in such events, we apply corrections derived from simulation to an estimate based on a control selection in data. The results are interpreted in the context of simplified stealth supersymmetry models with gluino and squark pair production. The observed data are consistent with the standard model predictions, and gluino (squark) masses of up to 2150 (1850) GeV are excluded at the 95% confidence level.
A search is presented for $\tau$ slepton pairs produced in proton-proton collisions at a center-of-mass energy of 13 TeV. The search is carried out in events containing two $\tau$ leptons in the final state, on the assumption that each $\tau$ slepton decays primarily to a $\tau$ lepton and a neutralino. Events are considered in which each $\tau$ lepton decays to one or more hadrons and a neutrino, or in which one of the $\tau$ leptons decays instead to an electron or a muon and two neutrinos. The data, collected with the CMS detector in 2016 and 2017, correspond to an integrated luminosity of 77.2 fb$^{-1}$. The observed data are consistent with the standard model background expectation. The results are used to set 95% confidence level upper limits on the cross section for $\tau$ slepton pair production in various models for $\tau$ slepton masses between 90 and 200 GeV and neutralino masses of 1, 10, and 20 GeV. In the case of purely left-handed $\tau$ slepton production and decay to a $\tau$ lepton and a neutralino with a mass of 1 GeV, the strongest limit is obtained for a $\tau$ slepton mass of 125 GeV at a factor of 1.14 larger than the theoretical cross section.
Supporting Data can be downloaded at: https://gain.inl.gov/content/uploads/4/2024/06/INL-RPT-24-77048-R1.xlsx Nuclear energy is a critical cornerstone of the current United States clean energy supply and may play a larger role in the future in support of a transition to a net-zero economy. The current fleet of nuclear reactors predominantly consists of large light-water reactors (LWRs), while many of the reactor designs under consideration are smaller and/or different technologies. Because these new designs have not yet been built, there is a high degree of uncertainty associated with their cost. This complicates energy-planning efforts because cost projections are not always standardized, consistent, and centralized in an easily accessible location. To help support energy planning in the US, this report provides advanced nuclear cost ranges using a transparent methodology along with other relevant information that can be used to help support decision making and energy planning. The purpose of this work was to conduct a methodical process for cost evaluation using only public information that was vetted with the end-goal to provide reference cost projections for nuclear energy. To provide a solid basis for these values, the approach and assumptions are explicitly laid out throughout the report allowing any user of the data to challenge or reconsider them. Because future US nuclear-reactor costs are still unknown due to little recent observed data, the report opted to compile a comprehensive list of bottom-up estimates and evaluate averages/trends within the data to identify reference ranges. This was deemed preferable to opining on the robustness or validity of one cost estimation versus another. To that end, the work evaluated thousands of lines of cost subaccounts from several bottom-up cost estimates. A wide variety of different reactor types captured in the data are of various sizes and technologies. Some of these reactors will be representative of advanced reactors under development while others will not. Thus, the results here are dependent on the data that are available and the accuracy of the estimates that are used. Each bottom-up estimate was reviewed to determine whether it was complete. Incomplete data sets were corrected to ensure an adequate basis of cross-comparison. The report is not without limitations and should be interpreted as an initial step to develop cost ranges for nuclear technology. Ultimately, future work can build upon the methodology with refined cost estimates to reduce uncertainty. US-based overnight capital cost (OCC) estimates were compiled from extensive data sets into ranges for both large and small reactor sizes for 2030. To project the cost declines over time, learning rates were sampled from literature sources. No SMRs were previously built; hence, learning rates based on bottom-up approaches (e.g., by quantifying the impact stemming from fabrication of different components, modular work, site construction, commissioning) were prioritized. For larger reactors, actual learning rates from deployments were used to project future costs (adjusted to account for standardization or lack thereof between designs). Other costs included are fixed and variable operations and maintenance costs. The final variables were capacity factors and ramp rates to support energy planning.
Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media.
The next generation of space telescopes will enable transformative science to understand the nature and origin of exoplanets. In particular, transit spectroscopy will reveal the chemical composition of the exoplanet atmospheres with unprecedented detail thanks to precise measurements of the visible-to-infrared transit depths down to 10 parts per million. Such a level of instrumental precision raises the challenge to obtain even more precise astrophysical models so as not to significantly influence the interpretation of the observed data. We must therefore critically revisit some of the commonly accepted assumptions that were adequate for analyzing past and current observations. A common approximation in the analysis of exoplanetary primary transits is that the planet does not contribute to the recorded flux, so-called dark planet hypothesis. In this paper, we investigate the impact of the dark planet hypothesis on the parameters obtained from the analysis of transits with particular attention to the transit depth. We develop mathematical formulae and release new software to estimate the magnitude of the potential bias. These tools will be useful in the preparation of observing proposals, as well as within the scientific consortia of the James Webb Space Telescope (JWST) and the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) missions. We probe the accuracy of the mathematical formulae through the analysis of synthetic observations with the JWST Mid-InfraRed Instrument. We find that self-blending from nightside emission attenuates the transit depth by >3σ for some of the known exoplanet systems, in agreement with previous work. An additional unreported effect caused by the nightside rotating into view can also impart a significant effect, but in the opposite direction (increasing the transit depth); this effect can largely be removed with conventional detrending practices, at the expense of a slight increase in noise, and mixing astrophysical variations and instrumental drifts.