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At least 235 records · Page 13

CHANDRA DETECTION OF SN 2010da FOUR MONTHS AFTER OUTBURST: EVIDENCE FOR A HIGH-MASS X-RAY BINARY IN NGC 300

We present the results of a 63 ks Chandra observation of the 'supernova impostor' SN 2010da four months after it was first observed on 2010 May 24. We detect an X-ray source at {approx}7{sigma} confidence coincident with the optical location of the outburst. Our imaging analysis has revealed a hard central point source, surrounded by soft diffuse emission extending {approx}8'' north of the central source. The diffuse emission has a hardness ratio ({approx}-0.4), 0.35-2 keV luminosity ({approx}6 x 10{sup 35} erg s{sup -1}), and size ({approx}70 pc) consistent with that of a supernova remnant, although the low number of counts prohibits detailed spectral modeling. The 0.5-6 keV spectrum of the central point source is well described by both a power law with {Gamma} {approx}0 and a blackbody with kT {approx} 1.8 keV, with no evidence for intrinsic absorption beyond the Galactic column. We estimate the 0.3-10 keV luminosity to be {approx}2 x 10{sup 37} erg s{sup -1}, a factor of {approx}25 decrease since the initial outburst. The high X-ray luminosity and slow fading rate is not consistent with this object being a single massive star undergoing an outburst. When combined with the optical and infrared properties of the outburst and progenitor systems, our X-ray observations suggest the SN 2010da system may be a Be X-ray binary system which experienced an extremely bright optical outburst near simultaneously with a bright, Type II X-ray outburst.

79 ASTRONOMY AND ASTROPHYSICS↗

FAINT END OF 1.3 mm NUMBER COUNTS REVEALED BY ALMA

We present the faint end of number counts at 1.3 mm (238 GHz) obtained with the Atacama Large Millimeter/submillimeter Array (ALMA). Band 6 observations were carried out targeting 20 star-forming galaxies at z ∼ 1.4 in the Subaru/XMM-Newton Deep Survey field. In the observations, we serendipitously detect 15 sources (≥3.8σ, S{sub 1.3} {sub mm} = 0.15-0.61 mJy) other than the targeted sources. We create number counts by using these ''sub-mJy sources'', which probe the faintest flux range among surveys at millimeter wavelengths. The number counts are consistent with (flux-scaled) number counts at 850 μm and 870 μm obtained with gravitational lensing clusters. The ALMA number counts agree well with model predictions, which suggest that these sub-mJy populations are more like ''normal'' star-forming galaxies than ''classical'' submillimeter galaxies with intense star-forming activity. In this flux range, ∼80% of the extragalactic background light at 1.3 mm is resolved into individual sources.

79 ASTRONOMY AND ASTROPHYSICS↗

HerMES: A DEFICIT IN THE SURFACE BRIGHTNESS OF THE COSMIC INFRARED BACKGROUND DUE TO GALAXY CLUSTER GRAVITATIONAL LENSING

We have observed four massive galaxy clusters with the SPIRE instrument on the Herschel Space Observatory and measure a deficit of surface brightness within their central region after removing detected sources. We simulate the effects of instrumental sensitivity and resolution, the source population, and the lensing effect of the clusters to estimate the shape and amplitude of the deficit. The amplitude of the central deficit is a strong function of the surface density and flux distribution of the background sources. We find that for the current best fitting faint end number counts, and excellent lensing models, the most likely amplitude of the central deficit is the full intensity of the cosmic infrared background (CIB). Our measurement leads to a lower limit to the integrated total intensity of the CIB of I{sub 250{mu}m}>0.69{sub -0.03}{sup +0.03}(stat.){sub -0.06}{sup +0.11}(sys.) MJy sr{sup -1}, with more CIB possible from both low-redshift sources and from sources within the target clusters. It should be possible to observe this effect in existing high angular resolution data at other wavelengths where the CIB is bright, which would allow tests of models of the faint source component of the CIB.

79 ASTRONOMY AND ASTROPHYSICS↗

Poisson hurdle model-based method for clustering microbiome features

Abstract Motivation High-throughput sequencing technologies have greatly facilitated microbiome research and have generated a large volume of microbiome data with the potential to answer key questions regarding microbiome assembly, structure and function. Cluster analysis aims to group features that behave similarly across treatments, and such grouping helps to highlight the functional relationships among features and may provide biological insights into microbiome networks. However, clustering microbiome data are challenging due to the sparsity and high dimensionality. Results We propose a model-based clustering method based on Poisson hurdle models for sparse microbiome count data. We describe an expectation–maximization algorithm and a modified version using simulated annealing to conduct the cluster analysis. Moreover, we provide algorithms for initialization and choosing the number of clusters. Simulation results demonstrate that our proposed methods provide better clustering results than alternative methods under a variety of settings. We also apply the proposed method to a sorghum rhizosphere microbiome dataset that results in interesting biological findings. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PHclust. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗

Life Cycle Analysis Data and Results for Geothermal and Other Electricity Generation Technologies

Life cycle analysis (LCA) is an environmental assessment method that quantifies the environmental performance of a product system over its entire lifetime, from cradle to grave. Based on a set of relevant metrics, the method is aptly suited for comparing the environmental performance of competing products systems. This file contains LCA data and results for electric power production including geothermal power. The LCA for electric power has been broken down into two life cycle stages, namely plant and fuel cycles. Relevant metrics include the energy ratio and greenhouse gas (GHG) ratios, where the former is the ratio of system input energy to total lifetime electrical energy out and the latter is the ratio of the sum of all incurred greenhouse gases (in CO2 equivalents) divided by the same energy output. Specific information included herein are material to power (MPR) ratios for a range of power technologies for conventional thermoelectric, renewables (including three geothermal power technologies), and coproduced natural gas/geothermal power. For the geothermal power scenarios, the MPRs include the casing, cement, diesel, and water requirements for drilling wells and topside piping. Also included herein are energy and GHG ratios for plant and fuel cycle stages for the range of considered electricity generating technologies. Some of this information are MPR data extracted directly from the literature or from models (eg. ICARUS - a subset of ASPEN models) and others (energy and GHG ratios) are results calculated using GREET models and MPR data. MPR data for wells included herein were based on the Argonne well materials model and GETEM well count results.

15 GEOTHERMAL ENERGY↗

Algorithmic and GPU enhancements for molecular dynamics in Cabana and LAMMPS

The focus of this milestone was to implement and test new algorithms and optimizations for improved GPU performance by molecular dynamics (MD) and related particle codes. The work was done in the CoPA Cabana library, the CabanaMD mini-app which uses Cabana to implement and benchmark prototypical MD models, and the LAMMPS MD code which is used by the ECP EXAALT project as well as for materials modeling generally. Efforts included (a) implementation of new or optimized algorithms for fundamental MD kernels like neighbor-list building and FFTs which are also widely used in other particle methods, (b) performance testing of data structure layouts for both simple pairwise and computationally intense neural network potentials, (c) optimizations aimed at improving GPU performance for models with small atom counts, and (d) optimizations specific to the SNAP interatomic potential due to its importance for the EXAALT project.

36 MATERIALS SCIENCE↗

AN EMPIRICAL METHOD FOR IMPROVING THE QUALITY OF RXTE HEXTE SPECTRA

We have developed a correction tool to improve the quality of Rossi X-ray Timing Explorer (RXTE) High Energy X-ray Timing Experiment (HEXTE) spectra by employing the same method we used earlier to improve the quality of RXTE Proportional Counter Array (PCA) spectra. We fit all of the hundreds of HEXTE spectra of the Crab individually to a simple power-law model, some 37 million counts in total for Cluster A and 39 million counts for Cluster B, and we create for each cluster a combined spectrum of residuals. We find that the residual spectrum of Cluster A is free of instrumental artifacts while that of Cluster B contains significant features with amplitudes ∼1%; the most prominent is in the energy range 30–50 keV, which coincides with the iodine K edge. Starting with the residual spectrum for Cluster B, via an iterative procedure we created the calibration tool hexBcorr for correcting any Cluster B spectrum of interest. We demonstrate the efficacy of the tool by applying it to Cluster B spectra of two bright black holes, which contain several million counts apiece. For these spectra, application of the tool significantly improves the goodness of fit, while affecting only slightly the broadband fit parameters. The tool may be important for the study of spectral features, such as cyclotron lines, a topic that is beyond the scope of this paper.

79 ASTRONOMY AND ASTROPHYSICS↗

Solar Wind Change Exchange from the Magnetosheath

We report the results of a long (approximately 100 ks) XMM-Newton observation designed to observe solar wind charge exchange emission (SWCX) from Earth's magnetosheath. By luck, the observation took place during a period of minimal solar wind flux so the SWCX emission was also minimal. Never-the-less, there is a significant if not stunning correlation between the observed O VIII count rate and our model for magnetosheath emission. We also report on the observed O VII and O VII emission.

Snowden, Steve↗

Additive Manufacturing Design Considerations for Liquid Engine Components

The Marshall Space Flight Center's Propulsion Systems Department has gained significant experience in the last year designing, building, and testing liquid engine components using additive manufacturing. The department has developed valve, duct, turbo-machinery, and combustion device components using this technology. Many valuable lessons were learned during this process. These lessons will be the focus of this presentation. We will present criteria for selecting part candidates for additive manufacturing. Some part characteristics are 'tailor made' for this process. Selecting the right parts for the process is the first step to maximizing productivity gains. We will also present specific lessons we learned about feature geometry that can and cannot be produced using additive manufacturing machines. Most liquid engine components were made using a two-step process. The base part was made using additive manufacturing and then traditional machining processes were used to produce the final part. The presentation will describe design accommodations needed to make the base part and lessons we learned about which features could be built directly and which require the final machine process. Tolerance capabilities, surface finish, and material thickness allowances will also be covered. Additive Manufacturing can produce internal passages that cannot be made using traditional approaches. It can also eliminate a significant amount of manpower by reducing part count and leveraging model-based design and analysis techniques. Information will be shared about performance enhancements and design efficiencies we experienced for certain categories of engine parts.

Whitten, Dave↗

Models of Sector Flows Under Local, Regional and Airport Weather Constraints

Recently, the ATM community has made important progress in collaborative trajectory management through the introduction of a new FAA traffic management initiative called a Collaborative Trajectory Options Program (CTOP). FAA can use CTOPs to manage air traffic under multiple constraints (manifested as flow constrained areas or FCAs) in the system, and it allows flight operators to indicate their preferences for routing and delay options. CTOPs also permits better management of the overall trajectory of flights by considering both routing and departure delay options simultaneously. However, adoption of CTOPs in airspace has been hampered by many factors that include challenges in how to identify constrained areas and how to set rates for the FCAs. Decision support tools providing assistance would be particularly helpful in effective use of CTOPs. Such DSTs tools would need models of demand and capacity in the presence of multiple constraints. This study examines different approaches to using historical data to create and validate models of maximum flows in sectors and other airspace regions in the presence of multiple constraints. A challenge in creating an empirical model of flows under multiple constraints is a lack of sufficient historical data that captures diverse situations involving combinations of multiple constraints especially those with severe weather. The approach taken here to deal with this is two-fold. First, we create a generalized sector model encompassing multiple sectors rather than individual sectors in order to increase the amount of data used for creating the model by an order of magnitude. Secondly, we decompose the problem so that the amount of data needed is reduced. This involves creating a baseline demand model plus a separate weather constrained flow reduction model and then composing these into a single integrated model. A nominal demand model is a flow model (gdem) in the presence of clear local weather. This defines the flow as a function of weather constraints in neighboring regions, airport constraints and weather in locations that can cause re-routes to the location of interest. A weather constrained flow reduction model (fwx-red) is a model of reduction in baseline counts as a function of local weather. Because the number of independent variables associated with each of the two decomposed models is smaller than that with a single model, need for amount of data is reduced. Finally, a composite model that combines these two can be represented as fwx-red (gdem(e), l) where e represents non-local constraints and l represents local weather. The approaches studied to developing these models are divided into three categories: (1) Point estimation models (2) Empirical models (3) Theoretical models. Errors in predictions of these different types of models have been estimated. In situations when there is abundant data, point estimation models tend to be very accurate. In contrast, empirical models do better than theoretical models when there is some data available. The biggest benefit of theoretical models is their general applicability in wider range situations once the degree of accuracy of these has been established.

Kulkarni, Deepak↗

Background and Lunar Neutron Populations Detected by LEND and Average Concentration of Near-Surface Hydrogen near the Moon's Poles

Neutron flux measurements by the Lunar Exploration Neutron Detector (LEND) on the Lunar Reconnaissance Orbiter (LRO) enable quantifying hydrogen-bearing volatiles in the lunar surface from orbit. Accurately determining hydrogen abundance requires discriminating between the instrument background detection rate and the population of lunar-sourced neutrons that are sensitive to surficial hydrogen. We have investigated the detection rate for lunar and non-lunar (spacecraft-sourced) neutrons in LEND by modeling maps of measured count rate in three LEND detector systems using linear combinations of maps compiled from LEND detectors and from the Lunar Prospector Neutron Spectrometer. We find that 30% of the global-average 24.926 +/- 0.020 neutron counts per second (cps) detected by the LEND STN3 thermal-energy neutron sensor are lunar-sourced neutrons in the thermal energy range (E < 0.4 eV), 65% are lunar-sourced neutrons in the epithermal and fast energy range (E > 0.4 eV), and 5% are from spacecraft-sourced background signal. In the SETN epithermal neutron detector, 90% of the 10.622 +/- 0.002 neutron detections per second are consistent with a lunar source of epithermal and fast neutrons combined (E > 0.4 eV), with 3% due to lunar-sourced thermal neutron leakage into the detector (E < 0.4 eV), and background signal accounting for 7% of total detections. Background signal due to spacecraft-derived neutrons is substantial in the CSETN collimated detector system, accounting for 57% of the global average detection rate of 5.082 +/- 0.001 cps, greater than the 48% estimated from cruise-phase data. Lunar-sourced epithermal and fast neutrons account for 43% of detected neutrons, including neutrons in collimation as well as neutrons that penetrate the collimator wall to reach the detector. We estimate a lower limit of 17% of lunar-sourced neutrons detected by CSETN are epithermal neutrons in collimation (0.37 cps), with an upper limit estimate of 54 +/- 11% of lunar-sourced neutrons received in collimation, or 1.2 +/- 0.2 cps global average. The pole-to-equator contrast ratio in epithermal and high-energy epithermal neutron flux indicates that the average concentration of hydrogen in the polar regolith above 80deg north or south latitude is ∼105 ppmw (parts per million by weight), or 0.095 +/- 0.01 wt% water-equivalent hydrogen. Above 88deg north or south, the concentration increases to ∼140 ppmw, or 0.13 +/- 0.02 wt% water-equivalent hydrogen. The similar pattern of neutron flux suppression at both poles suggests that hydrogen concentration generally increases nearer the pole and is not closely associated with a specific feature such as Shackleton Crater at the lunar south pole that has no northern counterpart. Epithermal neutron flux decreases with increasing latitude outside the polar regions, consistent with surface hydration that increases with latitude if that hydration extends to ∼13-40 cm into the surface.

T A Livengood↗

Background and Lunar Neutron Populations Detected by LEND and Average Concentration of Near-Surface Hydrogen near the Moon's Poles

Neutron flux measurements by the Lunar Exploration Neutron Detector (LEND) on the Lunar Reconnaissance Orbiter (LRO) enable quantifying hydrogen-bearing volatiles in the lunar surface from orbit. Accurately determining hydrogen abundance requires discriminating between the instrument background detection rate and the population of lunar-sourced neutrons that are sensitive to surficial hydrogen. We have investigated the detection rate for lunar and non-lunar (spacecraft-sourced) neutrons in LEND by modeling maps of measured count rate in three LEND detector systems using linear combinations of maps compiled from LEND detectors and from the Lunar Prospector Neutron Spectrometer. We find that 30% of the global-average 24.926 0.020 neutron counts per second (cps) detected by the LEND STN3 thermal-energy neutron sensor are lunar-sourced neutrons in the thermal energy range (E < 0.4 eV), 65% are lunar-sourced neutrons in the epithermal and fast energy range (E > 0.4 eV), and 5% are from spacecraft-sourced background signal. In the SETN epithermal neutron detector, 90% of the 10.622 0.002 neutron detections per second are consistent with a lunar source of epithermal and fast neutrons combined (E > 0.4 eV), with 3% due to lunar-sourced thermal neutron leakage into the detector (E < 0.4 eV), and background signal accounting for 7% of total detections. Background signal due to spacecraft-derived neutrons is substantial in the CSETN collimated detector system, accounting for 57% of the global average detection rate of 5.082 0.001 cps, greater than the 48% estimated from cruise-phase data. Lunar-sourced epithermal and fast neutrons account for 43% of detected neutrons, including neutrons in collimation as well as neutrons that penetrate the collimator wall to reach the detector. We estimate a lower limit of 17% of lunar-sourced neutrons detected by CSETN are epithermal neutrons in collimation (0.37 cps), with an upper limit estimate of 54 11% of lunar-sourced neutrons received in collimation, or 1.2 0.2 cps global average. The pole-to-equator contrast ratio inepithermal and high-energy epithermal neutron flux indicates that the average concentration of hydrogen in the polar regolith above 80 north or south latitude is ~105 ppmw (parts per million by weight), or 0.095 0.01 wt% water-equivalent hydrogen. Above 88 north or south, the concentration increases to ~140 ppmw, or 0.13 0.02 wt% water-equivalent hydrogen. The similar pattern of neutron flux suppression at both poles suggests that hydrogen concentration generally increases nearer the pole and is not closely associated with a specific feature such as Shackleton Crater at the lunar south pole that has no northern counterpart. Epithermal neutron flux decreases with increasing latitude outside the polar regions, consistent with surface hydration that increases with latitude if that hydration extends to ~13-40 cm into the surface.

Livengood, Timothy A.↗

Two-Tower Quantum Matrix Chain Multiplication: Trading Qubits for Depth

Matrix chain multiplication -- computing $\mathcal{W} = M^{(0)}\cdots M^{(K-1)}$ where $M^{(k)} \in \mathbb{R}^{P_k \times P_{k+1}}$-- arises in scientific computing, machine learning, and graph analysis. Despite the importance of this problem, for chains of distinct matrices, the classical number of operations grows linearly with the chain length $K$ and polynomially in the matrix dimensions. We present \emph{Two-Tower Matrix Multiplication}, a quantum subroutine that encodes the product $\mathcal{W}$ of the $K$ matrices into a quantum state in circuit depth $\mathcal{O}(\max_{k} \mathrm{polylog} (P_k P_{k+1}))$, which is independent of~$K$ within the QRAM-based state-preparation model, whereas the qubit count is $\mathcal{O}\bigl(\sum_{k} \log P_k \bigr)$; the total gate count remains linear in $K$, so the gain is in the circuit depth. The construction interleaves state-preparation operators across two layers; within each layer, all operators act on disjoint registers and execute in parallel. This subroutine can be specialized for the chain-vector case, which computes the product of $K-1$ matrices applied to a vector. We prove the correctness of the subroutine for all $K$ and provide two implementations using the Qiskit and QCLAB frameworks. The subroutine is applicable to any downstream quantum algorithm that operates on a matrix encoded in the statevector, including norm estimation, graph-matrix powers, linear system solving, and quantum machine learning kernels.

Antonioli, Giacomo [Pisa U.] (ORCID:00090000668703↗

Star counts and galactic structure

Star counts are discussed as an aid in studies of galactic structure. Iterative computations using a method-of-color-bins to match numerical models with observational data are described. The star counts provide bounds for the luminosity function and the color-magnitude diagram and allow spheroid normalization in a copycat Galaxy model which posits a galaxy with a Population I disk and a Population II spheroid. Good agreement has been obtained between star counts predicted by the model and observed distributions, including color and magnitude ranges and direction. Modifications being considered for the model include consideration of the halo, a galactic bulge and a thick disk. Several galactic structural features which have yet to be modeled and which may be tractable to the copycat model corrected with star counts are identified.

Bahcall, John N.↗

Allometric and Mobile Terrestrial LiDAR Modeling of Aboveground Woody Biomass of Populus in Coppice Production

Poplars ( Populus spp.) and their hybrids are increasingly being grown in coppice production to generate bioenergy feedstocks at frequent intervals. Allometric equations are re-quired to predict aboveground biomass (AGB) of coppiced individuals with minimal field measurements. Likewise, remote sensing tools like LiDAR (light detection and ranging) can be used if models are available to predict AGB from point cloud data. Therefore, this study sought to develop equations to predict dry woody AGB from field measurements and LiDAR data from coppiced poplar field trials containing eastern cottonwood ( P. del-toides ) and hybrid poplar taxa. We found that taxa-specific allometric models containing the summed basal area of the three largest stems in the coppice provided the best predictive model, with stem height and stem count failing to provide additional explanatory power. The best predictive LiDAR-based model was independent of taxa but had slightly lower adjusted R 2 and higher RMSE than the allometric model. It contained four parameters including crown volume, leaf area index, variance of height returns, and the top point density (i.e., density metric 9 or the proportion of points in the highest point interval when the point cloud is evenly divided into ten vertical intervals). In total, these models can be used to quickly and efficiently estimate dry woody AGB of Populus coppice systems for bioenergy feedstock production.

AGB↗

Model-Based Reconstruction for Collimated Beam Ultrasound Systems

Collimated beam ultrasound systems are a novel technology for imaging inside multi-layered structures such as geothermal wells. Such systems include a transmitter and multiple receivers to capture reflected signals. Common algorithms for ultrasound reconstruction use delay-and-sum (DAS) approaches; these have low computational complexity but produce inaccurate images in the presence of complex structures and specialized geometries such as collimated beams.In this paper, we propose a multi-layer, ultrasonic, model-based iterative reconstruction algorithm designed for collimated beam systems. We introduce a physics-based forward model to accurately ac-count for the propagation of a collimated ultrasonic beam in multi-layer media and describe an efficient implementation using binary search. We model direct arrival signals, detector noise, and a spatially varying image prior, then cast the reconstruction as a maximum a posteriori estimation problem. Using simulated and experimental data we obtain significantly fewer artifacts relative to DAS while running in near real time using commodity compute resources.

Alanazi, Abdulrahman M.↗

Tensor decompositions for count data that leverage stochastic and deterministic optimization

There is growing interest to extend low-rank matrix decompositions to multi-way arrays, or tensors. One fundamental low-rank tensor decomposition is the canonical polyadic decomposition (CPD). The challenge of fitting a low-rank, nonnegative CPD model to Poisson-distributed count data is of particular interest. Several popular algorithms use local search methods to approximate the maximum likelihood estimator (MLE) of the Poisson CPD model. Here, this work presents two new algorithms that extend state-of-the-art local methods for Poisson CPD. Hybrid GCP-CPAPR combines Generalized Canonical Decomposition (GCP) with stochastic optimization and CP Alternating Poisson Regression (CPAPR), a deterministic algorithm, to increase the probability of converging to the MLE over either method used alone. Restarted CPAPR with SVDrop uses a heuristic based on the singular values of the CPD model unfoldings to identify convergence toward optimizers that are not the MLE and restarts within the feasible domain of the optimization problem, thus reducing overall computational cost when using a multi-start strategy. We provide empirical evidence that indicates our approaches outperform existing methods with respect to converging to the Poisson CPD MLE.

CPAPR↗