The Starburst Nature of Lyman-Break Galaxies: Testing UV Extinction with X-rays

We derive the bolometric to X-ray correlation for a local sample of normal and starburst galaxies and use it, in combination with several UV reddening schemes, to predict the 2--8 keV X-ray luminosity for a sample of 24 Lyman-break galaxies in the HDF/CDF-N. We find that the mean X-ray luminosity, as predicted from the Meurer UV reddening relation for starburst galaxies, agrees extremely well with the Brandt stacking analysis. This provides additional evidence that Lyman-break galaxies can be considered as scaled-up local starbursts and that the locally derived starburst UV reddening relation may be a reasonable tool for estimating the UV extinction at high redshift. Our analysis shows that the Lyman-break sample can not have far-IR to far-UV flux ratios similar to nearby ULIGs, as this would predict a mean X-ray luminosity 100 times larger than observed, as well as far-IR luminosities large enough to be detected in the sub-mm. We calculate the UV reddening expected from the Calzetti effective starburst attenuation curve and the radiative transfer models of Witt & Gordon for low metallicity dust in a shell geometry with homogeneous or clumpy dust distributions and find that all are consistent with the observed X-ray emission. Finally, we show that the mean X-ray luminosity of the sample would be under predicted by a factor of 6 if the the far-UV is unattenuated by dust.Comment: 7 pages, 3 figures. Accepted for publication in A

Where is the USA Corn Belt, and how is it changing?

The “Corn Belt” is a commonly used term, but often referenced as a vaguely defined region in the Midwest USA. A few key studies have delineated synoptic maps of the Corn Belt boundaries going back to the early 20th century, but a modern flexible and accessible framework for mapping the Corn Belt in space and time is needed. New tools provide reference maps for the Corn Belt in the 21st century and the ability to quantify space-time changes in corn cropping patterns. The Land use and Agricultural Management Practices web-Service (LAMPS) was used to estimate the average corn (maize, Zea mays L.) area in each county of the contiguous 48 USA states for the years 2010–2016. LAMPS provides a modified areal Fraction of corn (Fc) used to map the Corn Belt at three intensity levels, for example. The resulting patterns illustrate a mostly contiguous Midwest Corn Belt surrounded by more scattered regions, including southern and eastern regions. We also mapped irrigated areas and temporal changes in Fc. Mapped patterns have the potential to help researchers study issues related to food, feed, biofuel, and water security

Fast non-negative deconvolution for spike train inference from population calcium imaging

Calcium imaging for observing spiking activity from large populations of neurons are quickly gaining popularity. While the raw data are fluorescence movies, the underlying spike trains are of interest. This work presents a fast non-negative deconvolution filter to infer the approximately most likely spike train for each neuron, given the fluorescence observations. This algorithm outperforms optimal linear deconvolution (Wiener filtering) on both simulated and biological data. The performance gains come from restricting the inferred spike trains to be positive (using an interior-point method), unlike the Wiener filter. The algorithm is fast enough that even when imaging over 100 neurons, inference can be performed on the set of all observed traces faster than real-time. Performing optimal spatial filtering on the images further refines the estimates. Importantly, all the parameters required to perform the inference can be estimated using only the fluorescence data, obviating the need to perform joint electrophysiological and imaging calibration experiments.Comment: 22 pages, 10 figure

Fe XXV and Fe XXVI Diagnostics of the Black Hole and Accretion Disk in Active Galaxies: Chandra Time-Resolved Spectroscopy of NGC 7314

We report the detection of Fe XXV and Fe XXVI $K\alpha$ emission lines from a Chandra HETGS observation of the Seyfert~1 galaxy NGC 7314, made simultaneously with RXTE. The lines are redshifted (cz ~ 1500 km/s) relative to the systemic velocity and unresolved. We argue that the lines originate in a near face-on (<7 degrees) disk having a radial line emissivity flatter than r^-2. Line emission from ionization states of Fe in the range ~Fe I up to Fe XXVI is observed. The ionization balance of Fe responds to continuum variations on timescales less than 12.5 ks, supporting an origin of the lines close to the X-ray source. We present additional, detailed diagnostics from this rich data set. These results identify NGC 7314 as a key source to study in the future if we are to pursue reverberation mapping of space-time near black-hole event horizons. This is because it is first necessary to understand the ionization structure of accretion disks and the relation between the X-ray continuum and Fe K line emission. However, we also describe how our results are suggestive of a means of measuring black-hole spin without a knowledge of the relation between the continuum and line emission. Finally, these data emphasize that one {\it can} study strong gravity with narrow (as opposed to very broad) disk lines. In fact narrow lines offer higher precision, given sufficient energy resolution.Comment: Accepted for publication in the Astrophysical Journal. 30 pages, six figures, five of them color. Abstract is abridge

The Virtual Machine (VM) Scaler: An Infrastructure Manager Supporting Environmental Modeling on IaaS Clouds

Infrastructure-as-a-service (IaaS) clouds provide a new medium for deployment of environmental modeling applications. Harnessing advancements in virtualization, IaaS clouds can provide dynamic scalable infrastructure to better support scientific modeling computational demands. Providing scientific modeling as-a-service requires dynamic scaling of server infrastructure to adapt to changing user workloads. This paper presents the Virtual Machine (VM) Scaler, an autonomic resource manager for IaaS Clouds. We have developed VM-Scaler, a REST/JSON-based web services application which supports infrastructure provisioning and management to support scientific modeling for the Cloud Services Innovation Platform (CSIP) [Lloyd et al. 2012]. VM-Scaler harnesses the Amazon Elastic Compute Cloud (EC2) application programming interface to support model- service scalability, cloud management, and infrastructure configuration for supporting modeling workloads. VM-Scaler provides cloud control while abstracting the underlying IaaS cloud from the end user. VM-Scaler is extensible to support any EC2 compatible cloud and currently supports the Amazon public cloud and Eucalyptus private clouds versions 3.1 and 3.3. VM-Scaler provides a platform to improve scientific model deployment by supporting experimentation with: hot spot detection schemes, VM management and placement approaches, and model job scheduling/proxy services

The Virtual Machine (VM) Scaler: An Infrastructure Manager Supporting Environmental Modeling on IaaS Clouds

Infrastructure-as-a-service (IaaS) clouds provide a new medium for deployment of environmental modeling applications. Harnessing advancements in virtualization, IaaS clouds can provide dynamic scalable infrastructure to better support scientific modeling computational demands. Providing scientific modeling as-a-service requires dynamic scaling of server infrastructure to adapt to changing user workloads. This paper presents the Virtual Machine (VM) Scaler, an autonomic resource manager for IaaS Clouds. We have developed VM-Scaler, a REST/JSON-based web services application which supports infrastructure provisioning and management to support scientific modeling for the Cloud Services Innovation Platform (CSIP) [Lloyd et al. 2012]. VM-Scaler harnesses the Amazon Elastic Compute Cloud (EC2) application programming interface to support model- service scalability, cloud management, and infrastructure configuration for supporting modeling workloads. VM-Scaler provides cloud control while abstracting the underlying IaaS cloud from the end user. VM-Scaler is extensible to support any EC2 compatible cloud and currently supports the Amazon public cloud and Eucalyptus private clouds versions 3.1 and 3.3. VM-Scaler provides a platform to improve scientific model deployment by supporting experimentation with: hot spot detection schemes, VM management and placement approaches, and model job scheduling/proxy services

Improved Oil Recovery in Mississippian Carbonate Reservoirs of Kansas -- Near-Term -- Class

The objective of this project is to demonstrate incremental reserves from Osagian and Meramecian (Mississippian) dolomite reservoirs in western Kansas through application of reservoir characterization to identify areas of unrecovered mobile oil. The project addresses producibility problems in two fields: Specific reservoirs target the Schaben Field in Ness County, Kansas, and the Bindley Field in Hodgeman County, Kansas. The producibility problems to be addressed include inadequate reservoir characterization, drilling and completion design problems, non-optimum recovery efficiency. The results of this project will be disseminated through various technology transfer activities. At the Schaben demonstration site, the Kansas team will conduct a field project to demonstrate better approaches to identify bypassed oil within and between reservoir units