Localizing coalescing massive black hole binaries with gravitational waves

Massive black hole binary coalescences are prime targets for space-based gravitational wave (GW) observatories such as {\it LISA}. GW measurements can localize the position of a coalescing binary on the sky to an ellipse with a major axis of a few tens of arcminutes to a few degrees, depending on source redshift, and a minor axis which is $2 - 4$ times smaller. Neglecting weak gravitational lensing, the GWs would also determine the source's luminosity distance to better than percent accuracy for close sources, degrading to several percent for more distant sources. Weak lensing cannot, in fact, be neglected and is expected to limit the accuracy with which distances can be fixed to errors no less than a few percent. Assuming a well-measured cosmology, the source's redshift could be inferred with similar accuracy. GWs alone can thus pinpoint a binary to a three-dimensional ``pixel'' which can help guide searches for the hosts of these events. We examine the time evolution of this pixel, studying it at merger and at several intervals before merger. One day before merger, the major axis of the error ellipse is typically larger than its final value by a factor of $\sim 1.5-6$. The minor axis is larger by a factor of $\sim 2-9$, and, neglecting lensing, the error in the luminosity distance is larger by a factor of $\sim 1.5-7$. This large change over a short period of time is due to spin-induced precession, which is strongest in the final days before merger. The evolution is slower as we go back further in time. For $z = 1$, we find that GWs will localize a coalescing binary to within $\sim 10\ \mathrm{deg}^2$ as early as a month prior to merger and determine distance (and hence redshift) to several percent.Comment: 30 pages, 10 figures, 5 tables. Version published in Ap

Post-processing partitions to identify domains of modularity optimization

We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics. Given a set of partitions, CHAMP identifies the domain of modularity optimization for each partition ---i.e., the parameter-space domain where it has the largest modularity relative to the input set---discarding partitions with empty domains to obtain the subset of partitions that are "admissible" candidate community structures that remain potentially optimal over indicated parameter domains. Importantly, CHAMP can be used for multi-dimensional parameter spaces, such as those for multilayer networks where one includes a resolution parameter and interlayer coupling. Using the results from CHAMP, a user can more appropriately select robust community structures by observing the sizes of domains of optimization and the pairwise comparisons between partitions in the admissible subset. We demonstrate the utility of CHAMP with several example networks. In these examples, CHAMP focuses attention onto pruned subsets of admissible partitions that are 20-to-1785 times smaller than the sets of unique partitions obtained by community detection heuristics that were input into CHAMP.Comment: http://www.mdpi.com/1999-4893/10/3/9

A Deep Spitzer Survey of Circumstellar Disks in the Young Double Cluster, h and chi Persei

We analyze very deep IRAC and MIPS photometry of $\sim$ 12,500 members of the 14 Myr old Double Cluster, h and $\chi$ Persei, building upon on our earlier, shallower Spitzer studies (Currie et al. 2007a, 2008a). Numerous likely members show infrared (IR) excesses at 8 {\mu}m and 24 $\mu$m indicative of circumstellar dust. The frequency of stars with 8 $\mu$m excess is at least 2% for our entire sample, slightly lower (higher) for B/A stars (later type, lower-mass stars). Optical spectroscopy also identifies gas in about 2% of systems but with no clear trend between the presence of dust and gas. Spectral energy distribution (SED) modeling of 18 sources with detections at optical wavelengths through MIPS 24 $\mu m$ reveals a diverse set of disk evolutionary states, including a high fraction of transitional disks, although similar data for all disk-bearing members would provide better constraints. We combine our results with those for other young clusters to study the global evolution of dust/gas disks. For nominal cluster ages, the e-folding times ($\tau_{o}$) for the frequency of warm dust and gas are 2.75 Myr and 1.75 Myr respectively. Assuming a revised set of ages for some clusters (e.g. Bell et al. 2013), these timescales increase to 5.75 and 3.75 Myr, respectively, implying a significantly longer typical protoplanetary disk lifetime. The transitional disk duration, averaged over multiple evolutionary pathways, is $\sim$ 1 Myr. Finally, 24 $\mu$m excess frequencies for 4-6 M$_{\odot}$ stars appear lower than for 1-2.5 M$_{\odot}$ stars in other 10-30 Myr old clusters.Comment: 35 pages, 6 tables, 21 figures; Accepted for publication in The Astrophysical Journa

Neue Strategien für die 18 F‐Radiochemie

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90201/1/1132_ftp.pd

LoCoH: nonparameteric kernel methods for constructing home ranges and utilization distributions.

Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: "fixed sphere-of-influence," or r-LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an "adaptive sphere-of-influence," or a-LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a), and compare them to the original "fixed-number-of-points," or k-LoCoH (all kernels constructed from k-1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a-LoCoH is generally superior to k- and r-LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu)

Finite difference time domain modeling of steady state scattering from jet engines with moving turbine blades

The approach chosen to model steady state scattering from jet engines with moving turbine blades is based upon the Finite Difference Time Domain (FDTD) method. The FDTD method is a numerical electromagnetic program based upon the direct solution in the time domain of Maxwell's time dependent curl equations throughout a volume. One of the strengths of this method is the ability to model objects with complicated shape and/or material composition. General time domain functions may be used as source excitations. For example, a plane wave excitation may be specified as a pulse containing many frequencies and at any incidence angle to the scatterer. A best fit to the scatterer is accomplished using cubical cells in the standard cartesian implementation of the FDTD method. The material composition of the scatterer is determined by specifying its electrical properties at each cell on the scatterer. Thus, the FDTD method is a suitable choice for problems with complex geometries evaluated at multiple frequencies. It is assumed that the reader is familiar with the FDTD method

Utilizing the Alarm Taxonomy and Classification System (ATACS) to Redesign Landing Gear Warnings

Alarms have been in use for many decades, yet there still needs to be more clarity about what makes a good alarm. Vendors and government agencies have developed several useful handbooks describing the Do’s and Don’ts of effective alarm design; however, to date, we cannot find a comprehensive quantitative taxonomy or classification system that allows researchers to easily score and rank various alarm designs in any field—while using a common language that users, engineers, designers, and human factors professionals can understand. The Alarm Taxonomy and Classification System (ATACS) fills this gap in the literature by breaking alarms down into categorical characteristics, providing a quantitative methodology for scoring each characteristic, and outlining a process by which users, vendors, and human factors professionals can agree on the efficacy of the alarm in question. We discuss this process in detail and show how this system was used to improve landing gear warnings