Parameter Estimation for Stellar-Origin Black Hole Mergers In LISA

The population of stellar origin black hole binaries (SOBHBs) detected by existing ground-based gravitational wave detectors is an exciting target for the future space-based Laser Interferometer Space Antenna (LISA). LISA is sensitive to signals at significantly lower frequencies than ground-based detectors. SOBHB signals will thus be detected much earlier in their evolution, years to decades before they merge. The mergers will then occur in the frequency band covered by ground-based detectors. Observing SOBHBs years before merger can help distinguish between progenitor models for these systems. We present a new Bayesian parameter estimation algorithm for LISA observations of SOBHBs that uses a time-frequency (wavelet) based likelihood function. Our technique accelerates the analysis by several orders of magnitude compared to the standard frequency domain approach and allows for an efficient treatment of non-stationary noise.Comment: 13 pages, 6 figures, 1 tabl

LISA Gravitational Wave Sources in A Time-Varying Galactic Stochastic Background

A unique challenge for data analysis with the Laser Interferometer Space Antenna (LISA) is that the noise backgrounds from instrumental noise and astrophysical sources will change significantly over both the year and the entire mission. Variations in the noise levels will be on time scales comparable to, or shorter than, the time most signals spend in the detector's sensitive band. The variation in the amplitude of the galactic stochastic GW background from galactic binaries as the antenna pattern rotates relative to the galactic center is a particularly significant component of the noise variation. LISA's sensitivity to different source classes will therefore vary as a function of sky location and time. The variation will impact both overall signal-to-noise and the efficiency of alerts to EM observers to search for multi-messenger counterparts.Comment: 15 pages, 9 figures, 3 table

LISA Galactic Binaries in the Roman Galactic Bulge Time-Domain Survey

Short-period Galactic white dwarf binaries detectable by LISA are the only guaranteed persistent sources for multi-messenger gravitational-wave astronomy. Large-scale surveys in the 2020s present an opportunity to conduct preparatory science campaigns to maximize the science yield from future multi-messenger targets. The Nancy Grace Roman Space Telescope Galactic Bulge Time Domain Survey will (in its Reference Survey design) image seven fields in the Galactic Bulge approximately 40,000 times each. Although the Reference Survey cadence is optimized for detecting exoplanets via microlensing, it is also capable of detecting short-period white dwarf binaries. In this paper, we present forecasts for the number of detached short-period binaries the Roman Galactic Bulge Time Domain Survey will discover and the implications for the design of electromagnetic surveys. Although population models are highly uncertain, we find a high probability that the baseline survey will detect of order ~5 detached white dwarf binaries. The Reference Survey would also have a $\gtrsim20\%$ chance of detecting several known benchmark white dwarf binaries at the distance of the Galactic Bulge.Comment: 9 pages, 4 figure, 1 tabl

(Not as) Big as a Barn: Upper Bounds on Dark Matter-Nucleus Cross Sections

Critical probes of dark matter come from tests of its elastic scattering with nuclei. The results are typically assumed to be model-independent, meaning that the form of the potential need not be specified and that the cross sections on different nuclear targets can be simply related to the cross section on nucleons. For point-like spin-independent scattering, the assumed scaling relation is $\sigma_{\chi A} \propto A^2 \mu_A^2 \sigma_{\chi N}\propto A^4 \sigma_{\chi N}$, where the $A^2$ comes from coherence and the $\mu_A^2\simeq A^2 m_N^2$ from kinematics for $m_\chi\gg m_A$. Here we calculate where model independence ends, i.e., where the cross section becomes so large that it violates its defining assumptions. We show that the assumed scaling relations generically fail for dark matter-nucleus cross sections $\sigma_{\chi A} \sim 10^{-32}-10^{-27}\;\text{cm}^2$, significantly below the geometric sizes of nuclei, and well within the regime probed by underground detectors. Last, we show on theoretical grounds, and in light of existing limits on light mediators, that point-like dark matter cannot have $\sigma_{\chi N}\gtrsim10^{-25}\;\text{cm}^2$, above which many claimed constraints originate from cosmology and astrophysics. The most viable way to have such large cross sections is composite dark matter, which introduces significant additional model dependence through the choice of form factor. All prior limits on dark matter with cross sections $\sigma_{\chi N}>10^{-32}\;\text{cm}^2$ with $m_\chi\gtrsim 1\;\text{GeV}$ must therefore be re-evaluated and reinterpreted.Comment: 17 pages, 7 figures, comments are welcom

Getting Ready for LISA: The Data, Support and Preparation Needed to Maximize US Participation in Space-Based Gravitational Wave Science

The NASA LISA Study Team was tasked to study how NASA might support US scientists to participate and maximize the science return from the Laser Interferometer Space Antenna (LISA) mission. LISA is gravitational wave observatory led by ESA with NASA as a junior partner, and is scheduled to launch in 2034. Among our findings: LISA science productivity is greatly enhanced by a full-featured US science center and an open access data model. As other major missions have demonstrated, a science center acts as both a locus and an amplifier of research innovation, data analysis, user support, user training and user interaction. In its most basic function, a US Science Center could facilitate entry into LISA science by hosting a Data Processing Center and a portal for the US community to access LISA data products. However, an enhanced LISA Science Center could: support one of the parallel independent processing pipelines required for data product validation; stimulate the high level of research on data analysis that LISA demands; support users unfamiliar with a novel observatory; facilitate astrophysics and fundamental research; provide an interface into the subtleties of the instrument to validate extraordinary discoveries; train new users; and expand the research community through guest investigator, postdoc and student programs. Establishing a US LISA Science Center well before launch can have a beneficial impact on the participation of the broader astronomical community by providing training, hosting topical workshops, disseminating mock catalogs, software pipelines, and documentation. Past experience indicates that successful science centers are established several years before launch; this early adoption model may be especially relevant for a pioneering mission like LISA.Comment: 93 pages with a lovely cover page thanks to Bernard Kelly and Elizabeth Ferrar

Impulsivity-related cognition in alcohol dependence: is it moderated by DRD2/ANKK1 gene status and executive dysfunction?

Perceived impaired control over alcohol use is a key cognitive construct in alcohol dependence that has been related prospectively to treatment outcome and may mediate the risk for problem drinking conveyed by impulsivity in non-dependent drinkers. The aim of the current study was to investigate whether perceived impaired control may mediate the association between impulsivity-related measures (derived from the Short-form Eysenck Personality Questionnaire Revised) and alcohol-dependence severity in alcohol-dependent drinkers. Furthermore, the extent to which this hypothesized relationship was moderated by genetic risk (Taq1A polymorphism in the DRD2/ANKK1 gene cluster) and verbal fluency as an indicator of executive cognitive ability (Controlled Oral Word Association Test) was also examined. A sample of 143 alcohol-dependent inpatients provided an extensive clinical history of their alcohol use, gave 10 ml of blood for DNA analysis, and completed self-report measures relating to impulsivity, impaired control and severity of dependence. As hypothesized, perceived impaired control (partially) mediated the association between impulsivity-related measures and alcohol-dependence severity. This relationship was not moderated by the DRD2/ANICK1 polymorphism or verbal fluency. These results suggest that, in alcohol dependence, perceived impaired control is a cognitive mediator of impulsivity-related constructs that may be unaffected by DRD2/ANKK1 and neurocognitive processes underlying the retrieval of verbal information. (C) 2014 Elsevier Ltd. All rights reserved

The NANOGrav 15-year Data Set: Bayesian Limits on Gravitational Waves from Individual Supermassive Black Hole Binaries

Evidence for a low-frequency stochastic gravitational wave background has recently been reported based on analyses of pulsar timing array data. The most likely source of such a background is a population of supermassive black hole binaries, the loudest of which may be individually detected in these datasets. Here we present the search for individual supermassive black hole binaries in the NANOGrav 15-year dataset. We introduce several new techniques, which enhance the efficiency and modeling accuracy of the analysis. The search uncovered weak evidence for two candidate signals, one with a gravitational-wave frequency of $\sim$4 nHz, and another at $\sim$170 nHz. The significance of the low-frequency candidate was greatly diminished when Hellings-Downs correlations were included in the background model. The high-frequency candidate was discounted due to the lack of a plausible host galaxy, the unlikely astrophysical prior odds of finding such a source, and since most of its support comes from a single pulsar with a commensurate binary period. Finding no compelling evidence for signals from individual binary systems, we place upper limits on the strain amplitude of gravitational waves emitted by such systems.Comment: 23 pages, 13 figures, 2 tables. Accepted for publication in Astrophysical Journal Letters as part of Focus on NANOGrav's 15-year Data Set and the Gravitational Wave Background. For questions or comments, please email [email protected]

Automated Bale Mapping Using Machine Learning and Photogrammetry

An automatic method of obtaining geographic coordinates of bales using monovision un-crewed aerial vehicle imagery was developed utilizing a data set of 300 images with a 20-megapixel resolution containing a total of 783 labeled bales of corn stover and soybean stubble. The relative performance of image processing with Otsu’s segmentation, you only look once version three (YOLOv3), and region-based convolutional neural networks was assessed. As a result, the best option in terms of accuracy and speed was determined to be YOLOv3, with 80% precision, 99% recall, 89% F1 score, 97% mean average precision, and a 0.38 s inference time. Next, the impact of using lower-cost cameras was evaluated by reducing image quality to one megapixel. The lower-resolution images resulted in decreased performance, with 79% precision, 97% recall, 88% F1 score, 96% mean average precision, and 0.40 s inference time. Finally, the output of the YOLOv3 trained model, density-based spatial clustering, photogrammetry, and map projection were utilized to predict the geocoordinates of the bales with a root mean squared error of 2.41 m

Automated Bale Mapping Using Machine Learning and Photogrammetry

An automatic method of obtaining geographic coordinates of bales using monovision un-crewed aerial vehicle imagery was developed utilizing a data set of 300 images with a 20-megapixel resolution containing a total of 783 labeled bales of corn stover and soybean stubble. The relative performance of image processing with Otsu’s segmentation, you only look once version three (YOLOv3), and region-based convolutional neural networks was assessed. As a result, the best option in terms of accuracy and speed was determined to be YOLOv3, with 80% precision, 99% recall, 89% F1 score, 97% mean average precision, and a 0.38 s inference time. Next, the impact of using lower-cost cameras was evaluated by reducing image quality to one megapixel. The lower-resolution images resulted in decreased performance, with 79% precision, 97% recall, 88% F1 score, 96% mean average precision, and 0.40 s inference time. Finally, the output of the YOLOv3 trained model, density-based spatial clustering, photogrammetry, and map projection were utilized to predict the geocoordinates of the bales with a root mean squared error of 2.41 m