From One to Many: A Deep Learning Coincident Gravitational-Wave Search

Gravitational waves from the coalescence of compact-binary sources are now routinely observed by Earth bound detectors. The most sensitive search algorithms convolve many different pre-calculated gravitational waveforms with the detector data and look for coincident matches between different detectors. Machine learning is being explored as an alternative approach to building a search algorithm that has the prospect to reduce computational costs and target more complex signals. In this work we construct a two-detector search for gravitational waves from binary black hole mergers using neural networks trained on non-spinning binary black hole data from a single detector. The network is applied to the data from both observatories independently and we check for events coincident in time between the two. This enables the efficient analysis of large quantities of background data by time-shifting the independent detector data. We find that while for a single detector the network retains $91.5\%$ of the sensitivity matched filtering can achieve, this number drops to $83.9\%$ for two observatories. To enable the network to check for signal consistency in the detectors, we then construct a set of simple networks that operate directly on data from both detectors. We find that none of these simple two-detector networks are capable of improving the sensitivity over applying networks individually to the data from the detectors and searching for time coincidences

Training Strategies for Deep Learning Gravitational-Wave Searches

Compact binary systems emit gravitational radiation which is potentially detectable by current Earth bound detectors. Extracting these signals from the instruments' background noise is a complex problem and the computational cost of most current searches depends on the complexity of the source model. Deep learning may be capable of finding signals where current algorithms hit computational limits. Here we restrict our analysis to signals from non-spinning binary black holes and systematically test different strategies by which training data is presented to the networks. To assess the impact of the training strategies, we re-analyze the first published networks and directly compare them to an equivalent matched-filter search. We find that the deep learning algorithms can generalize low signal-to-noise ratio (SNR) signals to high SNR ones but not vice versa. As such, it is not beneficial to provide high SNR signals during training, and fastest convergence is achieved when low SNR samples are provided early on. During testing we found that the networks are sometimes unable to recover any signals when a false alarm probability $<10^{-3}$ is required. We resolve this restriction by applying a modification we call unbounded Softmax replacement (USR) after training. With this alteration we find that the machine learning search retains $\geq 97.5\%$ of the sensitivity of the matched-filter search down to a false-alarm rate of 1 per month

Extending the PyCBC search for gravitational waves from compact binary mergers to a global network

The worldwide advanced gravitational-wave (GW) detector network has so far primarily consisted of the two Advanced LIGO observatories at Hanford and Livingston, with Advanced Virgo joining the 2016-7 O2 observation run at a relatively late stage. However Virgo has been observing alongside the LIGO detectors since the start of the O3 run; in the near future, the KAGRA detector will join the global network and a further LIGO detector in India is under construction. Gravitational-wave search methods would therefore benefit from the ability to analyse data from an arbitrary network of detectors. In this paper we extend the PyCBC offline compact binary coalescence (CBC) search analysis to three or more detectors, and describe resulting updates to the coincident search and event ranking statistic. For a three-detector network, our improved multi-detector search finds 20% more simulated signals at fixed false alarm rate in idealized colored Gaussian noise, and up to 40% more in real data, compared to the two-detector analysis previously used during O2

Costs and outcomes of noncardioembolic ischemic stroke in a managed care population

Nicole M Engel-Nitz1, Stephen D Sander2, Carolyn Harley3, Gabriel Gomez Rey1, Hemal Shah21Health Economic and Outcomes Research, i3 Innovus, Eden Prairie, MN, USA; 2Health Economic and Outcomes Research, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA; 3Health Economic and Outcomes Research, i3 Innovus, Palo Alto, CA, USAPurpose: To evaluate the clinical outcomes and incremental health care costs of ischemic stroke in a US managed care population.Patients and methods: A retrospective cohort analysis was done on patients (aged 18+ years) hospitalized with noncardioembolic ischemic stroke from January 1, 2002, through &amp;shy;December 31, 2003, identified from commercial health plan administrative claims. New or recurrent stroke was based on history in the previous 12 months, with index date defined as first date of &amp;shy;indication of stroke. A control group without stroke or transient ischemic attack (TIA) was matched (1:3) on age, sex, and geographic region, with an index date defined as the first &amp;shy;medical claim during the patient identification period. Patients with atrial fibrillation or mitral value abnormalities were excluded. Ischemic stroke and control cohorts were compared on 4-year clinical outcomes and 1-year costs.Results: Of 2180 ischemic stroke patients, 1808 (82.9%) had new stroke and 372 (17.1%) had a recurrent stroke. Stroke patients had higher unadjusted rates of additional stroke, TIA, and fatal outcomes compared with the 6540 matched controls. Recurrent stroke patients had higher rates of adverse clinical outcomes compared with new stroke patients; costs attributed to recurrent stroke were also higher. Stroke patients were 2.4 times more likely to be hospitalized in follow-up compared with controls (hazard ratio [HR] 2.4, 95% confidence interval [CI]: 2.2, 2.6). Occurrence of stroke following discharge was 21 times more likely among patients with index stroke compared with controls (HR 21.0, 95% CI: 16.1, 27.3). Stroke was also predictive of death (HR 1.8, 95% CI: 1.3, 2.5). Controlling for covariates, stroke patients had significantly higher costs compared with control patients in the year following the index event.Conclusion: Noncardioembolic ischemic stroke patients had significantly poorer outcomes and higher costs compared with controls. Recurrent stroke appears to contribute substantially to these higher rates of adverse outcomes and costs.Keywords: burden of illness, stroke&amp;frasl;cerebrovascular accident, cardiovascular disease, claims analysis, costs of care, health care outcome

Fall risk in people with MS: A Physiological Profile Assessment study.

INTRODUCTION: The Physiological Profile Assessment (PPA) is used in research and clinical practice for assessing fall risk. We compared PPA test performance between people with multiple sclerosis (MS) and healthy controls, determined the fall-risk profile for people with MS and developed a reference database for people with MS. METHODS: For this study, 416 ambulant people with MS (51.5 ± 12.0 years) and 352 controls (52.8 ± 12.2 years) underwent the PPA (tests of contrast sensitivity, proprioception, quadriceps strength, reaction time and sway) with composite fall-risk scores computed from these measures. MS participants were followed prospectively for falls for 3 months. RESULTS: The MS participants performed significantly worse than controls in each PPA test. The average composite fall-risk score was also significantly elevated, indicating a "marked" fall risk when compared with controls. In total, 155 MS participants (37.3%) reported 2 + falls in the follow-up period. Frequent fallers performed significantly worse than non-frequent fallers in the contrast sensitivity, reaction time and sway tests and had higher PPA composite scores. CONCLUSIONS: In line with poor PPA test performances, falls incidence in people with MS was high. This study provides comprehensive reference data for the PPA measures for people with MS that could be used to inform future research and clinical practice

3-OGC: Catalog of gravitational waves from compact-binary mergers

We present the third Open Gravitational-wave Catalog (3-OGC) of compact-binary coalescences, based on the analysis of the public LIGO and Virgo data from 2015 through 2019 (O1, O2, O3a). Our updated catalog includes a population of 57 observations, including four binary black hole mergers that had not previously been reported. This consists of 55 binary black hole mergers and the two binary neutron star mergers GW170817 and GW190425. We find no additional significant binary neutron star or neutron star--black hole merger events. The most confident new detection is the binary black hole merger GW190925\_232845 which was observed by the LIGO Hanford and Virgo observatories with $\mathcal{P}_{\textrm{astro}} > 0.99$; its primary and secondary component masses are $20.2^{+3.9}_{-2.5} M_{\odot}$ and $15.6^{+2.1}_{-2.6} M_{\odot}$, respectively. We estimate the parameters of all binary black hole events using an up-to-date waveform model that includes both sub-dominant harmonics and precession effects. To enable deep follow-up as our understanding of the underlying populations evolves, we make available our comprehensive catalog of events, including the sub-threshold population of candidates, and the posterior samples of our source parameter estimates

Adsorption and absorption energies of hydrogen with palladium

Thermal recombinative desorption rates of HD on Pd(111) and Pd(332) are reported from transient kinetic experiments performed between 523 and 1023 K. A detailed kinetic model accurately describes the competition between recombination of surface-adsorbed hydrogen and deuterium atoms and their diffusion into the bulk. By fitting the model to observed rates, we derive the dissociative adsorption energies (E0, adsH2 = 0.98 eV; E0, adsD2 = 1.00 eV; E0, adsHD = 0.99 eV) as well as the classical dissociative binding energy ϵads = 1.02 ± 0.03 eV, which provides a benchmark for electronic structure theory. In a similar way, we obtain the classical energy required to move an H or D atom from the surface to the bulk (ϵsb = 0.46 ± 0.01 eV) and the isotope specific energies, E0, sbH = 0.41 eV and E0, sbD = 0.43 eV. Detailed insights into the process of transient bulk diffusion are obtained from kinetic Monte Carlo simulations