RascalC: A Jackknife Approach to Estimating Single and Multi-Tracer Galaxy Covariance Matrices

To make use of clustering statistics from large cosmological surveys, accurate and precise covariance matrices are needed. We present a new code to estimate large scale galaxy two-point correlation function (2PCF) covariances in arbitrary survey geometries that, due to new sampling techniques, runs $\sim 10^4$ times faster than previous codes, computing finely-binned covariance matrices with negligible noise in less than 100 CPU-hours. As in previous works, non-Gaussianity is approximated via a small rescaling of shot-noise in the theoretical model, calibrated by comparing jackknife survey covariances to an associated jackknife model. The flexible code, RascalC, has been publicly released, and automatically takes care of all necessary pre- and post-processing, requiring only a single input dataset (without a prior 2PCF model). Deviations between large scale model covariances from a mock survey and those from a large suite of mocks are found to be be indistinguishable from noise. In addition, the choice of input mock are shown to be irrelevant for desired noise levels below $\sim 10^5$ mocks. Coupled with its generalization to multi-tracer data-sets, this shows the algorithm to be an excellent tool for analysis, reducing the need for large numbers of mock simulations to be computed.Comment: 29 pages, 8 figures. Accepted by MNRAS. Code is available at http://github.com/oliverphilcox/RascalC with documentation at http://rascalc.readthedocs.io

Self reported aggravating activities do not demonstrate a consistent directional pattern in chronic non specific low back pain patients: An observational study

Question: Do the self-reported aggravating activities of chronic non-specific low back pain patients demonstrate a consistent directional pattern? Design: Cross-sectional observational study. Participants: 240 chronic non specific low back pain patients. Outcome measure: We invited experienced clinicians to classify each of the three self-nominated aggravating activities from the Patient Specific Functional Scale by the direction of lumbar spine movement. Patients were described as demonstrating a directional pattern if all nominated activities moved the spine into the same direction. Analyses were undertaken to determine if the proportion of patients demonstrating a directional pattern was greater than would be expected by chance. Results: In some patients, all tasks did move the spine into the same direction, but this proportion did not differ from chance (p = 0.328). There were no clinical or demographic differences between those who displayed a directional pattern and those who did not (all p > 0.05). Conclusion: Using patient self-reported aggravating activities we were unable to demonstrate the existence of a consistent pattern of adverse movement in patients with chronic non-specific low back pain

Is there a link between agricultural land-use management and flooding?

International audienceOver the past fifty years, significant changes in UK land use and management practices have occurred, driven by UK and EU agricultural policies. There is substantial evidence that modern land-use management practices have enhanced surface runoff generation at the local scale, frequently creating impacts through "muddy floods". Such local impacts can be avoided or mitigated through the adoption of better land management practices and/or small scale surface runoff control measures. There is little evidence that local scale changes in runoff generation propagate downstream to create impacts at the larger catchment scale. This does not imply that impacts do not exist, but the very few studies in which evidence has been sought have not produced any conclusive findings. Multiscale catchment experimentation, linked to new developments in modelling, is needed which can lead to a better understanding of how small scale changes to runoff generation propagate to larger catchment scales. To facilitate the tracking of changes from the local to the catchment scale, a new modelling approach is demonstrated which allows a downstream flood hydrograph to be mapped back onto its source areas, thus presenting impact information to users in a useful and comprehensible form

Filamentary Star Formation in NGC 1275

We examine the star formation in the outer halo of NGC~1275, the central galaxy in the Perseus cluster (Abell 426), using far ultraviolet and optical images obtained with the Hubble Space Telescope. We have identified a population of very young, compact star clusters with typical ages of a few Myr. The star clusters are organised on multiple-kiloparsec scales. Many of these star clusters are associated with "streaks" of young stars, the combination of which has a cometary appearance. We perform photometry on the star clusters and diffuse stellar streaks, and fit their spectral energy distributions to obtain ages and masses. These young stellar populations appear to be normal in terms of their masses, luminosities and cluster formation efficiency; <10% of the young stellar mass is located in star clusters. Our data suggest star formation is associated with the evolution of some of the giant gas filaments in NGC~1275 that become gravitationally unstable on reaching and possibly stalling in the outer galaxy. The stellar streaks then could represent stars moving on ballistic orbits in the potential well of the galaxy cluster. We propose a model where star-forming filaments, switched on ~50~Myr ago and are currently feeding the growth of the NGC~1275 stellar halo at a rate of ~2-3 solar masses per year. This type of process may also build stellar halos and form isolated star clusters in the outskirts of youthful galaxies.Comment: 15 pages, 10 figures, accepted for publication in MNRA

Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature

A wind turbines' power curve is easily accessible damage sensitive data, and as such is a key part of structural health monitoring in wind turbines. Power curve models can be constructed in a number of ways, but the authors argue that probabilistic methods carry inherent benefits in this use case, such as uncertainty quantification and allowing uncertainty propagation analysis. Many probabilistic power curve models have a key limitation in that they are not physically meaningful - they return mean and uncertainty predictions outside of what is physically possible (the maximum and minimum power outputs of the wind turbine). This paper investigates the use of two bounded Gaussian Processes in order to produce physically meaningful probabilistic power curve models. The first model investigated was a warped heteroscedastic Gaussian process, and was found to be ineffective due to specific shortcomings of the Gaussian Process in relation to the warping function. The second model - an approximated Gaussian Process with a Beta likelihood was highly successful and demonstrated that a working bounded probabilistic model results in better predictive uncertainty than a corresponding unbounded one without meaningful loss in predictive accuracy. Such a bounded model thus offers increased accuracy for performance monitoring and increased operator confidence in the model due to guaranteed physical plausibility