Type IIP supernova SN 2004et: a multiwavelength study in X-ray, optical and radio

We present X-ray, broad-band optical and low-frequency radio observations of the bright type IIP supernova SN 2004et. The Chandra X-ray Observatory observed the supernova at three epochs, and the optical coverage spans a period of ∼470 d since explosion. The X-ray emission softens with time, and we characterize the X-ray luminosity evolution as   L _X∝ t ^−0.4  . We use the observed X-ray luminosity to estimate a mass-loss rate for the progenitor star of  ∼2 × 10⁻⁶ M_⊙ yr⁻¹  . The optical light curve shows a pronounced plateau lasting for about 110 d. Temporal evolution of photospheric radius and colour temperature during the plateau phase is determined by making blackbody fits. We estimate the ejected mass of ⁵⁶Ni to be  0.06 ± 0.03 M_⊙  . Using the expressions of Litvinova & Nadëzhin we estimate an explosion energy of  (0.98 ± 0.25) × 10⁵¹ erg  . We also present a single epoch radio observation of SN 2004et. We compare this with the predictions of the model proposed by Chevalier, Fransson & Nymark. These multiwavelength studies suggest a main-sequence progenitor mass of  ∼20 M_⊙  for SN 2004et.     

Solar x-ray spectrometer (SOXS) mission – Low energy payload – First results

We present the first results from the ‘Low Energy Detector’ pay-load of ‘Solar X-ray Spectrometer (SOXS)’ mission, which was launched onboard GSAT-2 Indian spacecraft on 08 May 2003 by GSLV-D2 rocket to study the solar flares. The SOXS Low Energy Detector (SLD) payload was designed, developed and fabricated by Physical Research Laboratory (PRL) in collaboration with Space Application Centre (SAC), Ahmedabad and ISRO Satellite Centre (ISAC), Bangalore of the Indian Space Research Organization (ISRO). The SLD payload employs the state-of-the-art solid state detectors viz., Si PIN and Cadmium-Zinc-Telluride (CZT) devices that operate at near room temperature (-20°C). The dynamic energy range of Si PIN and CZT detectors are 4–25 keV and 4–56 keV respectively. The Si PIN provides sub-keV energy resolution while CZT reveals ∼1.7keV energy resolution throughout the dynamic range. The high sensitivity and sub-keV energy resolution of Si PIN detector allows the measuring of the intensity, peak energy and equivalent width of the Fe-line complex at approximately 6.7 keV as a function of time in all 8 M-class flares studied in this investigation. The peak energy (E _p) of Fe-line feature varies between 6.4 and 6.8 keV with increase in temperature from 9 to 34 MK. We found that the equivalent width (ω) of Fe-line feature increases exponentially with temperature up to 20 MK but later it increases very slowly up to 28 MK and then it remains uniform around 1.55 keV up to 34 MK. We compare our measurements ofw with calculations made earlier by various investigators and propose that these measurements may improve theoretical models. We interpret the variation of both E_pand ω with temperature as the changes in the ionization and recombination conditions in the plasma during the flare interval and as a consequence the contribution from different ionic emission lines also varies.     

Extreme heat in New Zealand: a synthesis

Climatic Change - The usefulness of global integrated assessment model (IAM) results for policy recommendation in specific regions has not been fully assessed to date. This study presents the...     

SPIRE point source photometry: within the Herschel interactive processing environment (HIPE)

The different algorithms appropriate for point source photometry on data from the SPIRE instrument on-board the Herschel Space Observatory, within the Herschel Interactive Processing Environment (HIPE) are compared. Point source photometry of a large ensemble of standard calibration stars and dark sky observations is carried out using the 4 major methods within HIPE: SUSSEXtractor, DAOphot, the SPIRE Timeline Fitter and simple Aperture Photometry. Colour corrections and effective beam areas as a function of the assumed source spectral index are also included to produce a large number of photometric measurements per individual target, in each of the 3 SPIRE bands (250, 350, 500μm), to examine both the accuracy and repeatability of each of the 4 algorithms. It is concluded that for flux densities down to the level of 30mJy that the SPIRE Timeline Fitter is the method of choice. However, at least in the 250 and 350μm bands, all 4 methods provide photometric repeatability better than a few percent down to at approximately 100mJy. The DAOphot method appears in many cases to have a systematic offset of ～8 % in all SPIRE bands which may be indicative of a sub-optimal aperture correction. In general, aperture photometry is the least reliable method, i.e. largest scatter between observations, especially in the longest wavelength band. At the faintest fluxes, <30mJy, SUSSEXtractor or DAOphot provide a better alternative to the Timeline Fitter.     

Snow accumulation and ablation response to changes in forest structure and snow surface albedo after attack by mountain pine beetle

This study quantified changes in snow accumulation and ablation with forest defoliation in a young pine stand attacked by mountain pine beetle, a mature mixed species stand, and a clearcut in south‐central British Columbia. From 2006 to 2012, as trees in the pine stand turned from green to grey, average canopy transmittance increased from 27% to 49%. In the mixed stand, transmittance remained constant at 19%. In 2009, the year of greatest needle loss, average snow surface litter cover in the pine stand was 29% (range 4 – 61%), compared to ≤9% in other years and over double that in the mixed stand. By 2012, litter accumulation in the now‐grey pine stand was only a sixth of that in the mixed stand. Inter‐annual variability in the weather had the greatest effect on snow accumulation and ablation, with the greatest differences between both forested stands and the clearcut occurring in 2010, the year of lowest SWE. Differences in snow accumulation between the pine and mixed stand increased in 2010 as a result of decreased snow interception in the young stand after needlefall. Average ablation rates in the attacked stand were most different from the mixed stand in 2009 and 2012, the years with the largest and smallest over‐winter needle loss, respectively. This study shows that grey, non‐pine, and understory trees moderate snow response to changes in the main canopy. It also highlights the complex interrelationships between ecohydrological processes key to assessing watershed response to forest cover loss in snow dominated hydrologic regimes. Copyright © 2012 John Wiley & Sons, Ltd.     

Internal catchment process simulation in a snow‐dominated basin: performance evaluation with spatiotemporally variable runoff generation and groundwater dynamics

Hydrologic models have increasingly been used in forest hydrology to overcome the limitations of paired watershed experiments, where vegetative recovery and natural variability obscure the inferences and conclusions that can be drawn from such studies. Models are also plagued by uncertainty, however, and parameter equifinality is a common concern. Physically‐based, spatially‐distributed hydrologic models must therefore be tested with high‐quality experimental data describing a multitude of concurrent internal catchment processes under a range of hydrologic regimes. This study takes a novel approach by not only examining the ability of a pre‐calibrated model to realistically simulate watershed outlet flows over a four year period, but a multitude of spatially‐extensive, internal catchment process observations not previously evaluated, including: continuous groundwater dynamics, instantaneous stream and road network flows, and accumulation and melt period spatial snow distributions. Many hydrologic model evaluations are only on the comparison of predicted and observed discharge at a catchment outlet and remain in the ‘infant stage’ in terms of model testing. This study, on the other hand, tests the internal spatial predictions of a distributed model with a range of field observations over a wide range of hydroclimatic conditions. Nash‐Sutcliffe model efficiency was improved over prior evaluations due to continuing efforts in improving the quality of meteorological data collection. Road and stream network flows were generally well simulated for a range of hydrologic conditions, and snowpack spatial distributions were well simulated for one of two years examined. The spatial variability of groundwater dynamics was effectively simulated, except at locations where strong stream–groundwater interactions exist. Model simulations overall were quite successful in realistically simulating the spatiotemporal variability of internal catchment processes in the watershed, but the premature onset of simulated snowmelt for one of the simulation years has prompted further work in model development. Copyright © 2011 John Wiley & Sons, Ltd.     

Quantifying uncertainty in extreme values of design parameters with resampling techniques

In this paper, a methodology for the selection of statistical models for describing the extreme wave heights on the basis of resampling techniques is presented. Two such techniques are evaluated: the jackknife and the bootstrap. The methods are applied to two high-quality datasets of wave measurements in the Mediterranean and one from the East Coast of the USA. The robustness of the estimates of the extreme values of wave heights at return periods important for coastal engineering design is explored further. In particular, we demonstrate how an ensemble error norm can be used to select the most appropriate extreme probability model from a choice of cumulative distribution functions (CDFs). This error norm is based on the mean error norm of the optimised CDF for each resampled (replicate) data series. The resampling approach is also used to present confidence intervals of the CDF parameters. We provide a brief discussion of the sensitivity of these parameters and the suitability of each model in terms of uncertainty with resampling techniques. The advantages of resampling are outlined, and the superiority of the bootstrap over the jackknife in quantifying the uncertainty of extreme quantiles is demonstrated for these records.     

Exposure of sticklebacks (Gasterosteus aculeatus) to cadmium sulfide nanoparticles: biological effects and the importance of experimental design.

Effects of nanoparticles on aquatic organisms have been little studied to date and toxicological data are urgently needed for development of regulatory frameworks for these substances. Here, we report the findings of a study exposing sticklebacks to cadmium sulfide (CdS) as bulk material and quantum dots. Fish were exposed for 21 d in a flow through test system to 5, 50 or 500 microg l(-1) CdS nanoparticles (nCdS) coated in thiol terminated methyl polyethylene glycol (MPEG), bulk CdS or MPEG at 500 microg l(-1) (nominal concentrations). With the exception of the highest nCdS exposure, measured concentrations were approximately one order of magnitude below nominal. A single fish from each group (excluding MPEG) was examined using energy dispersive X-ray analysis (EDX) to localise cadmium, however, cadmium could not be detected in whole body sections. Elevated levels of oxidized glutathione were measured in the gills of fish exposed to 50 and 500 microg l(-1) nCdS. Induction of vitellogenin synthesis was not detected in any of the treatment groups. The number of males engaged in nest-building behaviour following exposure to 500 microg l(-1) nCdS was reduced and livers of 4/6 fish in the same treatment displayed hepatocellular nuclear pleomorphism. The results are discussed emphasising the fundamental importance of experimental design and the need to understand the behaviour of nanoparticles in the aqueous phase.     

基于深度学习卷积神经网络的地震波形自动分类与识别

发展高效、高精度、普适性强的自动波形拾取算法在地震大数据时代背景下显得越来越重要.波形自动拾取算法的主要挑战来自如何适应不同区域的不同类型地震事件的分类与筛选.本文针对地震事件-噪音分类这一问题,使用13839个汶川地震余震事件建立数据集,应用深度学习卷积神经网络(CNN)方法进行训练,并用8900个新的汶川余震事件作为检测数据集,其训练和检测准确率均达到95%以上.在对连续波形的检测中,CNN方法在精度和召回率上优于STA/LTA和Fbpicker传统方法,并能找出大量人工挑选极易遗漏的微震事件.最后,我们应用训练好的最优模型对选自全国台网的441个台站8天的连续波形数据进行了识别、到时挑取及与参考地震目录关联,CNN检出7016段波形,用自动挑选算法拾取到1380对P,S到时,并与540个地震目录事件成功关联,对1级以上事件总体识别准确率为54%,二级以上为80%,证明了CNN模型具有泛化能力,初步展示了CNN在发展兼具效率、精度、普适性算法,实时地震监测等应用上具有巨大潜力.