X-ray, Lyα and Hα emission from simulated disk galaxies

The X-ray properties of the haloes of disk galaxies formed in fully cosmological, hydro/gravity simulations are discussed. The results are found to be consistent with observational X-ray detections and upper limits. Disk galaxy haloes are predicted to be about an order of magnitude brighter at z ∼ 1 than at z=0. The Lyα and Hα surface brightness of an edge-on, Milky Way like model galaxy has been determined. The emission is found to be quite extended, with a scale height of about 600 pc, neglecting dust corrections. This revised version was published online in August 2006 with corrections to the Cover Date.     

Transport of suspended matter in a bar-built Danish estuary

The river Varde Å discharges into the bay of Ho Bugt on the western coast of Jutland forming a small, bar-built estuary. This paper deals with tidal fluxes of water and sediment in the Varde Å estuary.The inflowing water at flood tide is part of a turbidity maximum in the northern part of the bay. At high tide slack water the suspended material deposits inside the estuary. During ebb-tide it is resuspended, and the estuary bottom is washed clean coinciding with the influx of relatively pure freshwater from the drainage area.From one station in the estuary mouth, current velocities and concentrations of suspended material have been measured during 10 tidal periods covering all four seasons. It is shown how these data can be used in a quantitative calculation of the transport of water and suspended material through the cross-section of study.A model has been formulated which—based on half-tidal periods—quantifies the transport of water and suspended material through the estuary mouth.The model is calibrated on the basis of measurements made during the above-mentioned 10 tidal periods. The rather small number of measurements is to some extent compensated for by a carefully pre-arranged selection of tidal periods.The model is discussed in relation to the prediction of net suspended transport through the estuary mouth in different weather and tidal situations.     

Objectives of fisheries management: case studies from the UK, France, Spain and Denmark

The main objectives of fisheries management are generally similar throughout the world. These are often stated in policy documents such as the Common Fisheries Policy and the Magnuson–Stevens Fishery Conservation and Management Act. However, at the local level often the key objectives of management are more detailed, characterised by both the overriding management structure and the status and type of fishery concerned. In this paper, we consider case study fisheries from the UK, France, Spain and Denmark to compare some of the various types of fisheries and fisheries management systems that exist in the European Union. From this, we define the key objectives for each management system.     

Line-of-Sight Observables Algorithms for the <Emphasis Type="Italic">Helioseismic and Magnetic Imager</Emphasis> (HMI) Instrument Tested with <Emphasis Type="Italic">Interferometric Bidimensional Spectrometer</Emphasis> (IBIS) Observations

The Helioseismic and Magnetic Imager (HMI) instrument onboard the Solar Dynamics Observatory produces line-of-sight (LOS) observables (Doppler velocity, magnetic-field strength, Fe i line width, line depth, and continuum intensity) as well as vector magnetic-field maps at the solar surface. The accuracy of LOS observables is dependent on the algorithm used to translate a sequence of HMI filtergrams into the corresponding observables. Using one hour of high-cadence imaging spectropolarimetric observations of a sunspot in the Fe i line at 6173 Å through the Interferometric Bidimensional Spectrometer installed at the Dunn Solar Telescope, and the Milne–Eddington inversion of the corresponding Stokes vectors, we test the accuracy of the observables algorithm currently implemented in the HMI data-analysis pipeline: the MDI-like algorithm. In an attempt to improve the accuracy of HMI observables, we also compare this algorithm to others that may be implemented in the future: a least-squares fit with a Gaussian profile, a least-squares fit with a Voigt profile, and the use of second Fourier coefficients in the MDI-like algorithm.     

Global-Mode Analysis of Full-Disk Data from the <Emphasis Type="Italic">Michelson Doppler Imager</Emphasis> and the <Emphasis Type="Italic">Helioseismic and Magnetic Imager</Emphasis>

Building upon our previous work, in which we analyzed smoothed and subsampled velocity data from the Michelson Doppler Imager (MDI), we extend our analysis to unsmoothed, full-resolution MDI data. We also present results from the Helioseismic and Magnetic Imager (HMI), in both full resolution and processed to be a proxy for the low-resolution MDI data. We find that the systematic errors that we saw previously, namely peaks in both the high-latitude rotation rate and the normalized residuals of odd \(a\)-coefficients, are almost entirely absent in the two full-resolution analyses. Furthermore, we find that both systematic errors seem to depend almost entirely on how the input images are apodized, rather than on resolution or smoothing. Using the full-resolution HMI data, we confirm our previous findings regarding the effect of using asymmetric profiles on mode parameters, and also find that they occasionally result in more stable fits. We also confirm our previous findings regarding discrepancies between 360-day and 72-day analyses. We further investigate a six-month period previously seen in \(f\)-mode frequency shifts using the low-resolution datasets, this time accounting for solar-cycle dependence using magnetic-field data. Both HMI and MDI saw prominent six-month signals in the frequency shifts, but we were surprised to discover that the strongest signal at that frequency occurred in the mode coverage for the low-resolution proxy. Finally, a comparison of mode parameters from HMI and MDI shows that the frequencies and \(a\)-coefficients agree closely, encouraging the concatenation of the two datasets.     

Fraile Detection of Solar $$-Modes by Fossat et al.

The internal gravity modes of the Sun are notoriously difficult to detect, and the claimed detection of gravity modes presented by Fossat et al. (Astron. Astrophys.604, A40, 2017) is thus very exciting. Given the importance of these modes for understanding solar structure and dynamics, the results must be robust. While Fossat et al. described their method and parameter choices in detail, the sensitivity of their results to several parameters was not presented. Therefore, we test the sensitivity of the results to a selection of the parameters. The most concerning result is that the detection vanishes when we adjust the start time of the 16.5-year velocity time-series by a few hours. We conclude that this reported detection of gravity modes is extremely fragile and should be treated with utmost caution.     

Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets

In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four-dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four-dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics-based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks.