Extraction of ion distributions from magnetospheric ENA and EUV images

Energetic neutral atom (ENA) and extreme ultra-violet photon (EUV) imagers will soon be probing magnetospheric ion distributions from the NASA space missions IMAGE and TWINS. Although ENA and EUV images will differ greatly, the same basic mathematical approach can be applied to deducing the ion distributions: extracting the parameters of a model ion distribution in a model magnetic field (and, in the case of ENA, interacting with a model cold neutral population). The model ion distribution is highly non-linear in its many parameters (as many as 38 have been used) in order to describe the strong spatial gradients of ion intensities in the magnetosphere. We have developed several new computer algorithms to accomplish the extraction by minimizing the differences between a simulated (instrument-specific) image and an observed image (or set of images). Towards the goal of a truly automated `hands-off extraction algorithm, we have combined three algorithms into a Hierarchical Simplex Algorithm. At each step of the minimization, it first tries a sophisticated and efficient Adaptive Conjugate Gradient algorithm. Then, if the error function is not reduced, it defers to an intermediate Analytic Simplex algorithm, and (if this step also fails) it finally defaults to the robust but inefficient Downhill Simplex algorithm. Whenever a step is successful, the algorithm begins the next step at the top of the hierarchy. We also offer a completely different approach (without minimization) for the interpretation of sharp `edges in the images (e.g., the plasmapause in He ⁺ 30.4 nm EUV images of the plasmasphere). We demonstrate mathematically that the equatorial shape of the plasmapause can be constructed directly from the image using a simple graphical algorithm.     

Amplification of the influence of solar flux variations on the winter stratosphere by planetary waves

Recent observational evidence has suggested that variations in solar activity may affect winter stratospheric polar ozone and temperature levels. The paucity of direct sunlight available during this season points strongly to a dynamical mechanism. We have carried out several large ensemble experiments within the middle atmosphere and the coupled middle atmosphere and lower thermosphere to simulate the radiative/dynamical coupling via planetary waves for a range of solar fluxes. In the former case, the model response in the winter stratosphere was linear and of the order of the summer stratopause forcing, whilst in the latter, the level of correlation in the winter stratosphere remained high, but was diluted over a wider volume. The inclusion of the upper atmosphere enhanced the winter polar stratospheric response by a factor of three.     

Hectometric and kilometric solar radio emission observed from satellites in August 1972

Type II, III, and continuum solar radio events, as well as intense terrestrial magnetospheric radio emissions, were observed at low frequencies (10 MHz to 30 kHz) by the IMP-6 satellite during the period of high solar activity in August 1972. This review covers briefly the unique direction finding capability of the experiment, as well as a detailed chronology of the low frequency radio events, and, where possible, their association with both groundbased radio observations and solar flares. The attempted observation of solar bursts in the presence of intense magnetospheric noise may, as illustrated, lead to erroneous results in the absence of directional information. The problem of assigning an electron density scale and its influence on determining burst trajectories is reviewed. However, for the disturbed conditions existing during the period in question, we feel that such trajectories cannot be determined accurately by this method. In conclusion, the capabilities, limitations, and observing programs of present and future satellite experiments are briefly discussed.     

X-Ray spectroscopic investigation of the coronal structure of capella

The binary system Capella (G6 III + F9 III) has been observed on 1979 March 15 and on 1980 March 15–17 with the Objective Grating Spectrometer (OGS) onboard theEinstein Observatory. The spectrum measured with the 1000 l/mm grating covers the range 5–30 Å with a resolution < 1 Å. The spectra show evidence for a bimodal temperature distribution of emission measure in an optically thin plasma with one component 5 million degrees and the other one 10 million degrees. Spectral features can be identified with line emissions from O VIII, Fe XVII, Fe XVIII, Fe XXIV, and Ne X ions. Good spectral fits have been obtained assuming standard cosmic abundances. The data are interpreted in terms of emission from hot static coronal loops rather similar to the magnetic arch structures found on the Sun. It is shown that the conditions required by this model exist on Capella. Mean values of loop parameters are derived for both temperature components.     

The Rosat mission

The primary scientific objective of the ROSAT mission is to perform the first all sky survey with an imaging X-ray telescope leading to an improvement in sensitivity by several orders of magnitude compared with previous surveys. Consequently a large number of new sources (> 10⁵) will be discovered and located with an accuracy of 1 arcmin. After completion of the survey which will take about half a year the instrument will be used for detailed observations of selected targets.The X-ray telescope consists of a fourfold nested Wolter type I mirror system with 80 cm aperture and 240 cm focal length, and three focal plane detectors. In the baseline version these will be imaging proportional counters (0.1 – 2 keV) providing a field of view of 2⁰ × 2⁰.     

Spectra and time variability of GX 5-1

From a short observation of GX 5-1 with EXOSAT we have derived information on spectral and temporal behaviour in the energy range 1–20 keV. The source was found to be variable on time scales from 10 s to 1 h. Describing the spectrum one is forced to assume at least two spectral components. The best fit is reached using a spectrum composed of two blackbody functions with typical temperatures 1 keV and 2 keV, corresponding to apparent blackbody radii of 43 km and 11 km, respectively (for a distance of 10 kpc). With respect to the hot component there is evidence for variability in temperature as well as in apparent blackbody radius. No periodic variability has been found over the period range 0.25 s to 2000 s. There is no evidence for an iron emission line.     

First detection of an X-ray burst and a one hour intensity dip in 4U1323-62

We report the results of a 1.4 10⁴s observation of the region of 4U 1323-62 with the EXOSAT ME. The source has a flux of 7–8 10^-11 erg/cm²s (2–10 keV) and a power-law spectrum with 1.1 < < 1.8. During our observation, the source showed a symmetric 60% dip in its X-ray flux of R~1 hr. The spectrum hardens during the dip. Inside the dip we observed an X-ray burst with a 2–10 keV peak flux of 7 10^-10 erg/cm²s. The burst spectrum is black-body, and shows evidence of cooling during the burst decay. The discovery of a burst from 4U 1323-62 settles the classification of the source; the observation of a dip suggests that we may be able to measure its orbital period in the near future.     

Experiments in Adaptive Estimation of Unknown Binary Waveforms

A special-purpose adaptive machine is described which carries out estimation in real time of an unknown binary waveform which is perturbed with additive Gaussian noise. Unknown waveforms of over 103 samples in duration can be recovered. The unknown waveforms are of unknown epoch and can reappear at either random or periodic time intervals. The observed signal is received at moderate or low signal-to-noise ratios so that a single observation of the received data (even if one knew the precise signal arrival time) is not sufficient to provide a good estimate of the signal waveshape. Experimental results are described which show transient behavior waveform estimate. The transient behavior is expressed as the number of errors in the current estimate of the signal plotted vs. time. In a noisy environment, each ``learning' transient is a random time function. These learning transients are shown for several different signal-to-noise ratios and indicate the threshold noise levels for various types of initial states of the machine memory.