A novel method for telescope polarization modeling based on an artificial neural network

The polarization characteristics of an astronomical telescope is an important factor that affects polarimetry accuracy. Polarization modeling is an essential means to achieve high precision and efficient polarization measurement of the telescope, especially for the alt-azimuth mount telescope. At present, the polarization model for the telescope(i.e., the physical parametric model) is mainly constructed using the polarization parameters of each optical element. In this paper, an artificial neural network(ANN) is used to model the polarization characteristics of the telescope. The ANN model between the physical parametric model residual and the pointing direction of the telescope is obtained, which reduces the model deviation caused by the incompleteness of the physical parametric model. Compared with the physical parametric model, the model fitting and predictive accuracy of the New Vacuum Solar Telescope(NVST) is improved after adopting the ANN model. After using the ANN model, the polarization cross-talk from I to Q, U, and V can be reduced from 0.011 to 0.007, and the crosstalk among Q, U, and V can be reduced from 0.047 to 0.020, which effectively improves the polarization measurement accuracy of the telescope.     

Laser-induced breakdown spectroscopy with artificial neural network processing for material identification

Laser-induced breakdown spectroscopy (LIBS) has demonstrated its high potential in measurement of material composition in many areas including space exploration. LIBS instruments will be parts of payloads for the 2011 Mars Science Laboratory NASA-led mission and the ExoMars mission planned by ESA. This paper considers application of artificial neural networks (ANN) for material identification based on LIBS spectra that may be obtained with a portable instrument in ambient conditions. The several classes of materials used in this study included those selected to represent the sites analogues to Mars. In addition, metals and aluminum alloys were used to demonstrate ANN capabilities. Excellent material classification is achieved with single-shot measurements in real time.     

Reconstruct light curves from unevenly sampled variability data with artificial neural networks

Light curves are usually constructed from discrete observational data by interpolation. In most cases, the observation data is temporally uneven, and therefore the light curve is usually derived by the interpolation of the binned data with the spline function, which is intended for reducing the “high sample noise” (i.e., the variability in the timescales comparable with the bin width). Such a practice of course reduces the time resolution of the light curve. It is known that function approximation is one of the most important applications of the artificial neural networks (ANN). In this work, for the first time we tentatively use the ANN to construct light curves from unevenly sampled variability data. To demonstrate the advantages of ANN for signal reconstruction over commonly used cubic spline function scheme, two sets of simulated periodic functions are used with random noises of varying magnitudes, one single frequency based and one multiple (two) frequency based. These signal reconstruction tests show that the ANN is clearly superior to the cubic spline scheme. As a case study, we use the uneven long-term multi-band monitoring data of BL lacertae to derive the light curves with ANN. It is found that the light curves derived with ANN have higher time resolution than those with the cubic spline function adopted in previous works. We recommend using ANN for the signal reconstruction in astrophysical data analysis as well as that of in other fields.     

Removing cosmic-ray hits from CCD images in real-time mode by means of an artificial neural network

A feed-forward artificial neural network has been implemented to the problem of removing cosmic-ray hits (CRH) from CCD images. The results of a number of tests demonstrate the effectiveness of this method especially for undersampled stellar profiles. The problem of optimal and low price preparing of training data, which could enable real-time or at least fast post-processing filtering out of CRH is discussed. The training and test ensembles were composed of a number of synthetic stellar profiles involving different S/N ratios and CRH images taken from real data. Certain aspects of the network’s architecture and its training efficiency for different modes of the back-propagation procedure as well as for the pre-process normalization of data have been examined. It is shown that for training set composed of stellar images and CRH at a ratio of 1:2 recognition can reach 99% in the case of stars and 96% for CRH. To determine the extent to which the cognition power of a network trained using an ensemble of circular symmetric stellar profiles of a given radius can be generalised the test data included stellar profiles of different radii, as well as elongated profiles. The goal was to mimic temporal changes in seeing as well as such problems as image defocusing, the lack of isoplanatism and improper sideral tracking of a telescope. The experiments provided us with the conclusion that for S/N > 10 excellent classification property is maintained in cases where the change in the radius of a circular profile is up to 30%, as well as for elongated profiles where the longest dimension is almost double that of the shortest one. Moreover, the generalization capability has been investigated for test images of synthetic pairs of overlapping stars with different distances between components. Almost 99% recognition efficiency was achieved even if the separation was nearly three times the radius of the stellar profile, a case when two stars could be analyzed by appropriate software as separate objects. The example of removal of CRH from real CCD images is presented to give an idea of how an algorithm based on a neural network can work in practice. The result of such an experiment appears fully consistent with the conclusions drawn from the tests made on synthetic data.     

The link between abrupt climate change and basin stratigraphy: a numerical approach

To use basin stratigraphy for studying past climate change, it is important to understand the influence of evolving boundary conditions (river discharge and sediment flux, initial bathymetry, sea level, subsidence) and the complex interplay of the redistribution processes (plumes, turbidity currents, debris flows). To provide understanding of this complexity, we have employed source to sink numerical models to evaluate which process dominates the observed variability in a sedimentary record of two coastal Pacific basins, Knight Inlet in British Columbia and the Eel Margin of northern California.During the last glacial period, the Eel River supplied comparatively more sediment with a less variable flux to the ocean, while today the river is dominated by episodic events. Model results show this change in the variability of sediment flux to be as important to the deposit character as is the change in the volume of sediment supply. Due to the complex interaction of flooding events and ocean storm events, the more episodic flood deposits of recent times are less well preserved than the flood deposits associated with an ice-age climate.In Knight Inlet, the evolving boundary conditions (rapidly prograding coastline, secondary transport by gravity flows from sediment failures) are a strong influence on the sedimentary record. The delta and gravity flow deposits punctuate the sedimentary record formed by hemipelagic sedimentation from river plumes. Missing time intervals due to sediment failures can take away the advantage of the otherwise amplified lithologic record of discharge events, given the enclosed nature of the fjord basin.     

A Holocene and latest Pleistocene pollen record from Lake Poukawa, Hawke's Bay, New Zealand

Lake Poukawa is a small, shallow lake lying in the middle of extensive peatland in the Poukawa depression, central Hawke's Bay. Holocene peats (10 m at deepest point) overlie more than 200 m of sand, silt, clastic debris and infrequent thin peats and lacustrine sediments deposited during the late Pleistocene. Pollen analyses are presented for: a peat possibly dating to a late stage of the last interglacial or a warm interstadial of the last glacial; cool climate last glacial sediments; and a Holocene peat. The last interglacial or interstadial peat records a cool climate Nothofagus podocarp forest. During the last glacial, sparse shrubland and grassland grew within the depression under much drier and colder conditions than now. There is no pollen record for the Late Glacial and early Holocene period as conditions remained too dry for peat formation. Avian fossils indicate scrub and grassland persisted through until at least 10,600 years BP, and scrub or open forest may have prevailed until c. 6500 years BP. Closed podocarp broadleaved forest (Prumnopitys taxifolia dominant) occupied the depression from at least 6500 years BP until its destruction by Polynesian settlers after 800 years BP. Water levels rose from 6500 to 4500 years BP, culminating in the establishment of the present fluctuating lake-peatland system. Dry conditions in the Late Glacial and early Holocene may reflect a predominant northwesterly air flow, and a change to more easterly and southerly air flow in the mid- to late Holocene resulted in increased rainfall.     

Increased aridity in the Mediterranean region under greenhouse gas forcing estimated from high resolution simulations with a regional climate model

We use three measures of aridity, the Köppen climate classification, the UNEP aridity index and the Budyko dryness index, to estimate the possible effects of late 21st century climate change on the Mediterranean region under increased greenhouse gas concentrations (A2 and B2 IPCC emission scenarios) as simulated with a high resolution (20 km grid interval) regional climate model (the ICTP RegCM). A basic validation of the reference simulation along with a brief discussion of the surface climate changes for the A2 and B2 scenarios is also provided. Analysis of the changes in all three aridity measures indicates that by the end of the 21st century the Mediterranean region might experience a substantial increase in the northward extension of dry and arid lands, particularly in the central and southern portions of the Iberian, Italian, Hellenic and Turkish peninsulas and in areas of southeastern Europe (e.g. Romania and Bulgaria), the Middle East, northern Africa and major Islands (Corsica, Sardinia and Sicily). Most Ice-Cap areas of the Alps are also projected to disappear. These effects are due to a large warming and pronounced decrease in precipitation, especially during the spring and summer seasons. In addition, fine scale topography and coastline features affect the aridity change signal. We identify the southern Mediterranean as a region particularly vulnerable to water stress and desertification processes under climate change conditions.     

Modeling the effects of vegetation on Mediterranean climate during the Roman Classical Period: Part II. Model simulation

The purpose of this study is to perform a high-resolution general circulation model (GCM) experiment to quantify the sensitivity of regional climate to change in vegetation around the Mediterranean basin, corresponding to vegetation change during the Roman Classical Period (RCP), about 2000 years BP. First, an RCP vegetation distribution based on fossil pollen maps and historical records was defined. Second, the RCP vegetation inferred from palynology and other proxies was converted to the 12 vegetation types required by the biosphere model implemented in the GCM. The albedo change due to the change in vegetation significantly alters the atmospheric circulation over northern Africa and the Mediterranean. The consequences of this change involve a northward shift of the ITCZ in the African continent and a coupled circulation between northwestern Africa and the Mediterranean Sea. A large increase of precipitation occurs over the Sahel, the Nile valley and northwestern Africa. A smaller increase of precipitation occurs also over the Iberian Peninsula and the region corresponding to the south of the Caucasus range (Armenia). The increase of precipitation over northern Africa, the Iberian Peninsula and the Armenian region are consistent with the pollen, historical and geographical data. These results suggest that deforestation around the Mediterranean during the last 2000 years contributed to the dryness of the current climate.     

2D modelling of the light distribution of early-type galaxies in a volume-limited sample – I. Simulations with artificial data

This paper is the first of two dedicated to the application of a two-dimensional (2D) fit to the light distribution of 73 early-type galaxies belonging to the Virgo and Fornax clusters, a sample of which is 80 per cent complete, volume and magnitude limited down to M _B=−17.3 , and highly homogeneous. Two empirical models of the surface-brightness distribution of the early-type galaxies have been tested: the first uses a single spheroid represented by the r ^{1/ n} Sersic law, while the second is characterized by two components, 'bulge' and 'disc', by means of the ( r ^{1/ n} +exp) laws. The χ ² fitting routine ( minuit ), used to fit the 2D light distribution of real galaxies, is tested here on artificial galaxy images that were built with structural parameters chosen randomly from a large fixed interval. In this paper we present the properties of the two models, describe the minimization technique, and discuss the results of the tests of the 2D fit obtained from simulated artificial galaxies. By exploiting these tests we were able better to constrain the errors affecting the structural parameters, and the strategies that should be adopted to get the best fit.     

New analysis of Lunar Prospector radio tracking data brings the nearside gravity field of the Moon with an unprecedented resolution

A new analysis of the Doppler tracking data from the Lunar Prospector mission in 1999 revealed a number of previously-unseen gravity anomalies at spatial scales as small as 27 km over the nearside. The tracking data at low altitudes (50 km or below) were better analyzed to resolve the nearside features without dampening from a power law constraint, by partitioning the gravity parameters concentrated on either the nearside or farside. The resulting model presents gravity anomalies correlated with topography with a correlation coefficient of 0.7 or higher from degree 50 to 150, the widest bandwidth yet. The gravity-topography admittance of ∼70 mGal/km is found from numerous craters of which diameters are 60 km or less. In addition, the new model produces orbits that fit to independent radio tracking data from the Lunar Reconnaissance Orbiter and Kaguya (SELENE) better than previous gravity models. This high-resolution model can be of immediate use to geophysical analysis of small craters. Our technique could be applied to an upcoming mission, the Gravity Recovery And Interior Laboratory and useful to extract short wavelength signals from the MESSENGER Doppler data.