Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.     

模糊划分矩阵在岩土参数概率分布中的应用

讨论如何在小样本条件下用已有的过程经验与试验资料确定岩土参数概率分布,用模糊划分矩阵与BAYES方法相结合,给出由小样本试验数据确定岩土参数的概率分布。     

Evolution of the luminosity function and colours of galaxies in a Λ cold dark matter universe

The luminosity function of galaxies is derived from a cosmological hydrodynamic simulation of a Λ cold dark matter universe with the aid of a stellar population synthesis model. At     , the resulting B -band luminosity function has a flat faint-end slope of     with the characteristic luminosity and the normalization in fair agreement with observations, while the dark matter halo mass function is steep with a slope of     . The colour distribution of galaxies also agrees well with local observations. We also discuss the evolution of the luminosity function, and the colour distribution of galaxies from     to 5. A large evolution of the characteristic mass in the stellar mass function as a result of number evolution is compensated by luminosity evolution; the characteristic luminosity increases only by 0.8 mag from     to 2, and then declines towards higher redshift, while the B -band luminosity density continues to increase from     to 5 (but only slowly at     .     

Evidence of climatic warming in the southern Urals region derived from borehole temperatures and meteorological data

Thirty borehole temperature–depth profiles in the central and southern Urals, Russia were scrutinized for evidence of ground surface temperature histories. We explored two inversion schemes: a simple ramp inversion in which solutions are parameterized in terms of an onset time and magnitude of change and a more sophisticated functional space inverse algorithm in which the functional form of the solution is left unspecified. To enhance and potentially identify latitudinal differences in the ground surface temperature signal, we subdivided the data into three groups based on geographic proximity and simultaneously inverted the borehole temperature–depth logs. The simultaneous inversions highlighted 13 temperature–depth logs that could not both fit a common ground surface temperature history and a priori models within reasonable bounds. Our results confirm that this is an effective way to reduce site-specific noise from an ensemble of boreholes. Each inversion scheme gives comparable results indicating locally variable warming on the order of 1°C starting between 1800 and 1900 AD. Similarly surface air temperature records from 12 nearby meteorological stations exhibit locally variable warming also on the order of 1°C of warming during the 20th century. To explore the degree to which borehole temperatures and surface air temperature (SAT) time series are responding to the same signal, we average the SAT data into the same three groups and used these averages as a forcing function at the Earth's surface to generate synthetic transient temperature profiles. Root mean square (RMS) misfits between these synthetic temperature profiles and averaged temperature–depth profiles are low, suggesting that first-order curvature in borehole temperatures and variations in SAT records are correlated.     

Constraints on a quintessence model from gravitational lensing statistics

Constraints on an exact quintessence scalar-field model with an exponential potential are derived from gravitational lens statistics. An exponential potential can account for data from both optical quasar surveys and radio-selected sources. Based on the Cosmic Lens All-Sky Survey (CLASS) sample, lensing statistics provides, for the pressureless matter density parameter, an estimate of  Ω_M0= 0.31^+0.12_−0.14  .     

Cosmological constraints from the X-ray gas mass fraction in relaxed lensing clusters observed with Chandra

We present precise measurements of the X-ray gas mass fraction for a sample of luminous, relatively relaxed clusters of galaxies observed with the Chandra observatory, for which independent confirmation of the mass results is available from gravitational lensing studies. Parametrizing the total (luminous plus dark matter) mass profiles using the model of Navarro, Frenk & White, we show that the X-ray gas mass fractions in the clusters asymptote towards an approximately constant value at a radius r ₂₅₀₀, where the mean interior density is 2500 times the critical density of the Universe at the redshifts of the clusters. Combining the Chandra results on the X-ray gas mass fraction and its apparent redshift dependence with recent measurements of the mean baryonic matter density in the Universe and the Hubble constant determined from the Hubble Key Project, we obtain a tight constraint on the mean total matter density of the Universe,     , and measure a positive cosmological constant,     . Our results are in good agreement with recent, independent findings based on analyses of anisotropies in the cosmic microwave background radiation, the properties of distant supernovae, and the large-scale distribution of galaxies.