Non-parametric star formation histories for four dwarf spheroidal galaxies of the Local Group

We use recent Hubble Space Telescope colour–magnitude diagrams of the resolved stellar populations of a sample of local dSph galaxies (Carina, Leo I, Leo II and Ursa Minor) to infer the star formation histories of these systems, SFR ( t ). Applying a new variational calculus maximum likelihood method, which includes a full Bayesian analysis and allows a non-parametric estimate of the function one is solving for, we infer the star formation histories of the systems studied. This method has the advantage of yielding an objective answer, as one need not assume a priori the form of the function one is trying to recover. The results are checked independently using Saha's W statistic. The total luminosities of the systems are used to normalize the results into physical units and derive SN type II rates. We derive the luminosity-weighted mean star formation history of this sample of galaxies.     

Magnetic and optical spiral arms in the galaxy NGC 6946

The spiral pattern in the nearby spiral galaxy NGC 6946 has been studied using the wavelet transformation technique, applied to galaxy images in polarized and total non-thermal radio emission at λλ 3.5 and 6.2 cm, in broadband red light, in the λ 21.1 cm H  i line and in the optical Hα line. Well-defined, continuous spiral arms are visible in polarized radio emission and red light, where we can isolate a multi-armed pattern in the range of galactocentric distances 1.5–12 kpc, consisting of four long arms and one short spiral segment. The 'magnetic arms' (visible in polarized radio emission) are localized almost precisely between the optical arms. Each magnetic arm is similar in length and pitch angle to the preceding optical arm (in the sense of galactic rotation) and can be regarded as its phase-shifted image. Even details like a bifurcation of an optical arm have their phase-shifted counterparts in the magnetic arms. The average relative amplitude of the optical spiral arms (the stellar density excess over the azimuthal average) grows with galactocentric radius up to 0.3–0.7 at r ≃5 kpc, decreases by a factor of two at r =5–6 kpc and remains low at 0.2–0.3 in the outer parts of the galaxy. By contrast, the magnetic arms have a constant average relative amplitude (the excess in the regular magnetic field strength over the azimuthal average) of 0.3–0.6 in a wide radial range r =1.5–12 kpc. We briefly discuss implications of our findings for theories of galactic magnetic fields.     

The statistics of weak lensing at small angular scales: probability distribution function

Weak gravitational lensing surveys have the potential to probe mass density fluctuation in the Universe directly. Recent studies have shown that it is possible to model the statistics of the convergence field at small angular scales by modelling the statistics of the underlying density field in the highly non-linear regime. We propose a new method to model the complete probability distribution function of the convergence field as a function of smoothing angle and source redshift. The model relies on a hierarchical ansatz for the behaviour of higher order correlations of the density field. We compare our results with ray-tracing simulations and find very good agreement over a range of smoothing angles. Whereas the density probability distribution function is not sensitive to the cosmological model, the probability distribution function for the convergence can be used to constrain both the power spectrum and cosmological parameters.     

A stochastic Monte Carlo approach to model real star cluster evolution – II. Self-consistent models and primordial binaries

The new approach outlined in Paper I to follow the individual formation and evolution of binaries in an evolving, equal point-mass star cluster is extended for the self-consistent treatment of relaxation and close three- and four-body encounters for many binaries (typically a few per cent of the initial number of stars in the cluster mass). The distribution of single stars is treated as a conducting gas sphere with a standard anisotropic gaseous model. A Monte Carlo technique is used to model the motion of binaries, their formation and subsequent hardening by close encounters, and their relaxation (dynamical friction) with single stars and other binaries. The results are a further approach towards a realistic model of globular clusters with primordial binaries without using special hardware. We present, as our main result, the self-consistent evolution of a cluster consisting of 300 000 equal point-mass stars, plus 30 000 equal-mass binaries over several hundred half-mass relaxation times, well into the phase where most of the binaries have been dissolved and evacuated from the core. The cluster evolution is about three times slower than found by Gao et al. Other features are rather comparable. At every moment we are able to show the individual distribution of binaries in the cluster.     

Neural networks and the separation of cosmic microwave background and astrophysical signals in sky maps

We implement an independent component analysis (ICA) algorithm to separate signals of different origin in sky maps at several frequencies. Owing to its self-organizing capability, it works without prior assumptions on either the frequency dependence or the angular power spectrum of the various signals; rather, it learns directly from the input data how to identify the statistically independent components, on the assumption that all but, at most, one of the components have non-Gaussian distributions.
We have applied the ICA algorithm to simulated patches of the sky at the four frequencies (30, 44, 70 and 100 GHz) used by the Low Frequency Instrument of the European Space Agency's Planck satellite. Simulations include the cosmic microwave background (CMB), the synchrotron and thermal dust emissions, and extragalactic radio sources. The effects of the angular response functions of the detectors and of instrumental noise have been ignored in this first exploratory study. The ICA algorithm reconstructs the spatial distribution of each component with rms errors of about 1 per cent for the CMB, and 10 per cent for the much weaker Galactic components. Radio sources are almost completely recovered down to a flux limit corresponding to ≃0.7 σ _CMB, where σ _CMB is the rms level of the CMB fluctuations. The signal recovered has equal quality on all scales larger than the pixel size. In addition, we show that for the strongest components (CMB and radio sources) the frequency scaling is recovered with per cent precision. Thus, algorithms of the type presented here appear to be very promising tools for component separation. On the other hand, we have been dealing here with a highly idealized situation. Work to include instrumental noise, the effect of different resolving powers at different frequencies and a more complete and realistic characterization of astrophysical foregrounds is in progress.     

Testing the Gaussianity of the COBE DMR data with spherical wavelets

We investigate the Gaussianity of the 4-yr COBE DMR data (in HEALPix pixelization) using an analysis based on spherical Haar wavelets. We use all the pixels lying outside the Galactic cut and compute the skewness, kurtosis and scale–scale correlation spectra for the wavelet coefficients at each scale. We also take into account the sensitivity of the method to the orientation of the input signal. We find a detection of non-Gaussianity at >99 per cent level in just one of our statistics. Taking into account the total number of statistics computed, we estimate that the probability of obtaining such a detection by chance for an underlying Gaussian field is 0.69. Therefore, we conclude that the spherical wavelet technique shows no strong evidence of non-Gaussianity in the COBE DMR data.     

A new method to compensate for bias in magnetotellurics

Error estimates from statistical regression analysis are often obviously too small, leading to doubts about the given equations, the statistical method itself and finally, with resignation, to the conclusion that mathematical equations and reality never agree. However, for magnetotelluric data we have found an almost perfect fit between observed scattering and predicted confidence limits of regression coefficients after accounting for a systematic error—the bias.
Different methods to compensate for bias in magnetotelluric impedance estimation have been described using additional data from a reference station. However, sufficiently accurate reference data are often not available. A new method has been developed that enables bias compensation without additional data. For the new method we derive a linear relationship between the effect of bias and an expression depending on the data fit. From this we extrapolate the solution for the unbiased impedance. The new method assumes a special model of uncorrelated noise as well as an approximation for the structure of the impedance tensor. From each pair of components of the unrotated impedance tensor corresponding to the same output channel, one of the pair can be compensated if its magnitude is large compared to that of the other.
The method has been successfully applied in many cases. We claim that the solution is closer to the true impedance than any solution based on the selection of events. It gives a measure of the partitioning of noise between the electric and magnetic channels.
We applied the method to measurements from the North Anatolian Fault Zone (Turkey) and from the Merapi volcano (Central Java) in the period range 10–2500 s. Different instrumentation was used for the two sets of measurements, but in both cases we used fluxgate magnetometers to measure the magnetic variations.     

结构损伤初步定位的简便方法

提出将基于动态特性的检测方法与局部物理检测手段相结合的探伤思路。利用动力学方法进行实时监测及损伤区域的粗略定位,再由物理探测方法实现损伤程度和位置的精确判断,从而降低对动态检测方法的精度要求。此外,文中尝试直接由结构的动力响应信号构建能量指标识别结构损伤的方法,不需要进行模态参数识别,算法简单,有望应用于海洋平台、高层建筑等可简化为串联多自由度体系的结构的实时监测和损伤初步定位。