Synthetic horizontal branch morphology for different metallicities and ages under tidally enhanced stellar wind

It is believed that, except for metallicity, some other parameters are needed to explain the horizontal branch (HB) morphology of globular clusters (GCs). Furthermore, these parameters are considered to be correlated with the mass loss of the red giant branch (RGB) stars. In our previous work, we proposed that tidally enhanced stellar wind during binary evolution may affect the HB morphology by enhancing the mass loss of the red giant primary. As a further study, we now investigate the effects of metallicity and age on HB morphology by considering tidally enhanced stellar winds during binary evolution. We incorporated the tidally enhanced-stellar-wind model into Eggleton's stellar evolution code to study the binary evolution. To study the effects of metallicity and age on our final results, we conducted two sets of model calculations: (i) for a fixed age, we used three metallicities, namely Z=0.0001, 0.001, and 0.02. (ii) For a fixed metallicity, Z=0.001, we used five ages in our model calculations: 14, 13, 12, 10, and 7 Gyr. We found that HB morphology of GCs becomes bluer with decreasing metallicity, and old GCs present bluer HB morphology than young ones. These results are consistent with previous work. Although the envelope-mass distributions of zero-age HB stars produced by tidally enhanced stellar wind are similar for different metallicities, the synthetic HB under tidally enhanced stellar wind for Z=0.02 presented a distinct gap between red and blue HB. However, this feature was not seen clearly in the synthetic HB for Z=0.001 and 0.0001. We also found that higher binary fractions may make HB morphology become bluer, and we discussed the results with recent observations.Comment: 16 pages, 6 figures, 3 tables, accepted for publication in Astronomy & Astrophysic

Minimum Wage and Compliance in a Model of Search On-the-Job

minimum wages, compliance, job search, wage growth

A rescaled method for RBF approximation

A new method to compute stable kernel-based interpolants has been presented by the second and third authors. This rescaled interpolation method combines the standard kernel interpolation with a properly defined rescaling operation, which smooths the oscillations of the interpolant. Although promising, this procedure lacks a systematic theoretical investigation. Through our analysis, this novel method can be understood as standard kernel interpolation by means of a properly rescaled kernel. This point of view allow us to consider its error and stability properties. First, we prove that the method is an instance of the Shepard\u2019s method, when certain weight functions are used. In particular, the method can reproduce constant functions. Second, it is possible to define a modified set of cardinal functions strictly related to the ones of the not-rescaled kernel. Through these functions, we define a Lebesgue function for the rescaled interpolation process, and study its maximum - the Lebesgue constant - in different settings. Also, a preliminary theoretical result on the estimation of the interpolation error is presented. As an application, we couple our method with a partition of unity algorithm. This setting seems to be the most promising, and we illustrate its behavior with some experiments

A rescaled method for RBF approximation

In the recent paper [8], a new method to compute stable kernel-based interpolants has been presented. This \textit{rescaled interpolation} method combines the standard kernel interpolation with a properly defined rescaling operation, which smooths the oscillations of the interpolant. Although promising, this procedure lacks a systematic theoretical investigation. Through our analysis, this novel method can be understood as standard kernel interpolation by means of a properly rescaled kernel. This point of view allow us to consider its error and stability properties

Long-term wave growth and its linear and nonlinear interactions with wind fluctuations

Following Ge and Liu (2007), the simultaneously recorded time series of wave elevation and wind velocity are examined for long-term (on Lavrenov's τ<sub>4</sub>-scale or 3 to 6 h) linear and nonlinear interactions between the wind fluctuations and the wave field. Over such long times the detected interaction patterns should reveal general characteristics for the wave growth process. The time series are divided into three episodes, each approximately 1.33 h long, to represent three sequential stages of wave growth. The classic Fourier-domain spectral and bispectral analyses are used to identify the linear and quadratic interactions between the waves and the wind fluctuations as well as between different components of the wave field. <br><br> The results show clearly that as the wave field grows the linear interaction becomes enhanced and covers wider range of frequencies. Two different wave-induced components of the wind fluctuations are identified. These components, one at around 0.4 Hz and the other at around 0.15 to 0.2 Hz, are generated and supported by both linear and quadratic wind-wave interactions probably through the distortions of the waves to the wind field. The fact that the higher-frequency wave-induced component always stays with the equilibrium range of the wave spectrum around 0.4 Hz and the lower-frequency one tends to move with the downshifting of the primary peak of the wave spectrum defines the partition of the primary peak and the equilibrium range of the wave spectrum, a characteristic that could not be revealed by short-time wavelet-based analyses in Ge and Liu (2007). Furthermore, these two wave-induced peaks of the wind spectrum appear to have different patterns of feedback to the wave field. The quadratic wave-wave interactions also are assessed using the auto-bispectrum and are found to be especially active during the first and the third episodes. Such directly detected wind-wave interactions, both linear and nonlinear, may complement the existing theoretical and numerical models, and can be used for future model development and validation