Determination of fundamental properties of an M31 globular cluster from main-sequence photometry

M31 globular cluster B379 is the first extragalactic cluster, the age of which was determined by main-sequence photometry. In this method, the age of a cluster is obtained by fitting its CMD with stellar evolutionary models. However, different stellar evolutionary models use different parameters of stellar evolution, such as range of stellar masses, different opacities and equations of state, and different recipes, and so on. So, it is interesting to check whether different stellar evolutionary models can give consistent results for the same cluster. Brown et al. (2004a) constrained the age of B379 by comparing its CMD with isochrones of the 2006 VandenBerg models. Using SSP models of BC03 and its multi-photometry, Ma et al. (2007) independently determined the age of B379, which is in good agreement with the determination of Brown et al. (2004a). The BC03 models are calculated based on the Padova evolutionary tracks. It is necessary to check whether the age of B379 which, being determined based on the Padova evolutionary tracks, is in agreement with the determination of Brown et al. (2004a). So, in this paper, we re-determine its age using isochrones of the Padova stellar evolutionary models. In addition, the metal abundance, the distance modulus, and the reddening value for B379 are also determined in this paper. The results obtained in this paper are consistent with the previous determinations, which including the age obtained by Brown et al. (2004a). So, this paper confirms the consistence of the age scale of B379 between the Padova isochrones and the 2006 VandenBerg isochrones, i.e. the results' comparison between Brown et al. (2004a) and Ma et al. (2007) is meaningful. The results obtained in this paper are: the metallicity [M/H]=-0.325, the age $\tau=11.0\pm1.5$ Gyr, the reddening value E(B-V)=0.08, and the distance modulus $(m-M)_{0}=24.44\pm0.10$.Comment: Accepted for Publication in PASP, 7 pages, 1 figure and 1 tabl

A Calibration Method for Wide Field Multicolor Photometric System

The purpose of this paper is to present a method to self-calibrate the spectral energy distribution (SED) of objects in a survey based on the fitting of an SED library to the observed multi-color photometry. We adopt for illustrative purposes the Vilnius (Strizyz and Sviderskiene 1972) and Gunn & Stryker (1983) SED libraries. The self-calibration technique can improve the quality of observations which are not taken under perfectly photometric conditions. The more passbands used for the photometry, the better the results. This technique has been applied to the BATC 15-passband CCD survey.Comment: LateX file, 1 PS file, submitted to PASP number 99-025 The English has been improved and some mistakes have been correcte

Spectral Energy Distributions of M81 Globular Clusters in BATC Multicolor Survey

In this paper, we give the spectral energy distributions of 42 M81 globular clusters in 13 intermediate-band filters from 4000 to 10000 A, using the CCD images of M81 observed as part of the BATC multicolor survey of the Sky. The BATC multicolor filter system is specifically designed to exclude most of the bright and variable night-sky emission lines including the OH forest. Hence, it can present accurate SEDs of the observed objects. These spectral energy distributions are low-resolution spectra, and can reflect the stellar populations of the globular clusters. This paper confirms the conclusions of Schroder et al. that, M81 contains clusters as young as a few Gyrs, which also were observed in both M31 and M33Comment: Accepted for Publication in PASP, 10 pages, 3 figure

Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble

Precipitation is a significant index to measure the degree of drought and flood in a region, which directly reflects the local natural changes and ecological environment. It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy. In order to accurately predict precipitation, a new precipitation prediction model based on extreme learning machine ensemble (ELME) is proposed. The integrated model is based on the extreme learning machine (ELM) with different kernel functions and supporting parameters, and the submodel with the minimum root mean square error (RMSE) is found to fit the test data. Due to the complex mechanism and factors affecting precipitation change, the data have strong uncertainty and significant nonlinear variation characteristics. The mean generating function (MGF) is used to generate the continuation factor matrix, and the principal component analysis technique is employed to reduce the dimension of the continuation matrix, and the effective data features are extracted. Finally, the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June, July and August, and a comparative experiment is carried out by using ELM, long-term and short-term memory neural network (LSTM) and back propagation neural network based on genetic algorithm (GA-BP). The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models, and it has high stability and reliability, which provides a reliable method for precipitation prediction

Parallel Implementation of Katsevich's FBP Algorithm

For spiral cone-beam CT, parallel computing is an effective approach to resolving the problem of heavy computation burden. It is well known that the major computation time is spent in the backprojection step for either filtered-backprojection (FBP) or backprojected-filtration (BPF) algorithms. By the cone-beam cover method [1], the backprojection procedure is driven by cone-beam projections, and every cone-beam projection can be backprojected independently. Basing on this fact, we develop a parallel implementation of Katsevich's FBP algorithm. We do all the numerical experiments on a Linux cluster. In one typical experiment, the sequential reconstruction time is 781.3 seconds, while the parallel reconstruction time is 25.7 seconds with 32 processors