Red clump stars from the LAMOST dataⅠ: identification and distance

We present a sample of about 120 000 red clump candidates selected from the LAMOST DR2 catalog based on the empirical distribution model in the effective temperature vs. surface gravity plane. Although, in general, red clump stars are considered as standard candles, they do not exactly stay in a narrow range of absolute magnitude, but may have a range of more than one magnitude depending on their initial mass. Consequently, conventional oversimplified distance estimations with the assumption of a fixed luminosity may lead to systematic bias related to the initial mass or age, which can potentially affect the study of the evolution of the Galaxy with red clump stars. We therefore employ an isochrone-based method to estimate the absolute magnitude of red clump stars from their observed surface gravities, effective temperatures and metallicities. We verify that the estimation removes the systematics well and provides initial mass/age estimates that are independent of distance with accuracy better than 10%.

Photometric redshift estimation for quasars by integration of KNN and SVM

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches （such as the k-nearest neighbor （KNN） algorithm）. In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine （SVM） methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding Zphot. Our analysis has found two dense regions with catastrophic failure, one in the range of Zphot E [0.3, 1.2] and the other in the range of Zphot E [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey （SDSS） quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different /△z/ ranges and root mean square （rms） error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN （71.96%, 83.78%, 89.73% and 0.204）.

Steganalytic method based on short and repeated sequence distance statistics

According to the distribution characteristics of short and repeated sequence （SRS）, a steganaiytic method based on the correlation of image bit planes is proposed. Firstly, we provide the conception of SRS distance statistics and deduce its statistical distribution. Because the SRS distance statistics can effectively reflect the correlation of the sequence, SRS has statistical features when the image bit plane sequence equals the image width. Using this characteristic, the steganalytic method is fulfilled by the distinct test of Poisson distribution. Experimental results show a good performance for detecting LSB matching steganographic method in still images. By the way, the proposed method is not designed for specific steganographic algorithms and has good generality.