An efficient method to identify supernova remnants is provided,in order to iron out the great gap between the predicted number and the observed.We make an attempt to apply D4 wavelet to detect the useful structures bu...An efficient method to identify supernova remnants is provided,in order to iron out the great gap between the predicted number and the observed.We make an attempt to apply D4 wavelet to detect the useful structures buried in radio map,showing that it is an efficient way to separate noises from signals.展开更多
We present new statistical results on the spectral index-flux density relation for large samples of radio sources using archival data of the most sensitive surveys, such as 6C, Miyun, WENSS, B3, NVSS, GB87. Instrument...We present new statistical results on the spectral index-flux density relation for large samples of radio sources using archival data of the most sensitive surveys, such as 6C, Miyun, WENSS, B3, NVSS, GB87. Instrumental selection effects and the completeness of the catalogs are discussed. Based on the spectral indices calculated for about 200 000 sources from the WENSS (327 MHz) and NVSS (1.4 GHz) catalogs, we obtained (1) The median spectral index increases from amed --0.9 toαmed --0.8 (Svαvα), while S327 flux densities decrease from 0.1 Jy down to 25mJy. The median spectral indices nearly show no variation within the error bars when the flux density is larger than 0.1 Jy. (2) A dependence of the fraction of ultra-steep spectrum sources (USS, -1.5 <α<-1.0), steep spectrum sources (SSS, -1.0 <α< -0.5) and flat spectrum sources (FSS, -0.5≤α≤0.0) is partly responsible for the spectral flattening. Another contribution to the spectral flattening comes from the variation of αmed of steep spectrum sources (α<-0.5) themselves which increases with decreasing flux densities. (3) The spectral flattening for faint sources (down to S327- 20mJy) with steep spectra (α< -0.5) suggests that amed is correlated with luminosity rather than redshift according to the Condon' model. (4) A strong spectral selection effect occurs when spectral indices are calculated from samples with a large frequency separation.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos.19873009 and 19773017.
文摘An efficient method to identify supernova remnants is provided,in order to iron out the great gap between the predicted number and the observed.We make an attempt to apply D4 wavelet to detect the useful structures buried in radio map,showing that it is an efficient way to separate noises from signals.
文摘We present new statistical results on the spectral index-flux density relation for large samples of radio sources using archival data of the most sensitive surveys, such as 6C, Miyun, WENSS, B3, NVSS, GB87. Instrumental selection effects and the completeness of the catalogs are discussed. Based on the spectral indices calculated for about 200 000 sources from the WENSS (327 MHz) and NVSS (1.4 GHz) catalogs, we obtained (1) The median spectral index increases from amed --0.9 toαmed --0.8 (Svαvα), while S327 flux densities decrease from 0.1 Jy down to 25mJy. The median spectral indices nearly show no variation within the error bars when the flux density is larger than 0.1 Jy. (2) A dependence of the fraction of ultra-steep spectrum sources (USS, -1.5 <α<-1.0), steep spectrum sources (SSS, -1.0 <α< -0.5) and flat spectrum sources (FSS, -0.5≤α≤0.0) is partly responsible for the spectral flattening. Another contribution to the spectral flattening comes from the variation of αmed of steep spectrum sources (α<-0.5) themselves which increases with decreasing flux densities. (3) The spectral flattening for faint sources (down to S327- 20mJy) with steep spectra (α< -0.5) suggests that amed is correlated with luminosity rather than redshift according to the Condon' model. (4) A strong spectral selection effect occurs when spectral indices are calculated from samples with a large frequency separation.
