We have determined the Optical Luminosity Function (OLF) of a sample of 80946 Quasi Stellar Objects (QSOs) taken from the Sloan Digital Sky Survey Data Release Seven (SDSS DR7) with redshift range??0.3 z Mi < -22.5...We have determined the Optical Luminosity Function (OLF) of a sample of 80946 Quasi Stellar Objects (QSOs) taken from the Sloan Digital Sky Survey Data Release Seven (SDSS DR7) with redshift range??0.3 z Mi < -22.5. The Monte Carlo Technique of numerical integration is used. The sample of QSOs is divided into seven sub-samples with redshift in the ranges: 0.30 z z z < 1.05,?1.05 z z z < 1.80, and 1.80 z < 2.05. Each redshift interval is binned in absolute magnitude with bin width ΔMi = -0.5. A flat universe with cosmological parameters Ωm = 0.3, Ω∧ = 0.7, and Hubble constant Ho = 70.0 km·s-1·Mpc-1 is used. From the optical luminosity function a clear evidence of AGN downsizing is observed, i.e. the number density of the less luminous AGNs peaks at lower redshift than the number density of the more luminous AGNs.展开更多
We discuss the variation of the fine-structure constant, α. There are obvious discrepancies among the results of α-variation from recent Quasi-stellar observation experiments and from the Oklo uranium mine analysis....We discuss the variation of the fine-structure constant, α. There are obvious discrepancies among the results of α-variation from recent Quasi-stellar observation experiments and from the Oklo uranium mine analysis. We use dS Sitter invariant Special Relativity （SRc,R） and Dirac large number hypothesis to discuss this puzzle, and present a possible solution to the disagreement. By means of the observational data and the discussions presented in this paper, we estimate the radius of the Universe in SRc,R which is about -2√5× 10^11.y.展开更多
文摘We have determined the Optical Luminosity Function (OLF) of a sample of 80946 Quasi Stellar Objects (QSOs) taken from the Sloan Digital Sky Survey Data Release Seven (SDSS DR7) with redshift range??0.3 z Mi < -22.5. The Monte Carlo Technique of numerical integration is used. The sample of QSOs is divided into seven sub-samples with redshift in the ranges: 0.30 z z z < 1.05,?1.05 z z z < 1.80, and 1.80 z < 2.05. Each redshift interval is binned in absolute magnitude with bin width ΔMi = -0.5. A flat universe with cosmological parameters Ωm = 0.3, Ω∧ = 0.7, and Hubble constant Ho = 70.0 km·s-1·Mpc-1 is used. From the optical luminosity function a clear evidence of AGN downsizing is observed, i.e. the number density of the less luminous AGNs peaks at lower redshift than the number density of the more luminous AGNs.
文摘研究莱曼极限系统(Lyman limit systems,LLS)对于了解宇宙的大尺度结构、星系演化以及星系团内部气体分布具有重要意义.然而,由于LLS吸收特征的独特性,目前的研究主要采用传统方法,对柱密度在10^(19)cm^(-2)≤N(HI)<10^(20.3)cm^(-2)的小样本集上进行认证.本文利用深度学习技术,在暗能量光谱仪(The Dark Energy Spectroscopic Instrument,DESI)模拟光谱上,通过优化卷积神经网络(convolutional neural network,CNN)模型,提高了对LLS(10^(18.5)cm^(-2)≤N(HI)≤10^(20.0)cm^(-2))的识别精度(达到95%).随后,验证了该模型的完备度和纯度,并估计了LLS的柱密度和红移.结果显示:在S/N>6的情况下,当10^(19.0)cm^(-2)>N(HI)>1018.5cm^(-2)时,CNN模型的完备度超过0.5,而纯度也超过0.2;当10^(20.0)cm^(-2)>N(HI)>10^(19.0)cm^(-2)时,完备度超过0.9,而纯度超过0.7;当10^(20.0)cm^(-2)>N(HI)>10^(18.5)cm^(-2)时,CNN模型对LLS柱密度估计值与真实值的平均差值为-0.05161,标准差为0.239,对LLS红移估计值和真实值的平均差值为-0.0003,标准差为0.0009.这些结果表明:模型的完备度普遍高于纯度,尤其是在低柱密度的情况下,LLS在光谱中的吸收特征非常窄,极易与其他波段混淆,导致模型产生更多的FP(false positive)样本.此外,CNN模型对LLS的柱密度和红移的估计值略低于真实值,且估计误差的离散程度较小.本研究为未来的LLS研究提供了可参考的方法,鼓励研究人员适应并采用CNN模型进行各种光谱分析.
基金National Natural Science Foundation of China (90403021)PhD Program Funds of Education Ministry ofChina (20020358040)
文摘We discuss the variation of the fine-structure constant, α. There are obvious discrepancies among the results of α-variation from recent Quasi-stellar observation experiments and from the Oklo uranium mine analysis. We use dS Sitter invariant Special Relativity （SRc,R） and Dirac large number hypothesis to discuss this puzzle, and present a possible solution to the disagreement. By means of the observational data and the discussions presented in this paper, we estimate the radius of the Universe in SRc,R which is about -2√5× 10^11.y.
