Luminosity function（LF） is a very fundamental statistic,which provides one of the most important tools to probe the distribution and evolution of galaxies and active galactic nuclei（AGNs） over cosmic time.But the traditional LF methods all have more or less disadvantages.We propose a flexible method for estimating luminosity functions（LFs）based on kernel density estimation（KDE）,the most popular nonparametric density estimation approach developed in modern statistics.To implement our method,we have developed a public,open-source Python Toolkit,called kdeLF.With the support of kdeLF,our KDE method is expected to be a competitive alternative to existing nonparametric estimators,due to its high accuracy and excellent stability.
其他摘要
Luminosity function (LF) is a very fundamental statistic, which provides one of the most important tools to probe the distribution and evolution of galaxies and active galactic nuclei (AGNs) over cosmic time. But the traditional LF methods all have more or less disadvantages. We propose a flexible method for estimating luminosity functions (LFs) based on kernel density estimation (KDE), the most popular nonparametric density estimation approach developed in modern statistics. To implement our method, we have developed a public, open-source Python Toolkit, called kdeLF. With the support of kdeLF, our KDE method is expected to be a competitive alternative to existing nonparametric estimators, due to its high accuracy and excellent stability.
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