A flexible method for estimating luminosity functions via Kernel Density Estimation - III. Extending to Multiple Flux-Limited Samples

TL;DR

This paper extends kernel density estimation methods for luminosity functions to handle multiple flux-limited samples, enabling more comprehensive analysis of survey data with varying observational depths.

Contribution

It introduces a piecewise framework that combines data from disjoint surveys with different flux limits, improving the estimation of luminosity functions across complex datasets.

Findings

Validated with Monte Carlo simulations showing robustness.

Applied to SDSS DR7 and 2SLAQ quasar data with consistent results.

Ensures full utilization of available sources across surveys.

Abstract

As the third paper in a series regarding the estimation of luminosity functions (LFs) via kernel density estimation (KDE), we present a further generalization of our framework by extending its applicability to multiple flux-limited samples. While our previous works addressed single flux-limited datasets, many practical surveys collect data from several disjoint sky regions with varying observational depths and flux limits. We introduce a piecewise estimation framework that partitions the luminosity-redshift plane into disjoint regions according to the staggered flux limits of the sub-samples. Within each region, we integrate data from all surveys capable of detecting sources into a combined sample and apply the transformation-reflection KDE method using the corresponding local flux threshold as the truncation boundary. This strategy allows for the full utilization of all available sources while maintaining rigorous statistical consistency. The robustness of this approach is validated through Monte Carlo simulations. Furthermore, application to SDSS DR7 and 2SLAQ quasar data shows smooth transitions across flux limits and excellent agreement with parametric models. This approach is fully supported by our previously developed \texttt{kdeLF} Python package.