The miniJPAS survey quasar selection I: Mock catalogues for classification

TL;DR

This paper develops a pipeline to create realistic mock catalogues of quasars, galaxies, and stars for the miniJPAS survey, enabling machine learning classification without real spectroscopic training data.

Contribution

It introduces a novel method to generate synthetic photometry and noise models for mock catalogues tailored to miniJPAS, facilitating quasar candidate identification.

Findings

Synthetic photometry matches miniJPAS depths and S/N ratios.

Mock catalogues incorporate realistic non-detections and noise.

Pipeline adaptable to other photometric surveys.

Abstract

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper we develop a pipeline to compute synthetic photometry of quasars, galaxies and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey. To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range $17.5\leq r<24$, we augment our sample of available spectra by shifting the original $r$-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modeling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys.