Abstract
We present a measurement of the i-band galaxy luminosity function from the present-day to z = 2, using over 1.1 million galaxies from the Physics of the Accelerating Universe Survey (PAUS). PAUS combines broad-band imaging from the Canada–France–Hawaii Telescope Lensing Survey with narrow-band photometry from PAUCam, enabling high-precision photometric redshifts with an accuracy of σ68(z) = 0.019 down to iAB = 23. A synthetic light-cone mock catalogue built using the galform semi-analytic model is used to simulate PAUS selection effects and photometric uncertainties, and to derive a machine-learning based estimate of the k-correction. We recover rest frame i-band luminosities using a random forestregressortrained on simulated ugriz photometry and redshifts. Luminosity functions are estimated using the 1/Vmax method, accounting for photometric redshift and magnitude errors, and validated against mock data. We find good agreement between observations and models at z < 1, with increasing discrepancies at higherredshifts due to photometric redshift outliers. The bright-end of the luminosity function becomes flatter at high redshift, primarily driven by redshift errors. We show that the faint-end of the luminosity function becomes more incomplete with increasing redshift, but isstill useful for constraining models. We analyse the red and blue galaxy populations separately, observing distinct evolutionary trends. The model overpredicts the number of both faint red and blue galaxies. Our study highlights the importance of accurate redshift estimation and selection modelling for robust luminosity function recovery, and demonstratesthat PAUS can characterize the galaxy population with photometric redshifts across a wide redshift baseline.
| Original language | English |
|---|---|
| Article number | stag362 |
| Number of pages | 24 |
| Journal | Monthly Notices of the Royal Astronomical Society |
| Volume | 547 |
| Issue number | 3 |
| Early online date | 17 Mar 2026 |
| DOIs | |
| Publication status | Published - 1 Apr 2026 |
Keywords
- galaxies: distances and redshifts
- galaxies: luminosity function, mass function
- galaxies: statistics
- software: machine learning
- software: simulations
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