The ALHAMBRA survey⋆: B-band luminosity function of quiescent and star-forming galaxies at 0.2 ≤ z < 1 by PDF analysis

Lopez-Sanjuan, C. et. al.Aims. Our goal is to study the evolution of the B-band luminosity function (LF) since z ∼ 1 using ALHAMBRA data. Methods. We used the photometric redshift and the I-band selection magnitude probability distribution functions (PDFs) of those ALHAMBRA galaxies with I ≤ 24 mag to compute the posterior LF. We statistically studied quiescent and star-forming galaxies using the template information encoded in the PDFs. The LF covariance matrix in redshift - magnitude - galaxy type space was computed, including the cosmic variance. That was estimated from the intrinsic dispersion of the LF measurements in the 48 ALHAMBRA sub-fields. The uncertainty due to the photometric redshift prior is also included in our analysis. Results. We modelled the LF with a redshift-dependent Schechter function affected by the same selection effects than the data. The measured ALHAMBRA LF at 0.2 ≤ z < 1 and the evolving Schechter parameters both for quiescent and star-forming galaxies agree with previous results in the literature. The estimated redshift evolution of M∗B ∝ Qz is QSF = -1.03±0.08 and QQ = -0.80±0.08, and of log10φ∗ ∝ Pz is PSF = -0.01±0.03 and PQ = -0.41 ± 0.05. The measured faint-end slopes are αSF = -1.29 ± 0.02 and αQ = -0.53 ± 0.04. We find a significant population of faint quiescent galaxies with MB ≳ -18, modelled by a second Schechter function with slope β = -1.31 ± 0.11. Conclusions. We present a robust methodology to compute LFs using multi-filter photometric data. The application to ALHAMBRA shows a factor 2.55 ± 0.14 decrease in the luminosity density jB of star-forming galaxies, and a factor 1.25 ± 0.16 increase in the jB of quiescent ones since z = 1, confirming the continuous build-up of the quiescent population with cosmic time. The contribution of the faint quiescent population to jB increases from 3% at z = 1 to 6% at z = 0. The developed methodology will be applied to future multi-filter surveys such as J-PAS. © 2017 ESO.This work has been mainly funded by the FITE (Fondos de Inversiones de Teruel) and the projects AYA2015-66211-C2-1, AYA2012-30789, AYA200614056, and CSD2007-00060. We also acknowledge support from the Spanish Ministry for Economy and Competitiveness and FEDER funds through grants AYA2010-15081, AYA2010-15169, AYA2010-22111-C03-01, AYA201022111- C03-02, AYA2011-29517-C03-01, AYA2012-39620, AYA2013-40611P, AYA2013-42227-P, AYA2013-43188-P, AYA2013-48623-C2-1, AYA201348623- C2-2, ESP2013-48274, AYA2014-58861-C3-1, Aragon Government Research Group E103, Generalitat Valenciana projects Prometeo 2009/064 and PROMETEOII/2014/060, Junta de Andalucia grants TIC114, JA2828, P10FQM-6444, and Generalitat de Catalunya project SGR-1398. E. T. acknowledges the support by the ETAg grants IUT26-2, IUT40-2, and by the European Regional Development Fund (TK133). A. M. acknowledges the financial support of the Brazilian funding agency FAPESP (Post-doc fellowship - process number 2014/11806-9). B. A. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 656354.Peer reviewe