Our goal is to study the evolution of the B-band luminosity function (LF) since z = 1 using ALHAMBRA data. 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. 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 MB* ~ Qz is Q_SF = -1.03 +- 0.08 and Q_Q = -0.80 +- 0.08, and of log phi* ~ Pz is P_SF = -0.01 +- 0.03 and P_Q = -0.41 +- 0.05. The measured faint-end slopes are alpha_SF = -1.29 +- 0.02 and alpha_Q = -0.53 +- 0.04. We find a significant population of faint quiescent galaxies, modelled by a second Schechter function with slope beta = -1.31 +- 0.11. We find a factor 2.55 +- 0.14 decrease in the luminosity density j_B of star-forming galaxies, and a factor 1.25 +- 0.16 increase in the j_B 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 j_B 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.
Aims: The relative cosmic variance (σv) is a fundamental source of uncertainty in pencil-beam surveys and, as a particular case of count-in-cell statistics, can be used to estimate the bias between galaxies and their underlying dark-matter distribution. Our goal is to test the significance of the clustering information encoded in the σv measured in the ALHAMBRA survey.
Methods: We measure the cosmic variance of several galaxy populations selected with B-band luminosity at 0.35 ≤ z< 1.05 as the intrinsic dispersion in the number density distribution derived from the 48 ALHAMBRA subfields. We compare the observational σv with the cosmic variance of the dark matter expected from the theory, σv,dm. This provides an estimation of the galaxy bias b.
Results: The galaxy bias from the cosmic variance is in excellent agreement with the bias estimated by two-point correlation function analysis in ALHAMBRA. This holds for different redshift bins, for red and blue subsamples, and for several B-band luminosity selections. We find that b increases with the B-band luminosity and the redshift, as expected from previous work. Moreover, red galaxies have a larger bias than blue galaxies, with a relative bias of b_rel = 1.4 ± 0.2.
Conclusions: Our results demonstrate that the cosmic variance measured in ALHAMBRA is due to the clustering of galaxies and can be used to characterise the σv affecting pencil-beam surveys. In addition, it can also be used to estimate the galaxy bias b from a method independent of correlation functions.
Aims: We present MUFFIT, a new generic code optimized to retrieve the main stellar population parameters of galaxies in photometric multi-filter surveys, and check its reliability and feasibility with real galaxy data from the ALHAMBRA survey.
Methods: Making use of an error-weighted χ2-test, we compare the multi-filter fluxes of galaxies with the synthetic photometry of mixtures of two single stellar populations at different redshifts and extinctions, to provide the most likely range of stellar population parameters (mainly ages and metallicities), extinctions, redshifts, and stellar masses. To improve the diagnostic reliability, MUFFIT identifies and removes from the analysis those bands that are significantly affected by emission lines. The final parameters and their uncertainties are derived by a Monte Carlo method, using the individual photometric uncertainties in each band. Finally, we discuss the accuracies, degeneracies, and reliability of MUFFIT using both simulated and real galaxies from ALHAMBRA, comparing with results from the literature.
Results: MUFFIT is a precise and reliable code to derive stellar population parameters of galaxies in ALHAMBRA. Using the results from photometric-redshift codes as input, MUFFIT improves the photometric-redshift accuracy by ~10-20%. MUFFIT also detects nebular emissions in galaxies, providing physical information about their strengths. The stellar masses derived from MUFFIT show excellent agreement with the COSMOS and SDSS values. In addition, the retrieved age-metallicity locus for a sample of z ≤ 0.22 early-type galaxies in ALHAMBRA at different stellar mass bins are in very good agreement with the ones from SDSS spectroscopic diagnostics. Moreover, a one-to-one comparison between the redshifts, ages, metallicities, and stellar masses derived spectroscopically for SDSS and by MUFFIT for ALHAMBRA reveals good qualitative agreements in all the parameters, hence reinforcing the strengths of multi-filter galaxy data and optimized analysis techniques, like MUFFIT, to conduct reliable stellar population studies.
Aims: We present the main steps that will be taken to extract Hα emission flux from Javalambre Photometric Local Universe Survey (J-PLUS) photometric data.
Methods: For galaxies with z ≲ 0.015, the Hα+[N ii] emission is covered by the J-PLUS narrow-band filter F660. We explore three different methods to extract the Hα + [NII] flux from J-PLUS photometric data: a combination of a broad-band and a narrow-band filter (r' and F660), two broad-band and a narrow-band filter (r', i' and F660), and an SED-fitting based method using eight photometric points. To test these methodologies, we simulated J-PLUS data from a sample of 7511 SDSS spectra with measured Hα flux. Based on the same sample, we derive two empirical relations to correct the derived Hα+[NII] flux from dust extinction and [NII] contamination.
Results: We find that the only unbiased method is the SED-fitting based method. The combination of two filters underestimates the measurements of the Hα + [NII] flux by 22%, while the three filters method are underestimated by 9%. We study the error budget of the SED-fitting based method and find that, in addition to the photometric error, our measurements have a systematic uncertainty of 4.3%. Several sources contribute to this uncertainty: the differences between our measurement procedure and that used to derive the spectroscopic values, the use of simple stellar populations as templates, and the intrinsic errors of the spectra, which were not taken into account. Apart from that, the empirical corrections for dust extinction and [NII] contamination add an extra uncertainty of 14%.
Conclusions: Given the J-PLUS photometric system, the best methodology to extract Hα + [NII] flux is the SED-fitting based method. Using this method, we are able to recover reliable Hα fluxes for thousands of nearby galaxies in a robust and homogeneous way. Moreover, each stage of the process (emission line flux, dust extinction correction, and [NII] decontamination) can be decoupled and improved in the future. This method ensures reliable Hα measurements for many studies of galaxy evolution, from the local star formation rate density, to 2D studies in spatially well-resolved galaxies or the study of environmental effects, up to r' = 21.8 (AB; 3σ detection of Hα+[NII] emission).
Aims: Our goal is to develop and test a novel methodology to compute accurate close-pair fractions with photometric redshifts.
Methods: We improved the currently used methodologies to estimate the merger fraction fm from photometric redshifts by (i) using the full probability distribution functions (PDFs) of the sources in redshift space; (ii) including the variation in the luminosity of the sources with z in both the sample selection and the luminosity ratio constrain; and (iii) splitting individual PDFs into red and blue spectral templates to reliably work with colour selections. We tested the performance of our new methodology with the PDFs provided by the ALHAMBRA photometric survey.
Results: The merger fractions and rates from the ALHAMBRA survey agree excellently well with those from spectroscopic work for both the general population and red and blue galaxies. With the merger rate of bright (MB ≤ -20 - 1.1z) galaxies evolving as (1 + z)^n, the power-law index n is higher for blue galaxies (n = 2.7 ± 0.5) than for red galaxies (n = 1.3 ± 0.4), confirming previous results. Integrating the merger rate over cosmic time, we find that the average number of mergers per galaxy since z = 1 is N_red = 0.57 ± 0.05 for red galaxies and N_blue = 0.26 ± 0.02 for blue galaxies.
Conclusions: Our new methodology statistically exploits all the available information provided by photometric redshift codes and yields accurate measurements of the merger fraction by close pairs from using photometric redshifts alone. Current and future photometric surveys will benefit from this new methodology.
Context. Most observational results on the high redshift restframe UV-bright galaxies are based on samples pinpointed using the so-called dropout technique or Ly-α selection. However, the availability of multifilter data now allows the dropout selections to be replaced by direct methods based on photometric redshifts. In this paper we present the methodology to select and study the population of high redshift galaxies in the ALHAMBRA survey data.
Aims: Our aim is to develop a less biased methodology than the traditional dropout technique to study the high redshift galaxies in ALHAMBRA and other multifilter data. Thanks to the wide area ALHAMBRA covers, we especially aim at contributing to the study of the brightest, least frequent, high redshift galaxies.
Methods: The methodology is based on redshift probability distribution functions (zPDFs). It is shown how a clean galaxy sample can be obtained by selecting the galaxies with high integrated probability of being within a given redshift interval. However, reaching both a complete and clean sample with this method is challenging. Hence, a method to derive statistical properties by summing the zPDFs of all the galaxies in the redshift bin of interest is introduced.
Results: Using this methodology we derive the galaxy rest frame UV number counts in five redshift bins centred at z = 2.5,3.0,3.5,4.0, and 4.5, being complete up to the limiting magnitude at m_UV(AB) = 24, where mUV refers to the first ALHAMBRA filter redwards of the Ly-α line. With the wide field ALHAMBRA data we especially contribute to the study of the brightest ends of these counts, accurately sampling the surface densities down to m_UV(AB) = 21-22.
Conclusions: We show that using the zPDFs it is easy to select a very clean sample of high redshift galaxies. We also show that it is better to do statistical analysis of the properties of galaxies using a probabilistic approach, which takes into account both the incompleteness and contamination issues in a natural way.