Showing papers by "Narciso Benítez published in 2020"

The miniJPAS survey: a preview of the Universe in 56 colours

Abstract: The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) will soon start to scan thousands of square degrees of the northern extragalactic sky with a unique set of $56$ optical filters from a dedicated $2.55$m telescope, JST, at the Javalambre Astrophysical Observatory. Before the arrival of the final instrument (a 1.2 Gpixels, 4.2deg$^2$ field-of-view camera), the JST was equipped with an interim camera (JPAS-Pathfinder), composed of one CCD with a 0.3deg$^2$ field-of-view and resolution of 0.23 arcsec pixel$^{-1}$. To demonstrate the scientific potential of J-PAS, with the JPAS-Pathfinder camera we carried out a survey on the AEGIS field (along the Extended Groth Strip), dubbed miniJPAS. We observed a total of $\sim 1$ deg$^2$, with the $56$ J-PAS filters, which include $54$ narrow band (NB, $\rm{FWHM} \sim 145$Angstrom) and two broader filters extending to the UV and the near-infrared, complemented by the $u,g,r,i$ SDSS broad band (BB) filters. In this paper we present the miniJPAS data set, the details of the catalogues and data access, and illustrate the scientific potential of our multi-band data. The data surpass the target depths originally planned for J-PAS, reaching $\rm{mag}_{\rm {AB}}$ between $\sim 22$ and $23.5$ for the NB filters and up to $24$ for the BB filters ($5\sigma$ in a $3$~arcsec aperture). The miniJPAS primary catalogue contains more than $64,000$ sources extracted in the $r$ detection band with forced photometry in all other bands. We estimate the catalogue to be complete up to $r=23.6$ for point-like sources and up to $r=22.7$ for extended sources. Photometric redshifts reach subpercent precision for all sources up to $r=22.5$, and a precision of $\sim 0.3$% for about half of the sample. (Abridged)

Assessing the photometric redshift precision of the S-PLUS survey: the Stripe-82 as a test-case

Abstract: In this paper we present a thorough discussion about the photometric redshift (photo-z) performance of the Southern Photometric Local Universe Survey (S-PLUS). This survey combines a 7 narrow + 5 broad passband filter system, with a typical photometric-depth of r$\sim$21 AB. For this exercise, we utilize the Data Release 1 (DR1), corresponding to 336 deg$^{2}$ from the Stripe-82 region. We rely on the \texttt{BPZ2} code to compute our estimates, using a new library of SED models, which includes additional templates for quiescent galaxies. When compared to a spectroscopic redshift control sample of $\sim$100k galaxies, we find a precision of $\sigma_{z}<$0.8\%, $<$2.0\% or $<$3.0\% for galaxies with magnitudes r$<$17, $<$19 and $<$21, respectively. A precision of 0.6\% is attained for galaxies with the highest \texttt{Odds} values. These estimates have a negligible bias and a fraction of catastrophic outliers inferior to 1\%. We identify a redshift window (i.e., 0.26$

J-PAS: forecasts on dark energy and modified gravity theories

Abstract: The next generation of galaxy surveys will allow us to test one of the most fundamental assumptions of the standard cosmology, i.e. that gravity is governed by the general theory of relativity (GR). In this paper, we investigate the ability of the Javalambre Physics of the AcceleratingUniverseAstrophysical Survey (J-PAS) to constrainGR and its extensions. Based on the J-PAS information on clustering and gravitational lensing, we perform a Fisher matrix forecast on the effective Newton constant, mu, and the gravitational slip parameter, eta, whose deviations from unity would indicate a breakdown of GR. Similar analysis is also performed for the DESI and Euclid surveys and compared to J-PAS with two configurations providing different areas, namely an initial expectation with 4000 deg(2) and the future best case scenario with 8500 deg(2). We show that J-PAS will be able to measure the parameters mu and eta at a sensitivity of 2-7 per cent, and will provide the best constraints in the interval z = 0.3-0.6, thanks to the large number of ELGs detectable in that redshift range. We also discuss the constraining power of J-PAS for dark energy models with a time-dependent equation-of-state parameter of the type w(a) = w(0) + w(a)(1 - a), obtaining Delta w(0) = 0.058 and Delta w(a) = 0.24 for the absolute errors of the dark energy parameters.

The miniJPAS survey: star-galaxy classification using machine learning

Abstract: Future astrophysical surveys such as J-PAS will produce very large datasets, which will require the deployment of accurate and efficient Machine Learning (ML) methods. In this work, we analyze the miniJPAS survey, which observed about 1 deg2 of the AEGIS field with 56 narrow-band filters and 4 ugri broad-band filters. We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g. stars) objects, a necessary step for the subsequent scientific analyses. We aim at developing an ML classifier that is complementary to traditional tools based on explicit modeling. In order to train and test our classifiers, we crossmatched the miniJPAS dataset with SDSS and HSC-SSP data. We trained and tested 6 different ML algorithms on the two crossmatched catalogs. As input for the ML algorithms we use the magnitudes from the 60 filters together with their errors, with and without the morphological parameters. We also use the mean PSF in the r detection band for each pointing. We find that the RF and ERT algorithms perform best in all scenarios. When analyzing the full magnitude range of 15 21). We use our best classifiers, with and without morphology, in order to produce a value added catalog available at this https URL .

J-PAS: Measuring emission lines with artificial neural networks.

Abstract: Throughout this paper we present a new method to detect and measure emission lines in J-PAS up to $z = 0.35$. J-PAS will observe $8000$~deg$^2$ of the northern sky in the upcoming years with 56 photometric bands. The release of such amount of data brings us the opportunity to employ machine learning methods in order to overcome the difficulties associated with photometric data. We used Artificial Neural Networks (ANNs) trained and tested with synthetic J-PAS photometry from CALIFA, MaNGA, and SDSS spectra. We carry out two tasks: firstly, we cluster galaxies in two groups according to the values of the equivalent width (EW) of $H\alpha$, $H\beta$, $[NII]{\lambda 6584}$, and $ [OIII]{\lambda 5007}$ lines measured in the spectra. Then, we train an ANN to assign to each galaxy a group. We are able to classify them with the uncertainties typical of the photometric redshift measurable in J-PAS. Secondly, we utilize another ANN to determine the values of those EWs. Subsequently, we obtain the $[NII]/H\alpha$, $[OIII]/H\beta$, and \ion{O}{3}\ion{N}{2} ratios recovering the BPT diagram . We study the performance of the ANN in two training samples: one is only composed of synthetic J-PAS photo-spectra (J-spectra) from MaNGA and CALIFA (CALMa set) and the other one is composed of SDSS galaxies. We can reproduce properly the main sequence of star forming galaxies from the determination of the EWs. With the CALMa training set we reach a precision of 0.101 and 0.091 dex for the $[NII]/H\alpha$ and $[OIII]/H\beta$ ratios in the SDSS testing sample. Nevertheless, we find an underestimation of those ratios at high values in galaxies hosting an AGN. We also show the importance of the dataset used for both training and testing the model. ANNs are extremely useful to overcome the limitations previously expected concerning the detection and measurements of the emission lines in surveys like J-PAS.