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Models of stellar population synthesis (SPS) are the fundamental tool that relates the physical properties of a galaxy to its spectral energy distribution (SED). In this paper, we present DSPS: a python package for stellar population synthesis. All of the functionality in DSPS is implemented natively in the JAX library for automatic differentiation, and so our predictions for galaxy photometry are fully differentiable, and directly inherit the performance benefits of JAX, including portability onto GPUs. DSPS also implements several novel features, such as i) a flexible empirical model for stellar metallicity that incorporates correlations with stellar age, and ii) support for the diffstar model that provides a physically-motivated connection between the star formation history of a galaxy (SFH) and the mass assembly of its underlying dark matter halo. We detail a set of theoretical techniques for using autodiff to calculate gradients of predictions for galaxy SEDs with respect to SPS parameters that control a range of physical effects, including SFH, stellar metallicity, nebular emission, and dust attenuation. When forward modeling the colors of a synthetic galaxy population, we find that DSPS can provide a factor of 5 speedup over standard SPS codes on a CPU, and a factor of 300-400 on a modern GPU. When coupled with gradient-based techniques for optimization and inference, DSPS makes it practical to conduct expansive likelihood analyses of simulation-based models of the galaxy--halo connection that fully forward model galaxy spectra and photometry., Comment: 13 pages, 2 appendices. Version accepted for publication in MNRAS

Recent analyses show that $\Lambda$CDM-based models optimised to reproduce the clustering of massive galaxies overestimate their gravitational lensing by about 30\%, the so-called lensing is low problem. Using a state-of-the-art hydrodynamical simulation, we show that this discrepancy reflects shortcomings in standard galaxy-halo connection models rather than tensions within the $\Lambda$CDM paradigm itself. Specifically, this problem results from ignoring a variety of galaxy formation effects, including assembly bias, segregation of satellite galaxies relative to dark matter, and baryonic effects on the matter distribution. All these effects contribute towards overestimating gravitational lensing and, when combined, explain the amplitude and scale dependence of the lensing is low problem. We conclude that simplistic galaxy-halo connection models are inadequate to interpret clustering and lensing simultaneously, and that it is crucial to employ more sophisticated models for the upcoming generation of large-scale surveys., Comment: 16 pages, 12 figures, accepted by MNRAS

The spatial distribution of galaxies and their gravitational lensing signal offer complementary tests of galaxy formation physics and cosmology. However, their synergy can only be fully exploited if both probes are modelled accurately and consistently. In this paper, we demonstrate that this can be achieved using an extension of Subhalo Abundance Matching, dubbed SHAMe. Specifically, we use mock catalogues built from the TNG300 hydrodynamical simulation to show that SHAMe can simultaneously model the multipoles of the redshift-space galaxy correlation function and galaxy-galaxy lensing, without noticeable bias within the statistical sampling uncertainties of a SDSS volume and on scales r = [0.6-30] Mpc/h. Modelling the baryonic processes in galaxy-galaxy lensing with a baryonification scheme allows SHAMe's range of validity to be extended to r = [0.1-30] Mpc/h. Remarkably, our model achieves this level of precision with just five free parameters beyond those describing the baryonification model. At fixed cosmology, we find that galaxy-galaxy lensing provides a general consistency test but little additional information on galaxy modelling parameters beyond that encoded in the redshift-space multipoles. It does, however, improve constraints if only the projected correlation function is available, as in surveys with only photometric redshifts. We expect SHAMe to have a higher fidelity across a wider range of scales than more traditional methods such as Halo Occupation Distribution modelling. Thus it should provide a significantly more powerful and more robust tool for analysing next-generation large-scale surveys., 14 pages, 7 figures. Accepted by MNRAS

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies or quasars from multi-band photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a Machine Learning-based method that employs Convolutional Neural Networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the J-PAS collaboration covering $\sim$ 1 deg$^2$ of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established Machine Learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars and unresolved galaxies. Our results are a proof-of-concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence., 16 pages, 15 figures, published by MNRAS

One of the main predictions of excursion set theory is that the clustering of dark matter haloes only depends on halo mass. However, it has been long established that the clustering of haloes also depends on other properties, including formation time, concentration, and spin; this effect is commonly known as halo assembly bias. We use a suite of gravity-only simulations to study the dependence of halo assembly bias on cosmology; these simulations cover cosmological parameters spanning 10$\sigma$ around state-of-the-art best-fitting values, including standard extensions of the $\Lambda$CDM paradigm such as neutrino mass and dynamical dark energy. We find that the strength of halo assembly bias presents variations smaller than 0.05 dex across all cosmologies studied for concentration and spin selected haloes, letting us conclude that the dependence of halo assembly bias upon cosmology is negligible. We then study the dependence of galaxy assembly bias (i.e. the manifestation of halo assembly bias in galaxy clustering) on cosmology using subhalo abundance matching. We find that galaxy assembly bias also presents very small dependence upon cosmology ($\sim$ 2$\%$-4$\%$ of the total clustering); on the other hand, we find that the dependence of this signal on the galaxy formation parameters of our galaxy model is much stronger. Taken together, these results let us conclude that the dependence of halo and galaxy assembly bias on cosmology is practically negligible., Comment: 11 pages, 8 figures. Submitted to MNRAS, comments welcome

The concentrations of dark matter haloes provide crucial information about their internal structure and how it depends on mass and redshift -- the so-called concentration-mass-redshift relation, denoted $c(M,z)$. We present here an extensive study of the cosmology-dependence of $c(M,z)$ that is based on a suite of 72 gravity-only, full N-body simulations in which the following cosmological parameters were varied: $\sigma_{8}$, $\Omega_{\mathrm{M}}$, $\Omega_{\mathrm{b}}$, $n_{\mathrm{s}}$, $h$, $M_{\nu}$, $w_{0}$ and $w_{\mathrm{a}}$. We characterize the impact of these parameters on concentrations for different halo masses and redshifts. In agreement with previous works, and for all cosmologies studied, we find that there exists a tight correlation between the characteristic densities of dark matter haloes within their scale radii, $r_{-2}$, and the critical density of the Universe at a suitably defined formation time. This finding, when combined with excursion set modelling of halo formation histories, allows us to accurately predict the concentrations of dark matter haloes as a function of mass, redshift, and cosmology. We use our simulations to test the reliability of a number of published models for predicting halo concentration and highlight when they succeed or fail to reproduce the cosmological $c(M,z)$ relation., Comment: 11 pages, 9 figures

We compute the linear order, general relativistic corrections to angular redshift fluctuations (ARF), a new cosmological observable built upon density-weighted two-dimensional (2D) maps of galaxy redshifts. We start with an existing approach for galaxy/source counts developed in the Newtonian gauge, and generalize it to ARF, modifying for this purpose a standard Boltzmann code. Our calculations allow us identifying the velocity terms as the leading corrections on large scales, emphasizing the sensitivity of ARF to peculiar, cosmological velocity fields. Just like for standard 2D clustering, the impact of gravitational lensing on ARF is dominant on small angular scales and for wide redshift shells, while the signatures associated to gravitational potentials are extremely small and hardly detectable. The ARF also present interesting correlation properties to anisotropies of the Cosmic Microwave Background (CMB): they are highly correlated to CMB lensing potential fluctuations, while also exhibiting a significant (S/N$\sim 4$-$5$) {\em anti-}correlation with the Integrated Sachs-Wolfe effect (ISW). This negative ARF$\times$ISW signal is quite complementary to the standard 2D clustering$\times$ISW correlation, since the former appears mostly at higher redshift ($z\sim 2$) than the latter ($z\lesssim 1)$, and the combination of the two observables significantly increases the $\chi^2$ statistics testing the null (no ISW) hypothesis. We conclude that ARF constitute a novel, alternative, and potentially powerful tool to constrain the nature of Dark Energy component that gives rise to the ISW., Comment: 25 pages, 7 figures, comments welcome!

We present Diffstar, a smooth parametric model for the in-situ star formation history (SFH) of galaxies. Diffstar is distinct from conventional SFH models that are used to interpret the spectral energy distribution (SED) of an observed galaxy, because our model is parametrized directly in terms of basic features of galaxy formation physics. The Diffstar model assumes that star formation is fueled by the accretion of gas into the dark matter halo of the galaxy, and at the foundation of Diffstar is a parametric model for halo mass assembly, Diffmah. We include parametrized ingredients for the fraction of accreted gas that is eventually transformed into stars, $\epsilon_{\rm ms},$ and for the timescale over which this transformation occurs, $\tau_{\rm cons};$ some galaxies in Diffstar experience a quenching event at time $t_{\rm q},$ and may subsequently experience rejuvenated star formation. We fit the SFHs of galaxies predicted by the IllustrisTNG (TNG) and UniverseMachine (UM) simulations with the Diffstar parameterization, and show that our model is sufficiently flexible to describe the average stellar mass histories of galaxies in both simulations with an accuracy of $\sim0.1$ dex across most of cosmic time. We use Diffstar to compare TNG to UM in common physical terms, finding that: (i) star formation in UM is less efficient and burstier relative to TNG; (ii) galaxies in UM have longer gas consumption timescales, $\tau_{\rm cons}$, relative to TNG; (iii) rejuvenated star formation is ubiquitous in UM, whereas quenched TNG galaxies rarely experience sustained rejuvenation; and (iv) in both simulations, the distributions of $\epsilon_{\rm ms}$, $\tau_{\rm cons}$, and $t_{\rm q}$ share a common characteristic dependence upon halo mass, and present significant correlations with halo assembly history. [Abridged], Comment: 26 pages, 21 figures

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, 20 pages, 18 figures, submitted to MNRAS

Fluctuations in sky maps of the galaxy redshifts, dubbed as angular redshift fluctuations (ARF), contain precise information about the growth rate of structures and the nature of gravity in the Universe. Specifically, ARF constrain the combination of cosmological parameters $H/H_0\,f\sigma_8(z)$, while being an intrinsically tomographic probe and largely insensitive to many observational systematic errors, all this without requiring the assumption of any redshift-to-distance relation under a given fiducial cosmology. We present the first cosmological constraints derived from ARF by using BOSS LOWZ+CMASS DR12 galaxy samples, obtaining 7\%-accurate constraints on $H/H_0 f\sigma_8(z)$ at more than 20 redshifts over the range $z \in [0.26,0.72]$. Our best-fitting value is $10\%$ larger, but compatible at the $1.4\sigma$ level, than the $\Lambda$CDM expectation set by {\it Planck} observations of the Cosmic Microwave Background (CMB) radiation. Our tomographic measurements, combined with these CMB data, provides one of the strongest constraints on the gravity index $\gamma$, $\gamma=0.44^{+0.09}_{-0.07}$, which lies within $2\sigma$ from the prediction of General Relativity ($\gamma_{\rm GR}\simeq 0.55$)., Comment: 5 pages, 3 figures, matches accepted version in MNRAS letters

We present a new empirical model for the mass assembly of dark matter halos. We approximate the growth of individual halos as a simple power-law function of time, where the power-law index smoothly decreases as the halo transitions from the fast-accretion regime at early times, to the slow-accretion regime at late times. Using large samples of halo merger trees taken from high-resolution cosmological simulations, we demonstrate that our 3-parameter model, Diffmah, can approximate halo growth with a typical accuracy of 0.1 dex for t > 1 Gyr for all halos of present-day mass greater than 10^11Msun, including subhalos and host halos in gravity-only simulations, as well as in the TNG hydrodynamical simulation. We additionally present a new model for the assembly of halo populations, DiffmahPop, which not only reproduces average mass growth across time, but also faithfully captures the diversity with which halos assemble their mass. Our python implementation is based on the autodiff library JAX, and so our model self-consistently captures the mean and variance of halo mass accretion rate across cosmic time. We show that the connection between halo assembly and the large-scale density field, known as halo assembly bias, is accurately captured by Diffmah, and that residual errors in our approximations to halo assembly history exhibit a negligible residual correlation with the density field. Our publicly available source code can be used to generate Monte Carlo realizations of cosmologically representative halo histories; our differentiable implementation facilitates the incorporation of our model into existing analytical halo model frameworks., Main body: 11 pages, 8 figures. Appendices: 11 pages, 11 figures. Update to the version originally accepted by the Open Journal of Astrophysics; revisions limited to textual changes in the nomenclature used to refer to the model

Halo assembly bias is the secondary dependence of the clustering of dark-matter haloes on their assembly histories at fixed halo mass. This established dependence is expected to manifest itself on the clustering of the galaxy population, a potential effect commonly known as galaxy assembly bias. Using the IllustrisTNG300 magnetohydrodynamical simulation, we analyse the dependence of the properties and clustering of galaxies on the shape of the specific mass accretion history of their hosting haloes (sMAH). We first show that several halo and galaxy properties strongly correlate with the slope of the sMAH ($\beta$) at fixed halo mass. Namely, haloes with increasingly steeper $\beta$ increment their halo masses faster at early times, and their hosted galaxies present larger stellar-to-halo mass ratios, lose their gas faster, reach the peak of their star formation histories at higher redshift, and become quenched earlier. We also demonstrate that $\beta$ is more directly connected to these key galaxy formation properties than other broadly employed halo proxies, such as formation time. Finally, we measure the secondary dependence of galaxy clustering on $\beta$ at fixed halo mass as a function of redshift. By tracing back the evolution of individual haloes, we show that the amplitude of the galaxy assembly bias signal for the progenitors of $z=0$ galaxies increases with redshift, reaching a factor of 2 at $z = 1$ for haloes of $M_\mathrm{halo}=10^{11.5}-10^{12}$ $h^{-1}\mathrm{M}_\odot$. The measurement of the evolution of assembly bias along the merger tree provides a new theoretical perspective to the study of secondary bias. Our findings, which show a tight relationship between halo accretion and both the clustering and the observational properties of the galaxy population, have also important implications for the generation of mock catalogues for upcoming cosmological surveys., Comment: 10 pages, 6 figures. Submitted to MNRAS

Photometric redshift estimation algorithms are often based on representative data from observational campaigns. Data-driven methods of this type are subject to a number of potential deficiencies, such as sample bias and incompleteness. Motivated by these considerations, we propose using physically motivated synthetic spectral energy distributions in redshift estimation. In addition, the synthetic data would have to span a domain in colour-redshift space concordant with that of the targeted observational surveys. With a matched distribution and realistically modelled synthetic data in hand, a suitable regression algorithm can be appropriately trained; we use a mixture density network for this purpose. We also perform a zero-point re-calibration to reduce the systematic differences between noise-free synthetic data and the (unavoidably) noisy observational data sets. This new redshift estimation framework, SYTH-Z, demonstrates superior accuracy over a wide range of redshifts compared to baseline models trained on observational data alone. Approaches using realistic synthetic data sets can therefore greatly mitigate the reliance on expensive spectroscopic follow-up for the next generation of photometric surveys., Comment: 14 pages, 8 figures

Handling big data has largely been a major bottleneck in traditional statistical models. Consequently, when accurate point prediction is the primary target, machine learning models are often preferred over their statistical counterparts for bigger problems. But full probabilistic statistical models often outperform other models in quantifying uncertainties associated with model predictions. We develop a data-driven statistical modeling framework that combines the uncertainties from an ensemble of statistical models learned on smaller subsets of data carefully chosen to account for imbalances in the input space. We demonstrate this method on a photometric redshift estimation problem in cosmology, which seeks to infer a distribution of the redshift -- the stretching effect in observing the light of far-away galaxies -- given multivariate color information observed for an object in the sky. Our proposed method performs balanced partitioning, graph-based data subsampling across the partitions, and training of an ensemble of Gaussian process models.

Full list of authors: Bonoli, S.; Marín-Franch, A.; Varela, J.; Vázquez Ramió, H.; Abramo, L. R.; Cenarro, A. J.; Dupke, R. A.; Vílchez, J. M.; Cristóbal-Hornillos, D.; González Delgado, R. M.; Hernández-Monteagudo, C.; López-Sanjuan, C.; Muniesa, D. J.; Civera, T.; Ederoclite, A.; Hernán-Caballero, A.; Marra, V.; Baqui, P. O.; Cortesi, A.; Cypriano, E. S.; Daflon, S.; de Amorim, A. L.; Díaz-García, L. A.; Diego, J. M.; Martínez-Solaeche, G.; Pérez, E.; Placco, V. M.; Prada, F.; Queiroz, C.; Alcaniz, J.; Alvarez-Candal, A.; Cepa, J.; Maroto, A. L.; Roig, F.; Siffert, B. B.; Taylor, K.; Benitez, N.; Moles, M.; Sodré, L.; Carneiro, S.; Mendes de Oliveira, C.; Abdalla, E.; Angulo, R. E.; Aparicio Resco, M.; Balaguera-Antolínez, A.; Ballesteros, F. J.; Brito-Silva, D.; Broadhurst, T.; Carrasco, E. R.; Castro, T.; Cid Fernandes, R.; Coelho, P.; de Melo, R. B.; Doubrawa, L.; Fernandez-Soto, A.; Ferrari, F.; Finoguenov, A.; García-Benito, R.; Iglesias-Páramo, J.; Jiménez-Teja, Y.; Kitaura, F. S.; Laur, J.; Lopes, P. A. A.; Lucatelli, G.; Martínez, V. J.; Maturi, M.; Overzier, R. A.; Pigozzo, C.; Quartin, M.; Rodríguez-Martín, J. E.; Salzano, V.; Tamm, A.; Tempel, E.; Umetsu, K.; Valdivielso, L. ; von Marttens, R.; Zitrin, A.; Díaz-Martín, M. C.; López-Alegre, G.; López-Sainz, A.; Yanes-Díaz, A.; Rueda-Teruel, F.; Rueda-Teruel, S.; Abril Ibañez, J.; L Antón Bravo, J.; Bello Ferrer, R.; Bielsa, S.; Casino, J. M.; Castillo, J.; Chueca, S.; Cuesta, L.; Garzarán Calderaro, J.; Iglesias-Marzoa, R.; Íniguez, C.; Lamadrid Gutierrez, J. L.; Lopez-Martinez, F.; Lozano-Pérez, D.; Maícas Sacristán, N.; Molina-Ibáñez, E. L.; Moreno-Signes, A.; Rodríguez Llano, S.; Royo Navarro, M.; Tilve Rua, V.; Andrade, U.; Alfaro, E. J.; Akras, S.; Arnalte-Mur, P.; Ascaso, B.; Barbosa, C. E.; Beltrán Jiménez, J.; Benetti, M.; Bengaly, C. A. P.; Bernui, A.; Blanco-Pillado, J. J.; Borges Fernandes, M.; Bregman, J. N.; Bruzual, G.; Calderone, G.; Carvano, J. M.; Casarini, L.; Chaves-Montero, J.; Chies-Santos, A. L.; Coutinho de Carvalho, G.; Dimauro, P.; Duarte Puertas, S.; Figueruelo, D.; González-Serrano, J. I.; Guerrero, M. A.; Gurung-López, S.; Herranz, D.; Huertas-Company, M.; Irwin, J. A.; Izquierdo-Villalba, D.; Kanaan, A.; Kehrig, C.; Kirkpatrick, C. C.; Lim, J.; Lopes, A. R.; Lopes de Oliveira, R.; Marcos-Caballero, A.; Martínez-Delgado, D.; Martínez-González, E.; Martínez-Somonte, G.; Oliveira, N.; Orsi, A. A.; Penna-Lima, M.; Reis, R. R. R.; Spinoso, D.; Tsujikawa, S.; Vielva, P.; Vitorelli, A. Z.; Xia, J. Q.; Yuan, H. B.; Arroyo-Polonio, A.; Dantas, M. L. L.; Galarza, C. A.; Gonçalves, D. R.; Gonçalves, R. S.; Gonzalez, J. E.; Gonzalez, A. H.; Greisel, N.; Jiménez-Esteban, F.; Landim, R. G.; Lazzaro, D.; Magris, G.; Monteiro-Oliveira, R.; Pereira, C. B.; Rebouças, M. J.; Rodriguez-Espinosa, J. M.; Santos da Costa, S.; Telles, E., The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) will scan thousands of square degrees of the northern sky with a unique set of 56 filters using the dedicated 2:55m Javalambre Survey Telescope (JST) at the Javalambre Astrophysical Observatory. Prior to the installation of the main camera (4:2 deg2 field-of-view with 1.2 Gpixels), the JST was equipped with the JPAS-Pathfinder, a one CCD camera with a 0:3 deg2 field-of-view and plate scale of 0.23 arcsec pixel?1. To demonstrate the scientific potential of J-PAS, the JPAS-Pathfinder camera was used to perform miniJPAS, a _1 deg2 survey of the AEGIS field (along the Extended Groth Strip). The field was observed with the 56 J-PAS filters, which include 54 narrow band (FWHM _ 145 ) and two broader filters extending to the UV and the near-infrared, complemented by the u; g; r; i SDSS broad band filters. In this miniJPAS survey overview paper, we present the miniJPAS data set (images and catalogs), as we highlight key aspects and applications of these unique spectro-photometric data and describe how to access the public data products. The data parameters reach depths of magAB ' 22?23:5 in the 54 narrow band filters and up to 24 in the broader filters (5_ in a 300 aperture). The miniJPAS primary catalog contains more than 64 000 sources detected in the r band and with matched photometry in all other bands. This catalog is 99% complete at r = 23:6 (r = 22:7) mag for point-like (extended) sources. We show that our photometric redshifts have an accuracy better than 1% for all sources up to r = 22:5, and a precision of _0:3% for a subset consisting of about half of the sample. On this basis, we outline several scientific applications of our data, including the study of spatially-resolved stellar populations of nearby galaxies, the analysis of the large scale structure up to z _ 0:9, and the detection of large numbers of clusters and groups. Sub-percent redshift precision can also be reached for quasars, allowing for the study of the large-scale structure to be pushed to z 2. The miniJPAS survey demonstrates the capability of the J-PAS filter system to accurately characterize a broad variety of sources and paves the way for the upcoming arrival of J-PAS, which will multiply this data by three orders of magnitude. © 2021 EDP Sciences. All rights reserved., Funding for OAJ, UPAD, and CEFCA has been provided by the Governments of Spain and Aragon through the Fondo de Inversiones de Teruel; the Aragon Government through the Research Groups E96, E103, and E16_17R; the Spanish Ministry of Science, Innovation and Universities (MCIU/AEI/FEDER, UE) with grant PGC2018-097585-B-C21; the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER, UE) under AYA2015-66211-C2-1-P, AYA2015-66211-C2-2, AYA2012-30789, and ICTS2009-14; and European FEDER funding (FCDD10-4E-867, FCDD13-4E-2685). This work has made used of CEFCA's Scientific High Performance Computing system which has been funded by the Governments of Spain and Aragon through the Fondo de Inversiones de Teruel, and the Spanish Ministry of Economy and Competitiveness (MINECO-FEDER, grant AYA2012-30789). Funding for the J-PAS project has been provided also by the Brazilian agencies FINEP, FAPESP, FAPERJ and by the National Observatory of Brazil. Additional funding was also provided by the Tartu Observatory and by the Chinese Consortium from the Academy of Sciences SB acknowledges partial support from the project PGC2018-097585-B-C22. R.A.D. acknowledges support from the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico -CNPq through BP grant 308105/2018-4, and the Financiadora de Estudos e Projetos -FINEP grants REF. 1217/13 -01.13.0279.00 and REF 0859/10 -01.10.0663.00 and also FAPERJ PRONEX grant E-26/110.566/2010 for hardware funding support for the J-PAS project through the National Observatory of Brazil and Centro Brasileiro de Pesquisas Fisicas. LRA acknowledges financial support from CNPq (306696/2018-5) and FAPESP (2015/17199-0). VMthanks CNPq (Brazil) and FAPES (Brazil) for partial financial support and the H2020 project No 888258. L.A.D.G. and K.U. acknowledge support from the Ministry of Science and Technology of Taiwan (grant MOST 106-2628-M-001-003-MY3) and from the Academia Sinica (grant AS-IA-107-M01). J.M.D. and D.H acknowledge the support of project PGC2018-101814-B-100. MQ thanks CNPq (Brazil) and FAPERJ (Brazil) for financial support. PC acknowledges financial support from FAPESP (2018/05392-8) and CNPq (310041/2018-0). AAC acknowledges support from FAPERJ (E26/203.186/2016), CNPq (304971/2016-2 and 401669/2016-5), and the Universidad de Alicante (contract UATALENTO1802). C.Q. acknowledges support from FAPESP (2015/11442-0 and 2019/067661). V.M.P. is supported by NOIRLab, which is managed by AURA under a cooperative agreement with the NSF. P.B acknowledges support from CAPES -Finance Code 001. IAA researchers acknowledge financial support from the State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa" award to the Instituto de Astrofisica de Andalucia (SEV-2017-0709). Authors acknowledge support from the Generalitat Valenciana project of excellence Prometeo/2020/085. RGD, GMS, JRM, RGB, EP acknowledge financial support from the project AyA2016-77846-P. TC is supported by the INFN INDARK PD51 and PRIN-MIUR 2015W7KAWC. MAR and ALM acknowledge support from the MINECO project FIS2016-78859P(AEI/FEDER, UE). ET, AT and JL acknowledge the support by ETAg grants IUT40-2 and by EU through the ERDF CoE grant TK133 and MOBTP86. CK, JMV, JIP acknowledge financial support from project AYA2016-79724C4-4P. PAAL thanks the support of CNPq (309398/2018-5). LC thanks CNPq for partial support. Y.J-T acknowledges financial support from the FAPERJ (E26/202.835/2016), and from the Horizon 2020 Marie Sklodowska-Curie grant agreement No 898633. DMD acknowledges financial support from the SFB 881 of the DFG and from the MINECO grant AYA2016-81065-C2-2. FP acknowledges support of the project PGC2018-101931-B-I00. JC acknowledges support of the project E AYA2017-88007-C3-1-P, and co-financed by the FEDER. JIGs acknowledges support of projects of reference AYA2017-88007-C3-3-P, and PGC2018-099705-B-I00 and co-financed by the FEDER. EMG and PV would like to acknowledge financial support from the project ESP2017-83921C2-1-R. GMS acknowleges financial support from a predoctoral contract, ref. PRE2018-085523 (MCIU/AEI/FSE, UE). S.C. is partially supported by CNPq. R.G.L. acknowledges CAPES (process 88881.162206/2017-01) and Alexander von Humboldt Foundation for the financial support. JSA acknowledges support from FAPERJ (E26/203.024/2017), CNP (310790/2014-0 and 400471/2014-0) and FINEP (1217/13 -01.13.0279.00 and Ref. 0859/10 -01.10.0663.00). RvM acknowledges support from CNPq. AFS, PAM, VJM and FJB acknowledge support from project AYA2016-81065-C2-2. PAM acknowledges support from the "Subprograma Atraccio de Talent -Contractes Postdoctorals de la Universitat de Valencia". ESC acknowledges support from CNPq (308539/20184) and FAPESP (2019/19687-2). CMdO acknowledges support from CNPq (grant 312333/2014-5) and FAPESP (grant 2009/54202-8). LSJ acknowledges support from CNPq (grant 304819/2017-4) and FAPESP (grant 2012/008004). JMC acknowledges support from CNPq (grant 310727/2016-2). C.H.-M. and N. Greisel also acknowledge the support of the European Union via the Career Integration Grant CIG-PCIG9-GA-2011-294183. JJBP and AMC would like to acknowledge the support from the grant PGC2018-094626-B-C21 and the Basque Government grant IT-979-16. AMC acknowledges the postdoctoral contract from the University of the Basque Country UPV/EHU "Especializacioon de personal investigador doctor" program. MLLD acknowledges CAPES -Finance Code 001; and CNPq (142294/2018-7). GB acknowledges financial support from the UNAM through grant DGAPA/PAPIIT IG100319, from CONACyT through grant CB2015-252364, and from FAPESP projects 2017/02375-2 and 2018/05392-8. M.J. Reboucas acknowledges the support of FAPERJ under a CNE E-26/202.864/2017 grant, and CNPq. Support by CNPq (305409/2016-6) and FAPERJ (E-26/202.841/2017) is acknowledged by DL. AB acknowledges a CNPq fellowship. C.A.G.acknowledges support from CAPES. EA acknowledges support from FAPESP (2011/18729-1). AC acknowledges support from PNPD/CAPES. ABA and FSK acknowledge the Severo Ochoa program SEV-2015-0548. FSK also thanks the AYA2017-89891-P and the RYC2015-18693 grants. DF acknowledges support from the Atraccion del Talento Cientifico en Salamanca programme and the project PGC2018-096038B-I00.

We propose the use of angular fluctuations in the galaxy redshift field as a new way to extract cosmological information in the Universe. This new probe $\delta z (\hat{n})$ consists on the statistics of sky maps built by projecting redshifts under a Gaussian window of width $\sigma_z$ centred upon a redshift $z_{\rm obs}$, and weighted by the galaxy density field. We compute the angular power spectrum of the $\delta z (\hat{n})$ field in both numerical simulations and in linear perturbation theory. From these we find that the $\delta z (\hat{n})$ field: {\it (i)} is sensitive to the underlying density and peculiar velocity fields; {\it (ii)} is highly correlated, at the $\gtrsim 60\,\%$ level, to the line-of-sight projected peculiar velocity field; {\it (iii)} for narrow windows $(\sigma_z < 0.03$), it is almost completely uncorrelated to the projected galaxy angular density field under the same redshift window; and {\it (iv)} it is largely unaffected by multiplicative and additive systematic errors on the observed number of galaxies that are redshift-independent over $\sim\sigma_z$. We conclude that $\delta z (\hat{n})$ is a simple and robust tomographic measure of the cosmic density and velocity fields, complementary to angular clustering, that will contribute to more complete exploitations of current and upcoming galaxy redshift surveys., Comment: 6 pages, 3 figures, matches accepted version in MNRAS letters, companion paper of Chaves-Montero et al., arXiv:1911.10690

In the upcoming era of high-precision galaxy surveys, it becomes necessary to understand the impact of redshift uncertainties on cosmological observables. In this paper we explore the effect of sub-percent photometric redshift errors (photo-$z$ errors) on galaxy clustering and baryonic acoustic oscillations (BAO). Using analytic expressions and results from $1\,000$ $N$-body simulations, we show how photo-$z$ errors modify the amplitude of moments of the 2D power spectrum, their variances, the amplitude of BAO, and the cosmological information in them. We find that: a) photo-$z$ errors suppress the clustering on small scales, increasing the relative importance of shot noise, and thus reducing the interval of scales available for BAO analyses; b) photo-$z$ errors decrease the smearing of BAO due to non-linear redshift-space distortions (RSD) by giving less weight to line-of-sight modes; and c) photo-$z$ errors (and small-scale RSD) induce a scale dependence on the information encoded in the BAO scale, and that reduces the constraining power on the Hubble parameter. Using these findings, we propose a template that extracts unbiased cosmological information from samples with photo-$z$ errors with respect to cases without them. Finally, we provide analytic expressions to forecast the precision in measuring the BAO scale, showing that spectro-photometric surveys will measure the expansion history of the Universe with a precision competitive to that of spectroscopic surveys., Comment: 19 pages, 15 figures, resubmission. Included full analysis of power spectrum moments l=0, 2, and 4

A complete census of baryons in the late universe is a long-standing challenge due to the intermediate temperate and rarefied character of the majority of cosmic gas. To gain insight into this problem, we extract measurements of the kinematic Sunyaev-Zel'dovich (kSZ) effect from the cross-correlation of angular redshift fluctuations maps, which contain precise information about the cosmic density and velocity fields, and CMB maps high-pass filtered using aperture photometry; we refer to this technique as ARF-kSZ tomography. Remarkably, we detect significant cross-correlation for a wide range of redshifts and filter apertures using 6dF galaxies, BOSS galaxies, and SDSS quasars as tracers, yielding a 11 sigma detection of the kSZ effect. We then leverage these measurements to set constraints on the location, density, and abundance of gas inducing the kSZ effect, finding that this gas resides outside dark matter haloes, presents densities ranging from 10 to 250 times the cosmic average, and comprises half of cosmic baryons. Taken together, these findings indicate that ARF-kSZ tomography provides a nearly complete census of intergalactic gas from z=0 to 5., Comment: 17 pages, 14 figures, accepted for publication in MNRAS

Context. Knowing the exact shape of the ultraviolet (UV) luminosity function (LF) of high-redshift galaxies is important to understand the star formation history of the early Universe. However, the uncertainties, especially at the faint and bright ends of the LFs, remain significant. Aims. In this paper, we study the UV LF of redshift z = 2:5 4.5 galaxies in 2.38 deg of ALHAMBRA data with I ≤ 24. Thanks to the large area covered by ALHAMBRA, we particularly constrain the bright end of the LF. We also calculate the cosmic variance and the corresponding bias values for our sample and derive their host dark matter halo masses. Methods.We have used a novel methodology based on redshift and magnitude probability distribution functions (PDFs). This methodology robustly takes into account the uncertainties due to redshift and magnitude errors, shot noise, and cosmic variance, and models the LF in two dimensions (z; M). Results. We find an excess of bright ∼M∗ galaxies as compared to the studies based on broad-band photometric data. However, our results agree well with the LF of the magnitude-selected spectroscopic VVDS data. We measure high bias values, b ∼ 8 10, that are compatible with the previous measurements considering the redshifts and magnitudes of our galaxies and further reinforce the real high-redshift nature of our bright galaxies. Conclusions. We call into question the shape of the LF at its bright end; is it a double power-law as suggested by the recent broadband photometric studies or rather a brighter Schechter function, as suggested by our multi-filter analysis and the spectroscopic VVDS data.© ESO 2018., K. Viironen acknowledges the >Juan de la Cierva incorporacion> fellowship, IJCI-2014-21960, of the Spanish government. This work has mainly been funded by the FITE (Fondos de Inversiones de Teruel) and the projects AYA2015-66211-C2-1 and AYA2012-30789. We also acknowledge support from the Aragon Government Research Group E103 and support from the Spanish Ministry for Economy and Competitiveness and FEDER funds through grants AYA2010-15081, AYA2010-15169, AYA2010-22111-C03-01, AYA2010-22111-C03-02, AYA2011-29517-C03-01, AYA2012-39620, AYA2013-40611-P, AYA2013-42227-P, AYA2013-43188-P, AYA2013-48623-C2-1, AYA2013-48623-C2-2, ESP2013-48274, AYA2014-58861-C3-1, AYA2016-76682-C3-1-P, Generalitat Valenciana projects Prometeo 2009/064 and PROMETEOII/2014/060, Junta de Andalucia grants TIC114, JA2828, P10-FQM-6444, and Generalitat de Catalunya project SGR-1398. BA has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 656354. MP acknowledges financial supports from the Ethiopian Space Science and Technology Institute (ESSTI) under the Ethiopian Ministry of Science Science and Technology (MoST)

Subhalo abundance matching (SHAM) is a widely-used method to connect galaxies with dark matter structures in numerical simulations. SHAM predictions agree remarkably well with observations, yet they still lack strong theoretical support. We examine the performance, implementation, and assumptions of SHAM using the EAGLE project simulations. We find that $V_{\rm relax}$, the highest value of the circular velocity attained by a subhalo while it satisfies a relaxation criterion, is the subhalo property that correlates most strongly with galaxy stellar mass ($M_{\rm star}$). Using this parameter in SHAM, we retrieve the real-space clustering of EAGLE to within our statistical uncertainties on scales greater than $2$ Mpc for galaxies with $8.77, Comment: 20 pages, 14 figures

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-alpha selection. However, the availability of multifilter data allows now replacing the dropout selections 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 in the study of the brightest, less 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. With the wide field ALHAMBRA data we especially contribute in the study of the brightest ends of these counts, sampling well the surface densities down to m_UV(AB)=21-22. Conclusions. We show that using the zPDFs it is easy to select a clean sample of high redshift galaxies. We also show that statistical analysis of the properties of galaxies is better done using a probabilistic approach, which takes into account both the incompleteness and contamination in a natural way., Comment: 16 pages, 11 figures; Accepted for A&A; language corrected

We present ELDAR, a new method that exploits the potential of medium- and narrow-band filter surveys to securely identify active galactic nuclei (AGN) and determine their redshifts. Our methodology improves on traditional approaches by looking for AGN emission lines expected to be identified against the continuum, thanks to the width of the filters. To assess its performance, we apply ELDAR to the data of the ALHAMBRA (Advance Large Homogeneous Area Medium Band Redshift Astronomical) survey, which covered an effective area of 2.38 deg2 with 20 contiguous medium-band optical filters down to F814W ≃ 24.5. Using two different configurations of ELDAR in which we require the detection of at least two and three emission lines, respectively, we extract two catalogues of type-I AGN. The first is composed of 585 sources (79 per cent of them spectroscopically unknown) down to F814W = 22.5 at zphot > 1, which corresponds to a surface density of 209 deg−2. In the second, the 494 selected sources (83 per cent of them spectroscopically unknown) reach F814W = 23 at zphot > 1.5, for a corresponding number density of 176 deg−2. Then, using samples of spectroscopically known AGN in the ALHAMBRA fields, for the two catalogues we estimate a completeness of 73 per cent and 67 per cent, and a redshift precision of 1.01 per cent and 0.86 per cent (with outliers fractions of 8.1 per cent and 5.8 per cent). At z > 2, where our selection performs best, we reach 85 per cent and 77 per cent completeness and we find no contamination from galaxies., We acknowledge support from FITE (Fondos de Inversiones de Teruel), Grupos de Aragon E96 and E103, and the Spanish Ministry of Economy and Competitiveness (MINECO) through projects AYA2016-76682C3-1-P, AYA2015-66211-C2-1, AYA2015-66211-C2-2, AYA201342227-P and AYA2012-30789. This work was supported by FCT (ref. UID/FIS/04434/2013) through national funds and by FEDER through COMPETE2020 (ref. POCI-01-0145-FEDER-007672). JC acknowledges support from the Fundacion Bancaria Ibercaja for developing this research. BA has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 656354. MP acknowledges financial supports from the Ethiopian Space Science and Technology Institute (ESSTI) under the Ethiopian Ministry of Science and Technology (MoST). IM acknowledges support from an FCT postdoctoral grant (ref. SFRH/BPD/95578/2013).