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We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotationally, translationally, and permutation invariant and have no scale cutoff. By training on galaxy catalogs that only contain the 3D positions and radial velocities of approximately $1,000$ galaxies in tiny volumes of $(25~h^{-1}{\rm Mpc})^3$, our models achieve a precision of approximately $12$% when inferring the value of $\Omega_{\rm m}$. To test the robustness of our models, we evaluated their performance on galaxy catalogs from thousands of hydrodynamic simulations, each with different efficiencies of supernova and AGN feedback, run with five different codes and subgrid models, including IllustrisTNG, SIMBA, Astrid, Magneticum, and SWIFT-EAGLE. Our results demonstrate that our models are robust to astrophysics, subgrid physics, and subhalo/galaxy finder changes. Furthermore, we test our models on 1,024 simulations that cover a vast region in parameter space - variations in 5 cosmological and 23 astrophysical parameters - finding that the model extrapolates really well. Including both positions and velocities are key to building robust models, and our results indicate that our networks have likely learned an underlying physical relation that does not depend on galaxy formation and is valid on scales larger than, at least, $~\sim10~h^{-1}{\rm kpc}$., Comment: 33 pages, 12 figures, for a video summarizing the results, see https://youtu.be/b59ep7cyPOs

We present a list of quasar candidates including photometric redshift estimates from the miniJPAS Data Release constructed using SQUEzE. This work is based on machine-learning classification of photometric data of quasar candidates using SQUEzE. It has the advantage that its classification procedure can be explained to some extent, making it less of a `black box' when compared with other classifiers. Another key advantage is that using user-defined metrics means the user has more control over the classification. While SQUEzE was designed for spectroscopic data, here we adapt it for multi-band photometric data, i.e. we treat multiple narrow-band filters as very low-resolution spectra. We train our models using specialized mocks from Queiroz et al. (2022). We estimate our redshift precision using the normalized median absolute deviation, $\sigma_{\rm NMAD}$ applied to our test sample. Our test sample returns an $f_1$ score (effectively the purity and completeness) of 0.49 for quasars down to magnitude $r=24.3$ with $z\geq2.1$ and 0.24 for quasars with $z<2.1$. For high-z quasars, this goes up to 0.9 for $r<21.0$. We present two catalogues of quasar candidates including redshift estimates: 301 from point-like sources and 1049 when also including extended sources. We discuss the impact of including extended sources in our predictions (they are not included in the mocks), as well as the impact of changing the noise model of the mocks. We also give an explanation of SQUEzE reasoning. Our estimates for the redshift precision using the test sample indicate a $\sigma_{NMAD}=0.92\%$ for the entire sample, reduced to 0.81\% for $r<22.5$ and 0.74\% for $r<21.3$. Spectroscopic follow-up of the candidates is required in order to confirm the validity of our findings., Comment: Accepted in A&A 24 pages, 24 figures, 7 tables

This paper is part of large effort within the J-PAS collaboration that aims to classify point-like sources in miniJPAS, which were observed in 60 optical bands over $\sim$ 1 deg$^2$ in the AEGIS field. We developed two algorithms based on artificial neural networks (ANN) to classify objects into four categories: stars, galaxies, quasars at low redshift ($z < 2.1)$, and quasars at high redshift ($z \geq 2.1$). As inputs, we used miniJPAS fluxes for one of the classifiers (ANN$_1$) and colours for the other (ANN$_2$). The ANNs were trained and tested using mock data in the first place. We studied the effect of augmenting the training set by creating hybrid objects, which combines fluxes from stars, galaxies, and quasars. Nevertheless, the augmentation processing did not improve the score of the ANN. We also evaluated the performance of the classifiers in a small subset of the SDSS DR12Q superset observed by miniJPAS. In the mock test set, the f1-score for quasars at high redshift with the ANN$_1$ (ANN$_2$) are $0.99$ ($0.99$), $0.93$ ($0.92$), and $0.63$ ($0.57$) for $17 < r \leq 20$, $20 < r \leq 22.5$, and $22.5 < r \leq 23.6$, respectively, where $r$ is the J-PAS rSDSS band. In the case of low-redshift quasars, galaxies, and stars, we reached $0.97$ ($0.97$), $0.82$ ($0.79$), and $0.61$ ($0.58$); $0.94$ ($0.94$), $0.90$ ($0.89$), and $0.81$ ($0.80$); and $1.0$ ($1.0$), $0.96$ ($0.94$), and $0.70$ ($0.52$) in the same r bins. In the SDSS DR12Q superset miniJPAS sample, the weighted f1-score reaches 0.87 (0.88) for objects that are mostly within $20 < r \leq 22.5$. Finally, we estimate the number of point-like sources that are quasars, galaxies, and stars in miniJPAS.

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., Comment: 16 pages, 15 figures, published by MNRAS

We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotational, translational, and permutation invariant and do not impose any cut on scale. From galaxy catalogs that only contain $3$D positions and radial velocities of $\sim 1, 000$ galaxies in tiny $(25~h^{-1}{\rm Mpc})^3$ volumes our models can infer the value of $\Omega_{\rm m}$ with approximately $12$ % precision. More importantly, by testing the models on galaxy catalogs from thousands of hydrodynamic simulations, each having a different efficiency of supernova and AGN feedback, run with five different codes and subgrid models - IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE -, we find that our models are robust to changes in astrophysics, subgrid physics, and subhalo/galaxy finder. Furthermore, we test our models on $1,024$ simulations that cover a vast region in parameter space - variations in $5$ cosmological and $23$ astrophysical parameters - finding that the model extrapolates really well. Our results indicate that the key to building a robust model is the use of both galaxy positions and velocities, suggesting that the network have likely learned an underlying physical relation that does not depend on galaxy formation and is valid on scales larger than $\sim10~h^{-1}{\rm kpc}$., Comment: 34 pages, 12 figures. For a video summarizing the results, see https://youtu.be/b59ep7cyPOs

The relationship between galaxies and haloes is central to the description of galaxy formation, and a fundamental step towards extracting precise cosmological information from galaxy maps. However, this connection involves several complex processes that are interconnected. Machine Learning methods are flexible tools that can learn complex correlations between a large number of features, but are traditionally designed as deterministic estimators. In this work, we use the IllustrisTNG300-1 simulation and apply neural networks in a binning classification scheme to predict probability distributions of central galaxy properties, namely stellar mass, colour, specific star formation rate, and radius, using as input features the halo mass, concentration, spin, age, and the overdensity on a scale of 3 $h^{-1}$ Mpc. The model captures the intrinsic scatter in the relation between halo and galaxy properties, and can thus be used to quantify the uncertainties related to the stochasticity of the galaxy properties with respect to the halo properties. In particular, with our proposed method, one can define and accurately reproduce the properties of the different galaxy populations in great detail. We demonstrate the power of this tool by directly comparing traditional single-point estimators and the predicted joint probability distributions, and also by computing the power spectrum of a large number of tracers defined on the basis of the predicted colour-stellar mass diagram. We show that the neural networks reproduce clustering statistics of the individual galaxy populations with excellent precision and accuracy., Comment: 12 pages, 7 figures

It has been recently shown that a powerful way to constrain cosmological parameters from galaxy redshift surveys is to train graph neural networks to perform field-level likelihood-free inference without imposing cuts on scale. In particular, de Santi et al. (2023) developed models that could accurately infer the value of $\Omega_{\rm m}$ from catalogs that only contain the positions and radial velocities of galaxies that are robust to uncertainties in astrophysics and subgrid models. However, observations are affected by many effects, including 1) masking, 2) uncertainties in peculiar velocities and radial distances, and 3) different galaxy selections. Moreover, observations only allow us to measure redshift, intertwining galaxies' radial positions and velocities. In this paper we train and test our models on galaxy catalogs, created from thousands of state-of-the-art hydrodynamic simulations run with different codes from the CAMELS project, that incorporate these observational effects. We find that, although the presence of these effects degrades the precision and accuracy of the models, and increases the fraction of catalogs where the model breaks down, the fraction of galaxy catalogs where the model performs well is over 90 %, demonstrating the potential of these models to constrain cosmological parameters even when applied to real data., Comment: 29 pages, 11 figures. For the reference in the abstract (de Santi et al. 2023) see arXiv:2302.14101

It has been recently shown that a powerful way to constrain cosmological parameters from galaxy redshift surveys is to train graph neural networks to perform field-level likelihood-free inference without imposing cuts on scale. In particular, de Santi et al. (2023) developed models that could accurately infer the value of $\Omega_{\rm m}$ from catalogs that only contain the positions and radial velocities of galaxies that are robust to uncertainties in astrophysics and subgrid models. However, observations are affected by many effects, including 1) masking, 2) uncertainties in peculiar velocities and radial distances, and 3) different galaxy selections. Moreover, observations only allow us to measure redshift, intertwining galaxies' radial positions and velocities. In this paper we train and test our models on galaxy catalogs, created from thousands of state-of-the-art hydrodynamic simulations run with different codes from the CAMELS project, that incorporate these observational effects. We find that, although the presence of these effects degrades the precision and accuracy of the models, and increases the fraction of catalogs where the model breaks down, the fraction of galaxy catalogs where the model performs well is over 90 %, demonstrating the potential of these models to constrain cosmological parameters even when applied to real data., Comment: 39 pages, 25 figures. For the reference in the abstract (de Santi et al. 2023) see arXiv:2302.14101

Precise measurements of black hole (BH) masses are essential to understanding the coevolution of these sources and their host galaxies. In this work, we develop a novel approach to compute BH virial masses using measurements of continuum luminosities and emission line widths from partially-overlapping, narrow-band observations of quasars; we refer to this technique as single-epoch photometry. This novel method relies on forward-modelling quasar observations to estimate the previous properties, which enables accurate measurements of emission line widths even for lines poorly resolved by narrow-band data. We assess the performance of this technique using quasars from the Sloan Digital Sky Survey (SDSS) observed by the miniJPAS survey, a proof-of-concept project of the J-PAS collaboration covering $\simeq1\,\mathrm{deg}^2$ of the northern sky using the 56 J-PAS narrow-band filters. We find remarkable agreement between BH masses from single-epoch SDSS spectra and single-epoch miniJPAS photometry, with no systematic difference between these and a scatter ranging from 0.4 to 0.07 dex for masses from $\log(M_\mathrm{BH}/\mathrm{M}_\odot)\simeq8$ to 9.75, respectively. Reverberation mapping studies show that single-epoch masses approximately present 0.4 dex precision, letting us conclude that our novel technique delivers BH masses with only mildly worse precision than single-epoch spectroscopy. The J-PAS survey will soon start observing thousands of square degrees without any source preselection other than the photometric depth in the detection band, and thus single-epoch photometry has the potential to provide details on the physical properties of quasar populations not satisfying the preselection criteria of previous spectroscopic surveys.

Redshift evolution and peculiar velocities break the isotropy of cosmological surveys with respect to the directions parallel and transverse to the line of sight, limiting the accuracy of the Fourier representation to small areas and redshift ranges. In contrast to the Fourier space power spectrum, the full information about the two-point function of tracers of large-scale structure is encapsulated in the redshift-dependent angular power spectrum $C_\ell^{ij} (z_i,z_j)$ for the tracer species $i$ and $j$ at the redshift slices $z_i$ and $z_j$, expressed in harmonic space. In this paper we derive semi-analytical expressions for the multi-tracer Fisher matrix of angular power spectra, in real and in redshift space, which are exact in the linear regime of structure formation. Our expressions can be used to forecast the constraining power of galaxy surveys with many tracers and a large number of redshift slices, for which the derivation of the Fisher matrix from numerically evaluated covariance matrices may not be feasible or practical., Comment: 37 pages, 5 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 $17.5\leq r<24$, we augment our sample of available spectra by shifting the original $r$-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modeling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys., Comment: 20 pages, 18 figures, submitted to MNRAS

Elucidating the connection between the properties of galaxies and the properties of their hosting haloes is a key element in theories of galaxy formation. When the spatial distribution of objects is also taken under consideration, investigating the halo-galaxy connection becomes very relevant for cosmological measurements. In this paper, we use machine learning (ML) techniques to analyze these intricate relations in the IllustrisTNG300 magnetohydrodynamical simulation. We employ four different algorithms: extremely randomized trees (ERT), K-nearest neighbors (kNN), light gradient boosting machine (LGBM), and neural networks (NN), along with a stacked model where we combine results from all four approaches. Overall, the different ML algorithms produce consistent results in terms of predicting galaxy properties from a set of input halo properties that include halo mass, concentration, spin, and halo overdensity. For stellar mass, the (predicted v. true) Pearson correlation coefficient is 0.98, dropping down to 0.7-0.8 for specific star formation rate (sSFR), colour, and size. In addition, we test an existing data augmentation technique, designed to alleviate the problem of unbalanced datasets, and show that it improves slightly the shape of the predicted distributions. We also demonstrate that our predictions are good enough to reproduce the power spectra of multiple galaxy populations, defined in terms of stellar mass, sSFR, colour, and size with high accuracy. Our results align with previous reports suggesting that certain galaxy properties cannot be reproduced using halo features alone.

Precise measurements of black hole (BH) masses are essential to understanding the coevolution of these sources and their host galaxies. In this work, we develop a novel approach to compute BH virial masses using measurements of continuum luminosities and emission line widths from partially-overlapping, narrow-band observations of quasars; we refer to this technique as single-epoch photometry. This novel method relies on forward-modelling quasar observations to estimate the previous properties, which enables accurate measurements of emission line widths even for lines poorly resolved by narrow-band data. We assess the performance of this technique using quasars from the Sloan Digital Sky Survey (SDSS) observed by the miniJPAS survey, a proof-of-concept project of the J-PAS collaboration covering $\simeq1\,\mathrm{deg}^2$ of the northern sky using the 56 J-PAS narrow-band filters. We find remarkable agreement between BH masses from single-epoch SDSS spectra and single-epoch miniJPAS photometry, with no systematic difference between these and a scatter ranging from 0.4 to 0.07 dex for masses from $\log(M_\mathrm{BH}/\mathrm{M}_\odot)\simeq8$ to 9.75, respectively. Reverberation mapping studies show that single-epoch masses approximately present 0.4 dex precision, letting us conclude that our novel technique delivers BH masses with only mildly worse precision than single-epoch spectroscopy. The J-PAS survey will soon start observing thousands of square degrees without any source preselection other than the photometric depth in the detection band, and thus single-epoch photometry has the potential to provide details on the physical properties of quasar populations not satisfying the preselection criteria of previous spectroscopic surveys.

Precise measurements of black hole (BH) masses are essential to understanding the coevolution of these sources and their host galaxies. In this work, we develop a novel approach to compute BH virial masses using measurements of continuum luminosities and emission line widths from partially-overlapping, narrow-band observations of quasars; we refer to this technique as single-epoch photometry. This novel method relies on forward-modelling quasar observations to estimate the previous properties, which enables accurate measurements of emission line widths even for lines poorly resolved by narrow-band data. We assess the performance of this technique using quasars from the Sloan Digital Sky Survey (SDSS) observed by the miniJPAS survey, a proof-of-concept project of the J-PAS collaboration covering $\simeq1\,\mathrm{deg}^2$ of the northern sky using the 56 J-PAS narrow-band filters. We find remarkable agreement between BH masses from single-epoch SDSS spectra and single-epoch miniJPAS photometry, with no systematic difference between these and a scatter ranging from 0.4 to 0.07 dex for masses from $\log(M_\mathrm{BH}/\mathrm{M}_\odot)\simeq8$ to 9.75, respectively. Reverberation mapping studies show that single-epoch masses approximately present 0.4 dex precision, letting us conclude that our novel technique delivers BH masses with only mildly worse precision than single-epoch spectroscopy. The J-PAS survey will soon start observing thousands of square degrees without any source preselection other than the photometric depth in the detection band, and thus single-epoch photometry has the potential to provide details on the physical properties of quasar populations not satisfying the preselection criteria of previous spectroscopic surveys.

The Ly$\alpha$ emission is an important tracer of neutral gas in a circum-galactic medium (CGM) around high-z QSOs. The origin of Lya emission around QSOs is still under debate which has significant implications for galaxy formation and evolution. In this paper, we study Ly$\alpha$ nebulae around two high redshift QSOs, SDSS J141935.58+525710.7 at $z=3.218$ (hereafter QSO1) and SDSS J141813.40+525240.4 at $z=3.287$ (hereafter QSO2), from the miniJPAS survey within the AEGIS field. Using the contiguous narrow-band (NB) images from the miniJPAS survey and SDSS spectra, we analyzed their morphology, nature, and origin. We report the serendipitous detection of double-core Ly\al\ morphology around two QSOs which is rarely seen among other QSOs. The separations of the two Ly\al~cores are 11.07 $\pm$ 2.26 kpcs (1.47 $\pm$ 0.3$^{\prime\prime}$) and 9.73 $\pm$ 1.55 kpcs (1.31 $\pm$ 0.21$^{\prime\prime}$) with Ly$\alpha$~line luminosities of $\sim$ 3.35 $\times 10^{44}$ erg s $^{-1} $ and $\sim$ 6.99 $\times$ 10$^{44}$ erg s $^{-1}$ for QSO1 and QSO2, respectively. The miniJPAS NB images show evidence of extended Ly$\alpha$ and CIV morphology for both QSOs and extended HeII morphology for QSO1. These two QSOs may be potential candidates for the new enormous Lyman alpha nebula (ELAN) found from the miniJPAS survey due to their extended morphology in the shallow depth and relatively high Ly$\alpha$ luminosities. We suggest that galactic outflows are the major powering mechanism for the double-core Ly$\alpha$ morphology. Considering the relatively shallow exposures of miniJPAS, the objects found here could be the tip of the iceberg of a promising number of such objects that will be uncovered in the upcoming full J-PAS survey and deep IFU observations with 8-10m telescopes will be essential for constraining the underlying physical mechanism that is responsible for the double-cored morphology., Comment: Five figures and two tables (Accepted in Astronomy and Astrophysics)

Cosmological covariance matrices are fundamental for parameter inference, since they are responsible for propagating uncertainties from the data down to the model parameters. However, when data vectors are large, in order to estimate accurate and precise matrices we need huge numbers of observations, or rather costly simulations - neither of which may be viable. In this work we propose a machine learning approach to alleviate this problem in the context of the matrices used in the study of large-scale structure. With only a small amount of data (matrices built with samples of 50-200 halo power spectra) we are able to provide significantly improved matrices, which are almost indistinguishable from the ones built from much larger samples (thousands of spectra). In order to perform this task we trained convolutional neural networks to denoise the matrices, using in the training process a data set made up entirely of spectra extracted from simple, inexpensive halo simulations (mocks). We then show that the method not only removes the noise in the matrices of the cheap simulation, but it is also able to successfully denoise the matrices of halo power spectra from N-body simulations. We compare the denoised to the other matrices using several metrics, and in all of them they score better, without any signs of spurious artifacts. With the help of the Wishart distribution we derive an analytical extrapolation for the effective sample augmentation allowed by the denoiser. Finally, we show that, by using the denoised matrices, the cosmological parameters can be recovered with nearly the same accuracy as when using matrices built with a sample of 30,000 spectra in the case of the cheap simulations, and with 15,000 spectra in the case of the N-body simulations. Of particular interest is the bias in the Hubble parameter $H_0$, which was significantly reduced after applying the denoiser., Comment: Matches published version; very minor changes wrt V1

Redshift evolution and peculiar velocities break the isotropy of cosmological surveys with respect to the directions parallel and transverse to the line of sight, limiting the accuracy of the Fourier representation to small areas and redshift ranges. In contrast to the Fourier space power spectrum, the full information about the two-point function of tracers of large-scale structure is encapsulated in the redshift-dependent angular power spectrum $C_\ell^{ij} (z_i,z_j)$ for the tracer species $i$ and $j$ at the redshift slices $z_i$ and $z_j$, expressed in harmonic space. In this paper we derive semi-analytical expressions for the multi-tracer Fisher matrix of angular power spectra, in real and in redshift space, which are exact in the linear regime of structure formation. Our expressions can be used to forecast the constraining power of galaxy surveys with many tracers and a large number of redshift slices, for which the derivation of the Fisher matrix from numerically evaluated covariance matrices may not be feasible or practical., Comment: Minor changes. Matches version published in JCAP

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 $17.5\leq r<24$, we augment our sample of available spectra by shifting the original $r$-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modeling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys., Comment: 20 pages, 18 figures, submitted to MNRAS

Elucidating the connection between the properties of galaxies and the properties of their hosting haloes is a key element in galaxy formation. When the spatial distribution of objects is also taken under consideration, it becomes very relevant for cosmological measurements. In this paper, we use machine learning techniques to analyse these intricate relations in the IllustrisTNG300 magnetohydrodynamical simulation, predicting baryonic properties from halo properties. We employ four different algorithms: extremely randomized trees, K-nearest neighbours, light gradient boosting machine, and neural networks, along with a unique and powerful combination of the results from all four approaches. Overall, the different algorithms produce consistent results in terms of predicting galaxy properties from a set of input halo properties that include halo mass, concentration, spin, and halo overdensity. For stellar mass, the Pearson correlation coefficient is 0.98, dropping down to 0.7-0.8 for specific star formation rate (sSFR), colour, and size. In addition, we apply, for the first time in this context, an existing data augmentation method, synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN), designed to alleviate the problem of imbalanced data sets, showing that it improves the overall shape of the predicted distributions and the scatter in the halo-galaxy relations. We also demonstrate that our predictions are good enough to reproduce the power spectra of multiple galaxy populations, defined in terms of stellar mass, sSFR, colour, and size with high accuracy. Our results align with previous reports suggesting that certain galaxy properties cannot be reproduced using halo features alone., Comment: Matches published version; very minor changes wrt V1

We derive general expressions for the multi-tracer Fisher matrix, both assuming that the cross-spectra are constrained by the auto-spectra, and also allowing for independent degrees of freedom in the cross-spectra. We show that, just like the ratios of power spectra, the independent degrees of freedom of the cross-spectra are also not constrained by cosmic variance. Moreover, whereas the uncertainties in the ratios of power spectra decrease with the number density of the tracers as $\sim 1/\sqrt{\bar{n}}$, the uncertainties in the independent degrees of freedom of the cross-spectra decrease even faster, as $\sim 1/\bar{n}$. We also derive simple expressions for the optimal number of tracers in a survey., Comment: 17 pages, 3 figures

Precise measurements of black hole masses are essential to understanding the coevolution of these sources and their host galaxies. We develop a novel approach for computing black hole virial masses using measurements of continuum luminosities and emission line widths from partially overlapping, narrow-band observations of quasars; we refer to this technique as single-epoch photometry. This novel method relies on forward-modelling quasar observations for estimating emission line widths, which enables unbiased measurements even for lines coarsely resolved by narrow-band data. We assess the performance of this technique using quasars from the Sloan Digital Sky Survey (SDSS) observed by the miniJPAS survey, a proof-of-concept project of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) collaboration covering $\simeq1\,\mathrm{deg}^2$ of the northern sky using the 56 J-PAS narrow-band filters. We find remarkable agreement between black hole masses from single-epoch SDSS spectra and single-epoch miniJPAS photometry, with no systematic difference between these and a scatter ranging from 0.4 to 0.07 dex for masses from $\log(M_\mathrm{BH})\simeq8$ to 9.75, respectively. Reverberation mapping studies show that single-epoch masses present approximately 0.4 dex precision, letting us conclude that our novel technique delivers black hole masses with only mildly lower precision than single-epoch spectroscopy. The J-PAS survey will soon start observing thousands of square degrees without any source preselection other than the photometric depth in the detection band, and thus single-epoch photometry has the potential to provide details on the physical properties of quasar populations that do not satisfy the preselection criteria of previous spectroscopic surveys., Comment: 16 pages, 10 figures, accepted by A&A

We study the performance of the perturbative bias expansion when combined with the multi-tracer technique, and their impact on the extraction of cosmological parameters. We consider two populations of tracers of large-scale structure and perform a series of Markov chain Monte Carlo analysis for those two tracers separately. The constraints in $\omega_{\rm cdm}$ and $h$ using multi-tracer are less biased and approximately $60\%$ better than those obtained for a single tracer. The multi-tracer approach also provides stronger constraints on the bias expansion parameters, breaking degeneracies between them and with their error being typically half of the single-tracer case. Finally, we studied the impacts caused in parameter extraction when including a correlation between the stochastic field of distinct tracers., Comment: 28 pages, 12 figures

We study the performance of the perturbative bias expansion when combined with the multi-tracer technique, and their impact on the extraction of cosmological parameters. We consider two populations of tracers of large-scale structure and perform a series of Markov chain Monte Carlo analysis for those two tracers separately. The constraints in $\omega_{\rm cdm}$ and $h$ using multi-tracer are less biased and approximately $60\%$ better than those obtained for a single tracer. The multi-tracer approach also provides stronger constraints on the bias expansion parameters, breaking degeneracies between them and with their error being typically half of the single-tracer case. Finally, we studied the impacts caused in parameter extraction when including a correlation between the stochastic field of distinct tracers. We also include a study with galaxies showing that multi-tracer still lead to substantial gains in the cosmological parameters., Comment: 32 pages, 14 figures. The new appendix includes galaxy analysis

We derive general expressions for the multi-tracer Fisher matrix, both assuming that the cross-spectra are constrained by the auto-spectra, and also allowing for independent degrees of freedom in the cross-spectra. We show that, just like the ratios of power spectra, the independent degrees of freedom of the cross-spectra are also not constrained by cosmic variance. Moreover, whereas the uncertainties in the ratios of power spectra decrease with the number density of the tracers as $\sim 1/\sqrt{\bar{n}}$, the uncertainties in the independent degrees of freedom of the cross-spectra decrease even faster, as $\sim 1/\bar{n}$. We also derive simple expressions for the optimal number of tracers in a survey., Comment: 17 pages, 3 figures

Precise measurements of black hole masses are essential to understanding the coevolution of these sources and their host galaxies. We develop a novel approach for computing black hole virial masses using measurements of continuum luminosities and emission line widths from partially overlapping, narrow-band observations of quasars; we refer to this technique as single-epoch photometry. This novel method relies on forward-modelling quasar observations for estimating emission line widths, which enables unbiased measurements even for lines coarsely resolved by narrow-band data. We assess the performance of this technique using quasars from the Sloan Digital Sky Survey (SDSS) observed by the miniJPAS survey, a proof-of-concept project of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) collaboration covering $\simeq1\,\mathrm{deg}^2$ of the northern sky using the 56 J-PAS narrow-band filters. We find remarkable agreement between black hole masses from single-epoch SDSS spectra and single-epoch miniJPAS photometry, with no systematic difference between these and a scatter ranging from 0.4 to 0.07 dex for masses from $\log(M_\mathrm{BH})\simeq8$ to 9.75, respectively. Reverberation mapping studies show that single-epoch masses present approximately 0.4 dex precision, letting us conclude that our novel technique delivers black hole masses with only mildly lower precision than single-epoch spectroscopy. The J-PAS survey will soon start observing thousands of square degrees without any source preselection other than the photometric depth in the detection band, and thus single-epoch photometry has the potential to provide details on the physical properties of quasar populations that do not satisfy the preselection criteria of previous spectroscopic surveys., Comment: 16 pages, 10 figures, accepted by A&A

We study the performance of the perturbative bias expansion when combined with the multi-tracer technique, and their impact on the extraction of cosmological parameters. We consider two populations of tracers of large-scale structure and perform a series of Markov chain Monte Carlo analysis for those two tracers separately. The constraints in $\omega_{\rm cdm}$ and $h$ using multi-tracer are less biased and approximately $60\%$ better than those obtained for a single tracer. The multi-tracer approach also provides stronger constraints on the bias expansion parameters, breaking degeneracies between them and with their error being typically half of the single-tracer case. Finally, we studied the impacts caused in parameter extraction when including a correlation between the stochastic field of distinct tracers. We also include a study with galaxies showing that multi-tracer still lead to substantial gains in the cosmological parameters., Comment: 32 pages, 14 figures. The new appendix includes galaxy analysis

We consider a cosmological model where dark matter and dark energy feature a coupling that only affects their momentum transfer in the corresponding Euler equations. We perform a fit to cosmological observables and confirm previous findings within these scenarios that favour the presence of a coupling at more than $3\sigma$. This improvement is driven by the Sunyaev-Zeldovich data. We subsequently perform a forecast for future J-PAS data and find that clustering measurements will permit to clearly discern the presence of an interaction within a few percent level with the uncoupled case at more than $10\sigma$ when the complete survey, covering $8500$ sq. deg., is considered. We found that the inclusion of weak lensing measurements will not help to further constrain the coupling parameter. For completeness, we compare to forecasts for DESI and Euclid, which provide similar discriminating power., Comment: 34 pages, 17 figures, added some clarifications and discussions, matches published version

Errors in measurements are key to weighting the value of data, but are often neglected in Machine Learning (ML). We show how Convolutional Neural Networks (CNNs) are able to learn about the context and patterns of signal and noise, leading to improvements in the performance of classification methods. We construct a model whereby two classes of objects follow an underlying Gaussian distribution, and where the features (the input data) have varying, but known, levels of noise. This model mimics the nature of scientific data sets, where the noises arise as realizations of some random processes whose underlying distributions are known. The classification of these objects can then be performed using standard statistical techniques (e.g., least-squares minimization or Markov-Chain Monte Carlo), as well as ML techniques. This allows us to take advantage of a maximum likelihood approach to object classification, and to measure the amount by which the ML methods are incorporating the information in the input data uncertainties. We show that, when each data point is subject to different levels of noise (i.e., noises with different distribution functions), that information can be learned by the CNNs, raising the ML performance to at least the same level of the least-squares method -- and sometimes even surpassing it. Furthermore, we show that, with varying noise levels, the confidence of the ML classifiers serves as a proxy for the underlying cumulative distribution function, but only if the information about specific input data uncertainties is provided to the CNNs., Comment: 32 pages, 15 figures

Planned efforts to probe the largest observable distance scales in future cosmological surveys are motivated by a desire to detect relic correlations left over from inflation and the possibility of constraining novel gravitational phenomena beyond general relativity (GR). On such large scales, the usual Newtonian approaches to modelling summary statistics like the power spectrum and bispectrum are insufficient, and we must consider a fully relativistic and gauge-independent treatment of observables such as galaxy number counts in order to avoid subtle biases, e.g. in the determination of the fNL parameter.In this work, we present an initial application of an analysis pipeline capable of accurately modelling and recovering relativistic spectra and correlation functions. As a proof of concept, we focus on the non-zero dipole of the redshift-space power spectrum that arises in the cross-correlation of different mass bins of dark matter haloes, using strictly gauge-independent observable quantities evaluated on the past light cone of a fully relativistic N-body simulation in a redshift bin 1.7 ≤ z ≤ 2.9. We pay particular attention to the correct estimation of power spectrum multipoles, comparing different methods of accounting for complications such as the survey geometry (window function) and evolution/bias effects on the past light cone, and discuss how our results compare with previous attempts at extracting novel GR signatures from relativistic simulations.

At $z=0$, higher-spin haloes with masses above $\log(\text{M}_{\text{c}}/h^{-1}\text{M}_\odot)\simeq 11.5$ have a higher bias than lower-spin haloes of the same mass. However, this trend is known to invert below this characteristic crossover mass, $\text{M}_{\text{c}}$. In this paper, we measure the redshift evolution and scale dependence of halo spin bias at the low-mass end and demonstrate that the inversion of the signal is entirely produced by the effect of splashback haloes. These low-mass haloes tend to live in the vicinity of significantly more massive haloes, thus sharing their large-scale bias properties. We further show that the location of the redshift-dependent crossover mass scale $\text{M}_{\text{c}}(z)$ is completely determined by the relative abundance of splashbacks in the low- and high-spin subpopulations. Once splashback haloes are removed from the sample, the intrinsic mass dependence of spin bias is recovered. Since splashbacks have been shown to account for some of the assembly bias signal at the low-mass end, our results unveil a specific link between two different secondary bias trends: spin bias and assembly bias., Comment: 9 pages, 7 figures - final version after publication in MNRAS

The galaxy power spectrum is one of the central quantities in cosmology. It contains information about the primordial inflationary process, the matter clustering, the baryon-photon interaction, the effects of gravity, the galaxy-matter bias, the cosmic expansion, the peculiar velocity field, etc.. Most of this information is however difficult to extract without assuming a specific cosmological model, for instance $\Lambda$CDM and standard gravity. In this paper we explore instead how much information can be obtained that is independent of the cosmological model, both at background and linear perturbation level. We determine the full set of model-independent statistics that can be constructed by combining two redshift bins and two distinct tracers. We focus in particular on the statistics $r(k,z_1,z_2)$, defined as the ratio of $f\sigma_8(z)$ at two redshift shells, and we show how to estimate it with a Fisher matrix approach. Finally, we forecast the constraints on $r$ that can be achieved by future galaxy surveys, and compare it with the standard single-tracer result. We find that $r$ can be measured with a precision from 3 to 11%, depending on the survey. Using two tracers, we find improvements in the constraints up to a factor of two., Comment: 14 pages, 4 figures

We use the improved IllustrisTNG300 magneto-hydrodynamical cosmological simulation to revisit the effect that secondary halo bias has on the clustering of the central galaxy population. With a side length of 205 $h^{-1}$Mpc and significant improvements on the sub-grid model with respect to the previous Illustris boxes, IllustrisTNG300 allows us to explore the dependencies of galaxy clustering over a large cosmological volume and wide halo-mass range. We show, at high statistical significance, that the halo assembly bias signal (i.e., the secondary dependence of halo bias on halo formation redshift) manifests itself on the clustering of the central galaxy population when this is split by stellar mass, colour, specific star formation rate, and surface density. A significant detection is also obtained for galaxy size: at fixed halo mass, larger central galaxies are more tightly clustered than smaller central galaxies in haloes of mass M$_{\rm vir} \lesssim 10^{12.5}$ $h^{-1}$M$_{\odot}$. This effect, however, seems to be uncorrelated with halo formation time, unlike the rest of the secondary dependencies analysed. We also explore the transmission of the halo spin bias signal, i.e., the secondary dependence of halo bias on halo spin. Although galaxy spin retains little information about the total spin of the halo, the correlation is enough to produce a significant galaxy spin bias signal. We discuss possible ways to probe the spin bias effects with observations., Comment: 16 pages, 10 figures, submitted to MNRAS

Planned efforts to probe the largest observable distance scales in future cosmological surveys are motivated by a desire to detect relic correlations left over from inflation, and the possibility of constraining novel gravitational phenomena beyond General Relativity (GR). On such large scales, the usual Newtonian approaches to modelling summary statistics like the power spectrum and bispectrum are insufficient, and we must consider a fully relativistic and gauge-independent treatment of observables such as galaxy number counts in order to avoid subtle biases, e.g. in the determination of the $f_{\rm NL}$ parameter. In this work, we present an initial application of an analysis pipeline capable of accurately modelling and recovering relativistic spectra and correlation functions. As a proof of concept, we focus on the non-zero dipole of the redshift-space power spectrum that arises in the cross-correlation of different mass bins of dark matter halos, using strictly gauge-independent observable quantities evaluated on the past light cone of a fully relativistic N-body simulation in a redshift bin $1.7 \le z \le 2.9$. We pay particular attention to the correct estimation of power spectrum multipoles, comparing different methods of accounting for complications such as the survey geometry (window function) and evolution/bias effects on the past light cone, and discuss how our results compare with previous attempts at extracting novel GR signatures from relativistic simulations., Comment: Minor corrections in the text of version 2

We explore the potential of the kinetic Sunyaev-Zel'dovich (kSZ) effect as the cornerstone of a future observational probe for halo spin bias, the secondary dependence of halo clustering on halo spin at fixed halo mass. Using the IllustrisTNG magneto-hydrodynamical cosmological simulation, we measure both the kSZ and the thermal SZ (tSZ) effects produced by the baryonic content of more than 50,000 haloes within the halo mass range $11 < \log_{10} ({\rm M_{vir}}/ h^{-1} {\rm M_{\odot}}) \lesssim 14.5$. First, we confirm that the magnitude of both effects depends strongly on the total gas and virial mass of the haloes, and that the integrated kSZ signal displays a significant correlation with the angular momentum of the intra-halo gas, particularly for massive haloes. Second, we show that both the integrated kSZ signal and the ratio of the integrated kSZ and tSZ signals trace total halo spin, even though significant scatter exists. Finally, we demonstrate that, in the absence of observational and instrumental uncertainties, these SZ-related statistics can be used to recover most of the underlying IllustrisTNG halo spin bias signal. Our analysis represents the first attempt to develop a future observational probe for halo spin bias, bringing forward alternative routes for measuring the secondary bias effects., Comment: 17 pages, 10 figures, submitted to MNRAS

At $z=0$, higher-spin haloes with masses above $\log(\text{M}_{\text{c}}/h^{-1}\text{M}_\odot)\simeq 11.5$ have a higher bias than lower-spin haloes of the same mass. However, this trend is known to invert below this characteristic crossover mass, $\text{M}_{\text{c}}$. In this paper, we measure the redshift evolution and scale dependence of halo spin bias at the low-mass end and demonstrate that the inversion of the signal is entirely produced by the effect of splashback haloes. These low-mass haloes tend to live in the vicinity of significantly more massive haloes, thus sharing their large-scale bias properties. We further show that the location of the redshift-dependent crossover mass scale $\text{M}_{\text{c}}(z)$ is completely determined by the relative abundance of splashbacks in the low- and high-spin subpopulations. Once splashback haloes are removed from the sample, the intrinsic mass dependence of spin bias is recovered. Since splashbacks have been shown to account for some of the assembly bias signal at the low-mass end, our results unveil a specific link between two different secondary bias trends: spin bias and assembly bias., Comment: 9 pages, 7 figures - final version after publication in MNRAS

The galaxy power spectrum is one of the central quantities in cosmology. It contains information about the primordial inflationary process, the matter clustering, the baryon-photon interaction, the effects of gravity, the galaxy-matter bias, the cosmic expansion, the peculiar velocity field, etc.. Most of this information is however difficult to extract without assuming a specific cosmological model, for instance $\Lambda$CDM and standard gravity. In this paper we explore instead how much information can be obtained that is independent of the cosmological model, both at background and linear perturbation level. We determine the full set of model-independent statistics that can be constructed by combining two redshift bins and two distinct tracers. We focus in particular on the statistics $r(k,z_1,z_2)$, defined as the ratio of $f\sigma_8(z)$ at two redshift shells, and we show how to estimate it with a Fisher matrix approach. Finally, we forecast the constraints on $r$ that can be achieved by future galaxy surveys, and compare it with the standard single-tracer result. We find that $r$ can be measured with a precision from 3 to 11%, depending on the survey. Using two tracers, we find improvements in the constraints up to a factor of two., Comment: 14 pages, 4 figures

We use the improved IllustrisTNG300 magneto-hydrodynamical cosmological simulation to revisit the effect that secondary halo bias has on the clustering of the central galaxy population. With a side length of 205 $h^{-1}$Mpc and significant improvements on the sub-grid model with respect to the previous Illustris boxes, IllustrisTNG300 allows us to explore the dependencies of galaxy clustering over a large cosmological volume and wide halo-mass range. We show, at high statistical significance, that the halo assembly bias signal (i.e., the secondary dependence of halo bias on halo formation redshift) manifests itself on the clustering of the central galaxy population when this is split by stellar mass, colour, specific star formation rate, and surface density. A significant detection is also obtained for galaxy size: at fixed halo mass, larger central galaxies are more tightly clustered than smaller central galaxies in haloes of mass M$_{\rm vir} \lesssim 10^{12.5}$ $h^{-1}$M$_{\odot}$. This effect, however, seems to be uncorrelated with halo formation time, unlike the rest of the secondary dependencies analysed. We also explore the transmission of the halo spin bias signal, i.e., the secondary dependence of halo bias on halo spin. Although galaxy spin retains little information about the total spin of the halo, the correlation is enough to produce a significant galaxy spin bias signal. We discuss possible ways to probe the spin bias effects with observations., Comment: 16 pages, 10 figures, submitted to MNRAS

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 Accelerating Universe Astrophysical Survey (J-PAS) to constrain GR 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 $\mathrm{deg}^2$ and the future best case scenario with 8500 $\mathrm{deg}^2$. We show that J-PAS will be able to measure the parameters $\mu$ and $\eta$ at a sensitivity of $2\% - 7\%$, 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.

We use mock galaxy data from the VIMOS Public Extragalactic Redshift Survey (VIPERS) to test the performance of the Multi-Tracer Optimal Estimator (MTOE) of Abramo et al. as a tool to measure the monopoles of the power spectra of multiple tracers of the large-scale structure, $P^{(0)}_\alpha(k)$. We show that MTOE provides more accurate measurements than the standard technique of Feldman, Kaiser & Peacock (FKP), independently of the tracer-selection strategy adopted, on both small and large scales. The largest improvements on individual $P^{(0)}_\alpha(k)$ are obtained using a colour-magnitude selection on small scales, due to MTOE being naturally better equipped to deal with shot noise: we report an average error reduction with respect to FKP of $\sim$ 40$\%$ at $k \, [h$ Mpc$^{-1}]\gtrsim 0.3$. On large scales ($k[h$ Mpc$^{-1}]\lesssim0.1$), the gain in accuracy resulting from cosmic-variance cancellation is $\sim$ 10$\%$ for the ratios of $P^{(0)}_\alpha(k)$. We have carried out a Monte-Carlo Markov Chain analysis to determine the impact of these gains on several quantities derived from $P^{(0)}_\alpha(k)$. If we push the measurement to scales $0.3 < k \, [h$ Mpc$^{-1}]< 0.5$, the average improvements are $\sim$ 30 $\%$ for the amplitudes of the monopoles, $\sim$ 70 $\%$ for the monopole ratios, and $\sim$ 20 $\%$ for the galaxy biases. Our results highlight the potential of MTOE to shed light upon the physics that operate both on large and small cosmological scales. The effect of MTOE on cosmological constraints using VIPERS data will be addressed in a separate paper., Comment: 19 pages, 13 figures; submitted to MNRAS

We show how to obtain constraints on $\beta=f/b$, the ratio of the matter growth rate and the bias that quantifies the linear redshift-space distortions, that are independent of the cosmological model, using multiple tracers of large-scale structure. For a single tracer the uncertainties on $\beta$ are constrained by the uncertainties in the amplitude and shape of the power spectrum, which is limited by cosmic variance. However, for two or more tracers this limit does not apply, since taking the ratio of power spectra cosmic variance cancels out, and in the linear (Kaiser) approximation one measures directly the quantity $(1+ \beta_1 \mu^2)^2/(1+ \beta_2 \mu^2)^2$, where $\mu$ is the angle of a given mode with the line of sight. We provide analytic formulae for the Fisher matrix for one and two tracers, and quantify the signal-to-noise ratio needed to make effective use of the multiple-tracer technique. We also forecast the errors on $\beta$ for a survey like Euclid., Comment: 13 pages, 6 figures; matches version to be published on JCAP

We show how to obtain constraints on $\beta=f/b$, the ratio of the matter growth rate and the bias that quantifies the linear redshift-space distortions, that are independent of the cosmological model, using multiple tracers of large-scale structure. For a single tracer the uncertainties on $\beta$ are constrained by the uncertainties in the amplitude and shape of the power spectrum, which is limited by cosmic variance. However, for two or more tracers this limit does not apply, since taking the ratio of power spectra cosmic variance cancels out, and in the linear (Kaiser) approximation one measures directly the quantity $(1+ \beta_1 \mu^2)^2/(1+ \beta_2 \mu^2)^2$, where $\mu$ is the angle of a given mode with the line of sight. We provide analytic formulae for the Fisher matrix for one and two tracers, and quantify the signal-to-noise ratio needed to make effective use of the multiple-tracer technique. We also forecast the errors on $\beta$ for a survey like Euclid., Comment: 13 pages, 6 figures; matches version to be published on JCAP

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 Accelerating Universe Astrophysical Survey (J-PAS) to constrain GR 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 $\mathrm{deg}^2$ and the future best case scenario with 8500 $\mathrm{deg}^2$. We show that J-PAS will be able to measure the parameters $\mu$ and $\eta$ at a sensitivity of $2\% - 7\%$, 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.

We use mock galaxy data from the VIMOS Public Extragalactic Redshift Survey (VIPERS) to test the performance of the Multi-Tracer Optimal Estimator (MTOE) of Abramo et al. as a tool to measure the monopoles of the power spectra of multiple tracers of the large-scale structure, $P^{(0)}_\alpha(k)$. We show that MTOE provides more accurate measurements than the standard technique of Feldman, Kaiser & Peacock (FKP), independently of the tracer-selection strategy adopted, on both small and large scales. The largest improvements on individual $P^{(0)}_\alpha(k)$ are obtained using a colour-magnitude selection on small scales, due to MTOE being naturally better equipped to deal with shot noise: we report an average error reduction with respect to FKP of $\sim$ 40$\%$ at $k \, [h$ Mpc$^{-1}]\gtrsim 0.3$. On large scales ($k[h$ Mpc$^{-1}]\lesssim0.1$), the gain in accuracy resulting from cosmic-variance cancellation is $\sim$ 10$\%$ for the ratios of $P^{(0)}_\alpha(k)$. We have carried out a Monte-Carlo Markov Chain analysis to determine the impact of these gains on several quantities derived from $P^{(0)}_\alpha(k)$. If we push the measurement to scales $0.3 < k \, [h$ Mpc$^{-1}]< 0.5$, the average improvements are $\sim$ 30 $\%$ for the amplitudes of the monopoles, $\sim$ 70 $\%$ for the monopole ratios, and $\sim$ 20 $\%$ for the galaxy biases. Our results highlight the potential of MTOE to shed light upon the physics that operate both on large and small cosmological scales. The effect of MTOE on cosmological constraints using VIPERS data will be addressed in a separate paper., Comment: 19 pages, 13 figures; submitted to MNRAS

Halo bias is the main link between the matter distribution and dark matter halos. In its simplest form, halo bias is determined by halo mass, but there are known additional dependencies on other halo properties which are of consequence for accurate modeling of galaxy clustering. Here we present the most precise measurement of these secondary-bias dependencies on halo age, concentration, and spin, for a wide range of halo masses spanning from 10$^{10.7}$ to 10$^{14.7}$ $h^{-1}$ M$_{\odot}$. At the high-mass end, we find no strong evidence of assembly bias for masses above M$_{vir}$ $\sim10^{14}$ $h^{-1}$ M$_{\odot}$. Secondary bias exists, however, for halo concentration and spin, up to cluster-size halos, in agreement with previous findings. For halo spin, we report, for the first time, two different regimes: above M$_{vir}\sim$10$^{11.5}$ $h^{-1}$ M$_{\odot}$, halos with larger values of spin have larger bias, at fixed mass, with the effect reaching almost a factor 2. This trend reverses below this characteristic mass. In addition to these results, we test, for the first time, the performance of a multi-tracer method for the determination of the relative bias between different subsets of halos. We show that this method increases significantly the signal-to-noise of the secondary-bias measurement as compared to a traditional approach. This analysis serves as the basis for follow-up applications of our multi-tracer method to real data., Comment: 11 pages, 6 figures, submitted to MNRAS

We investigate the relationship between the turnaround radius ($R_t$) and the virial mass ($M_v$) of cosmic structures in the context of $\Lambda$CDM model and in an $f(R)$ model of modified gravity -- namely, the Hu-Sawicki model. The $R_t$ is the distance from the center of the cosmic structure to the shell that is detaching from the Hubble flow at a given time, while the $M_v$ is defined, for this work, as the mass enclosed within the volume where the density is $200$ times the background density. We consider that gravitationally bound astrophysical systems follow a Navarro-Frenk-White density profile, while beyond the virial radius ($R_v$) the profile is approximated by the 2-halo term of the matter correlation function. By combining them together with the information drawn from solving the spherical collapse for the structures, we are able to connect two observables: the $R_t$ and the $M_v$. We show that, in $\Lambda$CDM, the turnaround mass ($M_t$) at $z=0$ is related to the $M_v$ of that same structure by $M_t \simeq 3.07 \, M_v$, while in terms of the radii we have that $R_t \simeq 3.7 \, R_v$ (for $M_v$ of $10^{13} \, h^{-1} \, M_\odot$). In the $f(R)$ model, we have $M_t \simeq 3.43 \, M_v$ and $R_t \simeq 4.1 \, R_v$, for $|f_{R0}|=10^{-6}$ and the same mass scale. Therefore, the difference between $\Lambda$CDM and $f(R)$ in terms of these observable relations is of order $\sim 10-20\%$ even for a relatively mild strength of the modification of gravity ($|f_{R0}|=10^{-6}$). For the $R_t$ itself we find a difference of $\sim 9\%$ between the weakly modification in gravity considered in this work ($|f_{R0}|=10^{-6}$) and $\Lambda$CDM for a mass of $10^{13} \, h^{-1} \, M_\odot$. Once observations allow precisions of this order or better in measurements $R_t$, as well as the $M_v$, these quantities will become powerful tests of modified gravity., Comment: 17 pages, 5 figures. Comments are welcome

Halo bias is the main link between the matter distribution and dark matter halos. In its simplest form, halo bias is determined by halo mass, but there are known additional dependencies on other halo properties which are of consequence for accurate modeling of galaxy clustering. Here we present the most precise measurement of these secondary-bias dependencies on halo age, concentration, and spin, for a wide range of halo masses spanning from 10$^{10.7}$ to 10$^{14.7}$ $h^{-1}$ M$_{\odot}$. At the high-mass end, we find no strong evidence of assembly bias for masses above M$_{vir}$ $\sim10^{14}$ $h^{-1}$ M$_{\odot}$. Secondary bias exists, however, for halo concentration and spin, up to cluster-size halos, in agreement with previous findings. For halo spin, we report, for the first time, two different regimes: above M$_{vir}\sim$10$^{11.5}$ $h^{-1}$ M$_{\odot}$, halos with larger values of spin have larger bias, at fixed mass, with the effect reaching almost a factor 2. This trend reverses below this characteristic mass. In addition to these results, we test, for the first time, the performance of a multi-tracer method for the determination of the relative bias between different subsets of halos. We show that this method increases significantly the signal-to-noise of the secondary-bias measurement as compared to a traditional approach. This analysis serves as the basis for follow-up applications of our multi-tracer method to real data., Comment: 11 pages, 6 figures, submitted to MNRAS

We investigate the relationship between the turnaround radius ($R_t$) and the virial mass ($M_v$) of cosmic structures in the context of $\Lambda$CDM model and in an $f(R)$ model of modified gravity -- namely, the Hu-Sawicki model. The $R_t$ is the distance from the center of the cosmic structure to the shell that is detaching from the Hubble flow at a given time, while the $M_v$ is defined, for this work, as the mass enclosed within the volume where the density is $200$ times the background density. We consider that gravitationally bound astrophysical systems follow a Navarro-Frenk-White density profile, while beyond the virial radius ($R_v$) the profile is approximated by the 2-halo term of the matter correlation function. By combining them together with the information drawn from solving the spherical collapse for the structures, we are able to connect two observables: the $R_t$ and the $M_v$. We show that, in $\Lambda$CDM, the turnaround mass ($M_t$) at $z=0$ is related to the $M_v$ of that same structure by $M_t \simeq 3.07 \, M_v$, while in terms of the radii we have that $R_t \simeq 3.7 \, R_v$ (for $M_v$ of $10^{13} \, h^{-1} \, M_\odot$). In the $f(R)$ model, we have $M_t \simeq 3.43 \, M_v$ and $R_t \simeq 4.1 \, R_v$, for $|f_{R0}|=10^{-6}$ and the same mass scale. Therefore, the difference between $\Lambda$CDM and $f(R)$ in terms of these observable relations is of order $\sim 10-20\%$ even for a relatively mild strength of the modification of gravity ($|f_{R0}|=10^{-6}$). For the $R_t$ itself we find a difference of $\sim 9\%$ between the weakly modification in gravity considered in this work ($|f_{R0}|=10^{-6}$) and $\Lambda$CDM for a mass of $10^{13} \, h^{-1} \, M_\odot$. Once observations allow precisions of this order or better in measurements $R_t$, as well as the $M_v$, these quantities will become powerful tests of modified gravity., Comment: 17 pages, 5 figures. Comments are welcome

We study the detectability of large-scale velocity effects on galaxy clustering, by simulating galaxy surveys and combining the clustering of different types of tracers of large-scale structure. We employ a set of lognormal mocks that simulate a $20.000$ deg$^2$ near-complete survey up to $z=0.8$, in which each galaxy mock traces the spatial distribution of dark matter of that mock with a realistic bias prescription. We find that the ratios of the monopoles of the power spectra of different types of tracers carry most of the information that can be extracted from a multi-tracer analysis. In particular, we show that with a multi-tracer technique it will be possible to detect velocity effects with $\gtrsim 3 \sigma$. Finally, we investigate the degeneracy of these effects with the (local) non-Gaussianity parameter $f_{\rm NL}$, and how large-scale velocity contributions could be mistaken for the signatures of primordial non-Gaussianity., Comment: 17 pages, 25 figures

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 survey, which covered an effective area of $2.38\,{\rm deg}^2$ with 20 contiguous medium-band optical filters down to F814W$\simeq 24.5$. Using two different configurations of ELDAR in which we require the detection of at least 2 and 3 emission lines, respectively, we extract two catalogues of type-I AGN. The first is composed of 585 sources ($79\,\%$ of them spectroscopically-unknown) down to F814W$=22.5$ at $z_{\rm phot}>1$, which corresponds to a surface density of $209\,{\rm deg}^{-2}$. In the second, the 494 selected sources ($83\,\%$ of them spectroscopically-unknown) reach F814W$=23$ at $z_{\rm phot}>1.5$, for a corresponding number density of $176\,{\rm deg}^{-2}$. Then, using samples of spectroscopically-known AGN in the ALHAMBRA fields, for the two catalogues we estimate a completeness of $73\,\%$ and $67\,\%$, and a redshift precision of $1.01\,\%$ and $0.86\,\%$ (with outliers fractions of $8.1\,\%$ and $5.8\,\%$). At $z>2$, where our selection performs best, we reach $85\,\%$ and $77\,\%$ completeness and we find no contamination from galaxies., Comment: 23 pages, 18 figures, submitted to MNRAS

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 survey, which covered an effective area of $2.38\,{\rm deg}^2$ with 20 contiguous medium-band optical filters down to F814W$\simeq 24.5$. Using two different configurations of ELDAR in which we require the detection of at least 2 and 3 emission lines, respectively, we extract two catalogues of type-I AGN. The first is composed of 585 sources ($79\,\%$ of them spectroscopically-unknown) down to F814W$=22.5$ at $z_{\rm phot}>1$, which corresponds to a surface density of $209\,{\rm deg}^{-2}$. In the second, the 494 selected sources ($83\,\%$ of them spectroscopically-unknown) reach F814W$=23$ at $z_{\rm phot}>1.5$, for a corresponding number density of $176\,{\rm deg}^{-2}$. Then, using samples of spectroscopically-known AGN in the ALHAMBRA fields, for the two catalogues we estimate a completeness of $73\,\%$ and $67\,\%$, and a redshift precision of $1.01\,\%$ and $0.86\,\%$ (with outliers fractions of $8.1\,\%$ and $5.8\,\%$). At $z>2$, where our selection performs best, we reach $85\,\%$ and $77\,\%$ completeness and we find no contamination from galaxies., Comment: 23 pages, 18 figures, submitted to MNRAS