Unphysical properties in a class of interacting dark energy models

Models with non-gravitational interactions between the dark matter and dark energy components are an alternative to the standard cosmological scenario. These models are characterized by an interaction term, and a frequently used parameterization is $Q = 3\xi H \rho_{x}$, where $H$ is the Hubble parameter and $\rho_{x}$ is the dark energy density. Although current observations support such a model for negative values of the interaction parameter $\xi$, we show here that this interval of values of $\xi$ leads the model to predict a violation of the Weak Energy Condition (WEC) for the dark matter density, regardless of the value of the equation-of-state parameter of the dark energy component. This violation is accompanied by unphysical instabilities of matter perturbations.Comment: 17 pages, 7 figures. Accepted for publication in the EPJ

Total and dark mass from observations of galaxy centers with Machine Learning

The galaxy total mass inside the effective radius encode important information on the dark matter and galaxy evolution model. Total "central" masses can be inferred via galaxy dynamics or with gravitational lensing, but these methods have limitations. We propose a novel approach, based on Random Forest, to make predictions on the total and dark matter content of galaxies using simple observables from imaging and spectroscopic surveys. We use catalogs of multi-band photometry, sizes, stellar mass, kinematic "measurements" (features) and dark matter (targets) of simulated galaxies, from Illustris-TNG100 hydrodynamical simulation, to train a Mass Estimate machine Learning Algorithm (Mela). We separate the simulated sample in passive early-type galaxies (ETGs), both "normal" and "dwarf", and active late-type galaxies (LTGs) and show that the mass estimator can accurately predict the galaxy dark masses inside the effective radius in all samples. We finally test the mass estimator against the central mass estimates of a series of low redshift (z$\leq$0.1) datasets, including SPIDER, MaNGA/DynPop and SAMI dwarf galaxies, derived with standard dynamical methods based on Jeans equations. Dynamical masses are reproduced within 0.30 dex ($\sim2\sigma$), with a limited fraction of outliers and almost no bias. This is independent of the sophistication of the kinematical data collected (fiber vs. 3D spectroscopy) and the dynamical analysis adopted (radial vs. axisymmetric Jeans equations, virial theorem). This makes Mela a powerful alternative to predict the mass of galaxies of massive stage-IV surveys' datasets

J-PLUS DR3: Galaxy-Star-Quasar classification

The Javalambre Photometric Local Universe Survey (J-PLUS) is a 12-band photometric survey using the 83-cm JAST telescope. Data Release 3 includes 47.4 million sources (29.8 million with $r \le 21$) on 3192 deg$^2$ (2881 deg$^2$ after masking). J-PLUS DR3 only provides star-galaxy classification so that quasars are not identified from the other sources. Given the size of the dataset, machine learning methods could provide a valid alternative classification and a solution to the classification of quasars. Our objective is to classify J-PLUS DR3 sources into galaxies, stars and quasars, outperforming the available classifiers in each class. We use an automated machine learning tool called {\tt TPOT} to find an optimized pipeline to perform the classification. The supervised machine learning algorithms are trained on the crossmatch with SDSS DR12, LAMOST DR7 and \textit{Gaia} DR3. We checked that the training set of about 570 thousand galaxies, one million stars and 220 thousand quasars is both representative and pure to a good degree. We considered 37 features: besides the twelve photometric bands with their errors, six colors, four morphological parameters, galactic extinction with its error and the PSF relative to the corresponding pointing. After exploring numerous pipeline possibilities through the TPOT genetic algorithm, we found that XGBoost provides the best performance: the AUC for galaxies, stars and quasars is above 0.99 and the average precision is above 0.99 for galaxies and stars and 0.94 for quasars. XGBoost outperforms the star-galaxy classifiers already provided in J-PLUS DR3 and also efficiently classifies quasars. We also found that photometry was very important in the classification of quasars, showing the relevance of narrow-band photometry.Comment: 14 pages, 17 figure

J-PAS: forecasts on interacting vacuum energy models

The next generation of galaxy surveys will allow us to test some fundamental aspects of the standard cosmological model, including the assumption of a minimal coupling between the components of the dark sector. In this paper, we present the Javalambre Physics of the Accelerated Universe Astrophysical Survey (J-PAS) forecasts on a class of unified models where cold dark matter interacts with a vacuum energy, considering future observations of baryon acoustic oscillations, redshift-space distortions, and the matter power spectrum. After providing a general framework to study the background and linear perturbations, we focus on a concrete interacting model without momentum exchange by taking into account the contribution of baryons. We compare the J-PAS results with those expected for DESI and Euclid surveys and show that J-PAS is competitive to them, especially at low redshifts. Indeed, the predicted errors for the interaction parameter, which measures the departure from a $\Lambda$CDM model, can be comparable to the actual errors derived from the current data of cosmic microwave background temperature anisotropies.Comment: 34 pages, 9 figures, 14 table

J-PAS: forecasts on interacting vacuum energy models

The next generation of galaxy surveys will allow us to test some fundamental aspects of the standard cosmological model, including the assumption of a minimal coupling between the components of the dark sector. In this paper, we present the Javalambre Physics of the Accelerated Universe Astrophysical Survey (J-PAS) forecasts on a class of unified models where cold dark matter interacts with a vacuum energy, considering future observations of baryon acoustic oscillations, redshift-space distortions, and the matter power spectrum. After providing a general framework to study the background and linear perturbations, we focus on a concrete interacting model without momentum exchange by taking into account the contribution of baryons. We compare the J-PAS results with those expected for DESI and Euclid surveys and show that J-PAS is competitive to them, especially at low redshifts. Indeed, the predicted errors for the interaction parameter, which measures the departure from a ΛCDM model, can be comparable to the actual errors derived from the current data of cosmic microwave background temperature anisotropies. © 2021 IOP Publishing Ltd and Sissa Medialab.MB acknowledges Istituto Nazionale di Fisica Nucleare (INFN), sezione di Napoli, iniziativa specifica QGSKY. RvM acknowledges support from the Programa de Capacitacao Institucional do Observatorio Nacional PCI/ON/MCTI. SC is supported by CNPq (Grants No. 307467/2017-1 and 420641/2018-1). JA is supported by CNPq (Grants No. 310790/20140 and 400471/2014-0) and FAPERJ (Grant No. 233906). JCF is supported by CNPq (Grant No. 304521/2015-9) and FAPES (Grant No. 78/2017). ST is supported by the Grant-inAid for Scientific Research Fund of the JSPS No. 19K03854. VM thanks CNPq (Brazil) and FAPES (Brazil) for partial financial support. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 888258. This paper has gone through internal review by the J-PAS collaboration. Funding for the J-PAS Project has been provided by the Governments of Spain and Aragon through the Fondo de Inversion de Teruel, European FEDER funding and the MINECO and by the Brazilian agencies FINEP, FAPESP, FAPERJ and by the National Observatory of Brazil.With funding from the Spanish government through the Severo Ochoa Centre of Excellence accreditation SEV-2017-0709.Peer reviewe

A test of the standard cosmological model with geometry and growth

We perform a general test of the $\Lambda{\rm CDM}$ and $w {\rm CDM}$ cosmological models by comparing constraints on the geometry of the expansion history to those on the growth of structure. Specifically, we split the total matter energy density, $\Omega_M$, and (for $w {\rm CDM}$) dark energy equation of state, $w$, into two parameters each: one that captures the geometry, and another that captures the growth. We constrain our split models using current cosmological data, including type Ia supernovae, baryon acoustic oscillations, redshift space distortions, gravitational lensing, and cosmic microwave background (CMB) anisotropies. We focus on two tasks: (i) constraining deviations from the standard model, captured by the parameters $\Delta\Omega_M \equiv \Omega_M^{\rm grow}-\Omega_M^{\rm geom}$ and $\Delta w \equiv w^{\rm grow}-w^{\rm geom}$, and (ii) investigating whether the $S_8$ tension between the CMB and weak lensing can be translated into a tension between geometry and growth, i.e. $\Delta\Omega_M \neq 0$, $\Delta w \neq 0$. In both the split $\Lambda{\rm CDM}$ and $w {\rm CDM}$ cases, our results from combining all data are consistent with $\Delta\Omega_M = 0$ and $\Delta w = 0$. If we omit BAO/RSD data and constrain the split $w {\rm CDM}$ cosmology, we find the data prefers $\Delta w0$ at $4.2\sigma$ evidence. We also find that for both CMB and weak lensing, $\Delta\Omega_M$ and $S_8$ are correlated, with CMB showing a slightly stronger correlation. The general broadening of the contours in our extended model does alleviate the $S_8$ tension, but the allowed nonzero values of $\Delta\Omega_M$ do not encompass the $S_8$ values that would point toward a mismatch between geometry and growth as the origin of the tension.Comment: 27 pages, 10 figures. References updated; matches version published in JCA