Constraints from the CMB temperature and other common observational data-sets on variable dark energy density models

The thermodynamic and dynamical properties of a variable dark energy model with density scaling as rho_x \propto (1+z)^m, z being the redshift, are discussed following the outline of Jetzer et al. This kind of models are proven to lead to the creation/disruption of matter and radiation, which affect the cosmic evolution of both matter and radiation components in the Universe. In particular, we have concentrated on the temperature-redshift relation of radiation, which has been constrained using a very recent collection of cosmic microwave background (CMB) temperature measurements up to z ~ 3. For the first time, we have combined this observational probe with a set of independent measurements (Supernovae Ia distance moduli, CMB anisotropy, large-scale structure and observational data for the Hubble parameter), which are commonly adopted to constrain dark energy models. We find that, within the uncertainties, the model is indistinguishable from a cosmological constant which does not exchange any particles with other components. Anyway, while temperature measurements and Supernovae Ia tend to predict slightly decaying models, the contrary happens if CMB data are included. Future observations, in particular measurements of CMB temperature at large redshift, will allow to give firmer bounds on the effective equation of state parameter w_eff of this kind of dark energy models.Comment: 10 pages, 4 figures, 1 table, accepted for publication on PR

Rare Treasures in the KiDS Survey

The Kilo Degree Survey (KiDS) is one of the ESO public surveys carried out with the VLT Survey Telescope (VST), equipped with the one square degree field of view and high angular resolution (0.2''/pixel) OmegaCAM camera. KiDS is mainly designed for weak lensing studies, providing deep imaging in four optical bands (ugri), over a 1500 square degree of the sky with excellent seeing (e.g. 0.65'' median FWHM in r-band). The high image quality and deep photometry are ideal for galaxy evolution studies and for hunting peculiar and rare objects, as massive compact galaxies and gravitational lenses. For the latest Data release 3 we have determined structural parameters (effective radii, Re, and Sérsic indices, n), planning to collect at the end of the survey the largest sample of galaxies with measured structural parameters in u, g, r and i bands, up to redshift z=0.5. High-quality photometric redshifts are derived using a machine learning method, which has demonstrated to reach accuracies down to sigma_z 0.03 with optical band only. Stellar masses are derived from stellar population synthesis (SPS) and standard SED fitting. With our unprecedented homogeneous dataset, among the most massive galaxies (with M > 8*10^10) we search for the most compact objects (with sizes Re 2), which have survived intact having stellar populations with old ages. They represent a crucial test bench for galaxy formation processes. But, these galaxies do not have a spectral confirmation, thus we have started a multi-site/multi-facility program to determine their redshifts, velocity dispersions and the properties of the environment. Finally, the deep, subarcsecond seeing KiDS images are also suitable for a census of gravitational lensing systems, based on the (visual and automated) identification of arc-like structures around galaxies. I will discuss our first results using data from the second and third KiDS data releases

Probability density estimation of photometric redshifts based on machine learning

Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological problems. Among the various methods proposed over the years, supervised machine learning (ML) methods capable to interpolate the knowledge gained by means of spectroscopical data have proven to be very effective. METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) is a novel method designed to provide a reliable PDF (Probability density Function) of the error distribution of photometric redshifts predicted by ML methods. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine learning model chosen to predict photo-z's. After a short description of the software, we present a summary of results on public galaxy data (Sloan Digital Sky Survey - Data Release 9) and a comparison with a completely different method based on Spectral Energy Distribution (SED) template fitting.Comment: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 784995

METAPHOR: Probability density estimation for machine learning based photometric redshifts

We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).Comment: proceedings of the International Astronomical Union, IAU-325 symposium, Cambridge University pres

Limits on decaying dark energy density models from the CMB temperature-redshift relation

The nature of the dark energy is still a mystery and several models have been proposed to explain it. Here we consider a phenomenological model for dark energy decay into photons and particles as proposed by Lima (Phys Rev D 54:2571, 1996). He studied the thermodynamic aspects of decaying dark energy models in particular in the case of a continuous photon creation and/or disruption. Following his approach, we derive a temperature redshift relation for the cosmic microwave background (CMB) which depends on the effective equation of state w eff and on the "adiabatic index” γ. Comparing our relation with the data on the CMB temperature as a function of the redshift obtained from Sunyaev-Zel'dovich observations and at higher redshift from quasar absorption line spectra, we find w eff=−0.97 ± 0.03, adopting for the adiabatic index γ=4/3, in good agreement with current estimates and still compatible with w eff=−1, implying that the dark energy content being constant in tim

The central dark matter content of early-type galaxies: scaling relations and connections with star formation histories

We examine correlations between masses, sizes and star formation histories for a large sample of low-redshift early-type galaxies, using a simple suite of dynamical and stellar population models. We confirm an anticorrelation between the size and stellar age and go on to survey for trends with the central content of dark matter (DM). An average relation between the central DM density and galaxy size of 〈ρDM〉∝R−2eff provides the first clear indication of cuspy DM haloes in these galaxies - akin to standard Λ cold dark matter haloes that have undergone adiabatic contraction. The DM density scales with galaxy mass as expected, deviating from suggestions of a universal halo profile for dwarf and late-type galaxies. We introduce a new fundamental constraint on galaxy formation by finding that the central DM fraction decreases with stellar age. This result is only partially explained by the size-age dependencies, and the residual trend is in the opposite direction to basic DM halo expectations. Therefore, we suggest that there may be a connection between age and halo contraction and that galaxies forming earlier had stronger baryonic feedback, which expanded their haloes, or lumpier baryonic accretion, which avoided halo contraction. An alternative explanation is a lighter initial mass function for older stellar population

Anomaly detection in Astrophysics: a comparison between unsupervised Deep and Machine Learning on KiDS data

Every field of Science is undergoing unprecedented changes in the discovery process, and Astronomy has been a main player in this transition since the beginning. The ongoing and future large and complex multi-messenger sky surveys impose a wide exploiting of robust and efficient automated methods to classify the observed structures and to detect and characterize peculiar and unexpected sources. We performed a preliminary experiment on KiDS DR4 data, by applying to the problem of anomaly detection two different unsupervised machine learning algorithms, considered as potentially promising methods to detect peculiar sources, a Disentangled Convolutional Autoencoder and an Unsupervised Random Forest. The former method, working directly on images, is considered potentially able to identify peculiar objects like interacting galaxies and gravitational lenses. The latter instead, working on catalogue data, could identify objects with unusual values of magnitudes and colours, which in turn could indicate the presence of singularities.Comment: Preprint version of the manuscript to appear in the Volume "Intelligent Astrophysics" of the series "Emergence, Complexity and Computation", Book eds. I. Zelinka, D. Baron, M. Brescia, Springer Nature Switzerland, ISSN: 2194-728

Anomaly Detection in Astrophysics: A Comparison Between Unsupervised Deep and Machine Learning on KiDS Data

Every field of Science is undergoing unprecedented changes in the discovery process, and Astronomy has been a main player in this transition since the beginning. The ongoing and future large and complex multi-messenger sky surveys impose a wide exploiting of robust and efficient automated methods to classify the observed structures and to detect and characterize peculiar and unexpected sources. We performed a preliminary experiment on KiDS DR4 data, by applying to the problem of anomaly detection two different unsupervised machine learning algorithms, considered as potentially promising methods to detect peculiar sources, a Disentangled Convolutional Autoencoder and an Unsupervised Random Forest. The former method, working directly on images, is considered potentially able to identify peculiar objects like interacting galaxies and gravitational lenses. The latter instead, working on catalogue data, could identify objects with unusual values of magnitudes and colours, which in turn could indicate the presence of singularities

Scaling relations and baryonic cycling in local star-forming galaxies: I. The sample

Metallicity and gas content are intimately related in the baryonic exchange cycle of galaxies, and galaxy evolution scenarios can be constrained by quantifying this relation. To this end, we have compiled a sample of ~400 galaxies in the Local Universe, dubbed "MAGMA" (Metallicity And Gas for Mass Assembly), which covers an unprecedented range in parameter space, spanning more than 5 orders of magnitude in stellar mass (Mstar), star-formation rate (SFR), and gas mass (Mgas), and a factor of ~60 in metallicity [Z, 12+log(O/H)]. Stellar masses and SFRs have been recalculated for all the galaxies using IRAC, WISE and GALEX photometry, and 12+log(O/H) has been transformed, where necessary, to a common metallicity calibration. To assess the true dimensionality of the data, we have applied multi-dimensional principal component analyses (PCAs) to our sample. In confirmation of previous work, we find that even with the vast parameter space covered by MAGMA, the relations between Mstar, SFR, Z and Mgas (MHI+MH2) require only two dimensions to describe the hypersurface. To accommodate the curvature in the Mstar-Z relation, we have applied a piecewise 3D PCA that successfully predicts observed 12+log(O/H) to an accuracy of ~0.1dex. MAGMA is a representative sample of isolated star-forming galaxies in the Local Universe, and can be used as a benchmark for cosmological simulations and to calibrate evolutionary trends with redshift.Comment: 21 pages, 12 figures. Accepted for publication in A&A. Sample and results improved compared to previous versions. Some analysis has been removed and will be expanded in future paper