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Cosmological observations (redshifts, cosmic microwave background radiation, abundance of light elements, formation and evolution of galaxies, large-scale structure) find explanations within the standard Lambda-CDM model, although many times after a number of ad hoc corrections. Nevertheless, the expression ‘crisis in cosmology’ stubbornly reverberates in the scientific literature: the higher the precision with which the standard cosmological model tries to fit the data, the greater the number of tensions that arise. Moreover, there are alternative explanations for most of the observations. Therefore, cosmological hypotheses should be very cautiously proposed and even more cautiously received.

There are also sociological and philosophical arguments to support this scepticism. Only the standard model is considered by most professional cosmologists, while the challenges of the most fundamental ideas of modern cosmology are usually neglected. Funding, research positions, prestige, telescope time, publication in top journals, citations, conferences, and other resources are dedicated almost exclusively to standard cosmology. Moreover, religious, philosophical, economic, and political ideologies in a world dominated by anglophone culture also influence the contents of cosmological ideas.

Galaxy morphologies are one of the key diagnostics of galaxy evolutionary tracks, but visual classifications are extremely time-consuming. The sheer size of Big Data surveys, containing millions of galaxies, make this approach completely impractical. Deep Learning (DL) algorithms, where no image pre-processing is required, have already come to the rescue for image analysis of large data surveys. In this seminar, I will present the largest multi-band catalog of automated galaxy morphologies to date containing morphological classifications of ∼27 million galaxies from the Dark Energy Survey. The classification separates: (a) early-type galaxies (ETGs) from late-types (LTGs); and (b) face-on galaxies from edge-on. These classifications have been obtained using a supervised DL algorithm. Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications, but hese typically have mr < 17.7 mag. We overcome the lack ofa training sample by modeling fainter objects up to mr < 21.5 mag, i.e., by simulating what thebrighter objects with well-determined classifications would look like if they were at higher redshifts.The CNNs reach a 97% accuracy to mr < 21.5 on their training sets, suggesting that they are ableto recover features more accurately than the human eye. We obtain secure classifications for 87%and 73% of the catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively.

To understand the formation and evolution of galaxies, it is important to have a full comprehension of the role played by Metallicity, Star Formation Rate (SFR), and stellar mass of galaxies. The interplay of these parameters at different redshifts will substantially affect the evolution of galaxies and, as a consequence, the evolution of these parameters provides important constraints for the galaxy evolution models. We studied the relationships and dependencies between the SFR, stellar mass, and gas metallicity of star forming galaxies from the Sloan Digital Sky Survey-Data Release 7 (SDSS DR7) and Galaxy and Mass Assembly (GAMA) surveys. We have combined both surveys finding evidence of SFR and metallicity evolution for galaxies down to redshift ~0.2. Also, we have proved the existence of a Fundamental Plane in the 3D space formed by the SFR, mass and metallicity for the SDSS and GAMA samples.

We compare the Hubble type and the spectroscopic class of the galaxies with spectra in SDSS/DR7. As it is long known, elliptical galaxies tend to be red whereas spiral galaxies tend to be blue, however, this relationship presents a large scatter, which we measure and quantify in detail. We compare the Automatic Spectroscopic K-means based classification (ASK) with most of the commonly used morphological classifications. All of them provide consistent results. Given a spectral class, the morphological type wavers with a standard deviation between 2 and 3 T types, and the same large dispersion characterizes the variability of spectral classes fixed the morphological type. The distributions of Hubble types given an ASK class are very skewed -- they present long tails that go to the late morphological types for the red galaxies, and to the early morphological types for the blue spectroscopic classes. The scatter is not produced by problems in the classification, and it remains when particular subsets are considered. A considerable fraction of the red galaxies are spirals (40--60 %), but they never present very late Hubble types (Sd or later). Even though red spectra are not associated with ellipticals, most ellipticals do have red spectra: 97 % of the ellipticals in the morphological catalog by Nair & Abraham, used here for reference, belong to ASK 0, 2 or 3. It contains only a 3 % of blue ellipticals. The galaxies in the green valley class (ASK~5) are mostly spirals, and the AGN class (ASK 6) presents a large scatter of Hubble types from E to Sd. We investigate variations with redshift using a volume limited subsample. From redshift 0.25 to now the galaxies redden from ASK 2 to ASK 0, as expected from the passive evolution of their stellar populations. Two of the ASK classes (1 and 4) gather edge-on spirals, and they may be useful in studies requiring knowing the intrinsic shape of a galaxy (e.g., weak lensing calibration).

Using the k-means cluster analysis algorithm, we carry out an unsupervised classification of all galaxy spectra in the seventh and final Sloan Digital Sky Survey data release (SDSS/DR7). Except for the shift to rest-frame wavelengths and the normalization to the g-band flux, no manipulation is applied to the original spectra. The algorithm guarantees that galaxies with similar spectra belong to the same class. We find that 99% of the galaxies can be assigned to only 17 major classes, with 11 additional minor classes including the remaining 1%. The classification is not unique since many galaxies appear in between classes; however, our rendering of the algorithm overcomes this weakness with a tool to identify borderline galaxies. Each class is characterized by a template spectrum, which is the average of all the spectra of the galaxies in the class. These low-noise template spectra vary smoothly and continuously along a sequence labeled from 0 to 27, from the reddest class to the bluest class. Our Automatic Spectroscopic K-means-based (ASK) classification separates galaxies in colors, with classes characteristic of the red sequence, the blue cloud, as well as the green valley. When red sequence galaxies and green valley galaxies present emission lines, they are characteristic of active galactic nucleus activity. Blue galaxy classes have emission lines corresponding to star formation regions. We find the expected correlation between spectroscopic class and Hubble type, but this relationship exhibits a high intrinsic scatter. Several potential uses of the ASK classification are identified and sketched, including fast determination of physical properties by interpolation, classes as templates in redshift determinations, and target selection in follow-up works (we find classes of Seyfert galaxies, green valley galaxies, as well as a significant number of outliers). The ASK classification is publicly accessible through various Web sites.