Deep NIR Surveys in the Galactic Plane: A General Overview and the Study of Scutum’s Spiral Arm

Despite the impressive advances in Galactic structure studies, thanks to the large astronomical surveys, there remain several open questions. Although at low distances, optical surveys can bring us important information, the potential of NIR surveys, combined with the optical data, should be considered. In the present work, we explore the stellar distribution through the most recent NIR surveys toward low latitudes (|b| < 2° for 20° ≤ ℓ ≤ 346°) in the Galactic disk, such as 2MASS (entire plane), UKIDSS (20° ≤ ℓ ≤ 231°), and VVV-PSF data (295° ≤ ℓ ≤ 346°), avoiding directions toward the Galactic bar and bulge. Our final compilation contains nearly 140 million stars. We used this sample to perform total star counts at different longitudes, obtaining longitudinal profiles that are compared with those of other authors. For some directions, we obtained the stellar density as a function of distance to investigate the stellar distribution in the Galactic disk. As an example, the variation of the counts toward the Scutum arm tangential direction reveals the stellar content of two spiral arms, e.g., Sagittarius and Scutum. These are the preliminary results of a study that will cover a large extension of the Galactic disk.

GUASOM: an adaptive visualization tool for unsupervised clustering in spectrophotometric astronomical surveys

We present an adaptive visualization tool for unsupervised classification of astronomical objects in a Big Data context such as the one found in the increasingly popular large spectrophotometric sky surveys. This tool is based on an artificial intelligence technique, Kohonen’s self-organizing maps, and our goal is to facilitate the analysis work of the experts by means of oriented domain visualizations, which is impossible to achieve by using a generic tool. We designed a client-server that handles the data treatment and computational tasks to give responses as quickly as possible, and we used JavaScript Object Notation to pack the data between server and client. We optimized, parallelized, and evenly distributed the necessary calculations in a cluster of machines. By applying our clustering tool to several databases, we demonstrated the main advantages of an unsupervised approach: the classification is not based on pre-established models, thus allowing the “natural classes” present in the sample to be discovered, and it is suited to isolate atypical cases, with the important potential for discovery that this entails. Gaia Utility for the Analysis of self-organizing maps is an analysis tool that has been developed in the context of the Data Processing and Analysis Consortium, which processes and analyzes the observations made by ESA’s Gaia satellite (European Space Agency) and prepares the mission archive that is presented to the international community in sequential periodic publications. Our tool is useful not only in the context of the Gaia mission, but also allows segmenting the information present in any other massive spectroscopic or spectrophotometric database.

Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks

The problem of anomaly detection in astronomical surveys is becoming increasingly important as data sets grow in size. We present the results of an unsupervised anomaly detection method using a Wasserstein generative adversarial network (WGAN) on nearly one million optical galaxy images in the Hyper Suprime-Cam (HSC) survey. The WGAN learns to generate realistic HSC-like galaxies that follow the distribution of the data set; anomalous images are defined based on a poor reconstruction by the generator and outlying features learned by the discriminator. We find that the discriminator is more attuned to potentially interesting anomalies compared to the generator, and compared to a simpler autoencoder-based anomaly detection approach, so we use the discriminator-selected images to construct a high-anomaly sample of ∼13 000 objects. We propose a new approach to further characterize these anomalous images: we use a convolutional autoencoder to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images and perform UMAP clustering on these. We report detected anomalies of interest including galaxy mergers, tidal features, and extreme star-forming galaxies. A follow-up spectroscopic analysis of one of these anomalies is detailed in the Appendix; we find that it is an unusual system most likely to be a metal-poor dwarf galaxy with an extremely blue, higher-metallicity H ii region. We have released a catalog with the WGAN anomaly scores; the code and catalog are available at https://github.com/kstoreyf/anomalies-GAN-HSC, and our interactive visualization tool for exploring the clustered data is at https://weirdgalaxi.es.

Introducing high-school and university students to practical astronomy via Virtual observatory tools

Large-scale astronomical surveys from the last decades have turned the usage of catalogs and archival data into one of the primary skills of contemporary observational astronomers. Virtual observatory tools give high-school and university students the opportunity to conduct astronomical research by themselves, using freely available observational data. For this purpose, they need basic theoretical knowledge in astronomy. The current paper includes a review of this theoretical knowledge as well as a review of Virtual observatory tools suitable for students. Results obtained by students using VO tools at the Beli Brezi Summer School in Astronomy and Astrophysics are presented as well.

A deep learning approach to quasar continuum prediction

ABSTRACT We present a novel intelligent quasar continuum neural network (iQNet), predicting the intrinsic continuum of any quasar in the rest-frame wavelength range of $1020 \, {\mathring{\rm A}}\le \lambda _{\text{rest}} \le 1600 \, {\mathring{\rm A}}$. We train this network using high-resolution Hubble Space Telescope/Cosmic Origin Spectrograph ultraviolet quasar spectra at low redshift (z ∼ 0.2) from the Hubble Spectroscopic Legacy Archive (HSLA), and apply it to predict quasar continua in different astronomical surveys. We utilize the HSLA quasar spectra that are well defined in the rest-frame wavelength range of [1020, 1600] Å with an overall median signal-to-noise ratio of at least 5. The iQNet model achieves a median absolute fractional flux error of 2.24 per cent on the training quasar spectra, and 4.17 per cent on the testing quasar spectra. We apply iQNet and predict the continua of ∼3200 Sloan Digital Sky Survey Data Release 16 quasar spectra at higher redshift (2 < z ≤ 5) and measure the redshift evolution of mean transmitted flux (〈F〉) in the Ly α forest region. We measure a gradual evolution of 〈F〉 with redshift, which we characterize as a power-law fit to the effective optical depth of the Ly α forest. Our measurements are broadly consistent with other estimates of 〈F〉 in the literature but provide a more accurate measurement as we are directly measuring the quasar continuum where there is minimum contamination from the Ly α forest. This work proves that the deep learning iQNet model can predict the quasar continuum with high accuracy and shows the viability of such methods for quasar continuum prediction.

Dwarf galaxies without dark matter: constraints on modified gravity

ABSTRACT The discovery of 19 dwarf galaxies without dark matter (DM) provides, counterintuitively, strong support for the ΛCDM standard model of cosmology. Their presence is well accommodated in a scenario where the DM is in the form of cold dark particles. However, it is interesting to explore quantitatively what is needed from modified gravity models to accommodate the presence of these galaxies and what extra degree of freedom is needed in these models. To this end, we derive the dynamics at galaxy scales (Virial theorem) for a general class of modified gravity models. We distinguish between theories that satisfy the Jebsen–Birkhoff theorem, and those that do not. Our aim is to develop tests that can distinguish whether DM is part of the theory of gravity or a particle. The 19 dwarf galaxies discovered provide us with a stringent test for models of modified gravity. Our main finding is that there will always be an extra contribution to the Virial theorem coming from the modification of gravity, even if a certain galaxy shows very small, if not negligible, trace of DM, as has been reported recently. Thus, if these and more galaxies are confirmed as devoid (or negligible) of DM, while other similar galaxies have abundant DM, it seems interesting to find modifications of gravity to describe DM. Our result can be used by future astronomical surveys to put constraints on the parameters of modified gravity models at astrophysical scales where DM is described as such.

A Blended Artificial Intelligence Approach for Spectral Classification of Stars in Massive Astronomical Surveys

This paper analyzes and compares the sensitivity and suitability of several artificial intelligence techniques applied to the Morgan–Keenan (MK) system for the classification of stars. The MK system is based on a sequence of spectral prototypes that allows classifying stars according to their effective temperature and luminosity through the study of their optical stellar spectra. Here, we include the method description and the results achieved by the different intelligent models developed thus far in our ongoing stellar classification project: fuzzy knowledge-based systems, backpropagation, radial basis function (RBF) and Kohonen artificial neural networks. Since one of today’s major challenges in this area of astrophysics is the exploitation of large terrestrial and space databases, we propose a final hybrid system that integrates the best intelligent techniques, automatically collects the most important spectral features, and determines the spectral type and luminosity level of the stars according to the MK standard system. This hybrid approach truly emulates the behavior of human experts in this area, resulting in higher success rates than any of the individual implemented techniques. In the final classification system, the most suitable methods are selected for each individual spectrum, which implies a remarkable contribution to the automatic classification process.

Surveys for active galaxies: discovery and studies

We present surveys and related studies of active galaxies carried out at the Byurakan Astrophysical Observatory (BAO). This was one of the main research subjects at BAO during many years, since mid-1950s, when Viktor Ambartsumian suggested the hypothesis of the activity of the galactic nuclei. A number of surveys and searches for Active Galactic Nuclei (AGN) and other active galaxies were accomplished during 1960s-1980s. Since mid-1990s, our research group carried out new surveys and studies of active galaxies based on the First Byurakan Survey (FBS or Markarian Survey) and then a number of others. Here we also present the recent results of studies on active galaxies (both AGN and Starbursts) by the Extragalactic group of the Byurakan Astrophysical Observatory (BAO) Research Department “Astronomical Surveys”. These studies are characterized by multiwavelength approach to statistical analysis of large amount of data obtained in different wavelengths; from X-ray to radio. A fine classification scheme for active galaxies has also been suggested.