BEHIND THE ZONE OF AVOIDANCE OF THE MILKY WAY: WHAT CAN WE RESTORE BY DIRECT AND INDIRECT METHODS?

Abstract

Purpose: to present a brief overview of methods for restoring the large-scale structure of the Universe behind the Zone of Avoidance (ZoA) of the Milky Way; to propose a new “algorithm of darning the ZoA” and new approach based on the Generative adversarial network (GAN) to recover galaxy distribution in the ZoA using optical surveys as an additional platform for programming the artificial neural networks. Design/methodology/approach: Due to the extensive monitoring observations in radio (DOGS project, in HI line), infrared (IRAS and 2MASS surveys), and X-ray spectral ranges, the ZoA has been decreased significantly in size and now the obscured part is about 10% of the sky in the visible spectral range. The Cosmic Microwave Background (CMB) measurements showed a 180° asymmetry known as the dipole: despite the fact that the resulting vector lies within 20° of the observed CMB dipole, the calculations remain highly ambiguous, partly because the galaxies in the ZoA are not taken into account and the concept of “attractors” should be reconsidered. Hence, the analysis of the spatial distribution of galaxies and their groups in the regions surrounding and behind the ZoA of Milky Way remains a complex and unresolved problem, and estimation of the “invisible” content of the spatial galaxy distribution, which is obscured by this absorption zone, becomes a highly actual one. Restoring the ZoA is possible by indirect methods (signal processing applied to obscured and incomplete data; Voronoi tessellation, etc.). These recovery methods, however, work only for large-scale structures in the ZoA; they are practically not sensitive to individual galaxies and small galaxy systems. We suggest the machine learning technique such as the GAN to apply for modeling the “invisible” spatial galaxy distribution behind the ZoA. Findings: We present “the algorithm of darning the ZoA” for dividing the real extragalactic surveys (e.g, the SDSS DR 14 galaxy sample) on the slices by redshifts, stellar magnitudes, coordinates and other parameters to form a training sample, and the general GAN scheme for the ZoA filling. We discuss principal tasks to generate galaxy distributions and their properties in the ZoA from latent space of features and describe how the discriminative network will compare the obtained artificial survey with the real one and evaluate how it is a realistic one. Conclusions: The incompleteness of data depending on wavelengths indicates that there are steal not resolved problems such as the dynamics in the Local Group and the near Universe; the large-scale structure of the Universe in the sky region obscured by the Milky Way; the velocity flow fields towards the Great Attractor; the CMB dipole. Here, we propose a new “algorithm of darning the ZoA” and the general GAN scheme as an additional machine learning platform to recover a spatial distribution behind the ZoA of our Galaxy.

Keywords

• large-scale structure of the universe
• milky way
• galaxies
• galaxy clusters
• zone of avoidance
• machine learning
• generative adversarial network (gan)
• “algorithm of darning the zoa”