Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods?
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 distribut...
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| Zitieren: | Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods? / I.B. Vavilova, A.A. Elyiv, M.Yu. Vasylenko // Радиофизика и радиоастрономия. — 2018. — Т. 23, № 4. — С. 244-257. — Бібліогр.: 57 назв. — англ. |
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irk-123456789-1501952019-04-03T01:25:19Z Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods? Vavilova, I.B. Elyiv, A.A. Vasylenko, M.Yu. Радиоастрономия и астрофизика 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. Предмет и цель работы: представить краткий обзор методов, которые применяются для восстановления распределения крупномасштабных структур Вселенной за зоной избегания (ZoA) Млечного Пути; предложить новый “алгоритм штопки зоны избегания” и новый подход, основанный на Генерирующих состязательных сетях (GAN) для восстановления распределения галактик в ZoA с использованием оптических обзоров в качестве дополнительной платформы для программирования искусственных нейронных сетей. Предмет і мета роботи: подати короткий огляд методів, які застосовуються для відтворення розподілу великомасштабних структур Всесвіту за зоною уникнення (ZoA) Чумацького Шляху;запропонувати новий “алгоритм штопання зони уникнення” і новий підхід, що грунтується на генеруючій змагальній мережі (GAN) для відновлення розподілу галактик в ZoA з використанням оптичних оглядів у якості додаткової платформи для програмування штучних нейронних мереж. 2018 Article Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods? / I.B. Vavilova, A.A. Elyiv, M.Yu. Vasylenko // Радиофизика и радиоастрономия. — 2018. — Т. 23, № 4. — С. 244-257. — Бібліогр.: 57 назв. — англ. 1027-9636 PACS number: 98.35.-a DOI: https://doi.org/10.15407/rpra23.04.244 http://dspace.nbuv.gov.ua/handle/123456789/150195 en Радиофизика и радиоастрономия Радіоастрономічний інститут НАН України |
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Радиоастрономия и астрофизика Радиоастрономия и астрофизика Vavilova, I.B. Elyiv, A.A. Vasylenko, M.Yu. Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods? Радиофизика и радиоастрономия |
| description |
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. |
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Article |
| author |
Vavilova, I.B. Elyiv, A.A. Vasylenko, M.Yu. |
| author_facet |
Vavilova, I.B. Elyiv, A.A. Vasylenko, M.Yu. |
| author_sort |
Vavilova, I.B. |
| title |
Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods? |
| title_short |
Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods? |
| title_full |
Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods? |
| title_fullStr |
Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods? |
| title_full_unstemmed |
Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods? |
| title_sort |
behind the zone of avoidance of the milky way: what can we restore by direct and indirect methods? |
| publisher |
Радіоастрономічний інститут НАН України |
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2018 |
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Радиоастрономия и астрофизика |
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http://dspace.nbuv.gov.ua/handle/123456789/150195 |
| citation_txt |
Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods? / I.B. Vavilova, A.A. Elyiv, M.Yu. Vasylenko // Радиофизика и радиоастрономия. — 2018. — Т. 23, № 4. — С. 244-257. — Бібліогр.: 57 назв. — англ. |
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Радиофизика и радиоастрономия |
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2025-07-12T23:52:11Z |
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| fulltext |
ISSN 1027-9636. Радіофізика і радіоастрономія. Т. 23, № 4, 2018244
Радіофізика і радіоастрономія. 2018, Т. 23, № 4, c. 244–257
© I. B. Vavilova, A. A. Elyiv, M. Yu. Vasylenko, 2018
I. B. VAVILOVA, A. A. ELYIV, and M. YU. VASYLENKO
Main Astronomical Observatory, National Academy of Sciences of Ukraine,
27, Akademik Zabolotny St., Kyiv, 03143, Ukraine
Email: irivav@mao.kiev.ua; andrii.elyiv@gmail.com; maximka88@meta.ua
BEHIND THE ZONE OF AVOIDANCE OF THE MILKY WAY:
WHAT CAN WE RESTORE BY DIRECT AND INDIRECT METHODS?
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 tessella-
tion, 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.
Key words: 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”
DOI: https://doi.org/10.15407/rpra23.04.244
PACS number: 98.35.-a
1. Introduction
The Zone of Avoidance of the Milky Way was firstly
noted by R. Proctor in his paper as concerns with the
“General Catalogue of Nebulae” by J. Herschel (1878):
he called it as the Zone of Few Nebulae. In 1922,
using the data from the “New General Catalogue”
by Dreyer (1888, 1895), Charlier was the first who
has referred this sky region as a scientific problem
in recognizing the nebulae distribution in the area of
the sky that is obscured by the Milky Way. In 1961,
Shapley has proposed to call this region as the Zone
of Avoidance (ZoA) delimited by “the isopleth of
five galaxies per square degree from the Lick and
Harvard surveys”. During the long time this zone
was avoided by astronomers interested in the study
of extragalactic objects due to 1) the small number
of known objects; 2) decreasing the brightness of the
extragalactic objects toward to the galactic equator;
3) increasing the concentration of stars on the line
of sight which is resulted in increasing the over-
lapping of the extragalactic object with the star [1].
Because the Solar system is located not in the center
of our Galaxy, the ZoA is also heterogeneous and lon-
gitudinal.
Since 1990s the notion of the ZoA galaxy distribu-
tion has changed significantly. If it was previously
believed that this area closes an observer about 20 %
of the spatial distribution of galaxies in the optical
ISSN 1027-9636. Радіофізика і радіоастрономія. Т. 23, № 4, 2018 245
Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods?
range, then this value is now about 10 %. First of all,
this has happened due to studies in the infrared and
radio spectral ranges, since due to the decrease in
the amount of light absorption with increasing wave-
length, the Zone of Avoidance becomes more trans-
parent in these spectral ranges. As for the incom-
pleteness of ZoA galaxy catalogs as a function of the
foreground extinction, we note that optical ZoA sur-
veys are complete to an apparent diameter of 14D
(where the diameters correspond to an isophote of
m 224.5 arcsec ) for extinction levels less than
m3.0 .BA The incompleteness of galaxy sample
depending on the wavelength is an issue for studying
dynamic properties of Local Group; the large-scale
structure of the Universe (voids, filaments, walls,
galaxy clusters, etc.); the velocity flow fields towards
the Great Attractor; the dipole in the Cosmic Mic-
rowave Background (CMB), and other important
problems.
In this paper, 1) we describe briefly in the Chap-
ters 2 and 3 the direct (observational) and indirect
methods (data mining plus confirmation through ob-
servations), which were provided to recognize celes-
tial bodies in the ZoA; 2) we propose a new ap-
proach, the algorithm of darning the ZoA, based on
the machine learning technique to reconstruct gala-
xy distribution in the ZoA, which takes into account
the 3D distribution of galaxies and its photometry; the
algorithm and a general scheme of the GAN ma-
chine learning methods are given in Chapter 4. The
conclusions are made in Chapter 5.
2. A Brief Review of the Direct
and Indirect Methods for Restoring
the Zone of Avoidance
Due to the fact that galactic gas and dust close a signi-
ficant part of the sky from visual observation, the de-
tection of sources in this area becomes problematic.
Due to the incomplete sampling in the area of absorp-
tion, on the basis of which the velocity field is con-
structed, we cannot say of its homogeneity, which
gives an error in the definite direction of motion of our
Galaxy by this method. The problem of the discre-
pancy between the vectors of movement of galaxies
of the Local Group relative to the coordinate system
associated with the CMB relict radiation suggests that
there are a significant number of galaxies in the area
of absorption of our Galaxy.
The methodology to solve this problem includes
either direct or indirect techniques. Under direct
methods is meant the observation of whole-sky sur-
veys in different spectral ranges in the band near
the galactic equator ( [ 20 , 20 ]).b For exam-
ple, the currently actively used method is the search
for bright sources in the microwave energy range of
regions of the heated gas and of areas of star for-
mation HI. These areas are also monitored by radio
telescopes with purpose to confirm the assumption
of the presence of galaxies. In some cases, when
sources can be visible in optical spectral range, this
allows us to supplement the data on this source and
the Tally–Fisher method to determine the distance to
the galaxy.
2.1. Observational Programs in IR-, Radio-,
and X-ray Spectral Ranges
The first qualitative breakthrough in the study of the
ZoA belongs primarily to the Italian astronomer
P. Maffei, who in 1968 discovered two galaxies in the
ZoA using observations in the IR-range (see, paper
by Maffei, 2003, for review of his own works [2]).
The elliptical galaxy Maffei 1 together with its com-
panion, spiral galaxy Maffei 2, was discovered on
a hyper-sensitized I-N photographic plate exposed
on 29 Sept 1967 with the Schmidt telescope at
Asiago Observatory. These galaxies were named
after as the Maffei 1 and the Maffei 2. For examp-
le, the last updated data about Maffei 1 are as fol-
lows: morphology E3, constellation Cassiopeia,
h m sRA 02 36 35.4 , DEC + 59 39 19 , size 75,000 Lyy
(23,000 pc), radial velocity (66.4 5.0) km / s,rV
distance (2.85 0.36) Mpc, stellar magnitude in vi-
sible range 11.14 0.06.Vm Maffei 1 is located
only 0.55 from the galactic plane in the middle of
the ZoA and suffers from about m4.7 of extinction
(a factor of about 1 70) in visible range. If there were
no this absorption, it would be one of the largest
and brightest elliptical galaxy in the sky (about 3 4
the size of the full moon).
The Maffei’s discovery had a revolutionary effect
for our modern picture of the Local Universe and
promoted a lively discussion, first of all, about possi-
ble membership of these galaxies in the Local Group.
We note several important papers on the determi-
nation of a distance to Maffei 1 to show how this
problem was resolved. In 1970, Spinrad et al. [3]
suggested that Maffei 1 is a nearby heavily obscured
246 ISSN 1027-9636. Радіофізика і радіоастрономія. Т. 23, № 4, 2018
I. B. Vavilova, A. A. Elyiv, and M. Yu. Vasylenko
giant elliptical galaxy and the estimated distance to
Maffei 1 is equal to 1 Mpc (Local Group possible
member?). In 1983 this estimate was revised up to
1.3
0.82.1 Mpc by Buta and McCall [4] (Maffei 1 is
outside the Local Group!). In 2001, Davidge and van
den Bergh [5], using adaptive optics to observe the
brightest AGB stars in Maffei 1, concluded that dis-
tance is 0.6
0.54.4 Mpc. So, these larger ( 3 Mpc)
distances reported in the past 20 years would imply
that Maffei 1 has never been close enough to the
Local Group to significantly influence its dynamics.
The latest determination of the distance to Maffei 1
((2.85 0.36) Mpc) is based on the re-calibrated
luminosity/velocity dispersion relation for E-galaxies
and the updated extinction. Maffei 1 is a key member
of a nearby galaxy group, where among other large
members are the giant spiral galaxies IC342 and
Maffei 2 [6, 7]. Maffei 1 has a small satellite spiral
galaxy Dwingeloo 1, which was discovered in radio
spectral range, as well as a number of dwarf satel-
lites. The IC 342/Maffei Group is one of the closest
galaxy groups to the Milky Way.
A significant progress in a magnificent reduction
of the ZoA was connected with exploration of the
IRAS and the 2MASS surveys. For example, in 2000,
Jarret et al. [8] reported on the detection of newly
discovered sources from 2MASS Extended Survey
in the fields incorporating the Galactic plane at
40 70l and predicted that the area-normalized
detection rate is ~ 1 2 galaxies per deg2 brighter
than m12.1 (10 mJy). See, also, earlier paper by Lu
et al. [9] with results on identifying the HI spectra of
galaxies observed by the IRAS.
Observations of the neutral hydrogen (21 cm) in
frame of the DOGS project revealed new galaxies in
the ZoA, the Dwingeloo 1 [10] and the Dwingeloo 2
[11] (see, for example, on the estimates of their ki-
nematic and dynamic parameters, [4, 12, 13]). The
DOGS project was conducted with 25m Dwingeloo
radio telescope and covered almost the whole obser-
vational region of the Northern Galactic Plane
30 200l below a Galactic latitude 5 .b
Because of the transparency of the Galaxy to the
21 cm radiation of neutral hydrogen, systematic
HI-surveys are particularly powerful in mapping large-
scale structure (LSS) in this part of the sky. It should
be noted that the absence of a signal does not always
indicate the absence of a galaxy, but may be associa-
ted with a low HI content [14]. Nevertheless, that
this method is slow and requires a lot of time, the
conjunction of HI surveys and 2MASS will greatly
increase the current census of galaxies hidden be-
hind the Milky Way. Supplementary to these surveys,
the Parkes Multibeam HI ZoA Survey as a syste-
matic deep blind HI survey of the southern Milky
Way was begun in 1997 with the Multibeam receiver
at the 64m Parkes telescope (surveys were cente-
red on the southern Galactic Plane 52 196 ,l
5b (see, for example, [15])).
The X-ray spectral range is an excellent window
for studies of large-scale structure in the ZoA, be-
cause of the Milky Way is transparent to the hard X-
ray emission above a few keV, also the rich clus-
ters are strong X-ray emitters. Since the X-ray lumi-
nosity is roughly proportional to the cluster mass as
3 2
XL M or 2,M depending on the still uncertain
scaling law between the X-ray luminosity and tem-
perature (see our works [16–19] and references
therein), massive clusters hidden by the Milky Way
should be easily table through their X-ray emission
[20, 21]. This method is particularly attractive, be-
cause the clusters are primarily composed of early-
type galaxies, which are not recovered by IR ga-
laxy surveys or by systematic HI surveys.
2.2. A Brief Review of the Mathematical
Simulation, Data Mining,
and Machine Learning Methods
Indirect methods consist in applying the mathematical
simulation and data mining methods to fill the ZoA as
well as to determine the gravitational potentials of the
nearest galaxies in order to predict the positions of
galaxies and galaxy systems in the area of Milky Way
absorption. Now a great attention is also focused on
the machine learning technique.
The inhomogeneous distributed mass of matter
in the ZoA surrounding the Local Group may cause
the unbalanced gravity toward the Local Group (LG)
in one direction. The expected velocity of the Local
Group can be calculated by the sum of gravitational
forces from all known LG galaxies [22, 23]. Despite
the fact that the resulting vector lies within 20 of the
observed cosmic background dipole, the calculations
remain highly ambiguous, partly because galaxies in
the ZoA are not taken into account [24, 25].
CMB measurements showed an 180 asymmetry
known as dipole. It manifests itself in the heating of
0.1 % of CMB radiation in comparison with the
average in one direction and in the same cooling in
ISSN 1027-9636. Радіофізика і радіоастрономія. Т. 23, № 4, 2018 247
Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods?
the opposite direction. These measurements have
been confirmed yet by the COBE (1989–1990) stu-
dies indicating that the Milky Way and the Local
Group are moving at a velocity ~ 627 km/spV to
276 ,l 30 ,b towards the Hydra constellation
[26]. This motion arises as a result of the distribution
of matter iM in the Local Group and depends on the
cosmological parameter 0 [27]:
0.6
0
2
ˆ .i
p i
i i
M
V r
b r
The absence of objects in the absorption zone also
plays a key role in determining the value of the dipole
of the collective velocity of the galaxies. Filling the
zone 20b by galaxies changes the direction of
movement measured in the volume of 6000 km/s by
31 [28, 29]. Unknown galaxies that are closer to us
in the ZoA can make a larger contribution to the
definition of a vector of collective velocity than whole
clusters over long distances: 0.4 ˆ10 .m
p i
i
V r
What is the reason for this movement, which
manifests itself in a slight deviation from the homo-
geneous expansion of the Universe? To overcame
this discrepancy between the direction on the dipole
and the expected velocity vector made it necessary
to introduce the concept of “attractors” (the Great
Attractor at a distance of about 60 Mpc). The Local
Group is located at the same distance above the
Perseus-Fornax cluster (both of which are compo-
nents of a long chain of galaxies known as the Super-
galactic Plane). However a lot of well known nearby
large-scale structures are bisected by the Galactic
Plane, such as the Local Supercluster, the Perseus-
Pisces chain, and the Great Attractor. “What is their
true extent and their mass? It is curious that the two
major superclusters in the Local Universe, i.e. Per-
seus-Pisces and the Great Attractor overdensity, lie
at similar distances on opposite sides of the Local
Group, both partially obscured by the ZoA. Which
one of the two is dominant in the tug-of-war on the
Local Group? Do these features continue across the
Galactic Plane and are there other massive struc-
tures hidden in the ZoA for which so far no indication
exists? What is the size of the largest coherent struc-
tures?” – these questions remain unanswered. For
instance, the Great Wall and the Perseus-Pisces chain
are connected across the ZoA as it was suggested by
Giovanelli & Haynes [26] as the indicating structures
of 1(50 200)h Mpc. “The latter would be incom-
patible with the angular extent over which fluctua-
tions – the seeds of current large-scale structures –
have been measured in the CMB”. To answer these
questions, the superclusters need to be fully mapped
across the ZoA. We should take into account that the
ZoA is fully incomplete at low Galactic latitudes in
the larger Galactic Bulge area ( 0 90 ),l inclu-
ding the Great Attractor region. Even if the obscured
galaxies can be identified, the redshifts are deter-
mined very difficult if not impossible to obtain at the
higher extinction levels.
Attempts to solve the problem of the incompatibi-
lity of the vector apex motion of the Local Group
determined by the CMB and the velocity field did not
give a positive result, since the method involves the
uniform filling of the sky by the galaxies of the field,
and chaotic filling them with non-real objects leads to
the formation of non-existent fields [1]. This problem
can be solved by the machine learning methods.
So, the intensive multi-wavelength surveys of the
ZoA in the last decades were aimed at addressing
such key problems as the cosmological questions
about the dynamics of the Local Group, the possible
existence of nearby hidden massive galaxies, the dipole
determinations based on luminous galaxies, the con-
tinuity and size of nearby superclusters, the mapping
of cosmic flow fields. Their solution is possible by
indirect methods, which include the methods of signal
processing applied to obscured and incomplete data;
indirect estimates of averaged variables; the mask
inversion using Wiener filtering in spherical harmonic
analysis; reconstruction of the projected galaxy dis-
tribution in IR-, radio-, and X-ray spectral ranges;
2-D Wiener reconstruction to 3-D; methods of Voronoi
mosaic, cluster and fractal analysis; machine learn-
ing technique.
In this way, for example, the coordinates and
masses of new galaxy clusters in the Puppis and in
the Vela constellations were calculated [29], as well
as the length of the Supergalactic Plane in the ZoA.
The velocities of the galaxies near the two edges of
the ZoA were used to estimate the mass distribution
in it. For example, the center of the Great Attractor
was predicted to lie on a line joining the constellations
Centaurus and the Pavo. The Norma Supercluster
occupies region from 360 to 290 with a weak-
ly visible extension towards Vela (~ 270 ). Puppis
filament ( 240 ),l the Hydra-Antlia filament
( 280 ),l the overdense Great Attractor region
( 300 340 )l followed by an underdense region
(52 350 ),l which is strongly influenced by
the Local and Sagittarius Void [30].
248 ISSN 1027-9636. Радіофізика і радіоастрономія. Т. 23, № 4, 2018
I. B. Vavilova, A. A. Elyiv, and M. Yu. Vasylenko
Hence, till now the analysis of the spatial distribu-
tion of galaxies and their systems in the areas sur-
rounding the Milky Way Avoidance Zone remains a
complex and unresolved problem, as well as the es-
timation of the “invisible” content of the spatial gala-
xy distribution, which is obscured by this absorption
zone. The last successful results based on the 2MASS
Tully–Fisher Survey and the HI observational sur-
veys are presented in works by Said et al. [32–34],
where the optimized Tully–Fisher relation allowing
accurate measures of galaxy distances and peculiar
velocities for dust-obscured galaxies is also applied.
3. Scientific Problems as Concerns
with Incompleteness of the Data
on the Spatial Distribution in the Sky
Area Obscured by the Milky Way.
Gaps in Spectroscopic Observations
A state-of-art approach as concern with incomplete-
ness of the data is to use observations of galaxies and
their systems that surround the ZoA to reconstruct
missing information in it [25, 35, 36]. The study should
be conducted within the limited modeling of the Local
Universe and unlike ordinary cosmological simulations,
these simulations has restrictions on the initial condi-
tions limited by observational data. Thus, these obser-
vations may concerns with either own radial veloci-
ties of galaxies or redshift catalogues.
Classical 3D reconstruction of the extragalactic
objects behind the Milky Way to preserve the cohe-
rence of the large scale structure was triggered by
the search of the Great Attractor in the 1990s [31].
Problem of ZoA reconstruction is related to dealing
with gaps in the spectroscopic observations to re-
store homogeneous sky coverage. A good example is
the wide field imager and a multi-object spectrograph
(VIMOS) at the European Southern Observatory’s
Very Large Telescope. It consists of the 4 CCDs
with 2 arcmin space between them; the total area
coverage is 290 arcmin2. Almost 25 % of the field is
the unobserved region due to the constructed gaps
between the CCDs. It means that for 25 % galaxies
in sample is not possible to get spectroscopic redshifts
just less accurate photometric ones are presented.
Such regular pattern, which corresponds to footprint
of the spectrograph, creates issues for VIMOS
Public Extragalactic Redshift Survey (VIPERS).
Especially it creates the systematic effects of the
violation of the local Poisson hypothesis in cell coun-
ting statistics and makes galaxy counts in cells mea-
surements non valid [37].
Existing of unobserved zones in scales compara-
ble to size of investigated zone can have a serious
impact on the study of galaxy properties and local
environment. In this case the local and deterministic
recovery of the missing data is needed [38]. For
small scale reconstruction are common such tech-
niques as the direct cloning [39], wavelet analysis
[40, 41], cluster analysis [42, 43, 44], randomized
cloning of objects into unobserved areas or application
of the Wiener Filtering [45, 46], Voronoi tessellation
[47–50]. Cucciati et al. proposed two algorithms [51]
that use photometric redshift of target objects and
assign redshifts based on the spectroscopic redshifts
of the nearest galaxies. A Wiener filter applied in this
work was very efficient also to reconstruct the con-
tinuous density field instead of individual galaxy po-
sitions. This is a Bayesian method with basic as-
sumptions that both the distribution of the overdensity
field and the likelihood of observing galaxies are dis-
tributed by Gaussian. A true density distribution is
reconstructed by maximizing the posterior distribu-
tion given by Bayes formula. These methods can
clearly separate underdense from overdense regions
on scales of 15h Mpc at moderate redshifts
0.5 1.1,z which is important for studies of cos-
mic variance and rare population galaxy systems.
4. Generative Adversarial Neural
Networks for Recovering Damaged
Astronomical Images and Surveys
Optical observations of extended objects are limited
by random and systematic noise from detector, tele-
scope system and sky background. An image from
telescope can be interpreted as a convolution of the
real image with point spread function (PSF) and some
noise. The Shannon–Nyquist sampling theorem demon-
strates limits of deconvolution technique for improving
the observed images [52]. From other side, there is no
unique solution at deconvolution [53, 54). Schawinski
et al. estimated a possibility to recover artificially de-
graded images with a high noise better than a simple
deconvolution can do [55]. They proposed to use state-
of-the-art methods of Machine Learning, namely Deep
Learning – Generative adversarial network (GAN).
In case of galaxy images when we know how they
should look like, this information can be helpful for
decisions while choosing among many solutions.
ISSN 1027-9636. Радіофізика і радіоастрономія. Т. 23, № 4, 2018 249
Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods?
Generative adversarial neural networks as the type
of the unsupervised machine learning algorithms were
firstly invented by Goodfellow et al. in 2014 [56].
Core of these classes of algorithms are two neural
networks contesting with each other in a zero-sum
game. First neural network called “generative” (typi-
cally a deconvolutional), generates candidate images
and second neural network (a convolutional discrimi-
native) evaluates them. The generative network trains
to transfer from a space of features to a particular
data distribution. In the same time the discriminative
network discriminates between the produced candi-
dates and real examples. Schawinski et al. [55] ap-
plied the GAN to 4550 galaxies from the Sloan Digi-
tal Sky Survey DR12 (SDSS DR12). The authors
have proved that this method can reliably recover
features in images of galaxies and can go well be-
yond the limitation of deconvolutions. As the training
sample they used image pairs: one original image of
galaxy and the same artificially degraded (convolved
with PSF). In general, the GAN is going to learn how
to recover the degraded image by minimizing the
difference between the recovered and true images.
A main feature of this approach is the measurement
of the difference between these two images called
the loss function. With this purpose, the authors used
a second neural network, whose aim is to distinguish
the synthetic recovered image from true image. These
two neural networks are trained simultaneously.
Therefore by training on higher quality images, the
GAN method can learn how to recover information
from the less quality data by building priors. Such
approach has a potential for recovering partially dama-
ged images with gaps, dead CCD chips, ZoA etc.
We propose to apply such approach to the sample
of galaxies from the SDSS DR14 (a general ga-
laxy distribution at the redshifts 1z is shown in
Fig. 1(a), a galaxy distribution in the ZoA is shown in
Fig. 1(b). A general scheme of the GAN approach
for the filling of the ZoA in extragalactic surveys is
shown in Fig. 2. Principal problem with a whole-sky
galaxy distribution is that we have just unique sample
of galaxies, i.e. just one set for training. We cannot
use a set of many images for training likely in the
approach described above. A solution could be to
prepare the mock catalogues from numerical simula-
tions, which reproduce a target sample. In this case
we may generate as much as possible pairs – real
survey and survey with ZoA. Additionally position
of the ZoA could be randomized over survey field.
A goal of generative artificial neural network (ANN)
will be in the trying to generate galaxy distributions
and their properties in the ZoA from latent space of
features. In the same time, a discriminative network
will compare the obtained survey with the real one
and evaluate how realistic it is. The generative net-
work produces better surveys with iteration, while
the discriminative one becomes more experienced at
labeling the synthetic ones. In such a way the system
learns the sophisticated loss functions automatically
without its predefinition.
The “algorithm of darning the ZoA” for dividing
the real extragalactic surveys (for example, SDSS or
2MASS, or future LSST1 survey) on the slices by
redshifts, stellar magnitudes, coordinates and other pa-
rameters to form a training sample is given in Fig. 3.
To apply the algorithm, we should prepare a sample
of galaxies surrounding the ZoA, which is complete
by stellar magnitudes. To get a 3D spatial distribution
of galaxies in this sample, we must obtain their pho-
Fig. 1. Distribution of galaxies from the SDSS DR14 at the
redshifts 1z in Galactic coordinates: (a) whole-sky distribution
(Mollweide projection); (b) in the ZoA
1https://www.lsst.org/sites/default/files/docs/137.25_Borne_Data_
Mining_Research_8x10.pdf
250 ISSN 1027-9636. Радіофізика і радіоастрономія. Т. 23, № 4, 2018
I. B. Vavilova, A. A. Elyiv, and M. Yu. Vasylenko
tometric redshifts and to divide this sample on the
slices by coordinates, taking into account the cosmologi-
cal parameters. Each of these slices will contain a real
distribution and the damaged image (part of the ZoA
region), which will require a darning. The preliminary
step how the algorithm works and restores a galaxy
distribution should be conducted and tested with sub-
samples of real galaxies selected from the non-dama-
ged regions. The information about morphological clas-
sification of galaxies will be useful at this step and can
be obtained by another machine learning technique
(see, for example, our works [57] on applying the
Random Forest methods to obtain a binary morpholo-
gical classification (early and late types) or ternary
classification, which requires knowledge on the color
indices and photometry of galaxies). Another data
mining methods such as the Data visualization, Self-
Organizing Map, Classification, Bayesian Analysis
as well as 3D print models will be also engaged.
5. Conclusions
We presented a brief overview of methods for res-
toring the large-scale structure of the Universe be-
hind the Zone of Avoidance (ZoA) of the Milky Way.
Among them are the direct methods, which are con-
cerns with observational programs in IR-, radio,
and X-ray spectral ranges (IRAS, 2MASS, HI sur-
veys etc.). Due to their complanatory with optical da-
tabases the new galaxies and their systems were dis-
covered that allowed decreasing a size of the ZoA
(now it closes an observer about 10 % of the spatial
Fig. 2. Scheme of the data preparation, the training and testing phases for the ZoA recovering by the GAN method. The input is
a set of mock surveys from which the artificial ZoA were generated to train the GAN. A generative ANN is used to recover surveys
in the ZoA at the testing phase
ISSN 1027-9636. Радіофізика і радіоастрономія. Т. 23, № 4, 2018 251
Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods?
distribution of galaxies in the optical range. We also
described briefly the indirect methods for restoring
the ZoA, which were applied successfully with this
purpose (methods of average variables, mathema-
tical simulation, data mining, machine learning).
Nevertheless, the filling of the ZoA remains a com-
plex and unresolved problem, as well as the recogni-
tion of the invisible content of the spatial galaxy distri-
bution, which is obscured by this absorption zone, is a
highly actual. We described 1) the algorithms of “dar-
ning” the ZoA, which takes into account the photo-
metric redshifts of galaxies surrounding the ZoA,
and 2) new approach based on the Generative adver-
sarial network (GAN) to recover galaxy distribution
in the ZoA using optical surveys as an additional plat-
form for programing the artificial neural networks.
The work was partially supported in frame of the Tar-
get Complex Program of Scientific Space Research
of the NAS of Ukraine (2018–2022).
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И. Б. Вавилова, А. А. Элыив, М. Ю. Василенко
Главная астрономическая обсерватория НАН Украины,
ул. Академика Заболотного, 27, г. Киев, 03143, Украина
ЗА ЗОНОЙ ИЗБЕГАНИЯ МЛЕЧНОГО ПУТИ:
ЧТО МОЖНО ВОССОЗДАТЬ ПРЯМЫМИ
И НЕПРЯМЫМИ МЕТОДАМИ?
Предмет и цель работы: представить краткий обзор мето-
дов, которые применяются для восстановления распределе-
ния крупномасштабных структур Вселенной за зоной избе-
гания (ZoA) Млечного Пути; предложить новый “алгоритм
штопки зоны избегания” и новый подход, основанный на Ге-
нерирующих состязательных сетях (GAN) для восстановле-
ния распределения галактик в ZoA с использованием опти-
ческих обзоров в качестве дополнительной платформы
для программирования искусственных нейронных сетей.
Методы и методология: Благодаря мониторинговым на-
блюдениям всего неба в радио (проект DOGS, наблюдение
в линии HI), инфракрасном (IRAS и 2MASS обзоры) и рен-
тгеновском спектральных диапазонах, ZoA “уменьшилась”
в размерах и закрывает от наблюдателя около 10 % про-
странственного распределения галактик в оптическом диа-
пазоне. Измерения реликтового излучения (CMB) показали
асимметрию в 180 , известную как диполь: несмотря на
то, что результирующий вектор находится в пределах 20
наблюдаемого диполя CMB, расчеты остаются весьма нео-
днозначными, отчасти потому, что не учитываются галакти-
ки в ZoA и концепция “аттракторов” требует пересмотра.
На сегодняшний день анализ пространственного распреде-
ления галактик и их групп в областях, окружающих зону
избегания галактик, остается сложной и нерешенной пробле-
мой, а оценка “невидимого” пространственного распреде-
ления галактик, которое закрывает от наблюдателя зона
поглощения, – крайне своевременной. Для восстановления
распределения галактик в ZoA можно использовать косвен-
ные методы, включая методы обработки сигналов, применяе-
мые к скрытым и неполным данным; методы мозаики Воро-
ного и т. д. Эти методы восстановления, однако, работают
только для крупномасштабных структур в зоне избегания
галактик; они практически не чувствительны к отдельным
галактикам и малонаселенным скоплениям галактик. Одним
из решений является использование методик машинного обу-
чения, например GAN, для моделирования “невидимого”
пространственного распределения галактик за этой зоной.
Результаты: Мы предлагаем новый подход, названный нами
“алгоритм штопки зоны избегания”, для разбивания су-
ществующих внегалактических обзоров (например, SDSS
DR 14) на срезы в зависимости от красного смещения, звезд-
ных величин, координат и других параметров для формиро-
вания тренировочной выборки машинного обучения, а так-
же описываем общую схему GAN метода для применения
sing Systems 2014.
ISSN 1027-9636. Радіофізика і радіоастрономія. Т. 23, № 4, 2018 257
Behind the Zone of Avoidance of the Milky Way: What Can We Restore by Direct and Indirect Methods?
к восстановлению ZoA. Мы обсуждаем основные задачи
генерирования искусственных распределений галактик
и их свойств в ZoA и описываем, как дискриминационная
сеть будет сравнивать полученное распределение с реаль-
ным и оценивать его реалистичность.
Заключение: Неполнота данных, зависящая от длины волны,
на которой проводились обзоры, говорит о том, что оста-
лись такие проблемы, как динамика Местной Группы и ближ-
ней Вселенной; крупномасштабная структура Вселенной
в области неба, скрытой нашей Галактикой; поля потоков
скоростей галактик к Великому Аттрактору; диполь CMB.
Мы предлагаем новый “алгоритм штопки зоны избегания”
и общую схему GAN в качестве дополнительной платформы
машинного обучения для восстановления пространственно-
го распределения за зоной избегания нашей Галактики.
Ключевые слова: крупномасштабная структура Вселенной,
Млечный Путь, галактики, скопления галактик, зона избега-
ния галактик, машинное обучение, генерирующая состяза-
тельная сеть (GAN), “алгоритм штопки зоны избегания”
І. Б. Вавилова, А. А. Елиїв, М. Ю. Василенко
Головна астрономічна обсерваторія НАН України,
вул. Академіка Заболотного, 27, м. Київ, 03143, Україна
ЗА ЗОНОЮ УНИКНЕННЯ ЧУМАЦЬКОГО ШЛЯХУ:
ЩО МОЖНА ВІДТВОРИТИ ПРЯМИМИ
І НЕПРЯМИМ МЕТОДАМИ?
Предмет і мета роботи: подати короткий огляд методів,
які застосовуються для відтворення розподілу великомас-
штабних структур Всесвіту за зоною уникнення (ZoA)
Чумацького Шляху;запропонувати новий “алгоритм што-
пання зони уникнення” і новий підхід, що грунтується на
генеруючій змагальній мережі (GAN) для відновлення роз-
поділу галактик в ZoA з використанням оптичних оглядів
у якості додаткової платформи для програмування штучних
нейронних мереж.
Методи і методологія: Завдяки моніторинговим спостере-
женнями всього неба в радіо (проект DOGS, спостереження
в лінії НІ), інфрачервоному (IRAS та 2MASS огляди) і рент-
генівському спектральних діапазонах, ZoA “зменшила”
свої розміри і наразі закриває від спостерігача близько 10 %
просторового розподілу галактик в оптичному діапазоні.
Вимірювання реліктового випромінювання (CMB) показа-
ли асиметрію в 180 , відому як диполь: незважаючи на те,
що результуючий вектор знаходиться в межах 20 спосте-
режуваного диполя CMB, розрахунки залишаються досить
неоднозначними, почасти тому, що не враховуються галак-
тики в ZoA і концепція “атракторів” вимагає перегляду.
Наразі аналіз просторового розподілу галактик і їх скуп-
чень у областях, що оточують зону уникнення галактик, за-
лишається складною і невирішеною проблемою, а оцінка “не-
видимого” просторового розподілу галактик, яке закриває
від спостерігача зона поглинання, є вкрай своєчасною.
Для відновлення розподілу галактик в ZoA можливе вико-
ристання непрямих методів, включаючи методи обробки сиг-
налів, що застосовуються до прихованих і неповних даних;
методи мозаїки Вороного тощо. Ці методи відновлення, про-
те, працюють тільки для великомасштабних структур в зоні
уникнення галактик; вони практично не чутливі до окремих
галактик і малонаселених скупчень галактик. Одним з рішень
є використання методик машинного навчання, наприклад
GAN, для моделювання “невидимого” просторового розпо-
ділу галактик за цією зоною.
Результати: Ми пропонуємо новий підхід, названий нами
“алгоритм штопання зони уникнення”, для розбивання іс-
нуючих позагалактичних оглядів (наприклад, SDSS DR 14)
на зрізи залежно від червоного зміщення, зоряних величин,
координатів та інших параметрів для формування тренуваль-
ної вибірки машинного навчання, а також описуємо загальну
схему GAN методу для застосування до відновлення ZoA.
Ми обговорюємо основні завдання генерування штучних
розподілів галактик та їх властивостей в ZoA і описуємо,
як дискримінаційна мережа буде порівнювати отриманий роз-
поділ з реальним і оцінювати його реалістичність.
Висновок: Неповнота даних, що залежить від довжини хвилі,
на якій виконувалися огляди, свідчить про те, що залишили-
ся такі проблеми, як динаміка Місцевої Групи і ближнього
Всесвіту; великомасштабна структура Всесвіту в області
неба, прихованою нашою Галактикою; поля потоків швид-
кості галактик до Великого Атрактора; диполь CMB.
Ми пропонуємо новий “алгоритм штопання зони уникнен-
ня” і загальну схему GAN в якості додаткової платформи
машинного навчання для відновлення просторового розпо-
ділу галактик за зоною уникнення нашої Галактики.
Ключові слова: великомасштабна структура Всесвіту, Чу-
мацький Шлях, галактики, скупчення галактик, зона уник-
нення галактик, машинне навчання, генеруюча змагальна
мережа (GAN), “алгоритм штопання зони уникнення”
Received 19.10.2018
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