Milky Way galaxy-analogs and isolated galaxies with bars: environmental density in the Local Volume

1Kompaniiets, OV, 1Kukhar, OM, 1Vavilova, IB, 1Dobrycheva, DV, 2Fedorov, PM, 2Dmytrenko, AM, 2Khramtsov, VP, 1Sergijenko, O, 1Vasylenko, AA
1Main Astronomical Observatory of the National Academy of Sciences of Ukraine, Kyiv, Ukraine
2Institute of Astronomy of Kharkiv National University, Kharkiv, Ukraine
Space Sci. & Technol. 2025, 31 ;(6):134-148
https://doi.org/10.15407/knit2025.06.134
Язык публикации: English
Аннотация: 
The environmental density of galaxies within the cosmic web provides insight into their 3D locations in filaments, voids, groups, and clusters of the large-scale structure of the Universe. This parameter reflects the distribution of baryonic matter and the influence of dark matter halos on galaxy evolution. Understanding environmental density is crucial for identifying the external physical processes - such as feedback from supernovae and active galactic nuclei, tidal interactions, ram-pressure stripping, and large-scale matter flows - that shape galaxies beyond their internal properties. In this article, we focus on the isolated galaxies with bars as the ensemble of galaxies for which the Milky Way galaxy-analogs belong. To obtain local environmental density parameters and verify the isolation criterion (v  500 km/s), we developed a Python-based pipeline operating in two redshift regimes: low (z0 < 0.02) and high (z0  0.02). Local densities Σ were estimated using both k-nearest neighbor and Voronoi tessellation methods and classified as void (Σ < 0.05), filament (0.05  Σ < 0.5), group (0.5  Σ < 2.0), and cluster (Σ  2.0).
             Our sample of 311 isolated barred galaxies from the 2MIG catalog, supplemented by Milky Way-analog systems (z < 0.07), covers the northern sky. We find 157 galaxies in voids, 84 in filaments, 27 in groups, and 11 in clusters; 30 exhibit no detectable neighbors  A subset of 67 galaxies occupies extremely low-density regions (Σ₃D < 0.01 gal Mpc⁻³), while 22 have their nearest companion farther than 5 Mpc, suggesting their location within extended cosmological voids. The Milky Way (Σ₅NN  0.13 gal Mpc⁻³, R  2.1 Mpc) and its close analog NGC 3521 both reside in filamentary environments, consistent with intermediate-density surroundings at the boundary of a nearby void. For galaxies with z > 0.02, the estimated local densities of the Milky Way and the analyzed systems allow us to identify three additional candidates that satisfy the supplementary environmental density criterion. Based on 3D Voronoi tessellation density estimates, one Milky Way-analog candidate is CGCG 208-043, while according to the fifth-nearest-neighbor approach, the candidates are NGC 5231 and CGCG 047-026. These results highlight the importance of local environmental density
as an additional indicator in the search for Milky Way galaxy-analogs.
Ключевые слова: galaxies, Milky Way, Milky Way galaxy-analogs, voids, Voronoi tessellation
References: 
1. Alonso S., Mesa V., Padilla N., Lamb as D. G. (2012). Galaxy interactions. II. High density environments. Astron. and 
Astrophys., 539, id. A46, 9. 
https://doi.org/10.1051/0004-6361/201117901
 
2. Ansar S., Pearson S., Sanderson R. E., et al. (2025). Bar Formation and Destruction in the FIRE-2 Simulations. Astrophys. J., 
978, no. 1, Art. no. 37. 
https://doi.org/10.3847/1538-4357/ad8b45
 
3. Aragón-Calvo M. A., Platen E., van de Weygaert R. (2010). The Spine of the Cosmic Web. Astrophys. J., 723, Is. 1, 364-382. 
4. Argudo-Fernández M., Shen S., Sabater J., et al. (2016). The effect of local and large-scale environments on nuclear activity 
and star formation. Astron. and Astrophys., 592, 13. 
https://doi.org/10.1051/0004-6361/201628232
 
5. Baldwin J. A., Phillips M. M., Terlevich R. (1981). Classification parameters for the emission-line spectra of extragalactic 
objects. Publ. Astron. Soc. Pacif., 93, 5-19. 
https://doi.org/10.1086/130766
 
6. Bono G., Caputo F., Marconi M., Musella I. (2010). Insights into the Cepheid Distance Scale. Astrophys. J., 715(1), 277-291. 
7. Cheung E., Athanassoula E., Masters K. L., et al. (2013). Galaxy Zoo: Observing Secular Evolution through Bars. Astrophys. 
J., 779, Is. 2, id.A162, 18. 
https://doi.org/10.1088/0004-637X/779/2/162
 
8. Cybulski R. (2016). The Cosmic Web and the Role of Environment in Galaxy Evolution. PhD Thesis, University of Massachusetts. 
9. Darvish B., Mobasher B., Sobral D., et al. (2015). A Comparative Study of Density Field Estimation for Galaxies. Astrophys. 
J., 805, Is. 2, id. 121, 1-19.
https://doi.org/10.1088/0004-637X/805/2/121
 
10. Denyshchenko S. I., Fedorov P. N., Akhmetov V. S., et al. (2024). Determining the parameters of the spiral arms of the 
Galaxy from kinematic tracers based on Gaia DR3 data. Mon. Notic. Roy. Astron. Soc., 527, Is. 1, 1472-1480. 
11. de Vaucouleurs G. (1959). Classification and Morphology of External Galaxies. Handbuch der Physik, 53, 275 p. 
12. de Vaucouleurs G., de Vaucouleurs A., Corwin H. G., Jr., Buta R. J., Paturel G., Fouqu P. (1991). Third Reference Catalogue 
of Bright Galaxies. New York: Springer, 632 p. ISBN 0-387-97552-7
https://doi.org/10.1007/978-1-4757-4363-0_1
 
13. Dmytrenko A. M., Fedorov P. N., Akhmetov V. S., et al. (2025). Spatial orientation and shape of the velocity ellipsoids of
the Gaia DR3 giants and sub-giants in the Galactic plane. Mon. Notic. Roy. Astron. Soc., 542, Is. 3, 2542-2559. 
https://doi. org/10.1093/mnras/staf1408
 
14. Dobrycheva D. V., Hetmantsev O. O., Vavilova I. B., et al. (2025). Discovery of the Polar Ring Galaxies with deep 
learning.Astron. and Astrophys., 702, A258, 1-13. 
https://doi.org/10.1051/0004-6361/202555052
 
15. Dobrycheva D., Melnyk O., Elyiv A., et al. (2016). Environmental density of galaxies from SDSS via Voronoi 
tessellation.Proc. IAU, 308, 248-249. 
https://doi.org/10.1017/S1743921316009959
 
16. Dobrycheva D. V., Melnyk O. V., Vavilova I. B., et al. (2014). Environmental Properties of Galaxies at z < 0.1 from the SDSS 
via the Voronoi Tessellation. Odessa Astron. Publ., 27, 26-27.
 
17. Dobrycheva D. V., Melnyk O. V., Vavilova I. B., et al. (2015). Environmental Density vs. Colour Indices of the Low 
RedshiftsGalaxies. Astrophysics, 58, Is. 2, 168-180. 
https://doi.org/10.1007/s10511-015-9373-x
 
18. Dobrycheva D. V., Vavilova I. B., Khramtsov V. P., et al. (2025). The visual vs. CNN verification of catalogs of SDSS 
merginggalaxies, galaxies with bar, ring, and dust lane. Astron. and Astrophys.
 
19. Dobrycheva D., Vavilova I., Khramtsov V., et al. (2025). Machine Learning Mismatchings and Catalogues Creation: A Path to 
Finding the Milky Way Galaxies-Analogues. Publ. ASP.
 
20. Elyiv A., Melnyk O., Vavilova I. B. (2008). High-order 3D Voronoi tessellation for identifying Isolated galaxies, Pairs 
andTriplets. Mon. Notic. Roy. Astron. Soc., 394, Is. 3, 1409-1418. 
https://doi.org/10.1111/j.1365-2966.2008.14150.x
 
21. Ghosh S., Di Matteo P. (2024). Looking for a needle in a haystack: Measuring the length of a stellar bar. Astron. and 
Astrophys., 683, Art. no. A100. 
https://doi.org/10.1051/0004-6361/202347763
 
22. Gieren W., Pietrzyński G., Soszyński I., et al. (2005). The Araucaria Project: Near-Infrared Photometry of Cepheid Variables 
in the Sculptor Galaxy NGC 300. Astrophys. J., 628(2), 695-703. 
https://doi.org/10.1086/430903
 
23. Goh T., Primack J., Lee C. T., et al. (2019). Dark matter halo properties versus local density and cosmic web location. 
Mon.Notic. Roy. Astron. Soc., 483, Is. 2, 2101-2122.
https://doi.org/10.1093/mnras/sty3153
 
24. Hernández-Ibarra F. J., Dultzin D., Krongold Y., et al. (2013). Nuclear activity in isolated galaxies. Mon. Notic. Roy. 
Astron.Soc., 434, Is. 1, 336-346. 
https://doi.org/10.1093/mnras/stt1021
 
25. Jacobs B. A., Rizzi L., Tully R. B., et al.(2009). The Extragalactic Distance Database: Color-Magnitude Diagrams.Astrophys. 
J., 138(2), 332-337. 
https://doi.org/10.1088/0004-6256/138/2/332
 
26. Kauffmann G., Heckman T. M., Tremonti C., et al. (2003). The host galaxies of active galactic nuclei. Mon. Notic. 
Roy.Astron. Soc., 346, Is. 4, 1055-1077. 
https://doi.org/10.1111/j.1365-2966.2003.07154.x
 
27. Karachentseva V. E., Mitronova S. N., Melnyk O. V., et al. (2010). Catalog of Isolated Galaxies Selected from the 
2MASSSurvey. Astrophys. Bull., 65, Is. 1, 1-17. 
https://doi.org/10.1134/S1990341310010013
 
28. Kewley L. J., Dopita M. A., Sutherland R. S., et al. (2001). Theoretical Modeling of Starburst Galaxies. Astrophys. J., 
556,Is. 1, 121-140.
https://doi.org/10.1086/321545
 
29. Khramtsov V., Vavilova I. B., Dobrycheva D. V., et al. (2022). Machine learning technique for morphological classificationof 
galaxies from the SDSS. III. Image-based inference of detailed features. Space Sci. and Technol., 28, Is. 5, 27-55. 
https://doi.org/10.15407/knit2022.05.027
 
30. Khramtsov V., Dobrycheva D., Vavilova I., et al. (2025). Vision-Language Models for Spiral Galaxy Identification in SDSS: A 
Path to Finding Milky Way Analog Galaxies. Publ. ASP.
 
31. Kim T., Gadotti D. A., Athanassoula E., et al. (2016). Evidence of bar-induced secular evolution in the inner regions of 
stellardiscs in galaxies: what shapes disc galaxies? Mon. Notic. Roy. Astron. Soc., 462, Is. 4, 3430-3440. 
https://doi.org/10.1093/mnras/stw1899
 
32. Kuutma T., Tamm A., Tempel E. (2017). From voids to filaments: environmental transformations of galaxies in the SDSS. 
Astron. and Astrophys., 600, id. L6, 5. 
https://doi.org/10.1051/0004-6361/201730526
 
33. Laigle C., Pichon C., Arnouts S., et al. (2018). COSMOS2015 photometric redshifts probe the impact of filaments on galaxy 
properties. Mon. Notic. Roy. Astron. Soc., 474, Is. 4, 5437-5458. 
https://doi.org/10.1093/mnras/stx3055
 
34. Libeskind N. I., van de Weygaert R., Cautun M., et al. (2018). Tracing the Cosmic Web. Mon. Notic. Roy. Astron. Soc., 473, 
Is. 1, 1195-1217. 
https://doi.org/10.1093/mnras/stx1976
 
35. Lindner U., Einasto J., Einasto M., et al. (1995). The structure of supervoids. I. Void hierarchy in the Northern Local 
Supervoid. Astron. and Astrophys., 301, 329. 
https://doi.org/10.48550/arXiv.astro-ph/9503044
 
36. Makarov D., Prugniel P., Terekhova N., et al. (2014). HyperLEDA. III. The catalogue of extragalactic distances. Astron. and 
Astrophys., 570, id. A13, 1-12. 
https://doi.org/10.1051/0004-6361/201423496
37. Malandrino R., Lavaux G., Wandelt B. D., et al. (2025). A Bayesian catalog of 100 high-significance voids in the Local 
Universe. 
ArXiv:2507.06866. 
https://doi.org/10.48550/arXiv.2507.06866
https://doi.org/10.1051/0004-6361/202556345
 
38. Marius C., van de Weygaert R., Bernard J. T. J., et al. (2014) Evolution of the cosmic web. Mon. Notic. Roy. Astron. Soc., 
441, Is. 4, 2923-2973. 
https://doi.org/10.1093/mnras/stu768
 
39. Mazurenko S., Banik I., Kroupa P., et al. (2024). A simultaneous solution to the Hubble tension and observed bulk flow 
within 250 h-1 Mpc. Mon. Notic. Roy. Astron. Soc., 527, Is. 3, 4388-4396. 
https://doi.org/10.1093/mnras/stad3357
 
40. McCall M. L. (2014). A Council of Giants. Mon. Notic. Roy. Astron. Soc., 440, Is. 1, 405-426. 
https://doi.org/10.1093/mnras/stu199
 
41. Melnyk O., Elyiv A. A., Vavilova I. B. (2007). The Structure of the Local Supercluster by 3D Voronoi Tessellation. Kinemat. 
Phys. Celest. Bodies, 22, Is. 4, 283-296. 
arXiv:0712.1297 
https://www.mao.kiev.ua/index.php/ua/pdf-opener?vavilova/Melnyk-Elyiv-Va...
 
42. Mishenina T., Gorbaneva T., Dmytrenko A., et al. (2024). Specific Features of the Enrichment of Metal-Poor Stars with 
Neutron-Capture R-Process Elements. Odessa Astron. Publ., 37, 47-51. 
https://doi.org/10.18524/1810-4215.2024.37.312691
 
43. Naidu R. P., Conroy C., Bonaca A., et al. (2021). Reconstructing the Last Major Merger of the Milky Way with the H3 survey. 
Astrophys. J., 923, Is. 1, art. id. 92, 24 p. 
https://doi.org/10.3847/1538-4357/ac2d2d
 
44. Ostriker J. P., Peebles P. J. E. (1973). A Numerical Study of the Stability of Flattened Galaxies: or, can Cold Galaxies 
Survive? Astrophys. J., 186, 467-480. 
https://doi.org/10.1086/152513
 
45. Paranjape A., Alam S. (2020). Voronoi volume function: a new probe of cosmology and galaxy evolution. Mon. Notic. Roy.
Astron. Soc., 495, Is. 3, 3233-3251. 
https://doi.org/10.1093/mnras/staa1379
 
46. Pastoven O. S., Kompaniiets O. V., Vavilova I. B., Izviekova I. O. (2024). NGC 3521 as the Milky Way analogue: Spectral 
energy distribution from UV to radio and photometric variability. Space Sci. and Technol., 30, Is. 6(151), 67-83. 
47. Paturel G., Teerikorpi P., Theureau G., et al. (2002). Calibration of the distance scale from galactic Cepheids. II. Use of 
theHIPPARCOS calibration. Astron. and Astrophys., 389, 19-28. 
https://doi.org/10.1051/0004-6361:20020492
 
48. Pillepich A., Sotillo-Ramos D., Ramesh R. (2024). Milky Way and Andromeda analogues from the TNG50 simulatio Mon. Notic. 
Roy. Astron. Soc., 535, Is. 2, 1721-1762. 
https://doi.org/10.1093/mnras/stae2165
 
49. Pilyugin L. S., Grebel E. K., Kniazev A. Y. (2014). The Abundance Properties of Nearby Late-type Galaxies. I. The 
Data.Astrophys. J., 147, Is. 6, id. 131, 1-24. 
https://doi.org/10.1088/0004-6256/147/6/131
 
50. Pilyugin L. S.; Tautvaišienė G.; Lara-López M. A. (2023) Searching for Milky Way twins: Radial abundance distribution asa 
strict criterion. Astron. and Astrophys., 676, id. A57, 1-28. 
https://doi.org/10.1051/0004-6361/202346503
 
51. Poggianti B. M., Desai V., Finn R. (2008). The Relation between Star Formation, Morphology, and Local Density in High- 
Redshift Clusters and Groups. Astrophys. J., 684, Is. 2, 888-904.
https://doi.org/10.1086/589936
 
52. Poggianti B. M.; De Lucia G.; Varela J. (2010). The evolution of the density of galaxy clusters and groups: denser 
environments at higher redshifts. Mon. Notic. Roy. Astron. Soc., 405, Is. 2, 995-1005. 
https://doi.org/10.1111/j.1365-2966.2010.16546.x
 
53. Pulatova N. G.; Vavilova I. B.; Sawangwit U., et al. (2015). The 2MIG isolated AGNs - I. General and 
multiwavelengthproperties of AGNs and host galaxies in the northern sky. Mon. Notic. Roy. Astron. Soc., 447, Is. 3, 2209-2223. 
54. Pulatova N. G., Vavilova I. B., Vasylenko A. A., Ulyanov O. M. (2023). Radio properties of the low-redshift isolated 
galaxies with active nuclei. Kinemat. Phys. Celest. Bodies, 39, Is. 2, 98-115.
https://doi.org/10.3103/S088459132302006X
 
55. Sabater J., Best P. N., Heckman T. M. (2015). Triggering optical AGN: the need for cold gas, and the indirect roles of 
galaxy environment and interactions. Mon. Notic. Roy. Astron. Soc., 447, Is. 1, 110-116. 
https://doi.org/10.1093/mnras/stu2429
 
56. Sabater J., Leon S., Verdes-Montenegro L., et al. (2008). The AMIGA sample of isolated galaxies. VII. Far-infrared and radio 
continuum study of nuclear activity. Astron. and Astrophys., 486, Is. 1, 73-83. 
https://doi.org/10.1051/0004-6361:20078785
 
57. Schawinski K., Thomas D., Sarzi M., et al. (2007). Observational evidence for AGN feedback in early-type galaxies. Mon. 
Notic. Roy. Astron. Soc., 382, Is. 4, 1415-1431. 
https://doi.org/10.1111/j.1365-2966.2007.12487.x
 
58. Soszyński I., Gieren W., Pietrzyński G., et al. (2006). The Araucaria Project: Distance to the Local Group Galaxy NGC 3109 
from Near-Infrared Photometry of Cepheids. Astrophys. J., 648(1), 375-382. 
https://doi.org/10.1086/505789
 
59. Toomre A. (1964). On the gravitational stability of a disk of stars. Astrophys. J., 139, 1217-1238. 
60. Vavilova I. B.; Dobrycheva D. V., Khramtsov V., et al. (2024). Machine Learning of Galaxy Classification by their Images and 
Photometry. Publ. ASP, 535, 103-107.
 
61. Vavilova I. B., Dobrycheva D. V., Vasylenko M. Yu. (2021). Machine learning technique for morphological classification
of galaxies from the SDSS. I. Photometry-based approach. Astron. and Astrophys., 648, id. A122, 1-14. 
62. Vavilova I. B., Elyiv A. A., Dobrycheva D. V., et al. (2021). Voronoi tessellation method in astronomy. Intelligent 
Astrophysics. Springer, Cham, 57-79. ISBN: 978-3-030-65867-0.
https://doi.org/10.1007/978-3-030-65867-0_3
 
63. Vavilova I. B., Fedorov P. M., Dobrycheva D. V., et al. (2024). An advanced approach to the definition of the "Milky Way 
galaxies-analogues". Space Sci. and Technol., 30, Is. 4, 81-90. 
https://doi.org/10.15407/knit2024.04.081
 
64. Vavilova I. B., Ivashchenko G. Yu., Babyk Iu. V., et al. (2015) The astrocosmic databases for multi-wavelength and 
cosmological properties of extragalactic sources. Space Sci. and Technol., 21, Is. 5, 94-107. 
65. Vavilova I. B., Khramtsov V., Dobrycheva D. V., et al. (2022). Machine learning technique for morphological classification 
of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02 < z < 0.1. Space Sci. and Technol., 28, 
Is. 1, 3-22. 
https://doi.org/10.15407/knit2022.01.003
 
66. Vavilova I., Kompaniiets O., Vasylenko A., et al. (2025). Milky Way analogues as the isolated AGNs: multiwavelength data 
incompleteness. Publ. ASP.
 
67. Wang P., Kang X., Libeskind N. I., et al. (2020). A robust determination of halo environment in the cosmic field. New