arXiv subjects

D. V. Dobrycheva

Publications and source records attributed to D. V. Dobrycheva.

11 recordsLinked to original sources

arXiv2026

Milky Way Near Twins (MWNeTs). I. A Hierarchical Framework for Identifying the Evolutionary Counterparts of the Milky Way

The search for Milky Way (MW) analogues has traditionally relied on similarity in a limited set of present-day global properties, including morphology. However, galaxies with similar current properties may have experienced different assembly histories, secular evolution, nuclear activity, and environmental histories. We introduce Milky Way Near Twins (MWNeTs) as galaxies that resemble the Milky Way in present-day properties and exhibit observational signatures consistent with broadly similar evolutionary pathways. We reformulate the search for the closest extragalactic counterparts of the MW by shifting from parameter-based similarity toward evolutionary similarity. We propose a hierarchical methodology consisting of five stages: isolation and cosmic-web context, morphological and structural constraints, nuclear activity and supermassive black hole properties, global spectrophotometric and dynamical constraints, and advanced evolutionary diagnostics. The first four stages identify galaxies consistent with the present-day environmental, structural, nuclear, spectrophotometric, and dynamical state of the MW, while the fifth stage tests this similarity using independent signatures of comparable evolutionary histories. We introduce the concept of evolutionary memory, in which complementary diagnostics preserve information about physical processes operating on different timescales and probing different layers of galaxy formation and evolution. These diagnostics include the integrated spectral energy distribution, rotation-curve morphology, chemo-dynamical signatures, globular-cluster systems, merger history, circumgalactic-medium properties, and multiwavelength fossil tracers. Together, the MWNeT framework establishes an observational bridge between Galactic and extragalactic astronomy and supports future searches for the closest evolutionary counterparts of the Milky Way.

astro-ph.GA↗

arXiv2026

NGC 3521 as the Milky Way near twin: spectral energy distribution from UV to radio decameter ranges

Milky Way analogues (MWAs) are usually selected from structural and kinematic properties, but robust SED-based similarity criteria are limited by heterogeneous photometry and incomplete wavelength coverage. We present a homogeneous, aperture-photometry SED of the Milky Way near-twin NGC~3521 from the ultraviolet to the radio decameter range. Fluxes are measured within a fixed elliptical isophotal aperture using GALEX, SDSS, WISE, Spitzer/MIPS, Herschel/PACS+SPIRE, and VLA data, and supplemented by meter/decameter constraints. We report new observations obtained in Jan-Feb 2022 with the Ukrainian T-shape radio telescope and derive, for the first time, an upper limit in the 24--32~MHz band. The UV-to-decameter SED (27 points) is modelled with \textsc{CIGALE}, including a dedicated low-frequency radio prescription (\texttt{radio_extra}) that accounts for emission and absorption effects. Using ZTF and NEOWISE data (2014--2025), we detect genuine nuclear variability; optical trends at $\sim2^{\prime\prime}$ primarily trace the compact nucleus, while NEOWISE variability reflects a mix of nuclear changes and warm-dust emission within the larger aperture. The preferred fit yields $M_\star \simeq 6.0\times10^{10},M_\odot$, ${\rm SFR}\simeq1.65,M_\odot,{\rm yr}^{-1}$, $M_{\rm dust}\simeq1.3\times10^{8},M_\odot$, and an effective dust temperature of $\sim23$~K. The decameter constraint gives $S_{28,{\rm MHz}}<11.22$~Jy, consistent with expectations for a Milky Way-like system placed at 10.7~Mpc. We conclude that an integrated, homogeneous SED, especially below 100~MHz, provides a complementary diagnostic for identifying and validating MWAs and for interpreting how Milky Way properties would appear to an external observer.

astro-ph.GA↗

arXiv2026

Visual inspection of potential exocomet transits identified through machine learning and statistical methods

In this work, we explore several ways to detect possible exocomet transits in the TESS (The Transiting Exoplanet Survey Satellite) light curves. The first one has been presented in our previous work, a machine learning approach based on the Random Forest algorithm. It was trained on asymmetric transit profiles calculated as a result of the modelling of a comet transit, and then applied to real star light curves from Sector 1 of TESS. This allowed us to detect 32 candidates with weak and non-periodic brightness dips that may correspond to comet-like events. The aim of this work is to analyse the events identified by the visual inspection to make sure that the features detected were not caused by instrumental effects. The second approach to detect possible exocomet transits, which is proposed, is an independent statistical method to test the results of the machine learning algorithm and to look for asymmetric minima directly in the light curves. This approach was applied to \b{eta} Pictoris light curves using TESS data from Sectors 5, 6, 32, and 33. The algorithm reproduced nearly all previously known events deeper than 0.03 % of the star flux, showing that it is efficient to detect shallow and irregular flux changes in the different sectors of the TESS data and at the different levels of noise. The combination of machine learning, visual inspection, and statistical analysis facilitates the identification of faint and short-lived asymmetric transits in photometric data. Although the number of confirmed exocomet transits is still small, the growing amount of observations points to their likely presence in many young planetary systems.

astro-ph.EP↗

arXiv2026

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

The environmental density of galaxies within the cosmic web constrains their 3D locations in filaments, voids, groups, and clusters. It traces the distribution of baryons and the influence of dark-matter halos on galaxy evolution, and helps diagnose external processes such as supernova and AGN feedback, tidal interactions, ram-pressure stripping, and large-scale flows. We focus on isolated barred galaxies, the parent population that includes Milky Way analogs. To measure local environmental density and verify isolation (|Delta v| <= 500 km s^-1), we built a Python pipeline that works in two redshift regimes: low (z0 < 0.02) and high (z0 >= 0.02). Densities were estimated with k-nearest neighbors and Voronoi tessellations and classified as void (Sigma < 0.05), filament (0.05 <= Sigma < 0.5), group (0.5 <= Sigma < 2.0), and cluster (Sigma >= 2.0). Our northern-sky sample contains 311 isolated barred galaxies from 2MIG plus Milky Way analog systems (z < 0.07). We find 157 void, 84 filament, 27 group, and 11 cluster galaxies; 30 have no detected neighbors. Sixty-seven lie in extremely low-density regions (Sigma_3D < 0.01 gal Mpc^-3), and 22 have the nearest companion beyond 5 Mpc, indicating residence in extended voids. The Milky Way (Sigma_5NN ~ 0.13 gal Mpc^-3, R ~ 2.1 Mpc) and its close analog NGC 3521 both lie in filamentary environments at the edge of a nearby void. For z > 0.02 we identify three additional Milky Way analog candidates from the density metrics: CGCG 208-043 (3D Voronoi) and NGC 5231 and CGCG 047-026 (5th-nearest neighbor). These results show that local environmental density is an effective, physically motivated criterion for selecting Milky Way analogs.

astro-ph.GA↗

arXiv2025

Galaxy rotation curve based on RGB stars from the Gaia DR3 catalogue

In this paper, we construct a detailed circular velocity curve of the Milky Way out to 20 kpc based on the radial component of the Jeans equation in cylindrical coordinates, assuming an axisymmetric gravitational potential, and show its dependence on azimuth. We use only Gaia DR3 data and aim to minimize the use of model data and various assumptions. To build the rotation curves, we used a sample of 4,547,980 RGB stars with measured spatial velocities, covering the Galactic plane in the range of Galactocentric cylindrical coordinates $150^\circ < θ< 210^\circ$ and 0 < R < 20 kpc. We exclude systematics in the data that may arise from neglecting higher-order moments of the velocity distribution and their dispersions, as well as due to random measurement errors in Gaia. At the distance of the Sun, the circular velocity $V_{\rm c}(R_0)$ turned out to be ($229.63\pm0.30$) km s$^{-1}$, which is in good agreement with many previous estimates. The average slope of the circular velocity is $(-2.29\pm0.05)$ km s$^{-1}$ kpc$^{-1}$ obtained in the range of $R$ from 6 to 20 kpc and $θ$ from 150 to 210 degrees. The determined circular velocity curve has some peculiarities in behavior near $R\sim$13 and 18 kpc, but in general it does not contradict the results of other authors up to distances where our statistics are reliable.

astro-ph.GA↗

arXiv2025

Discovery of the Polar Ring Galaxies with deep learning

The aim of our research is to create a catalog of strong and good candidates for PRGs using existing catalogs of PRGs, develop an image-based approach with machine learning methods for the search and discovery of PRGs in a big sky survey, and explore the capability of the CIGALE software for determining their multiwavelength properties. For the first time, we applied a deep learning method to the search for PRGs. We visually inspected galaxies from existing catalogs of PRGs to create a training sample based on high-quality SDSS images. Since the resulting training sample was extremely small (87 strong and good PRGs), we applied augmentation, image segmentation, and ensemble learning techniques. However, most effective method was transfer learning with its ability to enlarge the training sample by synthetic images generated by GALFIT. To examine deep learning approach for finding new PRGs we used the SDSS catalog of galaxies at z < 0.1. The method with synthetic images showed that even with overtraining we were able to find galaxies with a ring pattern. Our deep learning approach has resulted in the discovery of three PRGs (SDSS J140644.42+471602.0; SDSS J133650.48+492745.3; SDSS J095717.30+364953.5). Also, we visually inspected the Catalog of the SDSS Ring galaxies at z < 0.1 and discovered four PRGs among ~2,200 ring galaxies (SDSS J095851.32+320422.9; SDSS J104211.05+234448.2; SDSS J162212.63+272032.2; SDSS J104600.10+090627.2). One of the discovered galaxies with transfer learning, SDSS J140644.42+471602.0, was studied with CIGALE software to determine its spectral energy distribution in IR-UV bands. The current SFR is 71 M_sun per year, although the lack of FUV data limits this estimate. The total stellar mass is 8.34x10^{10} M_sun. The predominance of an old stellar population (two-thirds of the total mass) suggests that this PRG is undergoing interaction process.

astro-ph.GA↗

arXiv2024

Spatial orientation and shape of the velocity ellipsoids of the Gaia DR3 giants and subgiants in the Galactic plane

We present the results of determining the parameters characterizing the shape and orientation of residual velocity ellipsoids from the Gaia DR3 red giants and subgiants. We show the distribution of velocity dispersions in the Galactic plane obtained from three components of the spatial velocity, as well as the coordinate distribution of the intersection points of the velocity ellipsoid axes with the celestial sphere, in particular the deviations of the longitudes and latitudes of the vertices of stellar regions located within spheres with a radius of 1 kpc centered in the Galactic mid-plane. The area of the Galactic disk under study is in the range of Galactocentric coordinates 0 < R < 15 kpc and $120^\circ < θ< 240^\circ$. We show that the vertex deviations in some regions of the Galactic mid-plane can reach $30^\circ$ in longitude, and $15^\circ$ in latitude. This indicates the presence of kinematic distortions of the stellar velocity field, especially noticeable in the angular range of $150^\circ < θ< 210^\circ$ at a distance of approximately 13 kpc. We propose the angles of deviation of longitudes and latitudes of the ellipsoid axes of residual stellar velocities to be considered as kinematic signatures of various Galactic deformations determined from real fields of spatial velocities. We present the distribution of parameters characterizing the shapes of velocity ellipsoids, as well as their distribution of the semi-axes length ratios. We note a local feature in this distribution and in the distribution of the elongation measurements of the ellipsoids. We perform a comparison of the results obtained from the tensor of deformation velocities and from the observed spatial velocities.

astro-ph.GA↗

arXiv2024

Hunting for exocomet transits in the TESS database using the Random Forest method

This study introduces an approach to detecting exocomet transits in the dataset of the Transiting Exoplanet Survey Satellite (TESS), specifically within its Sector 1. Given the limited number of exocomet transits detected in the observed light curves, creating a sufficient training sample for the machine learning method was challenging. We developed a unique training sample by encapsulating simulated asymmetric transit profiles into observed light curves, thereby creating realistic data for the model training. To analyze these light curves, we employed the TSFresh software, which was a tool for extracting key features that were then used to refine our Random Forest model training. Considering that cometary transits typically exhibit a small depth, less than 1% of the star's brightness, we chose to limit our sample to the CDPP parameter. Our study focused on two target samples: light curves with a CDPP of less than 40 ppm and light curves with a CDPP of up to 150 ppm. Each sample was accompanied by a corresponding training set. This methodology achieved an accuracy of approximately 96%, with both precision and recall rates exceeding 95% and a balanced F1-score of around 96%. This level of accuracy was effective in distinguishing between 'exocomet candidate' and 'non-candidate' classifications for light curves with a CDPP of less than 40 ppm, and our model identified 12 potential exocomet candidates. However, when applying machine learning to less accurate light curves (CDPP up to 150 ppm), we noticed a significant increase in curves that could not be confidently classified, but even in this case, our model identified 20 potential exocomet candidates. These promising results within Sector 1 motivate us to extend our analysis across all TESS sectors to detect and study comet-like activity in the extrasolar planetary systems.

astro-ph.EP↗

arXiv2022

Machine learning technique for morphological classification of galaxies from the SDSS. III. Image-based inference of detailed features

This paper follows series of our works on the applicability of various machine learning methods to the morphological galaxy classification (Vavilova et al., 2021, 2022). We exploited the sample of 315776 SDSS DR9 galaxies with absolute stellar magnitudes of -24m<Mr<-19.4m at 0.003<z<0.1 as a target data set for the CNN classifier based on the DenseNet-201. Because it is tightly overlapped with the Galaxy Zoo 2 (GZ2) sample, we use these annotated data as the training data set to classify galaxies into 34 detailed features. In the presence of a pronounced difference of visual parameters between galaxies from the GZ2 training data set and galaxies without known morphological parameters, we applied novel procedures, which allowed us for the first time to get rid of this difference for smaller and fainter SDSS galaxies. We describe in detail the adversarial validation technique as well as how we managed the optimal train-test split of galaxies from the training data set. We have also found optimal galaxy image transformations to increase the classifier generalization ability. It can be considered as another way to improve the human bias for those galaxy images that had a poor vote classification in the GZ project. Such an approach, likely auto-immunization, when the CNN classifier trained on very good images is able to retrain bad images from the same homogeneous sample, can be considered co-planar to other methods of combating the human bias. The accuracy of CNN classifier is in the range of 83.3-99.4 percent depending on 32 features. As a result, for the first time, we assigned the detailed morphological classification for more than 140K low-redshift galaxies, especially at the fainter end. We accentuate on the typical problem points of galaxy CNN image classification from the astronomical point of view. The catalogs will be available through the VizieR.

astro-ph.GA↗

arXiv2022

Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02<z<0.1

We applied the image-based approach with a convolutional neural network model to the sample of low-redshifts galaxies with $-24^{m}<M_{r}<-19.4^{m}$ from the SDSS DR9. We divided it into two subsamples, SDSS DR9 galaxy dataset and Galaxy Zoo 2 (GZ2) dataset, considering them as the inference and training datasets, respectively. As a result, we created the morphological catalog of 315782 galaxies at 0.02<z<0.1, where morphological five classes and 34 detailed features (bar, rings, number of spiral arms, mergers, etc.) were first defined for 216148 galaxies (inference dataset) by the image-based CNN classifier. For the rest of galaxies the initial morphological classification was re-assigned as in the GZ2 project. Our method shows the promising performance of morphological classification attaining more 93 % of accuracy for five classes morphology prediction except the cigar-shaped (75 %) and completely rounded (83 %) galaxies. Main results are presented in the catalog of 19468 completely rounded, 27321 rounded in-between, 3235 cigar-shaped, 4099 edge-on, 18615 spiral, and 72738 general low-redshift galaxies of the studied SDSS sample. As for the classification of galaxies by their detailed structural morphological features, our CNN model gives the accuracy in the range of 92-99 % depending on features, a number of galaxies with the given feature in the inference dataset, and the galaxy image quality. We demonstrate that implication of the CNN model with adversarial validation and adversarial image data augmentation improves classification of smaller and fainter SDSS galaxies with $m_{r}$ <17.7.

astro-ph.GA↗

arXiv2021

Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach

Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-Nearest Neighbors, and k-fold validation. Results. We present results of a binary automated morphological classification of galaxies conducted by human labeling, multiphotometry, and supervised Machine Learning methods. We applied its to the sample of galaxies from the SDSS DR9 with 0.02 < z < 0.1 and 24m < Mr < 19.4m. To study the classifier, we used absolute magnitudes: Mu, Mg, Mr , Mi, Mz, Mu-Mr , Mg-Mi, Mu-Mg, Mr-Mz, and inverse concentration index to the center R50/R90. Using the Support vector machine classifier and the data on color indices, absolute magnitudes, inverse concentration index of galaxies with visual morphological types, we were able to classify 316 031 galaxies from the SDSS DR9 with unknown morphological types. Conclusions. The methods of Support Vector Machine and Random Forest with Scikit-learn machine learning in Python provide the highest accuracy for the binary galaxy morphological classification: 96.4% correctly classified (96.1% early E and 96.9% late L types) and 95.5% correctly classified (96.7% early E and 92.8% late L types), respectively. Applying the Support Vector Machine for the sample of 316 031 galaxies from the SDSS DR9 at z < 0.1, we found 141 211 E and 174 820 L types among them.

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