arXiv subjects

Vladimir B. Ilin

Publications and source records attributed to Vladimir B. Ilin.

3 recordsLinked to original sources

arXiv2026

Analysis of Galactic cirrus filaments in HSC-SSP high-resolution deep images using artificial neural networks

The existence of Galactic optical cirrus poses a challenge for observing faint objects within our Galaxy and dim extragalactic structures. To investigate individual cirrus filaments in the Hyper Suprime-Cam Subaru Strategic Program public data release 3 (HSC-SSP DR3) we use a technique based on convolutional neural networks and ensemble learning. This approach allows us to distinguish cirrus filaments from foreground and background objects across the entire HSC-SSP, using optical images in the $g$, $r$, and $i$ wavebands. A comparison with previous work using deep Sloan Digital Sky Survey Stripe~82 (SDSS Stripe~82) data reveals that the cirrus clouds identified in this study are highly consistent in location within the overlapping survey region. However, in the deeper HSC-SSP dataset, we were able to detect $4.5$ times more cirrus clouds. Our study indicates that the sky background in HSC-SSP coadd images is over-subtracted, as evidenced by the surface brightness distribution in cirrus filaments and surrounding regions. Objects with surface brightness of $m = 29~\mbox{mag~arcsec}^{-2}$ near large filaments can be dimmed by over-subtraction of $0.5$ magnitude in the $r$ band. This suggests that cirrus clouds should be taken into account in algorithms for estimating the sky background. For practical use, we provide a catalog of filaments and a framework that allows one to train neural network models for segmenting cirri in HSC-SSP coadd images.

astro-ph.GA↗

arXiv2024

Galaxies decomposition with spiral arms -- II: A multiwavelength case study of M 51

Spiral structure can contribute significantly to a galaxy's luminosity. However, only rarely are proper photometric models of spiral arms used in decompositions. As we show in the previous work, including the spirals as a separate component in a photometric model of a galaxy would both allow to obtain their structural parameters, and reduce the systematic errors in estimating the parameters of other components. Doing so in different wavebands, one can explore how their properties vary with the wavelength. In this paper, second in this series, we perform decomposition of M 51 in 17 bands, from the far UV to far IR, using imaging from the DustPedia project. We use the same 2D photometric model of spiral structure where each arm is modelled independently. The complex and asymmetric spiral structure in M 51 is reproduced relatively well with our model. We analyze the differences between models with and without spiral arms, and investigate how the fit parameters change with wavelength. In particular, we find that the spiral arms demonstrate the largest width in the optical, whereas their contribution to the galaxy luminosity is most significant in the UV. The disk central intensity drops by a factor of 1.25--3 and its exponential scale changes by 5--10\% when spiral arms are included, depending on wavelength. Taking into account the full light distribution across the arms, we do not observe the signs of a long-lived density wave in the spiral pattern of M 51 as a whole.

astro-ph.GA↗

arXiv2023

Prospects for future studies using deep imaging: Analysis of individual Galactic cirrus filaments

The presence of Galactic cirrus is an obstacle for studying both faint objects in our Galaxy and low surface brightness extragalactic structures. With the aim of studying individual cirrus filaments in SDSS Stripe 82 data, we develop techniques based on machine learning and neural networks that allow one to isolate filaments from foreground and background sources in the entirety of Stripe 82 with a precision similar to that of the human expert. Our photometric study of individual filaments indicates that only those brighter than 26 mag arcsec$^{-2}$ in the SDSS $r$ band are likely to be identified in SDSS Stripe~82 data by their distinctive colours in the optical bands. We also show a significant impact of data processing (e.g. flat-fielding, masking of bright stars, and sky subtraction) on colour estimation. Analysing the distribution of filaments' colours with the help of mock simulations, we conclude that most filaments have colours in the following ranges: $0.55 \leq g-r \leq 0.73$ and $0.01 \leq r-i \leq 0.33$. Our work provides a useful framework for an analysis of all types of low surface brightness features (cirri, tidal tails, stellar streams, etc.) in existing and future deep optical surveys. For practical purposes, we provide the catalogue of dust filaments.

astro-ph.GA↗