The astrocosmic databases for multi-wavelength and cosmological properties of extragalactic sources

Vavilova, I. B.; Ivashchenko, G.; Babyk, Iu.; Sergijenko, O.; Dobrycheva, D. V.; Torbaniuk, O.; Vasylenko, A. A.; Pulatova, N. G.

doi:10.15407/knit2015.05.094

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…The mean values of w 0 from Tables 1-3 are much closer to −1 than in [4,19] (where the 2013 year data from Planck were used) for all combined datasets, making the constraints on c 2 a significantly weaker (indeed, as it can be seen in Fig. 1-3, the posteriors for c 2 a are cut from below by the prior).…”

Section: Resultsmentioning

confidence: 84%

Constraints on the dark energy with barotropic equation of state: assessing the importance of different observations

Sergijenko

2019

Adv.Astron.Spa.Physics

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For dynamical dark energy with the barotropic equation of state we determine the mean values of parameters and their confidence ranges together with other cosmological parameters on the basis of different combined datasets. The used observations include Planck data on CMB temperature anisotropy, E-mode polarization and lensing, BICEP2/Keck Array data on B-mode polarization, BAO from SDSS and 6dFGS, power spectrum of galaxies from WiggleZ, weak lensing from CFHTLenS and SN Ia data from the JLA compilation. We find that all but one mean models are phantom, mean values of the equation of state parameter at current epoch are close to −1 and constraints on the adiabatic sound speed of dark energy are weak. We investigate the effect of CMB polarization data on the dark energy parameters estimation. We discuss also which type of data on the large scale structure of the Universe allows to determine the dark energy parameters most precisely.

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Section: Resultsmentioning

confidence: 84%

Constraints on the dark energy with barotropic equation of state: assessing the importance of different observations

Sergijenko

2019

Adv.Astron.Spa.Physics

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“…For example, spectroscopy classification requires different methods to define simultaneously similar spectra for quiescent/starburst and starforming galaxies [94,95] or emission-line galaxies [96]. As well, a photometry-based approach gives an error when trying to classify red spirals and blue ellipticals [64,67,[97][98][99][100], i.e., galaxies with a high content of old stars or interacting galaxies which affect the photometric characteristics of each other [101][102][103][104]. Analyzing our obtained results and data products let us discuss several issues related to the CNN image-based galaxy classification.…”

Section: General Results and Discussionmentioning

confidence: 99%

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

Khramtsov¹,

Vavilova²,

Dobrycheva³

et al. 2022

Kosm. nauka tehnol.

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This paper follows a series of our works on the applicability of various machine learning methods to morphological galaxy classification (Vavilova et al., 2021, 2022). We exploited the sample of ~315800 low-redshift SDSS DR9 galaxies with absolute stellar magnitudes of −24m < Mr < −19.4m at 0.003 < z < 0.1 redshifts as a target data set for the CNN classifier. 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 in 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 with mr < 17.7. 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 to verify our CNN model based on the DenseNet-201 realistically. We have also found optimal galaxy image transformations, which help increase the classifier’s generalization ability. We demonstrate for the first time that implication of the CNN model with a train-test split of data sets and size-changing function simulating a decrease in magnitude and size (data augmentation) significantly improves the classification of smaller and fainter SDSS galaxies. 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, like autoimmunization, when the CNN classifier, trained on very good galaxy images, is able to retrain bad images from the same homogeneous sample, can be considered co-planar to other methods of combating such a human bias. The most promising result is related to the CNN prediction probability in the classification of detailed features. The accuracy of the CNN classifier is in the range of 83.3—99.4 % depending on 32 features (exception is for “disturbed” (68.55 %) and “arms winding medium” (77.39 %) features). As a result, for the first time, we assigned the detailed morphological classification for more than 140000 low-redshift galaxies, especially at the fainter end. A visual inspection of the samples of galaxies with certain morphological features allowed us to reveal typical problem points of galaxy image classification by shape and features from the astronomical point of view. The morphological catalogs of low-redshift SDSS galaxies with the most interesting features are available through the UkrVO website (http://ukr-vo.org/galaxies/) and VizieR.

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“…The answer to this question can lead us to a better understanding of AGNs structure and role of interactions with other galaxies on their structure, at least in pairs. We are planning to prepare a more detailed comparative analysis of X‐ray characteristics of the isolated AGNs at z < 0.1 selected from the 2MIG catalogue (Chesnok (2010); Pulatova et al (2015); Vasylenko et al (2015); Vavilova et al 2015a), AMIGA project Verdes‐Montenegro et al (2005) (http://amiga.iaa.es/p/1-homepage.htmand); and other databases.…”

Section: Discussionmentioning

confidence: 99%

Isolated AGNs NGC 5347, ESO 438‐009, MCG‐02‐04‐090, and J11366‐6002: Swift and NuSTAR joined view¹

2020

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We present the spectral analysis with the Nuclear Spectroscopic Telescope Array (NuSTAR) of four isolated galaxies with active galactic nuclei selected from the 2MIG catalogue: NGC 5347, ESO 438‐009, MCG‐02‐09‐040, and IGR J11366‐6002. We also used the Swift/Burst Alert Telescope (BAT) data up to ∼150 keV for MCG 02‐09‐040, ESO 438‐009, and IGR J11366‐6002 as well as the Swift/XRT data in 0.3–10 keV energy band for NGC 5347, ESO 438‐009, and IGR J11366‐6002. All the sources appear to have the reflected spectrum component with different reflection fractions in addition to the primary power‐law continuum. We found that power‐law indices for these sources lie between 1.6 and 1.8. The spectra of two sources, NGC 5347 and MCG‐02‐09‐040, show the Fe Kα emission line. For both of these sources, the Fe Kα lines have a significant value of EW ∼1 keV. The X‐ray spectrum of NGC 5347 is best fitted by a pure reflection model with Ecut ∼ 117 keV and without the presence of any additional primary power‐law component. We also found that the X‐ray spectrum of MCG‐02‐09‐040 shows the presence of heavy neutral obscuration of NH ∼ 1024 cm−2. However, this provides a non‐physical value of reflection in the case with fitting by a simple reflection model. A more appropriate fit is obtained with adopting the physical Monte Carlo‐based model as BNTorus. It allowed us to determine the absorption value of NH ∼ 1.04 × 1024 cm−2 and reasonable power‐law index of Γ ≈ 1.63. Results for MCG‐02‐09‐040 are presented for the first time.

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The astrocosmic databases for multi-wavelength and cosmological properties of extragalactic sources

Cited by 7 publications

References 32 publications

Constraints on the dark energy with barotropic equation of state: assessing the importance of different observations

Constraints on the dark energy with barotropic equation of state: assessing the importance of different observations

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

Isolated AGNs NGC 5347, ESO 438‐009, MCG‐02‐04‐090, and J11366‐6002: Swift and NuSTAR joined view¹

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The astrocosmic databases for multi-wavelength and cosmological properties of extragalactic sources

Cited by 7 publications

References 32 publications

Constraints on the dark energy with barotropic equation of state: assessing the importance of different observations

Constraints on the dark energy with barotropic equation of state: assessing the importance of different observations

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

Isolated AGNs NGC 5347, ESO 438‐009, MCG‐02‐04‐090, and J11366‐6002: Swift and NuSTAR joined view1

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Isolated AGNs NGC 5347, ESO 438‐009, MCG‐02‐04‐090, and J11366‐6002: Swift and NuSTAR joined view¹