Machine learning technique for morphological classification of galaxies at z<0.1 from the SDSS

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 redshifts of 0.02 < z < 0.1 and absolute stellar magnitudes of 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.Comment: 10 pages, 5 figures. The presentation of these results was given during the EWASS-2017, Symposium "Astroinformatics: From Big Data to Understanding the Universe at Large". It is vailable through \url{http://space.asu.cas.cz/~ewass17-soc/Presentations/S14/Dobrycheva_987.pdf

Criteria for galaxy classification in SDSS

We considered the sample of galaxy pairs collected from SDSS DR5. At first we made a morphological classification of approximately 1100 galaxies by visual inspection. As it is known the galaxy morphological types strongly depend on the color indices, the concentration parameters, galaxy magnitudes, de Vaucouleurs and exponential fit scale radii. Thus we used pairs of these parameters and found the universal criteria which can be used for galaxy morphological classi-cation with the help of our "by-eye" classification. According to our criteria we can select early type galaxies with 90% probability

Machine-learning computation of distance modulus for local galaxies

Quickly growing computing facilities and an increasing number of extragalactic observations encourage the application of data-driven approaches to uncover hidden relations from astronomical data. In this work we raise the problem of distance reconstruction for a large number of galaxies from available extensive observations. We propose a new data-driven approach for computing distance moduli for local galaxies based on the machine-learning regression as an alternative to physically oriented methods. We use key observable parameters for a large number of galaxies as input explanatory variables for training: magnitudes in U, B, I, and K bands, corresponding colour indices, surface brightness, angular size, radial velocity, and coordinates. We performed detailed tests of the five machine-learning regression techniques for inference of $m-M$: linear, polynomial, k-nearest neighbours, gradient boosting, and artificial neural network regression. As a test set we selected 91 760 galaxies at $z<0.2$ from the NASA/IPAC extragalactic database with distance moduli measured by different independent redshift methods. We find that the most effective and precise is the neural network regression model with two hidden layers. The obtained root-mean-square error of 0.35 mag, which corresponds to a relative error of 16\%, does not depend on the distance to galaxy and is comparable with methods based on the Tully-Fisher and Fundamental Plane relations. The proposed model shows a 0.44 mag (20\%) error in the case of spectroscopic redshift absence and is complementary to existing photometric redshift methodologies. Our approach has great potential for obtaining distance moduli for around 250 000 galaxies at $z<0.2$ for which the above-mentioned parameters are already observed.Comment: 8 pages, 5 figures, Accepted for publication in A&

THE NEW GALAXY SAMPLE FROM SDSS DR9 AT 0.003 ≤ Z ≤ 0.1

To test the relationships between morphological types of galaxies in pairs/groups and their physical properties (luminosity, mass, color index, the radial velocity, the inverse concentration index, the absolute magnitude, the radius of the de Vaucouleurs or scale radius, etc.) on a larger sample of the local Universe, we need the more representative data. With this aim we processed and prepared a sam­ple of galaxies with 0.003 ≤ z ≤ 0.1 based on the latest SDSS DR9. The initial sample was about 724,000 objects and, consequently, 407,000 galaxy images after the preliminary processing. Because of the large number of duplicate and faulty images, we checked its carefully and obtained finally about 260,000 galaxies in the studied sample at z &lt; 0.1. We discuss this procedure and properties of the studied galaxy sample

NO THE HOLMBERG EFFECT FOR GALAXY PAIRS SELECTED FROM THE SDSS DR9 AT Z ≤ 0:06

 We studied the Holmberg effect in galaxy pairs selected from the SDSS DR9, where 60561galaxies were limited by redshift 0.02 &lt; z &lt; 0.06 and absolute magnitude: Mr ≤ −20.7 m for central galaxies (N=18578) and Mr &gt; −21.5 m for neighbor galaxies (N=41983). We have made a morphological classification for each galaxy using both the visual inspection and machine learning methods. We considered four morphological types of galaxy pairs (E, early, and L, late, types) for testing the Holmberg effect: E- E, E-L, L-E, L-L (first companion of pairs is a central galaxy and second one is a faint satellite galaxy). We concluded about the absence of the Holmberg effect: Rg−i = 0.3 for L-E pairs at 0.04 &lt; z ≤ 0.06 and Rg−i = 0.2 for E-E and E-L pairs at 0.02 ≤ z ≤ 0.04. Summarizing, a correlation of color indices in pairs for the samples of galaxies composed with the half of large sky surveys likely SDSS was not confirmed or confirmed partially. The Holmberg effect is rather connected with morphological types of galaxies than with their color indices. Taking into account a scenario of the secular evolution, the presence of at least one elliptical galaxy in pair may be indicator of previous mergers in the earlier epoch. So, figuring manifestations of the Holmberg effect in its original interpretation no longer seems such urgent.

ENVIRONMENTAL PROPERTIES OF GALAXIES AT Z < 0.1 FROM THE SDSS VIA THE VORONOI TESSELLATION

The aim of our work was to investigate the environmental density of galaxies fromthe SDSS DR9 at z &lt; 0.1 using the 3D Voronoi tessellation. The inverse volume of the Voronoi cell was chosen as a parameter of local environmental density. We examined a density of given bright galaxy taking into account its faint satellites located in the Voronoi cell. We found that with the increase of total galaxy density around the central bright galaxy, the probability that it has the early type is increasing.The aim of our work was to investigate the environmental density of galaxies fromthe SDSS DR9 at z &lt; 0.1 using the 3D Voronoi tessellation. The inverse volume of the Voronoi cell was chosen as a parameter of local environmental density. We examined a density of given bright galaxy taking into account its faint satellites located in the Voronoi cell. We found that with the increase of total galaxy density around the central bright galaxy, the probability that it has the early type is increasing

New exocomets of β\beta Pic

Aims. The aim of our work is to analyze the light curves of $\beta$ Pic recently observed by TESS in sectors 32, 33, and 34, searching for the signatures of exocomet transits. Methods. We process the $\beta$ Pic light curves from the MAST database, applying the frequency analysis to remove harmonic signals due to the star's pulsations and use a simple 1-D model to fit the profiles of the found events. Results. We recover events previously found by other authors in sectors 5 and 6 and find five new distinct aperiodic dipping events with asymmetric shapes resembling the expected profiles due to the passage of a comet-like body across the star disk. These dips are rather shallow, with the flux drop at a level of 0.03\% and a duration of less than 1 day. No periodic transits were found in the sectors investigated. Conclusions. The depth and duration of the identified dips are similar to the recently discovered transits in the $\beta$ Pic light curves from sector 5 of the TESS observations as well as to those found in the light curves of KIC 354116 and KIC 1108472 from the Kepler database. It indicates that aperiodic shallow dips are not likely an exceptional phenomenon, at least for the $\beta$ Pic system.Comment: 8 pages, 13 Figs, 1 Table. Accepted by A&

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.Comment: 25 pages, 7 figures, 2 table