Related papers: Classifying galaxies according to their HI content
(abridged) Mass loss is a key parameter in the evolution of massive stars, with discrepancies between theory and observations and with unknown importance of the episodic mass loss. To address this we need increased numbers of classified…
While the selection of strongly lensed galaxies with 500{\mu}m flux density S_500>100 mJy has proven to be rather straightforward (Negrello et al. 2010), for many applications it is important to analyze samples larger than the ones obtained…
The environment plays a critical role in galaxy evolution, with galaxy clusters and their infall regions offering diverse conditions that shape galaxies before they enter the dense cluster core, a process known as ``pre-processing''.…
The integrated HI emission from hierarchical structures such as groups and clusters of galaxies can be detected by FAST at intermediate redshifts. Here we propose to use FAST to study the evolution of the global HI content of clusters and…
Context: We present the first Cosmological Parameter inferences from eROSITA X-ray observations of galaxy clusters using a Machine Learning algorithm. Methods: We train a Random Forest using mock catalogs of clusters from Magneticum…
The morphology of a galaxy has been shown to encode the evolutionary history and correlates strongly with physical properties such as stellar mass, star formation rates and past merger events. While the majority of galaxies in the local…
A standard parametric maximum-likelihood technique is used to determine both the probability distribution over total HI mass $M_{\rm HI}$ and the regression of this quantity on the linear optical diameter $D_{\rm o}$ for field giant spirals…
We present a machine learning (ML) approach for the prediction of galaxies' dark matter halo masses that achieves an improved performance over conventional methods. We train three ML algorithms (\texttt{XGBoost}, Random Forests, and neural…
Strong gravitational lensing provides a powerful tool to directly infer the dark matter (DM) subhalo mass function (SHMF) in lens galaxies. However, comparing observationally inferred SHMFs to theoretical predictions remains challenging, as…
In order to understand galaxy growth evolution, it is critical to constrain the evolution of its building block: gas. Mostly comprised by Hydrogen in its neutral (HI) and molecular (H2) phases, the latter is the one mostly directly…
We use spectral stacking to measure the contribution of galaxies of different masses and in different hierarchies to the cosmic atomic hydrogen (HI) mass density in the local Universe. Our sample includes 1793 galaxies at $z < 0.11$…
Classification will be an important first step for upcoming surveys that will detect billions of new sources such as LSST and Euclid, as well as DESI, 4MOST and MOONS. The application of traditional methods of model fitting and…
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…
Gas rich galaxies are typically star-forming. We make use of HI-MaNGA, a program of HI follow-up for the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey of the Sloan Digital Sky Surveys to construct a sample of unusual…
We apply novel survival analysis techniques to investigate the relationship between a number of the properties of galaxies and their atomic ($M_\mathrm{HI}$) and molecular ($M_{\mathrm{H_2}}$) gas mass, with the aim of devising efficient,…
In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…
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…
The classification of galaxies as spirals or ellipticals is a crucial task in understanding their formation and evolution. With the arrival of large-scale astronomical surveys, such as the Sloan Digital Sky Survey (SDSS), astronomers now…
Identifying neutral hydrogen (\hi) galaxies from observational data is a significant challenge in \hi\ galaxy surveys. With the advancement of observational technology, especially with the advent of large-scale telescope projects such as…
We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…