Related papers: Classifying galaxies according to their HI content
The atomic hydrogen HI content of galaxies is intimately related to star formation and galaxy evolution through the baryon cycle, which involves processes such as accretion, feedback, outflows, and gas recycling. While probing the HI gas…
We have developed an automated cross-correlation technique to detect 21cm emission in sample spectra obtained from the HI Parkes All Sky Survey. The initial sample selection was the nearest spectra to 2435 low surface brightness galaxies in…
We extend a machine learning (ML) framework presented previously to model galaxy formation and evolution in a hierarchical universe using N-body + hydrodynamical simulations. In this work, we show that ML is a promising technique to study…
The availability of high-quality spectra for a large number of galaxies in the SDSS survey allows for a more sophisticated extraction of information about their stellar populations than, e.g., the luminosity weighted age. Indeed,…
The connection between galaxies and dark matter halos encompasses a range of processes and play a pivotal role in our understanding of galaxy formation and evolution. Traditionally, this link has been established through physical or…
Unveiling the evolutionary history of galaxies necessitates a precise understanding of their physical properties. Traditionally, astronomers achieve this through spectral energy distribution (SED) fitting. However, this approach can be…
We present a random forest framework for predicting circumgalactic medium (CGM) physical conditions from quasar absorption line observables, trained on a sample of Voigt profile-fit synthetic absorbers from the Simba cosmological…
The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS, were cross-matched by Bilicki et al. (2016) (B16) to construct a novel photometric redshift catalogue on 70% of the sky. Galaxies were therein separated from…
Nowadays, the hyperspectral remote sensing imagery HSI becomes an important tool to observe the Earth's surface, detect the climatic changes and many other applications. The classification of HSI is one of the most challenging tasks due to…
We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…
Despite the existence of well-defined relationships between cold gas and star formation, there is evidence that some galaxies contain large amounts of HI that do not form stars efficiently. By systematically assessing the link between HI…
Upcoming galaxy surveys with the SKA Observatory will detect neutral hydrogen (HI) across unprecedented volumes, and their scientific return will crucially depend on predictive models for HI observables. In this work, we present a framework…
While massive quiescent galaxies are known to be poor in atomic hydrogen (HI), their true HI content remains poorly constrained due to the limited sensitivity and morphological biases of existing surveys. We present deep HI observations…
We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML). Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and…
To extract information from the clustering of galaxies on non-linear scales, we need to model the connection between galaxies and halos accurately and in a flexible manner. Standard halo occupation distribution (HOD) models make the…
Elucidating the connection between the properties of galaxies and the properties of their hosting haloes is a key element in galaxy formation. When the spatial distribution of objects is also taken under consideration, it becomes very…
This chapter describes the assumed specifications and sensitivities for HI galaxy surveys with SKA1 and SKA2. It addresses the expected galaxy number densities based on available simulations as well as the clustering bias over the…
Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the…
We perform a stacking analysis of the HI spectra from the Arecibo Legacy Fast ALFA (ALFALFA) survey for optically-selected local galaxies from the Sloan Digital Sky Survey (SDSS) to study the average gas fraction of galaxies at fixed…
Precision cosmology requires accurate galaxy redshifts, but next generation optical surveys will observe unprecedented numbers of resolved galaxies, placing strain on the amount of spectroscopic follow-up required. We show how useful…