Related papers: Galaxy merger challenge: A comparison study betwee…
Galaxy mergers are critical events that influence galaxy evolution by driving processes such as enhanced star formation, quenching, and active galactic nucleus (AGN) activity. However, constraining the timescales over which these processes…
We use machine learning techniques to classify galaxy merger stages, which can unveil physical processes that drive the star formation and active galactic nucleus (AGN) activities during galaxy interaction. The sample contains 4,690…
We present an automatic method to identify galaxy mergers using the morphological information contained in the residual images of galaxies after the subtraction of a Sersic model. The removal of the bulk signal from the host galaxy light is…
Machine learning (ML) techniques, in particular supervised regression algorithms, are a promising new way to use multiple observables to predict a cluster's mass or other key features. To investigate this approach we use the \textsc{MACSIS}…
Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…
Mergers are believed to play a pivotal role in galaxy evolution, and measuring the galaxy merger fraction is a longstanding goal of both observational and theoretical studies. In this work, we extend the consideration of the merger fraction…
One emerging application of machine learning methods is the inference of galaxy cluster masses. In this note, machine learning is used to directly combine five simulated multiwavelength measurements in order to find cluster masses. This is…
Galaxy mergers are crucial to understanding galaxy evolution, therefore we must determine their observational signatures to select them from large IFU galaxy samples such as MUSE and SAMI. We employ 24 high-resolution idealised…
Merging galaxy systems provides observational evidence of the existence of dark matter and constraints on its properties. Therefore, statistical uniform samples of merging systems would be a powerful tool for several studies. In this work…
Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable…
In recent years, automated, supervised classification techniques have been fruitfully applied to labeling and organizing large astronomical databases. These methods require off-line classifier training, based on labeled examples from each…
We present a catalogue of 39 multiple-mergers found using the mergers catalogue of the Galaxy Zoo project for $z<0.1$ and compare them to corresponding semi-analytical galaxies from the Millennium Simulation. We estimate the…
Accurately distinguishing between quiescent and star-forming galaxies is essential for understanding galaxy evolution. Traditional methods, such as spectral energy distribution (SED) fitting, can be computationally expensive and may…
We present a deep machine learning (ML) approach to constraining cosmological parameters with multi-wavelength observations of galaxy clusters. The ML approach has two components: an encoder that builds a compressed representation of each…
Identifying mergers from observational data has been a crucial aspect of studying galaxy evolution and formation. Tidal features, typically fainter than 26 ${\rm mag\,arcsec^{-2}}$, exhibit a diverse range of appearances depending on the…
It is difficult to accurately identify galaxy mergers and it is an even larger challenge to classify them by their mass ratio or merger stage. In previous work we used a suite of simulated mergers to create a classification technique that…
Galaxy cluster mergers are a powerful laboratory for testing cosmological and astrophysical models. However, interpreting individual merging clusters depends crucially on their merger configuration, defined by the masses, velocities, impact…
We investigate the environmental dependence of galaxy mergers using high-resolution imaging data from the Hyper Suprime-Cam (HSC) Subaru Strategic Program. We focus on galaxy groups and clusters at $z < 0.2$ identified by the Sloan Digital…
We use galaxy-galaxy lensing to investigate how the dark matter (DM) haloes and stellar content of galaxies with $0.012 \leq z \leq 0.32$ and $10 \leq \log_{10}(M_\star/\mathrm{M}_\odot) \leq 12$ change as a result of the merger process. To…
Aims. This work aims to reproduce the time before or after a merger event of merging galaxies from the IllustrisTNG cosmological simulation using machine learning. Methods. Images of merging galaxies were created in the u, g, r, and i bands…