Related papers: Estimating the mass of galactic components using m…
We present a novel technique for ranking the relative importance of galaxy properties in the process of quenching star formation. Specifically, we develop an artificial neural network (ANN) approach for pattern recognition and apply it to a…
Planet induced sub-structures, like annular gaps, observed in dust emission from protoplanetary disks provide a unique probe to characterize unseen young planets. While deep learning based model has an edge in characterizing the planet's…
Different ways of the determination of masses of galactic disks, based on the kinematic data, are briefly discussed. The analysis of the rotation curves which reach maximum inside of a disk, and N-body modeling, reproducing the rotation…
We perform automated bulge + disc decomposition on a sample of $\sim$7500 galaxies from the Galaxy And Mass Assembly (GAMA) survey in the redshift range of 0.002$<$z$<$0.06 using SIGMA, a wrapper around GALFIT3. To achieve robust profile…
The properties of dwarf galaxies orbiting the Milky Way (MW) are useful for testing models of the formation of our Galaxy, and by extension various theories of cosmology. Recent efforts to measure the masses of the MW's satellite dwarf…
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…
Weak gravitational lensing has been used extensively in the past decade to constrain the masses of galaxy clusters, and is the most promising observational technique for providing the mass calibration necessary for precision cosmology with…
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…
The formation of galaxies can be understood in terms of the assembly patterns of each type of galactic component. To perform this kind of analysis, is necessary to define some criteria to separate those components. Decomposition methods…
Understanding the connections between galaxy stellar mass, star formation rate, and dark matter halo mass represents a key goal of the theory of galaxy formation. Cosmological simulations that include hydrodynamics, physical treatments of…
Galaxy clusters are the most massive gravitationally bound structures in the Universe and key probes of cosmic evolution. The large data volume expected from upcoming surveys requires efficient automated analysis methods for tens of…
The vast quantity of strong galaxy-galaxy gravitational lenses expected by future large-scale surveys necessitates the development of automated methods to efficiently model their mass profiles. For this purpose, we train an approximate…
A galaxy cluster as the most massive gravitationally-bound object in the Universe, is dominated by Dark Matter, which unfortunately can only be investigated through its interaction with the luminous baryons with some simplified assumptions…
In Sedgwick et al. (2019) we introduced and utilised a method to combat surface brightness and mass biases in galaxy sample selection, using core-collapse supernovae (CCSNe) as pointers towards their host galaxies, in order to: (i) search…
By using a machine learning algorithms, we present an improved nuclear mass table with a root mean square deviation of less than $200$\.keV. The model is equipped with statistical error bars in order to compare with available experimental…
The simplest scheme for predicting real galaxy properties after performing a dark matter simulation is to rank order the real systems by stellar mass and the simulated systems by halo mass and then simply assume monotonicity - that the more…
A significant fraction of high redshift star-forming disc galaxies are known to host giant clumps, whose nature and role in galaxy evolution are yet to be understood. In this work we first present a new method based on neural networks to…
Dwarf galaxies are small, dark matter-dominated galaxies, some of which are embedded within the Milky Way. Their lack of baryonic matter (e.g., stars and gas) makes them perfect test beds for probing the properties of dark matter --…
We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0--S0a, Sa--Sb, Sbc--Scd, Sd--Irr) classes, using a combination of non-parametric…
Our goal is to estimate the mass of the Local Group (LG) and the individual masses of its primary galaxies, the M31 and the Milky Way (MW). We do this by means of a supervised machine learning algorithm, the gradient boosted decision trees…