Related papers: Galaxy stellar and total mass estimation using mac…
Galaxy clustering and galaxy-galaxy lensing probe the connection between galaxies and their dark matter haloes in complementary ways. On one hand, the halo occupation statistics inferred from the observed clustering properties of galaxies…
We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach.…
Galaxy edges or truncations are low-surface-brightness (LSB) features located in the galaxy outskirts that delimit the distance up to where the gas density enables efficient star formation. As such, they could be interpreted as a…
Cosmological simulations of galaxy formation predict a universal form for the mass profile of dark matter (DM) halos from cluster to galaxy scales. Remarkably few interesting constraints exist, however, on DM halos in early-type galaxies.…
We explore the capability of deep learning to classify cosmic structures. In cosmological simulations, cosmic volumes are segmented into voids, sheets, filaments and knots, according to the distribution and kinematics of dark matter (DM),…
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 --…
End-to-end deep learning models fed with multi-band galaxy images are powerful data-driven tools used to estimate galaxy physical properties in the absence of spectroscopy. However, due to a lack of interpretability and the associational…
Context. Convolutional neural networks (CNNs) have been established as the go-to method for fast object detection and classification on natural images. This opens the door for astrophysical parameter inference on the exponentially…
We study the evolution of galaxy clustering in various cosmological models with quintessence. We investigate how the analytical predictions vary with change of dark energy equation of state $w_X$. Comparing these predictions against…
Astronomical objects in our universe that are too faint to be directly detectable exist and are important - an obvious example being dark matter. The same can also apply to very faint baryonic objects, such as low luminosity dwarf galaxies…
Magnetic fields play a crucial role in various astrophysical processes within the intracluster medium, including heat conduction, cosmic ray acceleration, and the generation of synchrotron radiation. However, measuring magnetic field…
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…
All galaxies are thought to reside within large halos of dark matter, whose properties can only be determined from indirect observations. The formation and assembly of galaxies is determined from the interplay between these dark matter…
This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…
We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for…
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…
Whereas well-studied galaxies at cosmic noon are found to be baryon-dominated within the effective radius, recent JWST observations of $z\sim6-7$ galaxies with stellar masses of only $M_*\sim10^{8-9}\,{\rm M_\odot}$ surprisingly indicate…
Most existing star-galaxy classifiers depend on the reduced information from catalogs, necessitating careful data processing and feature extraction. In this study, we employ a supervised machine learning method (GoogLeNet) to automatically…
The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength surveys covering broad swaths of the sky. With multiple large datasets becoming available in the near future, we develop a likelihood-free Deep Learning technique…
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…