Related papers: A Robust and Efficient Deep Learning Method for Dy…

We present a machine-learning approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep machine learning tool commonly used in image recognition tasks. The CNN is trained…

We evaluate the ability of Convolutional Neural Networks (CNNs) to predict galaxy cluster masses in the BAHAMAS hydrodynamical simulations. We train four separate single-channel networks using: stellar mass, soft X-ray flux, bolometric…

We present a new method by which the total masses of galaxies including dark matter can be estimated from the kinematics of their globular cluster systems (GCSs). In the proposed method, we apply the convolutional neural networks (CNNs) to…

Traditional weak-lensing mass reconstruction techniques suffer from various artifacts, including noise amplification and the mass-sheet degeneracy. In Hong et al. (2021), we demonstrated that many of these pitfalls of traditional mass…

We introduce a novel method for reconstructing the projected matter distributions of galaxy clusters with weak-lensing (WL) data based on convolutional neural network (CNN). Training datasets are generated with ray-tracing through…

Galaxy clusters are useful laboratories to investigate the evolution of the Universe, and accurately measuring their total masses allows us to constrain important cosmological parameters. However, estimating mass from observations that use…

We study methods for reconstructing Bayesian uncertainties on dynamical mass estimates of galaxy clusters using convolutional neural networks (CNNs). We discuss the statistical background of approximate Bayesian neural networks and…

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…

Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning, which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions…

Clusters of galaxies mass can be inferred by indirect observations, see X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical. Unfortunately, all of them are affected by some bias. Alternatively, we provide an independent estimation…

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…

We present an algorithm for inferring the dynamical mass of galaxy clusters directly from their respective phase-space distributions, i.e. the observed line-of-sight velocities and projected distances of galaxies from the cluster centre.…

The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the…

Distinguishing galaxies as either fast or slow rotators plays a vital role in understanding the processes behind galaxy formation and evolution. Standard techniques, which are based on the $\lambda_R$-spin parameter obtained from stellar…

Precise determination of galaxy cluster masses is crucial for establishing reliable mass-observable scaling relations in cluster cosmology. We employ graph neural networks (GNNs) to estimate galaxy cluster masses from radially sampled…

We investigate the ability of machine learning to infer the virial mass ($M_{\rm vir}$) and the scale radius ($r_{\rm s}$) of galaxy clusters from their observables. Using the Uchuu--UniverseMachine galaxy catalog at $z=0.093$, we generate…

We explore the effectiveness of deep learning convolutional neural networks (CNNs) for estimating strong gravitational lens mass model parameters. We have investigated a number of practicalities faced when modelling real image data, such as…

We present a modern machine learning approach for cluster dynamical mass measurements that is a factor of two improvement over using a conventional scaling relation. Different methods are tested against a mock cluster catalog constructed…

In this work, we seek to improve the velocity reconstruction of clusters by using Graph Neural Networks -- a type of deep neural network designed to analyze sparse, unstructured data. In comparison to the Convolutional Neural Network (CNN)…

We present our results from training and evaluating a convolutional neural network (CNN) to predict galaxy shapes from wide-field survey images of the first data release of the Dark Energy Survey (DES DR1). We use conventional shape…