Related papers: Finding universal relations in subhalo properties …

The increasingly large amount of cosmological data coming from ground-based and space-borne telescopes requires highly efficient and fast enough data analysis techniques to maximise the scientific exploitation. In this work, we explore 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…

The interaction properties of cold dark matter (CDM) particle candidates, such as those of weakly interacting massive particles (WIMPs), generically lead to the structuring of dark matter on scales much smaller than typical galaxies,…

We study the impact of warm dark matter (WDM) particle mass on galaxy properties using 1,024 state-of-the-art cosmological hydrodynamical simulations from the DREAMS project. We begin by using a Multilayer Perceptron (MLP) coupled with a…

Neural networks are being used to make new types of empirical chemical models as inexpensive as force fields, but with accuracy close to the ab-initio methods used to build them. Besides modeling potential energy surfaces, neural-nets can…

We develop a new empirical methodology to study the relation between the stellar mass of galaxies and the mass of their host subhaloes. Our approach is similar to abundance matching, and is based on assigning a stellar mass to each subhalo…

The weight space of an artificial neural network can be systematically explored using tools from statistical mechanics. We employ a combination of a hybrid Monte Carlo algorithm which performs long exploration steps, a ratchet-based…

Accurately predicting the abundance and structural evolution of dark matter subhaloes is crucial for understanding galaxy formation, modeling galaxy clustering, and constraining the nature of dark matter. Due to the nonlinear nature of…

We examine the properties of dark matter halos within a rich galaxy cluster using a high resolution simulation that captures the cosmological context of a cold dark matter universe. The mass and force resolution permit the resolution of 150…

There has been a long history of works showing that neural networks have hard time extrapolating beyond the training set. A recent study by Balestriero et al. (2021) challenges this view: defining interpolation as the state of belonging to…

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…

We model the galaxy formation in a series of high-resolution N-body simulations using the semi-analytical approach. Unlike many earlier investigations based on semi-analytical models, we make use of the subhalos resolved in the $N$-body…

In this paper, we explain the universal approximation capabilities of deep residual neural networks through geometric nonlinear control. Inspired by recent work establishing links between residual networks and control systems, we provide a…

The average matter density within the turnaround scale, which demarcates where galaxies shift from clustering around a structure to joining the expansion of the Universe, is an important cosmological probe. However, a measurement of the…

Disentangling the stellar population in the central galaxy from the intrahalo light can help us shed light on the formation history of the host halo, as the properties of the stellar components are expected to retain traces of its formation…

This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a set is not fixed in advance. Many real-world problems, such as…

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…

Motivated by previous findings that the magnitude gap between certain satellite galaxy and the central galaxy can be used to improve the estimation of halo mass, we carry out a systematic study of the information content of different member…

Universal approximation theorem suggests that a shallow neural network can approximate any function. The input to neurons at each layer is a weighted sum of previous layer neurons and then an activation is applied. These activation…

Quantifying the connection between galaxies and their host dark matter halos has been key for testing cosmological models on various scales. Below $M_\star \sim 10^9\,M_\odot$, such studies have primarily relied on the satellite galaxy…