Related papers: A deep learning approach to halo merger tree const…
The Milky Way stellar halo contains relics of ancient mergers that tell the story of our Galaxy's formation. Some of them are identified due to their similarity in energy, actions and chemistry, referred to as the "chemodynamical space",…
Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as emerging families for generative model learning, have largely been considered as two…
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…
Quantifying the contribution of mergers to the stellar mass of galaxies is key for constraining the mechanisms of galaxy assembly across cosmic time. However, the mapping between observable galaxy properties and merger histories is not…
Galaxies are believed to be in one-to-one correspondence with simulated dark matter subhalos. We use high-resolution N-body simulations of cosmological volumes to calculate the statistical properties of subhalo (galaxy) major mergers at…
We have constructed merger trees for galaxies in the Illustris Simulation by directly tracking the baryonic content of subhalos. These merger trees are used to calculate the galaxy-galaxy merger rate as a function of descendant stellar…
The hierarchical mergers that form the haloes of dark matter surrounding galaxies, groups and clusters are not entirely efficient, leaving substantial amounts of dense substructure, in the form of stripped halo cores or `subhaloes',…
Generative adversarial networks (GANs) are deep neural networks that allow us to sample from an arbitrary probability distribution without explicitly estimating the distribution. There is a generator that takes a latent vector as input and…
We use TNG and EAGLE hydrodynamic simulations to investigate the central galaxy - dark matter halo relations that are needed for a halo-based empirical model of star formation in galaxies. Using a linear dimension reduction algorithm and a…
We make detailed theoretical predictions for the assembly properties of the Local Group (LG) in the standard LambdaCDM cosmological model. We use three cosmological N-body dark matter simulations from the CLUES project, which are designed…
In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a \textit {mode collapse} issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are…
Generative adversarial networks (GANs) are the state of the art in generative modeling. Unfortunately, most GAN methods are susceptible to mode collapse, meaning that they tend to capture only a subset of the modes of the true distribution.…
We extend a machine learning (ML) framework presented previously to model galaxy formation and evolution in a hierarchical universe using N-body + hydrodynamical simulations. In this work, we show that ML is a promising technique to study…
This paper explores the mapping between the observable properties of a stellar halo in phase- and abundance-space and the parent galaxy's accretion history in terms of the characteristic epoch of accretion and mass and orbits of progenitor…
We use semi-analytic models and cosmological merger trees to provide the initial conditions for multi-merger numerical hydrodynamic simulations, and exploit these simulations to explore the effect of galaxy interaction and merging on star…
Auto-encoding generative adversarial networks (GANs) combine the standard GAN algorithm, which discriminates between real and model-generated data, with a reconstruction loss given by an auto-encoder. Such models aim to prevent mode…
We present a model for the dynamical evolution of subhaloes based on an approach combining numerical and analytical methods. Our method is based on tracking subhaloes in an N-body simulation up to the last point that it can be resolved, and…
Recently, there has been a growing focus and interest in applying machine learning (ML) to the field of cybersecurity, particularly in malware detection and prevention. Several research works on malware analysis have been proposed, offering…
Model merging acquires general capabilities without extra data or training by combining multiple models' parameters. Previous approaches achieve linear mode connectivity by aligning parameters into the same loss basin using permutation…
We use the "Millennium Simulation" to study the mass function of accreted sub-halos during merger events in the dark halo assembly history. Our study includes three kinds of sub-halo mergers: (1) mergers that happen to the main progenitor…