Related papers: A deep learning approach to halo merger tree const…
Being able to distinguish between galaxies that have recently undergone major merger events, or are experiencing intense star formation, is crucial for making progress in our understanding of the formation and evolution of galaxies. As…
The connection between galaxies and dark matter halos encompasses a range of processes and play a pivotal role in our understanding of galaxy formation and evolution. Traditionally, this link has been established through physical or…
A fundamental prediction of the LambdaCDM cosmology is the hierarchical build-up of structure and therefore the successive merging of galaxies into more massive ones. As one can only observe galaxies at one specific time in cosmic history,…
Weak gravitational lensing mass maps play a crucial role in understanding the evolution of structures in the universe and our ability to constrain cosmological models. The prediction of these mass maps is based on expensive N-body…
In this work, I explore an empirically motivated model for investigating the relationship between galaxy stellar masses, star formation rates and their halo masses and mass accretion histories. The core statistical quantity in this model is…
We study the ability of PINOCCHIO (PINpointing Orbit-Crossing Collapsed HIerarchical Objects) to predict the merging histories of dark matter (DM) haloes, comparing the PINOCCHIO predictions with the results of two large N-body simulations…
Near-future large galaxy surveys will encounter blended galaxy images at a fraction of up to 50% in the densest regions of the universe. Current deblending techniques may segment the foreground galaxy while leaving missing pixel intensities…
Recently, deep-networks-based hashing (deep hashing) has become a leading approach for large-scale image retrieval. It aims to learn a compact bitwise representation for images via deep networks, so that similar images are mapped to nearby…
During a geosteering operation the well path is intentionally adjusted in response to the new data acquired while drilling. To achieve consistent high-quality decisions, especially when drilling in complex environments, decision support…
Dark matter subhalos are the remnants of (incomplete) halo mergers. Identifying them and establishing their evolutionary links in the form of merger trees is one of the most important applications of cosmological simulations. The…
We apply a novel method with machine learning to calibrate sub-grid models within numerical simulation codes to achieve convergence with observations and between different codes. It utilizes active learning and neural density estimators.…
One of the most significant challenges in statistical signal processing and machine learning is how to obtain a generative model that can produce samples of large-scale data distribution, such as images and speeches. Generative Adversarial…
To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating…
We investigate the evolution of dwarf galaxies using N -body/SPH simulations that incorporate their formation histories through merger trees constructed using the ex- tended Press-Schechter formalism. The simulations are computationally…
One of the generic predictions of modern cosmological models is that large galaxies should have experienced many mergers with smaller galaxies at some point in their past. Debris from such encounters will leave spatially distinct…
Despite the remarkable success of generative adversarial networks, their performance seems less impressive for diverse training sets, requiring learning of discontinuous mapping functions. Though multi-mode prior or multi-generator models…
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…
The utility of machine learning (ML) techniques in materials science has accelerated materials design and discovery. However, the accuracy of ML models - particularly deep neural networks - heavily relies on the quality and quantity of the…
Deep generative models have shown immense potential in generating unseen data that has properties of real data. These models learn complex data-generating distributions starting from a smaller set of latent dimensions. However, generative…
The relationship between galaxies and haloes is central to the description of galaxy formation, and a fundamental step towards extracting precise cosmological information from galaxy maps. However, this connection involves several complex…