Related papers: A machine--vision method for automatic classificat…
Subspace clustering (SC) is a promising clustering technology to identify clusters based on their associations with subspaces in high dimensional spaces. SC can be classified into hard subspace clustering (HSC) and soft subspace clustering…
We propose a method for detecting structural changes in a city using images captured from vehicular mounted cameras over traversals at two different times. We first generate 3D point clouds for each traversal from the images and approximate…
Machine learning (ML) techniques, in particular supervised regression algorithms, are a promising new way to use multiple observables to predict a cluster's mass or other key features. To investigate this approach we use the \textsc{MACSIS}…
We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive…
Joint lensing and dynamical mass profile determinations of galaxy clusters are an excellent tool to constrain modification of gravity at cosmological scales. However, search for tiny departures from General Relativity calls for an accurate…
Sub-milllimetre galaxies (SMGs) are some of the most luminous star-forming galaxies in the Universe, however their properties remain hard to determine due to the difficulty of identifying their optical\slash near-infrared counterparts. One…
Slice discovery methods (SDMs) are prominent algorithms for finding systematic weaknesses in DNNs. They identify top-k semantically coherent slices/subsets of data where a DNN-under-test has low performance. For being directly useful,…
Point cloud videos capture dynamic 3D motion while reducing the effects of lighting and viewpoint variations, making them highly effective for recognizing subtle and continuous human actions. Although Selective State Space Models (SSMs)…
Though quite challenging, leveraging large-scale unlabeled or partially labeled images in a cost-effective way has increasingly attracted interests for its great importance to computer vision. To tackle this problem, many Active Learning…
We quantify the effects of \emph{morphological k-correction} at $1<z<2$ by comparing morphologies measured in the K and I-bands in the COSMOS area. Ks-band data have indeed the advantage of probing old stellar populations for $z<2$,…
Context. Filaments are ubiquitous in the Galaxy, and they host star formation. Detecting them in a reliable way is therefore key towards our understanding of the star formation process. Aims. We explore whether supervised machine learning…
We use cosmological hydrodynamical simulations of Milky-Way-mass galaxies from the FIRE project to evaluate various strategies for estimating the mass of a galaxy's stellar halo from deep, integrated-light images. We find good agreement…
The lack of tangible evidence for non-gravitational interactions between dark and visible sectors drives the need for exploring new avenues of inferring dark matter properties through purely gravitational probes. In particular, addressing…
We present a new high-resolution N-body algorithm for cosmological simulations. The algorithm employs a traditional particle-mesh technique on a cubic grid and successive multilevel relaxations on the finer meshes, introduced recursively in…
We develop and implement two new methods for constraining the scatter in the relationship between galaxies and dark matter halos. These new techniques are sensitive to the scatter at low halo masses, making them complementary to previous…
We present CluSTAR-ND, a fast hierarchical galaxy/(sub)halo finder that produces {\bf Clu}stering {\bf S}tructure via {\bf T}ransformative {\bf A}ggregation and {\bf R}ejection in {\bf N}-{\bf D}imensions. It is designed to improve upon…
As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to…
We use a combination of self-supervised machine learning and visual classification to identify tidal features in a sample of 34,331 galaxies with stellar masses $\log_{10}(M_{*}/\rm{M}_{\odot})\geq9.5$ and redshift $z\leq0.4$, drawn from…
In this work we present a new catalogue of Cosmic Filaments obtained from the latest Sloan Digital Sky Survey (SDSS) public data. In order to detect filaments, we implement a version of the Subspace-Constrained Mean-Shift algorithm, boosted…
We apply Support Vector Machines -- a machine learning algorithm -- to the task of classifying structures in the Interstellar Medium. As a case study, we present a position-position velocity data cube of 12 CO J=3--2 emission towards…