Related papers: Quadtree features for machine learning on CMDs
We apply instance-based machine learning in the form of a k-nearest neighbor algorithm to the task of estimating photometric redshifts for 55,746 objects spectroscopically classified as quasars in the Fifth Data Release of the Sloan Digital…
The Minimum Covariance Determinant (MCD) approach robustly estimates the location and scatter matrix using the subset of given size with lowest sample covariance determinant. Its main drawback is that it cannot be applied when the dimension…
Using \textit{Hubble Space Telescope} images from the PHAT and PHAST surveys, we construct an updated catalogue of 747 OB cluster (OBC) candidates. We introduce a dimensionless structural metric, the trace coefficient of variation ($CV_{\rm…
We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical…
Hubble Space Telescope imaging for 26 giant early-type galaxies, all drawn from the MAST archive, is used to carry out photometry of their surrounding globular cluster (GC) systems. Most of these targets are Brightest Cluster Galaxies…
The cluster variation method (CVM) is an approximation technique which generalizes the mean field approximation and has been widely applied in the last decades, mainly for finding accurate phase diagrams of Ising-like lattice models. Here…
Recently, learning-based stereo matching methods have achieved great improvement in public benchmarks, where soft argmin and smooth L1 loss play a core contribution to their success. However, in unsupervised domain adaptation scenarios, we…
The COBE microwave background temperature fluctuations and the abundance of local rich clusters of galaxies provide the two most powerful constraints on cosmological models. When all variants of the standard cold dark matter (CDM) model are…
We develop a Maximum Likelihood estimator (MLE) to measure the masses of galaxy clusters through the impact of gravitational lensing on the temperature and polarization anisotropies of the cosmic microwave background (CMB). We show that, at…
The Minimum Covariance Determinant (MCD) method is a highly robust estimator of multivariate location and scatter, for which a fast algorithm is available. Since estimating the covariance matrix is the cornerstone of many multivariate…
We describe a method of determining the V magnitude of an asteroid using differential photometry, with the magnitudes of comparison stars derived from Carlsberg Meridian Catalogue 14 (CMC14) data. The availability of a large number of…
Clustering analysis is one of the most widely used statistical tools in many emerging areas such as microarray data analysis. For microarray and other high-dimensional data, the presence of many noise variables may mask underlying…
The use of distributions and high-level features from deep architecture has become commonplace in modern computer vision. Both of these methodologies have separately achieved a great deal of success in many computer vision tasks. However,…
Metallicity distributions (MDs) of globular clusters (GCs) provide crucial clues for the assembly and star formation history of their host galaxies. GC colors, when GCs are old, have been used as a proxy of GC metallicities. Bimodal GC…
The Red-Sequence Cluster Survey (RCS) provides a large and deep photometric catalog of galaxies in the $z'$ and $R_c$ bands for ~90 square degrees of sky, and supplemental $V$ and $B$ data have been obtained for 33.6 deg$^{2}$. We compile a…
In this paper, we discuss improvements of the Suto et al. (2000) model, in the light of recent theoretical developments (new theoretical mass functions, a more accurate mass-temperature relation and an improved bias model) to predict the…
We discuss combining gravitational lensing of galaxies and the cosmic microwave background (CMB) by clusters to measure cosmographic distance ratios, and hence dark energy parameters. Advantages to using the CMB as the second source plane,…
The detection of cardiovascular diseases (CVD) using machine learning techniques represents a significant advancement in medical diagnostics, aiming to enhance early detection, accuracy, and efficiency. This study explores a comparative…
XMM and Chandra opened a new area for the study of clusters of galaxies. Not only for cluster physics but also, for the detection of faint and distant clusters that were inaccessible with previous missions. This article presents 66…
In the framework of the hierarchical model the intra-cluster medium properties of galaxy clusters are tightly linked to structure formation, which makes X-ray surveys well suited for cosmological studies. To constrain cosmological…