相关论文: Introducing constrained matched filters for improv…

Aims: Implement a matched filter (MF) cross-correlation algorithm in multipole space and compare it to the standard Angular Cross Power Spectrum (ACPS) method. Apply both methods on a Integrated Sachs Wolfe (ISW) - Large Scale Structure…

The fundamental plane (FP) is a widely used tool to investigate the properties of early-type galaxies, and the tight relation between its parameters has spawned several cosmological applications, including its use as a distance indicator…

A method is proposed for combining multi-frequency maps in order to produce a catalogue of extragalactic point sources using data from future high-precision satellite experiments. We have found the optimal way for combining maps at…

The relationship between the clustering of dark matter and that of luminous matter is often described using the bias parameter. Here, we provide a new method to probe the bias of intermediate to high-redshift radio continuum sources for…

In this paper, we address a statistical model extension of multichannel nonnegative matrix factorization (MNMF) for blind source separation, and we propose a new parameter update algorithm used in the sub-Gaussian model. MNMF employs…

The detection of polarized sources in the WMAP 5-year data is a very difficult task. The maps are dominated by instrumental noise and only a handful of sources show up as clear peaks in the Q and U maps. Optimal linear filters applied at…

Multi-view clustering thrives in applications where views are collected in advance by extracting consistent and complementary information among views. However, it overlooks scenarios where data views are collected sequentially, i.e.,…

In this paper we derive a multi-sensor multi-Bernoulli (MS-MeMBer) filter for multi-target tracking. Measurements from multiple sensors are employed by the proposed filter to update a set of tracks modeled as a multi-Bernoulli random finite…

In this paper, we consider the filtering problem for partially observed diffusions, which are regularly observed at discrete times. We are concerned with the case when one must resort to time-discretization of the diffusion process if the…

This paper studies a factor modeling-based approach for clustering high-dimensional data generated from a mixture of strongly correlated variables. Statistical modeling with correlated structures pervades modern applications in economics,…

Extragalactic surveys provide significant statistical data for the study of crucial galaxy parameters used to constrain galaxy evolution, e.g. stellar mass (M$_*$) and star formation rate (SFR), under different environmental conditions.…

The search for gravitational-wave signals in detector data is often hampered by the fact that many data analysis methods are based on the theory of stationary Gaussian noise, while actual measurement data frequently exhibit clear departures…

The identification of X-ray and CMB sources as galaxy groups and clusters is a prerequisite for cluster cosmology. But the identification of groups, especially nearby ones, suffers from projection effects which in turn affect the purity of…

Wavelet-related techniques have proven useful in the processing and analysis of one and two dimensional data sets (spectra in the former case, images in the latter). In this work we apply adaptive filters, introduced in a previous work…

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training…

Matched filtering is a commonly used technique in gravitational wave searches for signals from compact binary systems and from rapidly rotating neutron stars. A common issue in these searches is dealing with four extrinsic parameters which…

Applying basic physical principles to recent observational results, we derive upper and lower limits on microwave anisotropy from point sources over the range of frequencies 10-1000 GHz. We examine the level of noise in the observations as…

The cluster-based Mean Field method (cMF) and it's second order perturbative correction[1], was introduced by Jim\'enez-Hoyos and Scuseria to reduce the cost of modeling strongly correlated systems by dividing an active space up into small…

We introduce ProFound, a source finding and image analysis package. ProFound provides methods to detect sources in noisy images, generate segmentation maps identifying the pixels belonging to each source, and measure statistics like flux,…

Density-based clustering aims to find groups of similar objects (i.e., clusters) in a given dataset. Applications include, e.g., process mining and anomaly detection. It comes with two user parameters ({\epsilon}, MinPts) that determine the…