Related papers: Computing Star Discrepancies with Numerical Black-…
We consider a general model for representing and manipulating parametric curves, in which a curve is specified by a black box mapping a parameter value between 0 and 1 to a point in Euclidean d-space. In this model, we consider the…
Point pattern matching problems are of fundamental importance in various areas including computer vision and structural bioinformatics. In this paper, we study one of the more general problems, known as LCP (largest common point set…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
Context: Calculating stellar pulsations requires a sufficient accuracy to match the quality of the observations. Many current pulsation codes apply a second order finite-difference scheme, combined with Richardson extrapolation to reach…
We address the problem of separating stars from galaxies in future large photometric surveys. We focus our analysis on simulations of the Dark Energy Survey (DES). In the first part of the paper, we derive the science requirements on…
The theory of digital sequences is a fundamental topic in QMC theory. Digital sequences are prototypes of sequences with low discrepancy. First examples were given by Il'ya Meerovich Sobol' and by Henri Faure with their famous…
This thesis describes a numerical study of binary boson stars within the context of an approximation to general relativity. The approximation we adopt places certain restrictions on the dynamical variables of general relativity (conformal…
Context. Calibration of optical polarimeters relies on the use of stars with negligible polarization (unpolarized standard stars) for determining the instrumental polarization zero-point. For wide-field polarimeters, calibration is often…
We outline the construction of an updated version of the New Luyten Two-Tenths (NLTT) catalog of high proper motion stars, which will contain improved astrometry and photometry for the vast majority of the ~59,000 stars in NLTT. The bright…
I show that the standard microlensing technique to measure the angular radius of a star using color/surface-brightness relations can be inverted, via late-time proper motion measurements, to calibrate these relations. The method is…
For highly compact neutron stars, constructing numerical initial data for black hole-neutron star binary evolutions is very difficult. We describe improvements to an earlier method that enable it to handle these more challenging cases. We…
A pointing accuracy better than 1"(3sigma) star tracker plays a significant role for the advanced scientific missions. This thesis makes a series of studies on the typical error sources associated with the positioning and calibration…
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models. Despite years of effort, direct optimization for these metrics remains a challenge due to their non-differentiable and…
We present a detailed analysis of the uncertainty on the theoretical population corrections to the LMC Red Clump (RC) absolute magnitude, by employing a population synthesis algorithm to simulate theoretically the photometric and…
We propose STARS, a randomized derivative-free algorithm for unconstrained optimization when the function evaluations are contaminated with random noise. STARS takes dynamic, noise-adjusted smoothing step-sizes that minimize the…
Finite-difference methods are a class of algorithms designed to solve black-box optimization problems by approximating a gradient of the target function on a set of directions. In black-box optimization, the non-smooth setting is…
The accurate automated classification of variable stars into their respective sub-types is difficult. Machine learning based solutions often fall foul of the imbalanced learning problem, which causes poor generalisation performance in…
We systematically investigate the error sources for high-precision astrometry from adaptive optics based near-infrared imaging data. We focus on the application in the crowded stellar field in the Galactic Center. We show that at the level…
We give independent proof of the deficit of stars in the in-plane central disc with respect to the predictions of a pure exponential density distribution. We use three different methods: 1) the inversion of the red clump giant distribution…
Markov chain Monte Carlo (MCMC) simulations are modeled as driven by true random numbers. We consider variance bounding Markov chains driven by a deterministic sequence of numbers. The star-discrepancy provides a measure of efficiency of…