University of Potsdam

About: University of Potsdam is a education organization based out in Potsdam, Germany. It is known for research contribution in the topics: Population & Computer science. The organization has 9629 authors who have published 26740 publications receiving 759745 citations. The organization is also known as: Universität Potsdam.

Quasar Microlensing at High Magnification and the Role of Dark Matter: Enhanced Fluctuations and Suppressed Saddle Points

Abstract: Contrary to naive expectation, diluting the stellar component of the lensing galaxy in a highly magnified system with smoothly distributed "dark" matter increases rather than decreases the microlensing fluctuations caused by the remaining stars. For a bright pair of images straddling a critical curve, the saddle point (of the arrival time surface) is much more strongly affected than the associated minimum. With a mass ratio of smooth matter to microlensing matter of 4 : 1, a saddle point with a macromagnification of μ = 9.5 will spend half of its time more than a magnitude fainter than predicted. The anomalous flux ratio observed for the close pair of images in MG 0414+0534 is a factor of 5 more likely than computed by Witt, Mao, & Schechter, if the smooth matter fraction is as high as 93%. The magnification probability histograms for macroimages exhibit a distinctly different structure that varies with the smooth matter content, providing a handle on the smooth matter fraction. Enhanced fluctuations can manifest themselves either in the temporal variations of a light curve or as flux ratio anomalies in a single epoch snapshot of a multiply imaged system. While the millilensing simulations of Metcalf & Madau also give larger anomalies for saddle points than for minima, the effect appears to be less dramatic for extended subhalos than for point masses. Moreover, microlensing is distinguishable from millilensing because it will produce noticeable changes in the magnification on a timescale of a decade or less.

Constructing boosting algorithms from SVMs: an application to one-class classification

Abstract: We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation procedure for a new algorithm: one-class leveraging, starting from the one-class support vector machine (1-SVM). This is a first step toward unsupervised learning in a boosting framework. Building on so-called barrier methods known from the theory of constrained optimization, it returns a function, written as a convex combination of base hypotheses, that characterizes whether a given test point is likely to have been generated from the distribution underlying the training data. Simulations on one-class classification problems demonstrate the usefulness of our approach.