Change-point tests under local alternatives for long-range dependent processes

We consider the change-point problem for the marginal distribution of subordinated Gaussian processes that exhibit long-range dependence. The asymptotic distributions of Kolmogorov-Smirnov- and Cram\'{e}r-von Mises type statistics are investigated under local alternatives. By doing so we are able to compute the asymptotic relative efficiency of the mentioned tests and the CUSUM test. In the special case of a mean-shift in Gaussian data it is always $1$. Moreover our theory covers the scenario where the Hermite rank of the underlying process changes. In a small simulation study we show that the theoretical findings carry over to the finite sample performance of the tests

Imprints of the quasar structure in time-delay light curves: Microlensing-aided reverberation mapping

Owing to the advent of large area photometric surveys, the possibility to use broad band photometric data, instead of spectra, to measure the size of the broad line region of active galactic nuclei, has raised a large interest. We describe here a new method using time-delay lensed quasars where one or several images are affected by microlensing due to stars in the lensing galaxy. Because microlensing decreases (or increases) the flux of the continuum compared to the broad line region, it changes the contrast between these two emission components. We show that this effect can be used to effectively disentangle the intrinsic variability of those two regions, offering the opportunity to perform reverberation mapping based on single band photometric data. Based on simulated light curves generated using a damped random walk model of quasar variability, we show that measurement of the size of the broad line region can be achieved using this method, provided one spectrum has been obtained independently during the monitoring. This method is complementary to photometric reverberation mapping and could also be extended to multi-band data. Because the effect described above produces a variability pattern in difference light curves between pairs of lensed images which is correlated with the time-lagged continuum variability, it can potentially produce systematic errors in measurement of time delays between pairs of lensed images. Simple simulations indicate that time-delay measurement techniques which use a sufficiently flexible model for the extrinsic variability are not affected by this effect and produce accurate time delays.Comment: Accepted for publication in Astronomy and Astrophysic

Stellar classification from single-band imaging using machine learning

Information on the spectral types of stars is of great interest in view of the exploitation of space-based imaging surveys. In this article, we investigate the classification of stars into spectral types using only the shape of their diffraction pattern in a single broad-band image. We propose a supervised machine learning approach to this endeavour, based on principal component analysis (PCA) for dimensionality reduction, followed by artificial neural networks (ANNs) estimating the spectral type. Our analysis is performed with image simulations mimicking the Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) in the F606W and F814W bands, as well as the Euclid VIS imager. We first demonstrate this classification in a simple context, assuming perfect knowledge of the point spread function (PSF) model and the possibility of accurately generating mock training data for the machine learning. We then analyse its performance in a fully data-driven situation, in which the training would be performed with a limited subset of bright stars from a survey, and an unknown PSF with spatial variations across the detector. We use simulations of main-sequence stars with flat distributions in spectral type and in signal-to-noise ratio, and classify these stars into 13 spectral subclasses, from O5 to M5. Under these conditions, the algorithm achieves a high success rate both for Euclid and HST images, with typical errors of half a spectral class. Although more detailed simulations would be needed to assess the performance of the algorithm on a specific survey, this shows that stellar classification from single-band images is well possible.Comment: 10 pages, 9 figures, 2 tables, accepted in A&

Bootstrap for U-Statistics: A new approach

Bootstrap for nonlinear statistics like U-statistics of dependent data has been studied by several authors. This is typically done by producing a bootstrap version of the sample and plugging it into the statistic. We suggest an alternative approach of getting a bootstrap version of U-statistics, which can be described as a compromise between bootstrap and subsampling. We will show the consistency of the new method and compare its finite sample properties in a simulation study

El voltor (Aegypius monachus) a Europa, un pas endavant en la seva recuperació

Abstract not availabl

The Effects of Incorporating Coding on Student Experience and Understanding of Middle School Mathematical Concepts

The purpose of this action research project was to study the effects of incorporating coding into a middle school math classroom on student experience with and understanding of mathematical concepts. The project used five data sources to examine these effects. Two of the data sources were used to examine the effects on student experience; 1) a survey to gauge student perception and 2) a chart to measure student engagement by tracking their time on task. Three of the data sources were used to examine the effects on student understanding; 1) a pre-assessment given before the coding project, 2) a post-assessment given after the coding project, these two data sources were used to determine if students grew in their understanding of the math concepts. Finally 3) a rubric was used to assess key learner outcomes, accuracy, application, coding efficiency, presentation, and creativity. After analyzing the data, it was found that incorporating coding into the middle school math classroom could have a positive impact on student math experiences and their understanding of math concepts