Natural data structure extracted from neighborhood-similarity graphs

'Big' high-dimensional data are commonly analyzed in low-dimensions, after performing a dimensionality-reduction step that inherently distorts the data structure. For the same purpose, clustering methods are also often used. These methods also introduce a bias, either by starting from the assumption of a particular geometric form of the clusters, or by using iterative schemes to enhance cluster contours, with uncontrollable consequences. The goal of data analysis should, however, be to encode and detect structural data features at all scales and densities simultaneously, without assuming a parametric form of data point distances, or modifying them. We propose a novel approach that directly encodes data point neighborhood similarities as a sparse graph. Our non-iterative framework permits a transparent interpretation of data, without altering the original data dimension and metric. Several natural and synthetic data applications demonstrate the efficacy of our novel approach

X-raying the AU Microscopii debris disk

AU Mic is a young, nearby X-ray active M-dwarf with an edge-on debris disk. Debris disk are the successors of the gaseous disks usually surrounding pre-main sequence stars which form after the first few Myrs of their host stars' lifetime, when - presumably - also the planet formation takes place. Since X-ray transmission spectroscopy is sensitive to the chemical composition of the absorber, features in the stellar spectrum of AU Mic caused by its debris disk can in principle be detected. The upper limits we derive from our high resolution Chandra LETGS X-ray spectroscopy are on the same order as those from UV absorption measurements, consistent with the idea that AU Mic's debris disk possesses an inner hole with only a very low density of sub-micron sized grains or gas.Comment: 11 pages, 10 figures, accepted for publication in A&

Minimizing Strong Telluric Absorption in Near Infra-red Stellar Spectra

We have obtained high resolution spectra (R = 25000) of an A star over varying airmass to determine the effectiveness of telluric removal in the limit of high signal to noise. The near infra-red line HeI at 2.058 microns, which is a sensitive indicator of physical conditions in massive stars, supergiants, HII regions and YSOs, resides among pressure broadened telluric absorption from carbon dioxide and water vapor that varies both in time and with observed airmass. Our study shows that in the limit of bright stars at high resolution, accuracies of 5% are typical for high airmass observations (greater than 1.9), improving to a photon-limited accuracy of 2% at smaller airmasses (less than 1.15). We find that by using the continuum between telluric absorption lines of a ro-vibrational fan a photon-limited 1% accuracy is achievable.Comment: 14 pages, 7 figures. Accepted for publication in PAS

Algorithm Development for Intrafraction Radiotherapy Beam Edge Verification from Cherenkov Imaging.

Imaging of Cherenkov light emission from patient tissue during fractionated radiotherapy has been shown to be a possible way to visualize beam delivery in real time. If this tool is advanced as a delivery verification methodology, then a sequence of image processing steps must be established to maximize accurate recovery of beam edges. This was analyzed and developed here, focusing on the noise characteristics and representative images from both phantoms and patients undergoing whole breast radiotherapy. The processing included temporally integrating video data into a single, composite summary image at each control point. Each image stack was also median filtered for denoising and ultimately thresholded into a binary image, and morphologic small hole removal was used. These processed images were used for day-to-day comparison computation, and either the Dice coefficient or the mean distance to conformity values can be used to analyze them. Systematic position shifts of the phantom up to 5 mm approached the observed variation values of the patient data. This processing algorithm can be used to analyze the variations seen in patients being treated concurrently with daily Cherenkov imaging to quantify the day-to-day disparities in delivery as a quality audit system for position/beam verification