The Development of a Fiber Optic Raman Temperature Measurement System for Rocket Flows

A fiberoptic Raman diagnostic system for H2/O2 rocket flows is currently under development. This system is designed for measurement of temperature and major species concentration in the combustion chamber and part of the nozzle of a 100 Newton thrust rocket currently undergoing testing. This paper describes a measurement system based on the spontaneous Raman scattering phenomenon. An analysis of the principles behind the technique is given. Software is developed to measure temperature and major species concentrations by comparing theoretical Raman scattering spectra with experimentally obtained spectra. Equipment selection and experimental approach are summarized. This experimental program is part of a program, which is in progress, to evaluate Navier-Stokes based analyses for this class of rocket

Stellar photometry with Multi Conjugate Adaptive Optics

We overview the current status of photometric analyses of images collected with Multi Conjugate Adaptive Optics (MCAO) at 8-10m class telescopes that operated, or are operating, on sky. Particular attention will be payed to resolved stellar population studies. Stars in crowded stellar systems, such as globular clusters or in nearby galaxies, are ideal test particles to test AO performance. We will focus the discussion on photometric precision and accuracy reached nowadays. We briefly describe our project on stellar photometry and astrometry of Galactic globular clusters using images taken with GeMS at the Gemini South telescope. We also present the photometry performed with DAOPHOT suite of programs into the crowded regions of these globulars reaching very faint limiting magnitudes Ks ~21.5 mag on moderately large fields of view (~1.5 arcmin squared). We highlight the need for new algorithms to improve the modeling of the complex variation of the Point Spread Function across the field of view. Finally, we outline the role that large samples of stellar standards plays in providing a detailed description of the MCAO performance and in precise and accurate colour{magnitude diagrams.Comment: 17 pages, 12 figures, SPIE 201

Detection of Dispersed Radio Pulses: A machine learning approach to candidate identification and classification

Searching for extraterrestrial, transient signals in astronomical data sets is an active area of current research. However, machine learning techniques are lacking in the literature concerning single-pulse detection. This paper presents a new, two-stage approach for identifying and classifying dispersed pulse groups (DPGs) in single-pulse search output. The first stage identified DPGs and extracted features to characterize them using a new peak identification algorithm which tracks sloping tendencies around local maxima in plots of signal-to-noise ratio vs. dispersion measure. The second stage used supervised machine learning to classify DPGs. We created four benchmark data sets: one unbalanced and three balanced versions using three different imbalance treatments.We empirically evaluated 48 classifiers by training and testing binary and multiclass versions of six machine learning algorithms on each of the four benchmark versions. While each classifier had advantages and disadvantages, all classifiers with imbalance treatments had higher recall values than those with unbalanced data, regardless of the machine learning algorithm used. Based on the benchmarking results, we selected a subset of classifiers to classify the full, unlabelled data set of over 1.5 million DPGs identified in 42,405 observations made by the Green Bank Telescope. Overall, the classifiers using a multiclass ensemble tree learner in combination with two oversampling imbalance treatments were the most efficient; they identified additional known pulsars not in the benchmark data set and provided six potential discoveries, with significantly less false positives than the other classifiers.Comment: 13 pages, accepted for publication in MNRAS, ref. MN-15-1713-MJ.R