- Direct exoplanet detection and characterization using the ANDROMEDA method: Performance on VLT/NaCo data
- Cantalloube, F.; Mouillet, D.; Mugnier, L. M.; Milli, J.; Absil, O.; Gomez Gonzalez, C. A.; Chauvin, G.; Beuzit, J.-L.; Cornia, A.
Context. The direct detection of exoplanets with high-contrast imaging requires advanced data processing methods to disentangle potential planetary signals from bright quasi-static speckles. Among them, angular differential imaging (ADI) permits pot …
- A method of complex background estimation in astronomical images
- Popowicz, A.; Smolka, B.
In this paper, we present a novel approach to the estimation of strongly varying backgrounds in astronomical images by means of small-objects removal and subsequent missing pixels interpolation. The method is based on the analysis of a pixel local n …
- Noise-based Detection and Segmentation of Nebulous Objects
- Akhlaghi, Mohammad; Ichikawa, Takashi
A noise-based non-parametric technique for detecting nebulous objects, for example, irregular or clumpy galaxies, and their structure in noise is introduced. “Noise-based” and “non-parametric” imply that this technique imposes negligible con …
- Galaxy morphology – An unsupervised machine learning approach
- Schutter, A.; Shamir, L.
Structural properties poses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a network of …
- Improving signal-to-noise in the direct imaging of exoplanets and circumstellar disks with MLOCI
- Wahhaj, Zahed; Cieza, Lucas A.; Mawet, Dimitri; Yang, Bin; Canovas, Hector; de Boer, Jozua; Casassus, Simon; Ménard, François; Schreiber, Matthias R.; Liu, Michael C.; Biller, Beth A.; Nielsen, Eric L.; Hayward, Thomas L.
We present a new algorithm designed to improve the signal-to-noise ratio (S/N) of point and extended source detections around bright stars in direct imaging data.One of our innovations is that we insert simulated point sources into the science image …
- Tensor anisotropy as a tracer of cosmic voids
- Bustamante, Sebastian; Forero-Romero, Jaime E.
We present a new method to find voids in cosmological simulations based on the tidal and the velocity shear tensors definitions of the cosmic web. We use the fractional anisotropy (FA) computed from the eigenvalues of each web scheme as a void trace …
- Extreme value statistics of cosmic microwave background lensing deflection angles
- Merkel, Philipp M.; Schäfer, Björn Malte
The smaller the angular scales on which the anisotropies of the cosmic microwave background (CMB) are probed the more important their distortion due to gravitational lensing becomes. Here we investigate the maxima and minima of the CMB lensing defle …
- Voronoi Tessellation and Non-parametric Halo Concentration
- Lang, Meagan; Holley-Bockelmann, Kelly; Sinha, Manodeep
We present and test Tessellation-based Recovery of Amorphous halo Concentrations (TesseRACt), a non-parametric technique for recovering the concentration of simulated dark matter halos using Voronoi tessellation. TesseRACt is tested on idealized N-b …
- Investigation of POWER8 processors for astronomical adaptive optics real-time control
- Basden, A. G.
The forthcoming Extremely Large Telescopes (ELTs) all require adaptive optics systems for their successful operation. The real-time control for these systems becomes computationally challenging, in part limited by the memory bandwidths required for …
- GalPak3D: A Bayesian Parametric Tool for Extracting Morphokinematics of Galaxies from 3D Data
- Bouché, N.; Carfantan, H.; Schroetter, I.; Michel-Dansac, L.; Contini, T.
We present a method to constrain galaxy parameters directly from three-dimensional data cubes. The algorithm compares directly the data with a parametric model mapped in x,y,λ coordinates. It uses the spectral line-spread function and the spatial p …
- Galaxy morphology — An unsupervised machine learning approach
- Andrew Schutter, Lior Shamir
Structural properties poses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a network of …
- The Morphologies of Massive Galaxies from z ~ 3—Witnessing the Two Channels of Bulge Growth
- M. Huertas-Company, P. G. Pérez-González, S. Mei, F. Shankar, M. Bernardi, E. Daddi, G. Barro, G. Cabrera-Vives, A. Cattaneo, P. Dimauro
We quantify the morphological evolution of $z\sim 0$ massive galaxies (${M}_{*}/{M}_{\odot }\sim {10}^{11.2\pm 0.3}$) from $z\sim 3$ in the 5 CANDELS fields. The progenitors are selected using abundance matching techniques to account for the mass gr …
- Krylov Iterative Methods for Support Vector Machines to Classify Galaxy Morphologies
- Matthew Freed, Jeonghwa Lee
Large catalogues of classified galaxy images have been useful in many studies of the universe in astronomy. There are too many objects to classify manually in the Sloan Digital Sky Survey, one of the premier data sources in astronomy. Therefore, eff …
- Everything I’d like to do with LSST data, but don’t know (yet) how
- Željko Ivezić
- Challenges in exascale radio astronomy: Can the SKA ride the technology wave?
- Erik Vermij,Leandro Fiorin, Rik Jongerius,Christoph Hagleitner, Koen Bertels
The Square Kilometre Array (SKA) will be the most sensitive radio telescope in the world. This unprecedented sensitivity will be achieved by combining and analyzing signals from 262,144 antennas and 350 dishes at a raw datarate of petabits per secon …
- Characterization of a Low-Frequency Radio Astronomy Prototype Array in Western Australia
- Sutinjo, A., Colegate, T. ; Wayth, R. ; Hall, P. ; de Lera Acedo, E. ; Booler, T. ; Faulkner, A. ; Feng, L. ; Hurley-Walker, N. ; Juswardy, B. ; Padhi, S. ; Razavi-Ghods, N. ; Sokolowski, M. ; Tingay, S. ; Vaate, J.
We report characterization results for an engineering prototype of a next-generation low-frequency radio astronomy array. This prototype, which we refer to as the Aperture Array Verification System 0.5 (AAVS0.5), is a sparse pseudo-random array of 1 …
- Parallel 2D Local Pattern Spectra of Invariant Moments for Galaxy Classification
- Ugo Moschini, Paul Teeninga, Scott C. Trager
In this paper, we explore the possibility to use 2D pattern spectra as suitable feature vectors in galaxy classification tasks. The focus is on separating mergers from projected galaxies in a data set extracted from the Sloan Digital Sky Survey Data …
- An Efficient Astronomical Cross-matching model Based on MapReduce Mechanism
- Kuei-sheng Lee, Meng-feng Tsai, Yuji Urata, Kui-yun Huang, Chi-sheng Huang
In order to perform an effective cross-matching computation on an enormous amount of text-file-based astronomical observation data, this study proposes an algorithm based on the MapReduce distributed architecture. Such an approach not only greatly e …
- Machine learning classification of SDSS transient survey images
- du Buisson, L.; Sivanandam, N.; Bassett, Bruce A.; Smith, M.
We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any transient scie …
- Dynamic temperature selection for parallel tempering in Markov chain Monte Carlo simulations
- Vousden, W. D.; Farr, W. M.; Mandel, I.
Modern problems in astronomical Bayesian inference require efficient methods for sampling from complex, high-dimensional, often multimodal probability distributions. Most popular methods, such as MCMC sampling, perform poorly on strongly multimodal …