The Astrophysics Source Code Library (ASCL) is a free online registry for source codes of interest to astronomers and astrophysicists and lists codes that have been used in research that has appeared in, or been submitted to, peer-reviewed publications. The ASCL is indexed by the SAO/NASA Astrophysics Data System (ADS) and is citable by using the unique ascl ID assigned to each code. The ascl ID can be used to link to the code entry by prefacing the number with ascl.net (i.e., ascl.net/1201.001).
SITELLE (installed this year at the Canada-France-Hawaii Telescope) and SpIOMM (a prototype attached to the Observatoire du Mont Mégantic) are the first imaging Fourier transform spectrometers capable of obtaining a hyperspectral data cube which samples a 12 arc-minutes field of view into 4 millions of visible spectra. The result of each observation is made up of two interferometric data cubes which need to be merged, corrected, transformed and calibrated in order to get a spectral cube of the observed region ready to be analysed. ORBS is a fully automatic data reduction software that has been entirely designed for this purpose. The size of the data and the computational needs have been challenging and the highly parallelized object-oriented architecture of ORBS reflects the solutions adopted which made possible to process 64 Go of raw data in less than 11 hours using 8 cores and 22.6 Go of RAM. Its core classes (ORB) have been designed to be used in a suite of softwares for data analysis (ORCS and OACS), data simulation (ORUS) and data acquisition (IRIS). We hope that they will provide a strong basis for the creation of specialized analysis modules that could benefit to the scientific community which works with SITELLE and SpIOMM.
iSpec is an integrated software framework written in Python for the treatment and analysis of stellar spectra and abundances. Spectra treatment functions include cosmic rays removal, continuum normalization, resolution degradation, and telluric lines identification. It can also perform radial velocity determination and correction and resampling. iSpec can also determine atmospheric parameters (i.e effective temperature, surface gravity, metallicity, micro/macroturbulence, rotation) and individual chemical abundances by using either the synthetic spectra fitting technique or equivalent widths method. The synthesis is performed with SPECTRUM (ascl:9910.002).
IFSFIT is a general-purpose IDL library for fitting the continuum, emission lines, and absorption lines in integral field spectra. It uses PPXF (ascl:1210.002) to find the best fit stellar continuum (using a user-defined library of stellar templates and including additive polynomials), or optionally a user-defined method to find the best fit continuum. It uses MPFIT (ascl:1208.019) to simultaneously fit Gaussians to any number of emission lines and emission line velocity components. It will also fit the NaI D feature using analytic absorption and/or emission-line profiles.
IFSRED is a general-purpose library for reducing data from integral field spectrographs (IFSs). For a general IFS data cube, it contains IDL routines to: (1) find and apply a zero-point shift in a wavelength solution on a spaxel-by-spaxel basis, using sky lines; (2) find the spatial coordinates of a flux peak; (3) empirically correct for differential atmospheric refraction; (4) mosaic dithered exposures; (5) (integer) rebin; and (6) apply a telluric correction. A sky-subtraction routine for data from the Gemini Multi-Object Spectrograph and Imager (GMOS) that can be easily modified for any instrument is also included. IFSRED also contains additional software specific to reducing data from GMOS and the Gemini Near-Infrared Integral Field Spectrograph (NIFS).
LANL* calculates the magnetic drift invariant L*, used for modeling radiation belt dynamics and other space weather applications, six orders of magnitude (~ one million times) faster than convectional approaches that require global numerical field lines tracing and integration. It is based on a modern machine learning technique (feed-forward artificial neural network) by supervising a large data pool obtained from the IRBEM library, which is the traditional source for numerically calculating the L* values. The pool consists of about 100,000 samples randomly distributed within the magnetosphere (r: [1.03, 11.5] Re) and within a whole solar cycle from 1/1/1994 to 1/1/2005. There are seven LANL* models, each corresponding to its underlying magnetic field configuration that is used to create the data sample pool. This model has applications to real-time radiation belt forecasting, analysis of data sets involving tens of satellite-years of observations, and other problems in space weather.
The Tsyganenko models are semi-empirical best-fit representations for the magnetic field, based on a large number of satellite observations (IMP, HEOS, ISEE, POLAR, Geotail, GOES, etc). The models include the contributions from major external magnetospheric sources: ring current, magnetotail current system, magnetopause currents, and large-scale system of field-aligned currents.
mixT accurately predicts T derived from a single-temperature fit for a multi-component thermal plasma. It can be applied in the deprojection analysis of objects with the temperature and metallicity gradients, for correction of the PSF effects, for consistent comparison of numerical simulations of galaxy clusters and groups with the X-ray observations, and for estimating how emission from undetected components can bias the global X-ray spectral analysis.
WSClean (w-stacking clean) is a fast generic widefield imager. It uses the w-stacking algorithm and can make use of the w-snapshot algorithm. It supports full-sky imaging and proper beam correction for homogeneous dipole arrays such as the MWA. WSClean allows Hogbom and Cotton-Schwab cleaning, and can clean polarizations joinedly. All operations are performed on the CPU; it is not specialized for GPUs.
PhotoRApToR (PHOTOmetric Research APplication TO Redshifts) solves regression and classification problems and is specialized for photo-z estimation. PhotoRApToR offers data table manipulation capabilities and 2D and 3D graphics tools for data visualization; it also provides a statistical report for both classification and regression experiments. The code is written in Java; the machine learning model is in C++ to increase the core execution speed.
APS finds Frequentist confidence limits on high-dimensional parameter spaces by using Gaussian Process interpolation to identify regions of parameter space for which chisquared is less than or equal to some specified limit. The code is written in C++, is robust against multi-modal chisquared functions and converges comparably fast to Monte Carlo methods. Code is also provided to draw Bayesian credible limits using the outputs of APS, though this code does not converge as well. APS requires the linear algebra libraries LAPACK, BLAS, and ARPACK (ascl:1311.010) to run.