Results 51-100 of 2352 (2312 ASCL, 40 submitted)
JetSeT reproduces radiative and accelerative processes acting in relativistic jets and fits the numerical models to observed data. This C/Python framework re-bins observed data, can define data sets, and binds to astropy tables and quantities. It can use Synchrotron Self-Compton (SSC), external Compton (EC) and EC against the CMB when defining complex numerical radiative scenarios. JetSeT can constrain the model in the pre-fitting stage based on accurate and already published phenomenological trends starting from parameters such as spectral indices, peak fluxes and frequencies, and spectral curvatures. The package fits multiwavelength SEDs using both frequentist approach and Bayesian MCMC sampling, and also provides self-consistent temporal evolution of the plasma under the effect of radiative and accelerative processes for both first order and second order (stochastic acceleration) processes.
Although large volumes of solar data are available for investigation and study, the vast majority of these data remain unlabeled and are therefore not amenable to modern supervised machine learning methods. Having a way to accurately and automatically classify spectra into categories related to the degree of solar activity is highly desirable and will assist and speed up future research efforts in solar physics. At the same time, the large volume of raw observational data is a serious bottleneck for machine learning, requiring powerful computational means that are not at the disposal of many laboratories. Additionally, the raw data communication imposes some restrictions on real time data observations and requires considerable bandwidth and energy for the onboard solar observation systems. To cope with the above mentioned issues, we propose a framework to classify solar activity on compressed data. To this end, we used a labeling scheme from a pre-existing vector quantization technique in conjunction with several machine learning algorithms to categorize Mg II spectra measured by NASA’s small explorer satellite IRIS into several groups characterizing solar activity. Our training data set is a human annotated list of 85 IRIS observations containing 29097 frames in total or equivalently 9 million Mg II spectra. The annotated types of Solar activity are: active region, pre-flare activity, Solar flare, Sunspot and quiet Sun. We used the vector quantization to compress these data and to reduce its complexity before training classifiers. From a host of classifiers, we found that the XGBoost classifier produced the most accurate results on the compressed data, yielding over a 95% prediction rate, and outperforming other ML methods like convolution neural networks, K-nearest neighbors, naive Bayes classifiers and support vector machines. A principle finding of this research is that the classification performance on compressed and uncompressed data is comparable under our particular architecture, implying the possibility of large compression rates for relatively low degrees of information loss.
HorizonGRound forward models general relativistic effects from the tracer luminosity function. It also compares relativistic corrections with the local primordial non-Gaussianity signature in ultra-large-scale clustering statistics. The package includes several recipes along with the data required to run them.
TDEmass interprets Tidal Disruption Event (TDE) observations. In TDEs, a supermassive black hole at the center of a galaxy tears apart an ordinary star; the debris is placed on highly eccentric orbits and ultimately produces a very bright flare. Using this TDEmass, one can infer the mass of the black hole (mbh) and the mass of the star (mstar) involved in a TDE.
TRISTAN (TRIdimensional STANford) is a fully electromagnetic code with full relativistic particle dynamics. The code simulates large-scale space plasma phenomena such as the formation of systems of galaxies. TRISTAN particles which hit the boundaries are arrested there and redistributed more uniformly by having the boundaries slightly conducting, thus allowing electrons to recombine with ions and provides a realistic way of eliminating escaping particles from the code. Fresh particle fluxes can then be introduced independently across the boundaries. Written in 1993, this code has largely been superceded by TRISTAN-MP (ascl:1908.008).
The original MUSIC code (ascl:1311.011) was designed to provide initial conditions for zoom initial conditions and is limited for applications to large-scale cosmological simulations. MUSIC2-monofonIC generates high order LPT/PPT cosmological initial conditions for single resolution cosmological simulations, and can be used for rapid predictions of large-scale structure. MUSIC2-monofonIC offers support for up to 3rd order Lagrangian perturbation theory, PPT (Semiclassical PT for Eulerian grids) up to 2nd order, and for mixed CDM+baryon sims. It direct interfaces with CLASS and can use file input from CAMB; it offers multiple output modules for RAMSES (ascl:1011.007), Arepo (ascl:1909.010), Gadget-2/3 (ascl:0003.001), and HACC via plugins, and new modules/plugins can be easily added.
DUCC (Distinctly Useful Code Collection) provides basic programming tools for numerical computation, including Fast Fourier Transforms, Spherical Harmonic Transforms, non-equispaced Fourier transforms, as well as some concrete applications like 4pi convolution on the sphere and gridding/degridding of radio interferometry data. The code is written in C++17 and provides a simple and comprehensive Python
healpy handles pixelated data on the sphere. It is based on the Hierarchical Equal Area isoLatitude Pixelization (HEALPix) scheme and bundles the HEALPix (ascl:1107.018) C++ library. healpy provides utilities to convert between sky coordinates and pixel indices in HEALPix nested and ring schemes and find pixels within a disk, a polygon or a strip in the sky. It can apply coordinate transformations between Galactic, Ecliptic and Equatorial reference frames, apply custom rotations either to vectors or full maps, and read and write HEALPix maps to disk in FITS format. healpy also includes utilities to upgrade and downgrade the resolution of existing HEALPix maps and transform maps to Spherical Harmonics space and back using multi-threaded C++ routines, among other utilities.
Eclaire is a GPU-accelerated image-reduction pipeline; it uses CuPy, a Python package for general-purpose computing on graphics processing units (GPGPU), to perform image processing, including bias subtraction, dark subtraction, flat fielding, bad pixel masking, alignment, and co-adding. It has been used for real-time image reduction of MITSuME observational data, and can be used with data from other observatories.
iFIT determines the Sérsic law model for galaxies with imperfect Sérsic law profiles by searching for the best match between the observationally determined and theoretically expected radial variation of the mean surface brightness and light growth curve. The technique ensures quick convergence to a unique solution for both perfect and imperfect Sérsic profiles, even shallow and resolution-degraded SBPs. iFIT allows for correction of PSF convolution effects, offering the user the option of choosing between a Moffat, Gaussian, or user-supplied PSF, and is an efficient tool for the non-supervised structural characterization of large galaxy samples, such as those expected to become available with Euclid and LSST.
maxsmooth fits derivative constrained functions (DCF) such as Maximally Smooth Functions (MSFs) to data sets. MSFs are functions for which there are no zero crossings in derivatives of order m >= 2 within the domain of interest. They are designed to prevent the loss of signals when fitting out dominant smooth foregrounds or large magnitude signals that mask signals of interest. Here "smooth" means that the foregrounds follow power law structures and do not feature turning points in the band of interest. maxsmooth uses quadratic programming implemented with CVXOPT (ascl:2008.017) to fit data subject to a fixed linear constraint, Ga <= 0, where the product Ga is a matrix of derivatives. The code tests the <= 0 constraint multiplied by a positive or negative sign and can test every available sign combination but by default, it implements a sign navigating algorithm.
CVXOPT makes the development of software for convex optimization applications straightforward by building on Python’s extensive standard library and on the strengths of Python as a high-level programming language. It offers efficient Python classes for dense and sparse matrices (real and complex) with Python indexing and slicing and overloaded operations for matrix arithmetic, an interface to the fast Fourier transform routines from FFTW, and an interface to most of the double-precision real and complex BLAS. It contains routines for linear, second-order cone, and semidefinite programming problems, and for nonlinear convex optimization. CVXOPT also provides an interface to LAPACK routines for solving linear equations and least-squares problems, matrix factorizations (LU, Cholesky, LDLT and QR), symmetric eigenvalue and singular value decomposition, and Schur factorization, and a modeling tool for specifying convex piecewise-linear optimization problems.
ParaMonte contains serial and parallel Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions. It is used for posterior distributions of Bayesian models in data science, Machine Learning, and scientific inference and unifies the automation of Monte Carlo simulations. ParaMonte is user friendly and accessible from multiple programming environments, including C, C++, Fortran, MATLAB, and Python, and offers high performance at runtime and scalability across many parallel processors.
CMEchaser looks for the occultation of background astronomical sources by CMEs to enable measurement of effects such as variations in the ionized content and the associated Faraday rotation of polarized signals along the line of sight to the background source. The code transforms a given Galactic coordinate to its concordant point in the Helioprojective, Sun-centered plane and estimates the point at which the line of sight from the source to the Earth passes through it. It then searches a user selected database to detect if any CMEs which launched before the observation date would have crossed the line of sight at the epoch of observation, and produces a number of useful plots. CMEchaser can run as a flat script orcan be installed as a package.
SuperRAENN performs photometric classification of supernovae in the following categories: Type I superluminos supernovae, Type II, Type IIn, Type Ia and Type Ib/c. Though the code is optimized for use with complete (rather than realtime) light curves from the Pan-STARRS Medium Deep Survey, the classifier can be trained on other data. SuperRAENN can be used on a dataset containing both spectroscopically labelled and unlabelled SNe; all events will be used to train the RAENN, while labelled events will be used to train the random forest.
SEDBYS (Spectral Energy Distribution Builder for Young Stars) provides command-line tools and uses existing functions from standard packages such as Astropy (ascl:1304.002) to collate archival photometric and spectroscopic data. It also builds and inspects SEDS, and automatically collates the necessary software references.
Ujti calculates geodesics, gravitational lenses and gravitational redshift in principle, for any metric. Special attention has been given to compact objects, so the current implementation considers only metrics in spherical coordinates.
Magnetizer computes time and radial dependent magnetic fields for a sample of galaxies in the output of a semi-analytic model of galaxy formation. The magnetic field is obtained by numerically solving the galactic dynamo equations throughout history of each galaxy. Stokes parameters and Faraday rotation measure can also be computed along a random line-of-sight for each galaxy.
Zeus is a pure-Python implementation of the Ensemble Slice Sampling method. Ensemble Slice Sampling improves upon Slice Sampling by bypassing some of that method's difficulties; it also exploits an ensemble of parallel walkers, thus making it immune to linear correlations. Zeus offers fast and robust Bayesian inference and efficient Markov Chain Monte Carlo without hand-tuning. The code provides excellent performance in terms of autocorrelation time and convergence rate, can scale to multiple CPUs without any extra effort, and includes convergence diagnostics.
SuperNNova performs photometric classification by leveraging recent advances in deep neural networks. It can train either a recurrent neural network or random forest to classify light-curves using only photometric information. It also allows additional information, such as host-galaxy redshift, to be incorporated to improve performance.
Barry compares different BAO models. It removes as many barriers and complications to BAO model fitting as possible and allows each component of the process to remain independent, allowing for detailed comparisons of individual parts. It contains datasets, model fitting tools, and model implementations incorporating different descriptions of non-linear physics and algorithms for isolating the BAO (Baryon Acoustic Oscillation) feature.
sslf is a simple, effective and useful spectral line finder for 1D data. It utilizes the continuous wavelet transform from SciPy, which is a productive way to find even weak spectral lines.
Umbrella detects, validates, and identifies asteroids. The core of this software suite, Umbrella2, includes algorithms and interfaces for all steps of the processing pipeline, including a novel detection algorithm for faint trails. A detection pipeline accessible as a desktop program (ViaNearby) builds on the library to provide near real-time data reduction of asteroid surveys on the Wide Field Camera of the Isaac Newton Telescope. Umbrella can read and write MPC optical reports, supports SkyBoT and VizieR querying, and can be extended by user image processing functions to take advantage of the algorithms framework as a multi-threaded CPU scheduler for easy algorithm parallelization.
PySAP (Python Sparse data Analysis Package) provides a common API for astronomical and neuroimaging datasets and access to iSAP's (ascl:1303.029) Sparse2D executables with both wrappers and bindings. It also offers a graphical user interface for exploring the provided functions and access to application specific plugins.
Spin-Orbit Tomography (SOT) is a retrieval technique of a two-dimensional map of an Exo-Earth from time-series data of integrated reflection light. The software provides code for the Bayesian version of the static SOT and dynamic mapping (time-varying mapping) with full Bayesian modeling, and tutorials for L2 and Bayesian SOT are available in jupyter notebooks.
KLLR (Kernel Localized Linear Regression) generates estimates of conditional statistics in terms of the local slope, normalization, and covariance. This method provides a more nuanced description of population statistics appropriate for very large samples with non-linear trends. The code uses a bootstrap re-sampling technique to estimate the uncertainties and also provides tools to seamlessly generate visualizations of the model parameters.
PhaseTracer maps out cosmological phases, and potential transitions between them, for Standard Model extensions with any number of scalar fields. The code traces the minima of effective potential as the temperature changes, and then calculates the critical temperatures at which the minima are degenerate. PhaseTracer can use potentials provided by other packages and can be used to analyze cosmological phase transitions which played an important role in the early evolution of the Universe.
Kinesis fits the internal kinematics of a star cluster with astrometry and (incomplete) radial velocity data of its members. In the most general model, the stars can be a mixture of background (contamination) and the cluster, for which the (3,3) velocity dispersion matrix and velocity gradient (i.e., dv_x/dx and dv_y/dx) are included. There are also simpler versions of the most general model and utilities to generate mock clusters and mock observations.
CaTffs predicts the strength of calcium triplet indices (CaT*, PaT and CaT) on the basis of empirical fitting functions and performs required interpolations between the different local functions. Together with the indices predictions, the program also computes the random errors associated to such predictions resulting from the covariance matrices of the fits (for the indices CaT* and PaT). This ensures a reliable error index estimation for any combination of input atmospheric parameters.
CosmoGRaPH explores cosmological problems in a fully general relativistic setting. Written in C++, it implements various novel methods for numerically solving the Einstein field equations, including an N-body solver, full AMR capabilities via SAMRAI, and raytracing.
SPARTA analyzes periodically-variable spectroscopic observations. Intended for common astronomical uses, SPARTA facilitates analysis of single- and double-lined binaries, high-precision radial velocity extraction, and periodicity searches in complex, high dimensional data. It includes two modules, UNICOR and USuRPER. UNICOR analyzes spectra using 1-d CCF. It includes maximum-likelihood analysis of multi-order spectra and detection of systematic shifts. USuRPER (Unit Sphere Representation PERiodogram) is a phase-distance correlation (PDC) based periodogram and is designed for very high-dimensional data such as spectra.
JB2008 (Jacchia-Bowman 2008) is an empirical thermospheric density model developed as an improved revision to the Jacchia-Bowman 2006 model, based on Jacchia’s diffusion equations. Driving solar indices are computed from on-orbit sensor data, which are used for the solar irradiances in the extreme through far ultraviolet, including x-ray and Lyman-α wavelengths. Exospheric temperature equations are developed to represent the thermospheric EUV and FUV heating. Semiannual density equations based on multiple 81-day average solar indices are used to represent the variations in the semiannual density cycle that result from EUV heating, and geomagnetic storm effects are modeled using the Dst index as the driver of global density changes.
pygwinc processes and plots noise budgets for ground-based gravitational wave detectors. Its primary feature is a collection of mostly analytic noise calculation functions for various sources of noise affecting detectors, including quantum and seismic noise, mirror coating and substrate thermal noise, suspension fiber thermal noise, and residual gas noise. It is also a generalized noise budgeting tool that allows users to create arbitrary noise budgets for any experiment, not just ground-based GW detectors, using measured or analytically calculated data.
TROVE (Theoretical ROVibrational Energies) performs variational calculations of rovibrational energies for general polyatomic molecules of arbitrary structure in isolated electronic states. The software numerically constructs the kinetic energy operator, which is represented as an expansion in terms of internal coordinates. The code is self-contained, requiring no analytical pre-derivation of the kinetic energy operator. TROVE is also general and can be used with any internal coordinates.
OSPEX (Object Spectral Executive) is an object-oriented interface for X-ray spectral analysis of solar data. The next generation of SPEX (ascl:2007.017), it reads and displays input data, selects and subtracts background, selects time intervals of interest, selects a combination of photon flux model components to describe the data, and fits those components to the spectrum in each time interval selected. During the fitting process, the response matrix is used to convert the photon model to the model counts to compare with the input count data. The resulting time-ordered fit parameters are stored and can be displayed and analyzed with OSPEX. The entire OSPEX session can be saved in the form of a script and the fit results stored in the form of a FITS file. Part of the SolarSoft (ascl:1208.013) package, OSPEX works with any type of data structured in the form of time-ordered count spectra; RHESSI, Fermi, SOXS, MESSENGER, Yohkoh, SMM, and SMART data analysis have all been implemented in OSPEX.
SPEX provides a uniform interface suitable for the X-ray spectral analysis of a number of solar (or other) instruments in the X and Gamma Ray energy ranges. Part of the SolarSoft (ascl:1208.013) library, this package is suitable for any datastream which can be placed in the form of response vs interval where the response is usually a counting rate (spectrum) and the interval is normally an accumulation over time. Together with an algorithm which can be used to relate a model input spectrum to the observed response, generally a response matrix, the dataset is amenable to analysis with this package. Currently the data from a large number of instruments, including SMM (HXRBS, GRS Gamma, GRS X1, and GRS X2), Yohkoh (HXT, HXS, GRS, and SXT,) CGRO (BATSE SPEC and BATSE LAD), WIND (TGRS), HIREX, and NEAR (PIN). SPEX's next generation software is available in OSPEX (ascl:2007.018), an object-oriented package that is also part of and dependent on SolarSoft.
ReadPDS reads in and visualizes data from the Planetary Data System in PDS4 format. Tools are available in Python as PDS4Viewer and in IDL as PDS4-IDL. These tools support PDS4 data, including images, spectra, and arrays and provide multiple views of the data, including summary, image, plot, and table views in addition to easy access to metadata such as structure labels and spectral characteristics.
MAGI (MAny-component Galaxy Initializer) generates initial conditions for numerical simulations of galaxies that resemble observed galaxies and are dynamically stable for time-scales longer than their characteristic dynamical times, taking into account galaxy bulges, discs, and haloes. MAGI adopts a distribution-function-based method and supports various kinds of density models, including custom-tabulated inputs and the presence of more than one disc, and is fast and easy to use.
PARS (Paint the Atmospheres of Rotating Stars) quickly computes magnitudes and spectra of rotating stellar models. It uses the star's mass, equatorial radius, rotational speed, luminosity, and inclination as input; the models incorporate Roche mass distribution (where all mass is at the center of the star), solid body rotation, and collinearity of effective gravity and energy flux.
wdtools characterizes the atmospheric parameters of white dwarfs using spectroscopic data. The flagship class is the generative fitting pipeline (GFP), which fits ab initio theoretical models to observed spectra in a Bayesian framework using high-speed neural networks to interpolate synthetic spectra.
Line-Stacker stacks both 3D cubes or already extracted spectra and is an extension of Stacker (ascl:1912.019). It is an ensemble of both CASA tasks and native python tasks. Line-Stacker supports image stacking and some additional tools, allowing further analysis of the stack product, are also included in the module.
FleCSPH is a multi-physics compact application that exercises FleCSI parallel data structures for tree-based particle methods. In particular, the software implements a smoothed-particle hydrodynamics (SPH) solver for the solution of Lagrangian problems in astrophysics and cosmology. FleCSPH includes support for gravitational forces using the fast multipole method (FMM). Particle affinity and gravitation is handled using the parallel implementation of the octree data structure provided by FleCSI.
DarkHistory calculates the global temperature and ionization history of the universe given an exotic source of energy injection, such as dark matter annihilation or decay. The software simultaneously solves for the evolution of the free electron fraction and gas temperature, and for the cooling of annihilation/decay products and the secondary particles produced in the process. Consequently, we can self-consistently include the effects of both astrophysical and exotic sources of heating and ionization, and automatically take into account backreaction, where modifications to the ionization/temperature history in turn modify the energy-loss processes for injected particles.
MPSolve (Multiprecision Polynomial SOLVEr) provides an easy-to-use universal blackbox for solving polynomials and secular equations. Its features include arbitrary precision approximation and guaranteed inclusion radii for the results. It can exploit polynomial structures, taking advantage of sparsity as well as coefficients in a particular domain (i.e., integers or rationals), and can be specialized for specific classes of polynomials.
PSRVoid performs RFI excision, flux calibration and timing of folded pulsar data. RFI excision is administered via both traditional and multi-layered deep learning neural network algorithms. The software offers full neural network control (over training set creation and manipulation and network parameters). PSRVoid also contains useful data miners for the ATNF, a multitude of plotting tools, as well as many useful pulsar processing macros such as space velocity simulators and Tempo2 (ascl:1210.015) wrappers.
PoPE (Population Profile Estimator) analyzes spatial distribution or internal spatial structure problems of samples of astronomical systems. This population-based Bayesian inference model uses the conditional statistics of spatial profile of multiple observables assuming the individual observations are measured with errors of varying magnitude. Assuming the conditional statistics of the observables can be described with a multivariate normal distribution, the model reduces to the conditional average profile and conditional covariance between all observables. The method consists of two steps: (1) reconstructing the average profile using non-parametric regression with Gaussian Processes and (2) estimating the property profiles covariance given a set of independent variable. PoPE is computationally efficient and capable of inferring average profiles of a population from noisy measurements without stacking and binning nor parameterizing the shape of the average profile.
The N-body code PETAR (ParticlE Tree & particle-particle & Algorithmic Regularization) combines the methods of Barnes-Hut tree, Hermite integrator and slow-down algorithmic regularization (SDAR). It accurately handles an arbitrary fraction of multiple systems (e.g. binaries, triples) while keeping a high performance by using the hybrid parallelization methods with MPI, OpenMP, SIMD instructions and GPU. PETAR has very good agreement with NBODY6++GPU results on the long-term evolution of the global structure, binary orbits and escapers and is significantly faster when used on a highly configured GPU desktop computer. PETAR scales well when the number of cores increase on the Cray XC50 supercomputer, allowing a solution to the ten million-body problem which covers the region of ultra compact dwarfs and nuclear star clusters.
spex_to_xspec takes the output from the collisional ionisation equilibrium model in the SPEX spectral modelling and fitting package (ascl:1308.014), and converts it into a form usable by the XSPEC spectral fitting package (ascl:9910.005). For a list of temperatures it computes the line strengths and continuum spectra using SPEX. These are collated and written into an APEC-format table model which can be loaded into Xspec. By allowing SPEX models to be loaded into XSPEC, the program allows easy comparison between the results of the SPEX and APEC codes.
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