The Astrophysics Source Code Library (ASCL) is a free online registry for source codes of interest to astronomers and astrophysicists, including solar system astronomers, 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 Web of Science 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).
The Fokker-Planck equation is a kinetic equation which models the dynamics of a particle distribution function. This equation is applied to describe non-thermal particles in astrophysical scenarios. With Paramo (PArticle and RAdiation MOnitor), the kinetic equation is solved numerically with the proper modeling of the acceleration processes, and accounts for accurate cooling coefficient (e.g., radiative cooling with Klein-Nishina corrections). The numerical solution at every time step is used to calculate radiations processes, namely synchrotron and IC, with sophisticated numerical techniques, obtaining the multi-wavelength spectral evolution of the system.
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.
Large-scale surveys have brought about a revolution in astronomy. To analyse the resulting wealth of data, we need automated tools to identify, classify, and quantify the important underlying structures. J plots can classify and quantify a pixelated structure, based on its principal moments of inertia. This enables us to automatically detect, and objectively compare, centrally condensed cores, elongated filaments, and hollow rings. A Python code is available on GitHub with examples of how to analyse 2D or 3D datasets, enabling an unbiased analysis and comparison of simulated and observed structures.
Stellar parameters are required in a variety of contexts, ranging from the characterisation of exoplanets to Galactic archaeology. Among them, the age of stars cannot be directly measured, while the mass and radius can be measured in some particular cases (binary systems, interferometry). Stellar ages, masses, and radii have to be inferred from stellar evolution models by appropriate techniques. We have designed a Python tool named SPInS. It takes a set of photometric, spectroscopic, interferometric, and/or asteroseismic observational constraints and, relying on a stellar model grid, provides the age, mass, and radius of a star, among others, as well as error bars and correlations. We make the tool available to the community via a dedicated website. SPInS uses a Bayesian approach to find the PDF of stellar parameters from a set of classical constraints. At the heart of the code is a MCMC solver coupled with interpolation within a pre-computed stellar model grid. Priors can be considered, such as the IMF or SFR. SPInS can characterise single stars or coeval stars, such as members of binary systems or of stellar clusters. We illustrate the capabilities of SPInS by studying stars that are spread over the Hertzsprung-Russell diagram. We then validate the tool by inferring the ages and masses of stars in several catalogues and by comparing them with literature results. We show that in addition to the age and mass, SPInS can efficiently provide derived quantities, such as the radius, surface gravity, and seismic indices. We demonstrate that SPInS can age-date and characterise coeval stars that share a common age and chemical composition. The SPInS tool will be very helpful in preparing and interpreting the results of large-scale surveys, such as the wealth of data expected or already provided by space missions, such as Gaia, Kepler, TESS, and PLATO.
CASI-3D (Convolutional Approach to Structure Identification - 3D) is a deep learning method to identify signatures of stellar feedback in molecular line spectra, such as 12CO and 13CO. CASI-3D is developed from CASI-2D (Van Oort+2019) in order to exploit the full 3D spectral information.
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