Results 701-750 of 3553 (3462 ASCL, 91 submitted)

[ascl:1712.007]
SFoF: Friends-of-friends galaxy cluster detection algorithm

SFoF is a friends-of-friends galaxy cluster detection algorithm that operates in either spectroscopic or photometric redshift space. The linking parameters, both transverse and along the line-of-sight, change as a function of redshift to account for selection effects.

[ascl:1304.013]
SFH: Star Formation History

SFH is an efficient IDL tool that quickly computes accurate predictions for the baryon budget history in a galactic halo.

[ascl:2001.003]
sf3dmodels: Star-forming regions 3D modelling package

sf3dmodels models star-forming regions; it brings together analytical models in order to compute their physical properties in a 3-dimensional grid. The package can couple different models in a single grid to recreate complex star forming systems such as those being revealed by current instruments. The output data can be read with LIME (ascl:1107.012) or RADMC-3D (ascl:1108.016) to carry out radiative transfer calculations of the modeled region.

[ascl:2212.010]
sf_deconvolve: PSF deconvolution and analysis

sf_deconvolve performs PSF deconvolution using a low-rank approximation and sparsity. It can handle a fixed PSF for the entire field or a stack of PSFs for each galaxy position. The code accepts Numpy binary files or FITS as input, takes the observed (*i.e.* with PSF effects and noise) stack of galaxy images and a known PSF, and attempts to reconstruct the original images. sf_deconvolve can be run in a terminal or in an active Python session, and includes options for initialization, optimization, low-Rank approximation, sparsity, PSF estimation, and other attributes.

[ascl:1010.064]
SExtractor: Source Extractor

This new software optimally detects, de-blends, measures and classifies sources from astronomical images: SExtractor (Source Extractor). A very reliable star/galaxy separation can be achieved on most images using a neural network trained with simulated images. Salient features of SExtractor include its ability to work on very large images, with minimal human intervention, and to deal with a wide variety of object shapes and magnitudes. It is therefore particularly suited to the analysis of large extragalactic surveys.

[ascl:1508.006]
SExSeg: SExtractor segmentation

SExSeg forces SExtractor (ascl:1010.064) to run using a pre-defined segmentation map (the definition of objects and their borders). The defined segments double as isophotal apertures. SExSeg alters the detection image based on a pre-defined segmenation map while preparing your "analysis image" by subtracting the background in a separate SExtractor run (using parameters you specify). SExtractor is then run in "double-image" mode with the altered detection image and background-subtracted analysis image.

[ascl:2206.019]
SEVN: Stellar EVolution for N-body

The population synthesis code SEVN (Stellar EVolution for N-body) includes up-to-date stellar evolution (through look-up tables), binary evolution, and different recipes for core-collapse supernovae. SEVN also provides an up-to-date formalism for pair-instability and pulsational pair-instability supernovae, and is designed to interface with direct-summation N-body codes such as STARLAB (ascl:1010.076) and HiGPUs (ascl:1207.002).

[ascl:1803.009]
SETI-EC: SETI Encryption Code

The SETI Encryption code, written in Python, creates a message for use in testing the decryptability of a simulated incoming interstellar message. The code uses images in a portable bit map (PBM) format, then writes the corresponding bits into the message, and finally returns both a PBM image and a text (TXT) file of the entire message. The natural constants (c, G, h) and the wavelength of the message are defined in the first few lines of the code, followed by the reading of the input files and their conversion into 757 strings of 359 bits to give one page. Each header of a page, *i.e.*, the little-endian binary code translation of the tempo-spatial yardstick, is calculated and written on-the-fly for each page.

[ascl:2203.025]
SetCoverPy: A heuristic solver for the set cover problem

SetCoverPy finds an (near-)optimal solution to the set cover problem (SCP) as fast as possible. It employs an iterative heuristic approximation method, combining the greedy and Lagrangian relaxation algorithms. It also includes a few useful tools for a quick chi-squared fitting given two vectors with measurement errors.

[ascl:2006.011]
SERVAL: SpEctrum Radial Velocity AnaLyser

Zechmeister, M.; Reiners, A.; Amado, P. J.; Azzaro, M.; Bauer, F. F.; Béjar, V. J. S.; Caballero, J. A.; Guenther, E. W.; Hagen, H. -J.; Jeffers, S. V.; Kaminski, A.; Kürster, M.; Launhardt, R.; Montes, D.; Morales, J. C.; Quirrenbach, A.; Reffert, S.; Ribas, I.; Seifert, W.; Tal-Or, L. Wolthoff, V.

SERVAL calculates radial velocities (RVs) from stellar spectra. The code uses least-squares fitting algorithms to derive the RVs and additional spectral diagnostics. Forward modeling in pixel space is used to properly weight pixel errors, and the stellar templates are reconstructed from the observations themselves to make optimal use of the RV information inherent in the stellar spectra.

[ascl:1304.009]
Sérsic: Exact deprojection of Sérsic surface brightness profiles

Sérsic is an implementation of the exact deprojection of Sérsic surface brightness profiles described in Baes and Gentile (2011). This code depends on the mpmath python library for an implementation of the Meijer G function required by the Baes and Gentile (hereafter B+G) formulas for rational values of the Sérsic index. Sérsic requires rational Sérsic indices, but any irrational number can be approximated arbitrarily well by some rational number. The code also depends on scipy, but the dependence is mostly for testing. The implementation of the formulas and the formulas themselves have undergone comprehensive testing.

[ascl:1312.001]
SERPent: Scripted E-merlin Rfi-mitigation PipelinE for iNTerferometry

SERPent is an automated reduction and RFI-mitigation procedure that uses the SumThreshold methodology. It was originally developed for the LOFAR pipeline. SERPent is written in Parseltongue, enabling interaction with the Astronomical Image Processing Software (AIPS) program. Moreover, SERPent is a simple "out of the box" Python script, which is easy to set up and is free of compilers.

[ascl:1102.010]
SEREN: A SPH code for star and planet formation simulations

Hubber, David; Batty, Chris; McLeod, Andrew; Whitworth, Anthony; Bisbas, Thomas; Stamatellos, Dimitrios; Walch, Stefanie; Rawiraswattana, Krisada; Goodwin, Simon

SEREN is an astrophysical Smoothed Particle Hydrodynamics code designed to investigate star and planet formation problems using self-gravitating hydrodynamics simulations of molecular clouds, star-forming cores, and protostellar disks.

SEREN is written in Fortran 95/2003 with a modular philosophy for adding features into the code. Each feature can be easily activated or deactivated by way of setting options in the Makefile before compiling the code. This has the added benefit of allowing unwanted features to be removed at the compilation stage resulting in a smaller and faster executable program. SEREN is written with OpenMP directives to allow parallelization on shared-memory architecture.

[ascl:1404.005]
SER: Subpixel Event Repositioning Algorithms

Subpixel Event Repositioning (SER) techniques significantly improve the already unprecedented spatial resolution of Chandra X-ray imaging with the Advanced CCD Imaging Spectrometer (ACIS). Chandra CCD SER techniques are based on the premise that the impact position of events can be refined, based on the distribution of charge among affected CCD pixels. Unlike ACIS SER models that are restricted to corner split (3- and 4-pixel) events and assume that such events take place at the split pixel corners, this IDL code uses two-pixel splits as well, and incorporates more realistic estimates of photon impact positions.

[ascl:1811.004]
SEP: Source Extraction and Photometry

SEP (Source Extraction and Photometry) makes the core algorithms of Source Extractor (ascl:1010.064) available as a library of standalone functions and classes. These operate directly on in-memory arrays (no FITS files or configuration files). The code is derived from the Source Extractor code base (written in C) and aims to produce results compatible with Source Extractor whenever possible. SEP consists of a C library with no dependencies outside the standard library and a Python module that wraps the C library in a Pythonic API. The Python wrapper operates on NumPy arrays with NumPy as its only dependency. It is generated using Cython.

From Source Extractor, SEP includes background estimation, image segmentation (including on-the-fly filtering and source deblending), aperture photometry in circular and elliptical apertures, and source measurements such as Kron radius, "windowed" position fitting, and half-light radius. It also adds the following features that are not available in Source Extractor: optimized matched filter for variable noise in source extraction; circular annulus and elliptical annulus aperture photometry functions; local background subtraction in shape consistent with aperture in aperture photometry functions; exact pixel overlap mode in all aperture photometry functions; and masking of elliptical regions on images.

[ascl:1807.026]
SENR: Simple, Efficient Numerical Relativity

SENR (Simple, Efficient Numerical Relativity) provides the algorithmic framework that combines the C codes generated by NRPy+ (ascl:1807.025) into a functioning numerical relativity code. It is part of the numerical relativity code package SENR/NRPy+. The package extends previous implementations of the BSSN reference-metric formulation to a much broader class of curvilinear coordinate systems, making it suitable for modeling physical configurations with approximate or exact symmetries, such as modeling black hole dynamics.

[ascl:2012.003]
Sengi: Interactive viewer for spectral outputs from stellar population synthesis models

Sengi enables online viewing of the spectral outputs of stellar population synthesis (SPS) codes. Typical SPS codes require significant disk space or computing resources to produce spectra for simple stellar populations with arbitrary parameters, making it difficult to present their results in an interactive, web-friendly format. Sengi uses Non-negative Matrix Factorisation (NMF) and bilinear interpolation to estimate output spectra for arbitrary values of stellar age and metallicity; this reduces the disk requirements and computational expense, allowing Sengi to serve the results in a client-based Javascript application.

[ascl:1504.009]
Self-lensing binary code with Markov chain

The self-lensing binary code with Markov chain code was used to analyze the self-lensing binary system KOI-3278. It includes the MCMC modeling and the key figures.

[ascl:2301.006]
Self-cal: Optical/IR long-baseline interferometry

Self-cal produces radio-interferometric images of an astrophysical object. The code is an adaptation of the self-calibration algorithm to optical/infrared long-baseline interferometry, especially to make use of differential phases and differential visibilities. It works together with the Mira image reconstruction software and has been used mainly on VLTI data. Self-cal, written in Yorick, is also available as part of fitsOmatic (ascl:2301.005).

[ascl:2110.019]
SELCIE: Screening Equations Linearly Constructed and Iteratively Evaluated

SELCIE (Screening Equations Linearly Constructed and Iteratively Evaluated) investigates the chameleon model that arises from screening a scalar field introduced in some modified gravity models that is coupled to matter. The code provides tools to construct user defined meshes by utilizing the GMSH mesh generation software. These tools include constructing shapes whose boundaries are defined by some function or by constructing it out of basis shapes such as circles, cones and cylinders. The mesh can also be separated into subdomains, each of which having its own refinement parameters. These meshes can then be converted into a format that is compatible with the finite element software FEniCS. SELCIE uses FEniCS (ascl:2110.018) with a nonlinear solving method (Picard or Newton method) to solve the chameleon equation of motion for some parameters and density distribution. These density distributions are constructed by having the density profile of each subdomain being set by a user defined function, allowing for extremely customizable setups that are easy to implement.

[ascl:1411.007]
segueSelect: SDSS/SEGUE selection function modelling

The Python package segueSelect automatically models the SDSS/SEGUE selection fraction -- the fraction of stars with good spectra -- as a continuous function of apparent magnitude for each plate. The selection function can be determined for any desired sample cuts in signal-to-noise ratio, u-g, r-i, and E(B-V). The package requires Pyfits (ascl:1207.009) and, for coordinate transformations, galpy (ascl:1411.008). It can calculate the KS probability that the spectropscopic sample was drawn from the underlying photometric sample with the model selection function, plot the cumulative distribution function in r-band apparent magnitude of the spectroscopic sample (red) and the photometric sample+selection-function-model for this plate, and, if galpy is installed, can transform velocities into the Galactic coordinate frame. The code can also determine the selection function for SEGUE K stars.

[ascl:2303.003]
SeeKAT: Localizer for transients detected in tied-array beams

SeeKAT is a Python implementation of a novel maximum-likelihood estimation approach to localizing transients and pulsars detected in multiple MeerKAT tied-array beams at once to (sub-)arcsecond precision. It reads in list of detections (RA, Dec, S/N) and the beam PSF and computes a covariance matrix of the S/N value ratios, assuming 1-sigma Gaussian errors on each measurement. It models the aggregate beam response by arranging beam PSFs appropriately relative to each other and calculates a likelihood distribution of obtaining the observed S/N in each beam according to the modeled response. In addition, SeeKAT can plot the likelihood function over RA and Dec with 1-sigma uncertainty, overlaid on the beam coordinates and sizes.

[ascl:1607.020]
SEEK: Signal Extraction and Emission Kartographer

Akeret, Joel; Seehars, Sebastian; Chang, Chihway; Monstein, Christian; Amara, Adam; Refregier, Alexandre

SEEK (Signal Extraction and Emission Kartographer) processes time-ordered-data from single dish radio telescopes or from the simulation pipline HIDE (ascl:1607.019), removes artifacts from Radio Frequency Interference (RFI), automatically applies flux calibration, and recovers the astronomical radio signal. With its companion code HIDE (ascl:1607.019), it provides end-to-end simulation and processing of radio survey data.

[ascl:1905.026]
SEDPY: Modules for storing and operating on astronomical source spectral energy distribution

SEDPY performs a variety of tasks for astronomical spectral energy distributions. It can generate synthetic photometry through any filter, provides detailed modeling of extinction curves, and offers basic aperture photometry algorithms. SEDPY can also store and interpolate model SEDs, convolve absolute or apparent fluxes, and calculate rest-frame magnitudes.

[ascl:2012.013]
sedop: Optimize discrete versions of common SEDs

sedop is a Monte-Carlo minimization code designed to optimally construct spectral energy distributions (SEDs) for sources of ultraviolet and X-ray radiation employed in numerical simulations of reionization and radiative feedback.

[ascl:1901.008]
SEDobs: Observational spectral energy distribution simulation

SEDobs uses state-of-the-art theoretical galaxy SEDs (spectral energy distributions) to create simulated observations of distant galaxies. It used BC03 and M05 theoretical models and allows the user to configure the simulated observation that are needed. For a given simulated galaxy, the user is able to simulate multi-spectral and multi-photometric observations.

[ascl:2011.014]
SEDkit: Spectral energy distribution construction and analysis tools

SEDkit constructs and analyzes simple spectral energy distributions (SED). This collection of pure Python modules creates individual SEDs or SED catalogs from spectra and/or photometry and calculates fundamental parameters (fbol, Mbol, Lbol, Teff, mass, log(g)).

[ascl:2008.013]
SEDBYS: Spectral Energy Distribution Builder for Young Stars

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.

[ascl:1909.003]
SecularMultiple: Hierarchical multiple system secular evolution model

SecularMultiple computes the secular (orbit-averaged) gravitational dynamics of hierarchical multiple systems composed of nested binary orbits (simplex-type systems) with any configuration and any number of bodies. A particle can represent a binary or a body. The structure of the system is determined by linking to other particles with the attributes child1 and child2, and tidal interactions and relativistic corrections are included in an ad hoc fashion. SecularMultiple also includes routines for external perturbations such as flybys and supernovae.

[ascl:1101.001]
Second-order Tight-coupling Code

Prior to recombination photons, electrons, and atomic nuclei rapidly scattered and behaved, almost, like a single tightly-coupled photon-baryon plasma. In order to solve the cosmological perturbation equations during that time, Cosmic Microwave Background (CMB) codes use the so-called tight-coupling approximation in which the problematic terms (i.e. the source of the stiffness) are expanded in inverse powers of the Thomson Opacity. Most codes only keep the terms linear in the inverse Thomson Opacity. We have developed a second-order tight-coupling code to test the validity of the usual first-order tight-coupling code. It is based on the publicly available code CAMB.

[ascl:1201.003]
SeBa: Stellar and binary evolution

SeBa is the stellar and binary evolution module, fully integrated into the kira N-body integrator, for Starlab (ascl:1010.076), although it can also be used as a stand-alone module for non-dynamical applications. Due to the interaction between stellar evolution and stellar dynamics, it is difficult to solve for the evolution of both systems in a completely self-consistent way. The trajectories of stars are computed using a block timestep scheme, as described earlier. Stellar and binary evolution is updated at fixed intervals (every 1/64 of a crossing time, typically a few thousand years). Any feedback between the two systems may thus experience a delay of at most one timestep. Internal evolution time steps may differ for each star and binary, and depend on binary period, perturbations due to neighbors, and the evolutionary state of the star. Time steps in this treatment vary from several milliseconds up to (at most) a million years.

[ascl:1210.012]
SearchCal: The JMMC Evolutive Search Calibrator Tool

SearchCal builds an evolutive catalog of stars suitable as calibrators within any given user-defined angular distance and magnitude around a scientific target. SearchCal can select suitable bright calibration stars (V ≤ 10; K ≤ 5.0) for obtaining the ultimate precision of current interferometric instruments like the VLTI and faint calibration stars up to K ~ 15 around the scientific target. Star catalogs available at the CDS are searched via web requests and provide the useful astrometric and photometric informations for selecting calibrators. The missing photometries are computed with an accuracy of about 0.1 mag. The stellar angular diameter is estimated with a precision of about 10% through newly determined surface-brightness versus color-index relations based on the I, J, H and K magnitudes. For each star the squared visibility is computed taking into account the central wavelength and the maximum baseline of the predicted observations.

[ascl:2012.015]
seaborn: Statistical data visualization

Waskom, Michael; Botvinnik, Olga; Gelbart, Maoz; Ostblom, Joel; Hobson, Paul; Lukauskas, Saulius; Gemperline, David C; Augspurger, Tom; Halchenko, Yaroslav; Warmenhoven, Jordi; Cole, John B.; De Ruiter, Julian; Vanderplas, Jake; Hoyer, Stephan; Pye, Cameron; Miles, Alistair; Swain, Corban; Meyer, Kyle; Martin, Marcel; Bachant, Pete Quintero, Eric; Kunter, Gero; Villalba, Santi; Brian; Fitzgerald, Clark; Evans, C. G.; Williams, Mike Lee; O'Kane, Drew; Yarkoni, Tal; Brunner, Thomas

Seaborn provides a high-level interface for drawing attractive statistical graphics. Written in Python, it builds on matplotlib and integrates closely with pandas data structures. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API allows the user to focus on what the different elements of the plots mean, rather than on the details of how to draw them.

[submitted]
SDSS Dual Active Nuclei Galaxy Detection Pipeline

Dual Active Nuclei Galaxies (DAGNs) are rare occurrences in the sky. Until now, most AGNs have been described to be found serendipitously, or by manual observation. In recent years, there has been an increasing interest in such dual AGNs and their astrophysical properties. Their study is important to the understanding of galaxy formation, star formation and these objects are the precursors to Gravitational Wave Sources.

Hence, we have devised a pipeline, that along with systematic data collection, can detect such dual AGN candidates. A novel algorithm 'Graph-Boosted Gradient Ascent' has been devised to detect whether an R-band image of a galaxy is a potential candidate for a DAGN or not. The pipeline can be cloned to a user's machine, and by joining the AstrIRG_DAGN group on SciServer, astronomers can collectively contribute to the mining of DAGNs.

[ascl:2002.001]
SDAR: Slow-Down Algorithmic Regularization code for solving few-body problems

SDAR (Slow-Down Algorithmic Regularization) simulates the long-term evolution of few-body systems such as binaries and triples. The algorithm used provides a few orders of magnitude faster performance than the classical N-body method. The secular evolution of hierarchical systems, *e.g.* Kozai-Lidov oscillation, can be well reproduced. The code is written in the C++ language and can be used either as a stand-alone tool or a library to be plugged in other N-body codes. The high precision of the floating point to 62 digits is also supported.

[ascl:2202.018]
Sculptor: Interactive modeling of astronomical spectra

Sculptor manipulates, models and analyzes spectroscopic data; the code facilitates reproducible scientific results and easy to inspect model fits. A built-in graphical user interface around LMFIT (ascl:1606.014) offers interactive control to set up and combine multiple spectral models to fully fit the spectrum of choice. Alternatively, all core functionality can be scripted to facilitate the design of spectral fitting and analysis pipelines.

[ascl:2204.013]
SCRIPT: Semi-numerical Code for ReIonization with PhoTon-conservation

SCRIPT (Semi-numerical Code for ReIonization with PhoTon-conservation) generates the ionization field during the epoch of cosmological reionization using a photon-conserving algorithm. The code depends on density and velocity files obtained using a N-body simulation, which can be generated with a 2LPT code such as MUSIC (ascl:1311.011).

[ascl:2303.011]
Scri: Manipulate time-dependent functions of spin-weighted spherical harmonics

Scri manipulates time-dependent functions of spin-weighted spherical harmonics. It implements the BMS transformations of the most common gravitational waveforms, including the Newman-Penrose quantity ψ4, the Bondi news function, the shear spin coefficient σ, and the transverse-traceless metric perturbation h, as well as the remaining Newman-Penrose quantities ψ0 through ψ3.

[ascl:2003.004]
scousepy: Semi-automated multi-COmponent Universal Spectral-line fitting Engine

scousepy is a Python implementation of spectral line-fitting IDL code SCOUSE (ascl:1601.003). It fits a large amount of complex astronomical spectral line data in a systematic way.

[ascl:1601.003]
SCOUSE: Semi-automated multi-COmponent Universal Spectral-line fitting Engine

Henshaw, J. D.; Longmore, S. N.; Kruijssen, J. M. D.; Davies, B.; Bally, J.; Barnes, A.; Battersby, C.; Burton, M.; Cunningham, M. R.; Dale, J. E.; Ginsburg, A.; Immer, K.; Jones, P. A.; Kendrew, S.; Mills, E. A. C.; Molinari, S.; Moore, T. J. T.; Ott, J.; Pillai, T.; Rathborne, J.; Schilke, P.; Schmiedeke, A.; Testi, L.; Walker, D.; Walsh, A.; Zhang, Q.

The Semi-automated multi-COmponent Universal Spectral-line fitting Engine (SCOUSE) is a spectral line fitting algorithm that fits Gaussian files to spectral line emission. It identifies the spatial area over which to fit the data and generates a grid of spectral averaging areas (SAAs). The spatially averaged spectra are fitted according to user-provided tolerance levels, and the best fit is selected using the Akaike Information Criterion, which weights the chisq of a best-fitting solution according to the number of free-parameters. A more detailed inspection of the spectra can be performed to improve the fit through an iterative process, after which SCOUSE integrates the new solutions into the solution file.

[ascl:2112.003]
SCORPIO: Sky COllector of galaxy Pairs and Image Output

The Python package SCORPIO retrieves images and associated data of galaxy pairs based on their position, facilitating visual analysis and data collation of multiple archetypal systems. The code ingests information from SDSS, 2MASS and WISE surveys based on the available bands and is designed for studies of galaxy pairs as natural laboratories of multiple astrophysical phenomena for, among other things, tidal force deformation of galaxies, pressure gradient induced star formation regions, and morphological transformation.

[ascl:2209.005]
SCORE: Shape COnstraint REstoration

The Shape COnstraint REstoration algorithm (SCORE) is a proximal algorithm based on sparsity and shape constraints to restore images. Its main purpose is to restore images while preserving their shape information. It can, for example, denoise a galaxy image by instanciating SCORE and using its denoise method and then visualize the results, and can deconvolve multiple images with different parameter values.

[submitted]
ScopeSim Templates

Templates and helper functions for creating on-sky Source description objects for the ScopeSim instrument data simulation engine.

[submitted]
ScopeSim Instrument Reference Database

A reference database for astronomical instrument and telescope characteristics for all types of visual and infrared systems. Instrument packages are used in conjunction with the ScopeSim instrument data simulator.

[submitted]
ScopeSim

An attempt at creating a common pythonic framework for visual and infrared telescope instrument data simulators.

[ascl:2306.013]
SCONCE-SCMS: Spherical and conic cosmic web finders with extended SCMS algorithms

SCONCE-SCMS detects cosmic web structures, primarily cosmic filaments and the associated cosmic nodes, from a collection of discrete observations with the extended subspace constrained mean shift (SCMS) algorithms on the unit (hyper)sphere (in most cases, the 2D (RA,DEC) celestial sphere), and the directional-linear products space (most commonly, the 3D (RA,DEC,redshift) light cone).

The subspace constrained mean shift (SCMS) algorithm is a gradient ascent typed method dealing with the estimation of local principal curves, more widely known as density ridges. The one-dimensional density ridge traces over the curves where observational data are highly concentrated and thus serves as a natural model for cosmic filaments in our Universe. Modeling cosmic filaments as density ridges enables efficient estimation by the kernel density estimator (KDE) and the subsequent SCMS algorithm in a statistically consistent way. While the standard SCMS algorithm can identify the density ridges in any "flat" Euclidean space, it exhibits large bias in estimating the density ridges on the data space with a non-linear curvature. The extended SCMS algorithms used in SCONCE-SCMS are adaptive to the spherical and conic geometries and resolve the estimation bias of the standard SCMS algorithm on a 2D (RA,DEC) celestial sphere or 3D (RA,DEC,redshift) light cone.

[ascl:2011.019]
Scintools: Pulsar scintillation data tools

SCINTOOLS (SCINtillation TOOLS) simulates and analyzes pulsar scintillation data. The code can be used for processing observed dynamic spectra, computing secondary spectra and ACFs, measuring scintillation arcs, simulating dynamic spectra, and modeling pulsar transverse velocities through scintillation arcs or diffractive timescales.

[ascl:1609.006]
SCIMES: Spectral Clustering for Interstellar Molecular Emission Segmentation

SCIMES identifies relevant molecular gas structures within dendrograms of emission using the spectral clustering paradigm. It is useful for decomposing objects in complex environments imaged at high resolution.

[ascl:1311.001]
SciDB: Open Source DMAS for Scientific Research

SciDB is a DMAS (Data Management and Analytics Software System) optimized for data management of big data and for big analytics. SciDB is organized around multidimensional array storage, a generalization of relational tables, and is designed to be scalable up to petabytes and beyond. Complex analytics are simplified with SciDB because arrays and vectors are first-class objects with built-in optimized operations. Spatial operators and time-series analysis are easy to express. Interfaces to common scientific tools like R as well as programming languages like C++ and Python are provided.

[ascl:2202.007]
SciCatalog: Tools for scientific data catalogs

SciCatalog handles catalogs of scientific data in a way that is easily extensible, including the ability to create nicely formatted AASTex deluxe tables for use in AAS Publishing manuscripts. It handles catalogs of values, their positive and negative uncertainties, and references for those values with methods for easily adding columns and changing values. The catalog is also backed up every time it is loaded under the assumption that it is about to be modified.

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