Astrophysics Source Code Library

Making codes discoverable since 1999

Searching for codes credited to 'de Souza, Rafael S.'

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[ascl:1408.018] CosmoPhotoz: Photometric redshift estimation using generalized linear models

CosmoPhotoz determines photometric redshifts from galaxies utilizing their magnitudes. The method uses generalized linear models which reproduce the physical aspects of the output distribution. The code can adopt gamma or inverse gaussian families, either from a frequentist or a Bayesian perspective. A set of publicly available libraries and a web application are available. This software allows users to apply a set of GLMs to their own photometric catalogs and generates publication quality plots with no involvement from the user. The code additionally provides a Shiny application providing a simple user interface.

[ascl:1503.006] AMADA: Analysis of Multidimensional Astronomical DAtasets

AMADA allows an iterative exploration and information retrieval of high-dimensional data sets. This is done by performing a hierarchical clustering analysis for different choices of correlation matrices and by doing a principal components analysis in the original data. Additionally, AMADA provides a set of modern visualization data-mining diagnostics. The user can switch between them using the different tabs.

[ascl:1512.009] DRACULA: Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy

DRACULA classifies objects using dimensionality reduction and clustering. The code has an easy interface and can be applied to separate several types of objects. It is based on tools developed in scikit-learn, with some usage requiring also the H2O package.

[ascl:2208.002] qrpca: QR-based Principal Components Analysis

qrpca uses QR-decomposition for fast principal component analysis. The software is particularly suited for large dimensional matrices. It makes use of torch for internal matrix computations and enables GPU acceleration, when available. Written in both R and python languages, qrpca provides functionalities similar to the prcomp (R) and sklearn (python) packages.

[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:2404.005] GalMOSS: GPU-accelerated galaxy surface brightness fitting via gradient descent

GalMOSS performs two-dimensional fitting of galaxy profiles. This Python-based, Torch-powered tool seamlessly enables GPU parallelization and meets the high computational demands of large-scale galaxy surveys. It incorporates widely used profiles such as the Sérsic, Exponential disk, Ferrer, King, Gaussian, and Moffat profiles, and allows for the easy integration of more complex models. Tested on over 8,000 galaxies from the Sloan Digital Sky Survey (SDSS) g-band with a single NVIDIA A100 GPU, GalMOSS completed classical Sérsic profile fitting in about 10 minutes. Benchmark tests show that GalMOSS achieves computational speeds that are significantly faster than those of default implementations.