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Astrophysics Source Code Library

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Searching for codes credited to 'Shen, Shiyin'

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Found 4 codes.

[ascl:2510.016] pPXF-SEW: Full-spectrum fitting with penalized pixel-fitting and equivalent width extraction
pPXF-SEW implements a full‑spectrum fitting method that combines the penalized pixel‑fitting (pPXF, ascl:1210.002) technique with an “Equivalent Widths Spectrum” (SEW) approach. It derives stellar and gas kinematics via pPXF and extracts stellar‑population parameters and dust attenuation without assuming a prior attenuation curve. The code processes observed spectra and outputs fits to both continuum and line features, incorporating the SEW method’s linear treatment of equivalent widths to handle multiple galaxy components. pPXF-SEW can be applied to galaxy and stellar datasets for joint kinematic and population synthesis.
[ascl:2507.023] Capivara: Scalable spectral-based segmentation package
Capivara implements a spectral-based segmentation method for Integral Field Unit (IFU) data cubes. The code uses hierarchical clustering in the spectral domain, grouping similar spectra to improve the signal-to-noise ratio without compromising astrophysical similarity among regions, and leverages advanced matrix operations via torch for GPU acceleration.
[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.
[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.