Capivara: A Spectral-based Segmentation Method for IFU Data Cubes
Rafael S. de Souza, Luis G. Dahmer-Hahn, Shiyin Shen, Ana L. Chies-Santos, Mi Chen, P. T. Rahna, Paula Coelho, Rog\'erio Riffel, Renhao Ye, Behzad Tahmasebzadeh

TL;DR
Capivara is a scalable spectral segmentation tool for IFU galaxy data that improves structural analysis by grouping similar spectral regions, enhancing signal quality, and enabling detailed stellar population studies.
Contribution
It introduces a novel, GPU-accelerated spectral segmentation algorithm for IFU data that moves beyond traditional methods like Voronoi binning, providing detailed galactic component analysis.
Findings
Successfully applied to MaNGA galaxies
Enhances signal-to-noise ratio by spectral grouping
Effectively identifies regions with similar spectral features
Abstract
We present capivara, a fast and scalable spectral-based segmentation package designed to study astrophysical properties within distinct structural components of galaxies. This spectro-segmentation code for integral field unit (IFU) data provides a holistic view of galactic structure, moving beyond conventional radial gradients and the bulge-plus-disk dichotomy. It enables detailed comparisons of stellar ages and metallicities across components, and naturally identifies outliers by grouping spaxels according to dominant spectral features. The algorithm leverages Torch's scalability and GPU acceleration, outputting a masked FITS file that assigns each pixel to its respective group and generates the corresponding one-dimensional spectrum per group, without relying on Voronoi binning. We demonstrate the capabilities of the method using a sample of MaNGA galaxies, combining capivara…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEmbedded Systems Design Techniques · Sensor Technology and Measurement Systems · Fault Detection and Control Systems
