Defines path-signature descriptors for spectral line profiles and shows they enable unsupervised clustering of MaNGA Hα spaxels into spatially coherent classes whose stacked spectra recover large-scale velocity patterns.
Spectral Classification of Galaxies: An Orthogonal Approach
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Classification of galaxy spectral energy distributions in terms of orthogonal basis functions provides an objective means of estimating the number of significant spectral components that comprise a particular galaxy type. We apply the Karhunen-Lo\`{e}ve transform to derive a spectral eigensystem from a sample of ten galaxy spectral energy distributions. These spectra cover a wavelength range of 1200 \AA\ to 1 $\mu$m and galaxy morphologies from elliptical to starburst. We find that the distribution of spectral types can be fully described by the first two eigenvectors (or eigenspectra). The derived eigenbasis is affected by the normalization of the original spectral energy distributions. We investigate different normalization and weighting schemes, including weighting to the same bolometric magnitude and weighting by the observed distribution of morphological types. Projecting the spectral energy distributions on to their eigenspectra we find that the coefficients define a simple spectral classification scheme. The galaxy spectral types can then be described in terms of a one parameter family (the angle in the plane of the first two eigenvectors). We find a strong correlation in the mean between our spectral classifications and those determined from published morphological classifications.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DES Y3 weak lensing analysis with hybrid map-level statistics and simulation-based inference yields S8 = 0.808 ± 0.017, Ωm = 0.325 ± 0.024, and w < -0.766, improving the figure of merit by 60% over prior state-of-the-art.
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The Hidden Geometry of Astrophysical Spectra: Path-Signatures of Line Profiles
Defines path-signature descriptors for spectral line profiles and shows they enable unsupervised clustering of MaNGA Hα spaxels into spatially coherent classes whose stacked spectra recover large-scale velocity patterns.
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Dark Energy Survey Year 3 results: optimized $w$CDM simulation-based inference with weak lensing map-level hybrid statistics
DES Y3 weak lensing analysis with hybrid map-level statistics and simulation-based inference yields S8 = 0.808 ± 0.017, Ωm = 0.325 ± 0.024, and w < -0.766, improving the figure of merit by 60% over prior state-of-the-art.