sedkit
Compare the light of a single star with the physical flux sum of a coeval pair, combining optical spectra and infrared photometry.
For exploring unresolved companions and their contributions to the observed SED.
RESEARCH SOFTWARE
From starlight
to stellar physics.
I build tools to connect the light we observe to the stars that produce it: assembling spectra, testing hidden companions, modelling atmospheres, and measuring stellar populations.
FROM THE HOMEPAGE DEMO TO REAL DATA
sedkit
One source. One star—or two?
Download Gaia XP spectra and linked infrared photometry, then compare single-star and coeval-binary SED models using absolute fluxes.
A compact empirical model runs with NumPy and SciPy. Examples connect SED fitting to SPHEREx data and joint radial-velocity modelling.
DOWNLOAD → FIT → PLOT
from sedkit import download, fit, plot
sed = download("1521154374020165376")
result = fit(sed, age_gyr=5.0, feh=0.0)
fig = plot(sed, result)This example fixes age and metallicity. A preference for the binary model is a diagnostic, not a confirmed companion.
01 / OBSERVATIONS & BINARIES
Build the spectrum.
Test the hidden companion.
XphereX
Extract SPHEREx spectrophotometry with aperture and PSF methods, account for source proper motion, and associate the results with Gaia XP spectra.
For extending the observed stellar SED from optical to infrared wavelengths.
02 / SPECTRAL PHYSICS
Connect an atmosphere
to the light it produces.
Kurucz-a1
A neural emulator of one-dimensional LTE stellar atmospheres, incorporating hydrostatic equilibrium as a physical constraint.
For predicting atmospheric structure as a function of stellar parameters and optical depth.
Jorg
A JAX-based stellar spectrum synthesis library with atmosphere interpolation, line and continuum opacity, and radiative transfer.
For connecting stellar parameters to synthetic spectra and exploring derivatives. The direct-fitting interface is experimental.
03 / STELLAR POPULATIONS
Measure individual stars.
Read the Galaxy.
AspGap
Infer stellar parameters and chemical abundances from low-resolution Gaia XP spectra, using the information learned from APOGEE.
For studying the chemistry and assembly history of the Milky Way with large stellar samples.
M Dwarf Machine
Estimate temperatures and metallicities from LAMOST M-dwarf spectra using models trained with the Stellar Label Machine.
For characterising the low-mass stars that dominate the Galaxy by number.
Explore the code, follow the examples, or get in touch about a scientific application.
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