Data for "The Dust Extinction Curve: Beyond R(V)"
<p>24 million dust extinction curves, detemined from Gaia XP spectra, as described in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241022537G/abstract">Green, Zhang & Zhang (2025)</a>.</p> <p>We represent the extinction curves using a set of 16 basis vectors. For each star, there are 16 coefficients, which can be used to reconstruct the extinction curves. After loading <code>A_zp</code> and <code>G_subspace</code> from the file <code>G_subspace.json</code>, and <code>coeffs</code> from the file <code>coeffs.h5</code>, the extinction curves can be reconstructed using:</p> <p> <code>A = A_zp + np.sum(coeffs[None,:] * G_subspace[:,:], axis=1)</code></p> <p>The output <code>A</code> will have shape (star, wavelength). The wavelengths at which <code>A</code> is sampled are stored in the field <code>wavelengths_nm</code> (in nanometers), in <code>G_subspace.json</code>.</p> <p>The covariance matrix of the coefficients for each star is stored in the files <code>coeffs_cov_?.h5</code>. We store the diagonals and the upper triangles of the covariances separately. They can be reconstructed using the function <code>reconstruct_symm_matrices</code> from <code>symm_matrix_utils.py</code>:</p> <p> <code>from symm_matrix_utils.py import reconstruct_symm_matrices</code><br> <code>cov = reconstruct_symm_matrices(cov_diag, cov_triu_wo_diag)</code></p> <p>Additionally, we store the inverse covariance matrices in the files <code>coeffs_icov_?.h5</code>, in the same manner as the covariance matrices.</p> <p>The file <code>source_info.h5</code> contains a few useful Gaia fields and parameter estimates (with corresponding uncertainties) from <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240714594Z/abstract">Zhang & Green (2025)</a>.</p> <p>The file <code>feature_EW.h5</code> contains the equivalent widths (in nanometers) of the VBS and the 770 and 850 nm extinction features.</p> <p>Every file contains the Gaia DR3 <code>source_id</code> of every star, labeled <code>gdr3_source_id</code>.</p>
ShareScore
32/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 0
- Engagement
- 4