SLAM

SLAM (Stellar Labels Machine) estimates stellar parameters for M dwarfs from BOSS coadded spectra. It was developed by Bo Zhang and adapted for SDSS-V by Zach Way.

What it does

SLAM determines the following stellar labels for M dwarf stars:

  • Effective temperature (teff)

  • Surface gravity (logg)

  • Iron abundance (fe_h)

  • Iron abundance from NIU calibration (fe_h_niu)

  • Alpha-element abundance (alpha_fe)

How it works

  1. Target selection: Spectra are filtered by photometric and program criteria before fitting. Objects must satisfy at least one of:

    • Magnitude cut: Gaia G - RP > 0.56 and absolute G-band magnitude > 5.553 (with valid parallax).

    • Program match: The source is assigned to the mwm_yso or mwm_snc program.

    Objects failing both criteria are flagged and skipped.

  2. Spectral preparation: Observed BOSS spectra are rebinned onto a standard wavelength grid used during SLAM training. For coadded spectra, no radial velocity correction is applied; for visit spectra, the XCSAO radial velocity is used to shift to the rest frame.

  3. Continuum normalization: Spectra are block-normalized using the laspec normalization routines over the wavelength range 6002–8957 Angstroms with a polynomial approach (quantile 0.7).

  4. Label prediction: Labels are predicted in two passes:

    • A fast initial prediction (predict_labels_quick) provides starting guesses.

    • A refined prediction (predict_labels_multi) uses the initial guesses to perform a full optimization, returning labels, covariance, and optimizer status.

  5. Post-processing flags: After fitting, results are flagged if the effective temperature or metallicity falls outside the expected bounds, or if the optimizer reports a bad status.

Output fields

Field

Description

teff

Effective temperature (K)

e_teff

Uncertainty on effective temperature

logg

Surface gravity (log cm/s^2)

e_logg

Uncertainty on surface gravity

fe_h

Iron abundance [Fe/H] (dex)

e_fe_h

Uncertainty on [Fe/H]

fe_h_niu

Iron abundance from NIU calibration (dex)

e_fe_h_niu

Uncertainty on NIU [Fe/H]

alpha_fe

Alpha-element abundance [alpha/Fe] (dex)

e_alpha_fe

Uncertainty on [alpha/Fe]

rho_*

Correlation coefficients between pairs of labels

initial_*

Initial guesses for each label from the quick prediction

success

Whether the optimizer converged

status

Optimizer exit status code

optimality

Whether the solution satisfies optimality conditions

chi2

Chi-squared of the fit

rchi2

Reduced chi-squared of the fit

result_flags

Bitfield summarizing result quality

Correlation coefficients

The model provides pairwise correlation coefficients between the fitted labels (e.g., rho_teff_logg, rho_teff_fe_h, etc.), which quantify covariances in the label estimates.

Flags

Flag

Bit

Meaning

flag_bad_optimizer_status

2^0

Optimizer returned a bad status (not 0 or 2)

flag_teff_outside_bounds

2^1

Teff is outside [2800, 4500] K

flag_fe_h_outside_bounds

2^2

[Fe/H] is outside [-1.0, +0.5] dex

flag_outside_photometry_range

2^3

Source photometry is outside expected M dwarf range

flag_not_magnitude_cut

2^4

Source fails the photometric magnitude cut

flag_not_carton_match

2^5

Source is not in the mwm_yso or mwm_snc programs

Composite flags

  • flag_warn: Set if flag_bad_optimizer_status or flag_outside_photometry_range is set.

  • flag_bad: Set if any of flag_teff_outside_bounds, flag_fe_h_outside_bounds, flag_outside_photometry_range, or flag_bad_optimizer_status is set.

Key caveats

  • SLAM is trained on M dwarf spectra. Applying it to stars outside the M dwarf parameter range (approximately 2800–4500 K) will produce unreliable results, which is indicated by flag_teff_outside_bounds.

  • The photometric filtering criteria (Gaia color and absolute magnitude) are designed to select M dwarfs. Stars without valid Gaia parallax will only be analyzed if they belong to the mwm_yso or mwm_snc programs.

  • The fe_h_niu label is an alternative metallicity calibration; both metallicity estimates are provided for comparison.

  • Spectral data arrays (model flux, continuum) are stored in intermediate pickle files and can be accessed through the result object.