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solar_long_slit

Long-slit solar reduction from a raw 2-D frame: geometric rectification, hot-pixel rejection, band extraction (the Sun fills the slit), wavelength calibration on the Fraunhofer lines (no lamp needed), continuum normalisation and BeSS export. The instrument geometry and the exposure metadata are variables, so one profile per spectrograph serves every solar spectrum.
Kind reduction
Status draft - draft: conventions still open for discussion
Version 1.0.0
Source package:spectro-kernel-recipes
Requires spectro-kernel >=0.7

Conventions

Extraction sums the brightest row +/- 40 rows; continuum normalised on 6400-6500 Angstrom; a 3rd-order dispersion solution on at least six Fraunhofer lines.

References

  • Howell 2006, Handbook of CCD Astronomy, 2nd ed., ch. 5 - long-slit reduction
  • Tody 1986, Proc. SPIE 627, 733 - the IRAF reduction tasks this chain mirrors

Variables

The instrument- or observer-dependent values. Provide them with a profile file, --set name=value, or variables={...} in Python.

Variable Type Required / default Description
tilt_deg float default 0.0 slit tilt (deg); 0 = none
slant_deg float default 0.0 line slant (deg); 0 = none
smile_radius float default 0.0 smile radius (px); 0 = none
reference_row int required row the geometry is measured on
approx_wavelength_min_angstrom float required approximate wavelength of pixel 0
approx_dispersion_angstrom_per_pixel float required approximate dispersion
instrument str required
observer str required
site str required
date_obs_utc str required ISO-8601 start of exposure (UTC)
exposure_seconds float required

Steps

# Algorithm Parameters
1 correct_tilt_affine tilt_deg='${tilt_deg}'
2 correct_slant_affine slant_deg='${slant_deg}', pivot_row='${reference_row}'
3 correct_smile_polynomial reference_row='${reference_row}', smile_radius='${smile_radius}', polynomial_order=4
4 outlier_rejection_mad_adaptive kernel_size=3, threshold=3.0
5 extract_spectrum_sum dispersion_axis=1, half_width=40
6 wavelength_calibration_solar approx_wavelength_min_angstrom='${approx_wavelength_min_angstrom}', approx_dispersion_angstrom_per_pixel='${approx_dispersion_angstrom_per_pixel}', poly_order=3, min_lines_for_fit=6
7 normalize_to_region wave_lo=6400.0, wave_hi=6500.0
8 export_fits_bess object_name='Sun', instrument='${instrument}', site='${site}', observer='${observer}', date_obs_utc='${date_obs_utc}', exposure_seconds='${exposure_seconds}', vhelio_kms=0.0

Run it

# profile.yaml holds your instrument values:
#   tilt_deg: 0.0
#   slant_deg: 0.0
#   smile_radius: 0.0
#   reference_row: 0
#   approx_wavelength_min_angstrom: 0.0
#   approx_dispersion_angstrom_per_pixel: 0.0
#   instrument: <instrument>
#   observer: <observer>
#   site: <site>
#   date_obs_utc: <date_obs_utc>
#   exposure_seconds: 0.0
spectro pipeline solar_long_slit --input spectrum.fits --profile profile.yaml
spectro preset show solar_long_slit      # variables and steps

from spectro_kernel import WorkContext
from spectro_kernel.pipeline import PipelineBuilder

variables = {"tilt_deg": 0.0, "slant_deg": 0.0, "smile_radius": 0.0, "reference_row": 0, "approx_wavelength_min_angstrom": 0.0, "approx_dispersion_angstrom_per_pixel": 0.0, "instrument": "<instrument>", "observer": "<observer>", "site": "<site>", "date_obs_utc": "<date_obs_utc>", "exposure_seconds": 0.0}
pipeline = PipelineBuilder().from_preset("solar_long_slit", variables).build()
result = pipeline.execute(ctx)        # ctx holds the spectrum / frames
print(result.history[-1])             # pipeline:<name> vX.Y.Z + variables

{
  "tool": "run_preset",
  "arguments": {
    "session_id": "<session_id>",
    "preset_name": "solar_long_slit",
    "variables": {
      "tilt_deg": 0.0,
      "slant_deg": 0.0,
      "smile_radius": 0.0,
      "reference_row": 0,
      "approx_wavelength_min_angstrom": 0.0,
      "approx_dispersion_angstrom_per_pixel": 0.0,
      "instrument": "<instrument>",
      "observer": "<observer>",
      "site": "<site>",
      "date_obs_utc": "<date_obs_utc>",
      "exposure_seconds": 0.0
    }
  }
}