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star_activity_caii_hk

Mount Wilson S index from the Ca II H and K line cores of a reduced stellar spectrum (Vaughan, Preston & Wilson 1978), and log R'_HK when the star's B-V colour is given (Noyes et al. 1984). The standard chromospheric activity measurement for exoplanet-host and solar-type star monitoring.
Kind campaign
Status draft - draft: conventions still open for discussion
Version 1.0.0
Source package:spectro-kernel-recipes
Requires spectro-kernel >=0.7

Conventions

Triangular 1.09 Angstrom H and K bands at 3968.47 and 3933.66 Angstrom, 20 Angstrom R and V continuum bands at 4001 and 3901 Angstrom, alpha = 2.3 (echelle convention). The instrumental S is NOT on the Mount Wilson scale until a linear calibration is derived for your spectrograph: pass it as s_index_calibration once you have it, otherwise compare only your own measurements with each other.

References

  • Vaughan, Preston & Wilson 1978, PASP 90, 267 - the HKP-2 S index bandpasses
  • Duncan et al. 1991, ApJS 76, 383 - the Mount Wilson survey and its alpha calibration
  • Noyes et al. 1984, ApJ 279, 763 - R'_HK from S and B-V

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
b_v float default None B-V colour of the star; enables log R'_HK (leave unset otherwise)
s_index_calibration list default None [slope, offset] mapping your instrumental S onto the Mount Wilson scale, once derived

Steps

# Algorithm Parameters
1 snr_der Signal-to-noise (DER_SNR) -
2 activity_index_caii_hk Ca II H and K S index alpha=2.3, b_v='${b_v}', s_index_calibration='${s_index_calibration}'

Run it

# profile.yaml holds your instrument values:
#   b_v: 0.0
#   s_index_calibration: []
spectro pipeline star_activity_caii_hk --input spectrum.fits --profile profile.yaml
spectro preset show star_activity_caii_hk      # variables and steps

from spectro_kernel import WorkContext
from spectro_kernel.pipeline import PipelineBuilder

variables = {"b_v": 0.0, "s_index_calibration": []}
pipeline = PipelineBuilder().from_preset("star_activity_caii_hk", 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": "star_activity_caii_hk",
    "variables": {
      "b_v": 0.0,
      "s_index_calibration": []
    }
  }
}