DOI: 10.5281/zenodo.12663685
title: CRIRES+ reduced spectroscopic observations of WASP-189

description: Reduced CRIRES+ spectroscopy observations of WASP-189 from 2022-07-01

This records contains the reduced data as used in the publication of Lesjak et al. 2024 "Retrieving wind properties from the ultra-hot dayside of WASP-189 b with CRIRES+" in press with Astronomy & Astrophysics.

The raw data from the CRIRES+ instrument were reduced using the instrument pipeline and the reduction steps are described in the article.

Acknowledgements

If you make use of this data in your research, please cite the Lesjak et al. 2024 article and this record with its DOI (10.5281/zenodo.12663686) and its reference, the bibtex reference is:

@dataset{lavail_2024_12663686,
  author       = {Lavail, Alexis},
  title        = {{CRIRES+ reduced spectroscopic observations of 
                   WASP-189}},
  month        = jul,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {1.0},
  doi          = {10.5281/zenodo.12663686},
  url          = {https://doi.org/10.5281/zenodo.12663686}
}

 Note also that ESO requests an acknowledgement, which in this case would be: "Based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme ID 109.23HN.002".

This data is released under a Creative Commons Attribution 4.0 International license.

Format of the data

The data are contained in a pickle file. Instructions on how to read pickle files can be found on the python wiki at https://wiki.python.org/moin/UsingPickle:

import pickle
data = pickle.load(open('wasp189_220630.pickle', 'rb'))

The data consists of a dictionary, the keys can be explored with the following command:

print(data.keys())

The keys are the following:



script_version

nodpos

rawfilename

nodpair

wave

wave_model

spec

err

snr

airmass

bjd_tdb

berv

orders

rawheaders

slitfunctionFWHM-det1

slitfunctionFWHM-det2

slitfunctionFWHM-det3


The keys in boldface (wave, spec, err) contain the reduced data, respectively the wavelength (expressed in vacuum in nanometres), the extracted spectrum (in ADU), and the error spectrum (in ADU). The other keys contain metadata and supplementary information as explained below. Each key contains a numPy array. The shape of the arrays can be expressed using three sizes:



n_obs: the number of observations in the dataset

n_pix = 2008: the number of pixels in each segment

n_orders : the numbers of segment (each spectral order is split over three detectors creating three segments)


The size of each array can be investigated with e.g

for key in data.keys():
    try:
        print(key,':', data[key].shape)
    except:
        print(key)


which results in 

script_version
nodpos : (152,)
rawfilename : (152,)
nodpair : (152,)
wave : (19, 152, 2008)
wave_model : (19, 152, 2008)
spec : (19, 152, 2008)
err : (19, 152, 2008)
snr : (19, 152)
airmass : (152,)
bjd_tdb : (152,)
berv : (152,)
orders : (19,)
rawheaders : (152, 2484, 3)
slitfunctionFWHM-det1 : (152, 8, 3)
slitfunctionFWHM-det2 : (152, 7, 3)
slitfunctionFWHM-det3 : (152, 7, 3)


What's in the data?



script_version: version number of the python script used to produce the data

nodpos: string, ('A' or 'B') the nodding position of the observation

rawfilename: string, the filename of the raw science file from the ESO archive 

nodpair: string, ('pairNN') where NN is the number of nodding pair used in the data reduction: science files are reduced in pair with one nodding 'A' spectrum and one 'B'

wave: array containing the pipeline-derived wavelength solution for the spectrum in nanometers in vacuum

wave_model: refined and more precise wavelength solution using molecfit fitting the telluric spectrum as explained in Sect. 2.1 of the paper

spec: reduced spectrum in ADU from the CRIRES+ pipeline. The spectra have been divided by the blaze spectrum (extracted spectrum from the flat-field lamp) to rectify the shape of the continuum and simplify spectrum, normalization

err: the error spectrum in ADU corresponding to the spec (signal to noise ration can be computed using spec/err)

snr: median signal to noise ratio for each segment/observation

airmass: airmass for each observation taken from the raw file header (mean of airmass at start and end of each exposure)

bjd_tdb: bjd_tdb (barycentric julian date expressed in temps dynamique barycentrique) time at the middle of the exposure computed with barycorrpy (https://github.com/shbhuk/barycorrpy)

berv: berv correction at middle of exposure computed with barycorrpy

orders: string (D-OO) identifying the segment where D is the detector number (1-3) and OO is the order number (02-08)

rawheaders: the fits header from the raw science files

slitfunctionFWHM-det1: contains information on the FWHM of the slit function taken from the reduced file headers for the orders of detector 1

slitfunctionFWHM-det2: same for detector 2

slitfunctionFWHM-det3: same for detector 3