Reduced CRIRES+ spectroscopy observations of GJ 3470

Reduced CRIRES+ spectroscopy observations of GJ 3470 from 2022-02-10 This records contains yet unpublished reduced data obtained with the CRIRES+ spectrograph at the Very Large Telescope. The time-series of observations was acquired covering a transit of the exoplanet GJ3470b. The raw data from the CRIRES+ instrument were reduced using the instrument pipeline and the reduction steps are identical as the step described in e.g Lesjak et al. 2024 "Retrieving wind properties from the ultra-hot dayside of WASP-189 b with CRIRES+". Acknowledgements If you make use of this data in your research, please cite this record with its DOI (10.5281/zenodo.15130190) and its reference, the bibtex reference is: @dataset{lavail_2025_15130190, author = {Lavail, Alexis and Chabrol, Estelle}, title = {Reduced CRIRES+ spectroscopy observations of GJ 3470 }, month = apr, year = 2025, publisher = {Zenodo}, doi = {10.5281/zenodo.15130190}, url = {https://doi.org/10.5281/zenodo.15130190},} 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 108.22PH.003". 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('GJ3470-220209.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_modelA', 'wave_modelB', 'spec', 'err', 'snr', 'airmass', 'bjd_tdb', 'berv', 'orders', 'spec_interpolated', 'tell', '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_versionnodpos : (46,)rawfilename : (46,)nodpair : (46,)wave : (19, 46, 2008)wave_modelA : (19, 2008)wave_modelB : (19, 2008)spec : (19, 46, 2008)err : (19, 46, 2008)snr : (19, 46)airmass : (46,)bjd_tdb : (46,)berv : (46,)orders : (19,)spec_interpolated : (19, 46, 2008)tell : (19, 2008)rawheaders : (46, 2484, 3)slitfunctionFWHM-det1 : (46, 8, 3)slitfunctionFWHM-det2 : (46, 7, 3)slitfunctionFWHM-det3 : (46, 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_modelA: refined and more precise wavelength solution for spectra in nodding position A, using molecfit fitting the telluric spectrum wave_modelB: refined and more precise wavelength solution for spectra in nodding position B, using molecfit fitting the telluric spectrum spec: reduced spectrum in ADU from the CRIRES+ pipeline. 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) spec_interpolated: the spectrum array (spec) interpolated on the wave_modelA wavelength solution tell: a telluric spectrum as output by molecfit. 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 32022年2月10日获取的GJ 3470精简版CRIRES+光谱观测数据集 本数据集包含尚未正式发表的、依托欧洲南方天文台（European Southern Observatory, ESO）甚大望远镜（Very Large Telescope, VLT）上的CRIRES+摄谱仪获取的精简后观测数据。本次观测为时间序列观测，覆盖系外行星GJ 3470 b的凌星过程。原始CRIRES+仪器数据已通过仪器自带数据管线完成精简处理，精简步骤与Lesjak等人2024年发表的论文《利用CRIRES+反演WASP-189 b超热昼侧的大气风场特性》中描述的步骤完全一致。 致谢 若您在研究中使用本数据集，请引用本记录的DOI（10.5281/zenodo.15130190）及相关参考文献，其BibTeX格式引用如下： @dataset{lavail_2025_15130190, author = {Lavail, Alexis and Chabrol, Estelle}, title = {Reduced CRIRES+ spectroscopy observations of GJ 3470}, month = apr, year = 2025, publisher = {Zenodo}, doi = {10.5281/zenodo.15130190}, url = {https://doi.org/10.5281/zenodo.15130190}, } 另请注意，欧洲南方天文台（ESO）要求致谢声明，本案例的致谢内容为："基于欧洲南方天文台拉西拉帕瑞纳天文台的望远镜观测数据，项目编号为108.22PH.003"。 本数据集采用知识共享署名4.0国际许可协议进行发布。 数据格式 数据存储于pickle文件中。读取pickle文件的操作指南可参阅Python官方Wiki：https://wiki.python.org/moin/UsingPickle，示例代码如下： import pickle data = pickle.load(open('GJ3470-220209.pickle','rb')) 数据集为字典格式，可通过以下命令查看所有键名： print(data.keys()) 具体键名如下： 'script_version', 'nodpos', 'rawfilename', 'nodpair', 'wave', 'wave_modelA', 'wave_modelB', 'spec', 'err', 'snr', 'airmass', 'bjd_tdb', 'berv', 'orders', 'spec_interpolated', 'tell', 'rawheaders', 'slitfunctionFWHM-det1', 'slitfunctionFWHM-det2', 'slitfunctionFWHM-det3' 其中标粗的键（wave、spec、err）分别对应精简后的数据：波长（以真空纳米为单位）、提取后的光谱（以ADU为单位）以及误差光谱（以ADU为单位）。其余键包含元数据与补充信息，详述如下。每个键对应一个NumPy数组（Numerical Python Array）。 数组的形状可通过三个维度参数表征： n_obs：数据集中的观测次数 n_pix = 2008：每个光谱分段的像素数 n_orders：光谱分段的数量（每个光谱阶被分配至三台探测器，形成三个分段） 可通过以下代码查看各键对应数组的形状： for key in data.keys(): try: print(key,':', data[key].shape) except: print(key) 运行结果如下： script_version : (46,) nodpos : (46,) rawfilename : (46,) nodpair : (46,) wave : (19, 46, 2008) wave_modelA : (19, 2008) wave_modelB : (19, 2008) spec : (19, 46, 2008) err : (19, 46, 2008) snr : (19, 46) airmass : (46,) bjd_tdb : (46,) berv : (46,) orders : (19,) spec_interpolated : (19, 46, 2008) tell : (19, 2008) rawheaders : (46, 2484, 3) slitfunctionFWHM-det1 : (46, 8, 3) slitfunctionFWHM-det2 : (46, 7, 3) slitfunctionFWHM-det3 : (46, 7, 3) 数据各字段说明 script_version: 用于生成本数据集的Python脚本版本号 nodpos: 字符串类型，取值为'A'或'B'，代表天文光谱观测中的摇头位置 rawfilename: 字符串类型，对应欧洲南方天文台存档中的原始科学观测文件文件名 nodpair: 字符串类型，格式为'pairNN'，其中NN为数据精简中使用的摇头对编号：科学文件以摇头A光谱与摇头B光谱组成的对进行精简处理 wave: 数组类型，包含由数据管线导出的光谱波长解，单位为真空纳米 wave_modelA: 基于molecfit拟合大气光谱得到的、针对摇头位置A的高精度精修波长解 wave_modelB: 基于molecfit拟合大气光谱得到的、针对摇头位置B的高精度精修波长解 spec: CRIRES+数据管线精简得到的光谱数据，单位为ADU（模拟数字单位） err: 对应spec的误差光谱，单位为ADU，可通过spec/err计算信噪比 snr: 每个分段/观测的中位数信噪比 airmass: 从原始文件头中提取的各次观测的大气质量，为每次曝光开始与结束时大气质量的平均值 bjd_tdb: 采用barycorrpy工具计算得到的、对应曝光中点的质心儒略日（以质心动力学时间为基准） berv: 采用barycorrpy工具计算得到的、对应曝光中点的质心径向速度修正量 orders: 字符串类型，格式为'D-OO'，其中D为探测器编号（1-3），OO为光谱阶编号（02-08） spec_interpolated: 基于wave_modelA波长解进行插值后的光谱数组 tell: molecfit输出的大气光谱 rawheaders: 原始科学观测文件的FITS（Flexible Image Transport System）头文件 slitfunctionFWHM-det1: 从探测器1的各分段精简文件头中提取的狭缝函数半高全宽（Full Width at Half Maximum, FWHM）信息 slitfunctionFWHM-det2: 探测器2的狭缝函数半高全宽信息，同上 slitfunctionFWHM-det3: 探测器3的狭缝函数半高全宽信息，同上