IRTF-TEXES data, March 5-6 2025

Description

Each folder contains observations recorded by TEXES (Texas Echelon Cross Echelle Spectrograph) on NASA's IRTF (Infrared Telescope Facility) on March 5 and 6, 2025. The slit of the spectrograph was scanned across Jupiter's mid-to-high northern latitudes with a spectrum recorded at each step thereby producing spectral maps. The goal of recording these observations was to detect the spectral features of propadiene (CH2CCH2), propene (C3H6) and propane (C3H8) at Jupiter's mid-to-high northern latitudes. Spectra were recorded in discrete settings centered at 587, 748, 843, 912 and 1248 cm-1. The 587 and 1248 cm-1 spectra capture the readily detectable features of the hydrogen S(1) quadrupole and methane emission. The 748, 843 and 912 cm-1 settings contain the spectral features of acetylene, ethane and ethylene, and the targeted spectral features of propadiene at 845.25 cm-1 and propene at 912.56 cm-1. Each .latlon.sav file represents spectra recorded by a single scan. The 'jup<setting>list' text files list all the filenames containing scans recorded in that setting. Further details are provided

Steps to reproduce

The .latlon.sav files can be opened and read using IDL or Python. IDL: IDL> restore, <savefile> This will produce arrays with the following names: wavenumber: 1-D wavenumber grid in cm^-1 latitude: 2-D planetocentric latitude map longitude: 2-D System III longitude map vel_map: 2-D velocity map capturing the relative velocity due to Jupiter's radial and rotation motion in km/s airm: 2-D Jovian airmass map. The inverse cosine of 1/airm produces the observer zenith emission angle. spec_cube: 3-D map containing the spectra recorded at each point in the map in units of erg/s/cm^2/steradian/cm^-1. hdr: FITS-style header containing information about the spectral scan. Python: python >>> import numpy as np python >>> from scipy.io import readsav python >>> data = readsav(<savefile>) np.array(data.wavenumber) np.array(data.latitude), np.array(data.longitude), np.array(data.vel_map), np.array(data.airm), np.array(data.spec_cube) would produce arrays containing the wavenumber, geometry information and spectra recorded in each scan - see list under IDL instructions for further details of each array. data.hdr would produce a FITS-style header containing further information about the spectral scan. Individual spectra need to be coadded spatially in order to increase the effective signal-to-noise ratio and then spectrally fit using radiative transfer code - see linked paper for further details.

Institutions

• Jet Propulsion Laboratory

  California, Pasadena

Categories

Funders

• National Aeronautics and Space Administration

  Government of the United States of America

  Washington

  Grant ID: 80HQTR24DA010
• University of Hawaii System

  Honolulu

Licence