INSPIRESat-1/DAXSS data release version 2.0 and new python analysis tools
The Miniature X-Ray Solar Spectrometer 3, or MinXSS-3, successfully launched on the INSPIRESat-1
small satellite on February 14, 2022 on the Indian Space Research Organization’s Polar Satellite Launch
Vehicle (PSLV-C52). Also known as the Dual Aperture X-ray Solar Spectrometer, or DAXSS, MinXSS-3 is
the third in the series of NASA-funded MinXSS instruments. DAXSS observes the solar soft X-ray (SXR)
spectral irradiance over an energy range of 0.4 keV to 12 keV (0.1 nm to 3 nm). DAXSS has improved
energy resolution of 0.05 keV at 1 keV, which is 3 times better than MinXSS-1, and has improved
responsivity that is 600 times better than MinXSS-1 (as measured at 4 keV). The Version 2.0 of the
DAXSS Level 1 data product of solar SXR spectral irradiance has been publicly released at:
https://lasp.colorado.edu/minxss/data.
In addition to the MinXSS and DAXSS data products and data descriptions on that website, there are two
new ways of accessing and analyzing DAXSS data using Python.
DAXSS Graphical User Interface (GUI) based utility
https://github.com/minxsscubesat/minxsscubesat/tree/master/daxss_library/DAXSS_GUI_Utility
This utility provides a simple Graphical User Interface (GUI) for plotting and fitting DAXSS data. The user
can select variables in the DAXSS Level-1 data product and then plot parameters using the GUI utility,
such as making time-series plots and energy spectrum overlay plots. The utility also contains functionality to fit a particular spectrum to standard models available in XSPEC (such as vvapec). After selecting a spectrum and desired energy range, the fitted parameters include corona plasma temperature, emission measure, and elemental abundance factors. The ReadMe page in the link shared above provides more instructions on installing dependencies and running this utility.
DAXSS Google Colab notebook
https://colab.research.google.com/drive/1ImlpPcPMvgYHMIHaS2bTSFWEd0RwhA7h?usp=sharing
This Jupyter notebook provides code snippets for accessing DAXSS Level-1 data, viewing variable
metadata, creating spectra plots, and generating Pulse Height Analyzer (.PHA) files for spectral fitting
using XSPEC (and PyXSPEC). These code snippets can be run directly in your browser, without the need
for installing Python on your local machine. The tutorial also contains a code snippet for fitting
DAXSS data to XSPEC models, that can be run on your local machine after installation of XSPEC and
PyXSPEC.
For more information on the MinXSS and DAXSS data products, please contact Tom Woods (Email: tom.woods@lasp.colorado.edu) and James Mason (Email: james.mason@jhuapl.edu).
For more information on the Python GUI Utility and the Google Colab notebook, please contact Anant Kumar T K (Email: anant.telikicherla@ualberta.ca).
