Guide to the X-ray Light Curve Generator, XGEN.
XGEN was written by Abygail R. Waggoner(1*) and L. Ilsedore Cleeves(1,2)
(1) University of Virginia, Department of Chemistry (2) University of Virginia, Department of Astronomy
This model is published in Waggoner & Cleeves (2022), and any use of XGEN should cite this work.
Purpose of XGEN:
XGEN is capable of producing a light curve based on the power law distribution
dN/dE = beta * logE ** -alpha
where N is the number of flares that over the energy range dE, E = the total energy of the flare beta = a normalization constant dependent on average flare frequency logE = the total integrated energy of the flaring event alpha = the power law index The script xray_generator.py and Waggoner & Cleeves (2022) describe how the model produces the light curve in further detail.
Included files
XGEN itself is a single python script, xray_generator.py, but this directory includes two additional scripts, an observed energy distribution for TTauri stars, and an schematic of the model. All python scripts are executable in Python 3, and last updated for Python 3.7.11.
Included with model:
- xray_generator.py This script generates the light curves, as described above and in the script.
- plot_curve.py This script plots individual light curves simulated by xray_generator.py. Modeled flare peaks can also be plotted.
- run_stats.py This script determines the observed energy distribution and observable flare frequency of the generated light curve(s). This script is optimized for comparing the simulated light curve to observed flare statistics.
- wolk05_edistdata.csv This csv file contains the observed energy distribution of solar mass TTauri stars from Wolk et al. (2005). This csv file is read into run_stats.py to compare simulated and observed flare statistics.
- xgen_visualized.png This image demonstrates how xray_generator.py creates the light curve and how run_stats.py determines the energy distribution of observable flares.
Additional Notes:
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The default input parameters used for XGEN are found to yeild the best fit to the energy distribution and flare frequency observed for a TTauri star as discussed in Waggoner & Cleeves (2022)
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Flares of different energy values can occur simultaneously
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XGEN is currently written where all flares have a uniform rise and decay time, but is written such that a non-uniform rise and decay time can be introduced
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XGEN distinguishes between
observable' andmodeled' flares, as defined below: modeled: any and all flares modeled. This includes any nano or microflares that can occur within the set energy range observable: these are only flares that would be considered distinguishable if the flare were observed. The user defines the minimum flare energy considered observable, and any flares with Etot < Emin (observed) are considered non-observable. Additionally: only flares with a distinguishable flare peak in the integrated light curve are considered observable. The final flare frequency and energy distribution takes overlap of modeled flares into account when determining observable flares.
See run_stats.py and Waggoner & Cleeves (2022) for further details. -
xray_generator considers both a modeled (L_characteristics) and an observed characteristic luminosity (Lchar_obs). This distinction is necessary, because micro and nano-flaring events can raise the modeled characterstic luminosity. If you find that XGEN is yielding a higher baseline luminosity than desired, (i.e. deltaLXR always > 1), then decrease Lcharacteristic.
Acknowledgements
A.R.W. would like to thank Dana Anderson, Rachel Gross, and Korash Assani for testing XGEN.
end of readme.txt