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Running MADHAT

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Pearl Sandick edited this page Oct 4, 2019 · 3 revisions

Running MADHAT

To run MADHAT with command line input, type the command:

./madhat [options]

where [options] are described below.

Run Options

There are three run options. For Input File Formatting and Output Options and Formatting, see the relevant sections below.

Run Option A) Specify the set of dwarfs to be analyzed, the confidence level (beta) for Nbound, and the dark matter model parameters. This option requires three arguments to run:

./madhat [dwarfset.dat] [beta] [model.in]
  • [dwarfset.dat] is the file containing the parameters for the dwarfs you'd like to analyze and must be located in the directory Input.
  • [beta] is a number between zero and one that specifies the confidence level (eg. 0.95 for 95% CL).
  • [model.in] is a file containing a list of dark matter masses and integrated photon spectra as described below and must be located in the directory Input. MADHAT will read [model.in] and calculate output for each line until it reaches the end of the [model.in] file.

Output prints to a file in the Output directory named [model_dwarfset_beta.out]. The data from [dwarfset.dat] is copied into the header of the output file, below which are the following columns: mass, integrated photon spectrum, beta, Nbound(beta), PhiPP, +dPhiPP, -dPhiPP.

Run Option B) Specify the set of dwarfs to be analyzed, the confidence level (β) for Nbound, and the mass and integrated photon spectrum for single dark matter model point:

./madhat [dwarfset.dat] [beta] [mass] [integrated spectrum]

This option is a duplicate of option A, but allows the user to quickly check a single model point. Output will print to the screen.

Run Option C) Specify the set of dwarfs to be analyzed and the confidence level (beta) for Nbound. This option requires two arguments to run:

./madhat [dwarfset.dat] [beta]
  • [dwarfset.dat] is the file containing the parameters for the dwarfs you'd like to analyze and must be located in the directory Input.
  • [beta] is a number between zero and one that specifies the confidence level (eg. 0.95 for 95% CL).

Output prints to the screen in the following format: beta, Nbound(beta), PhiPP, +dPhiPP, -dPhiPP.

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