Huntsman Dust

This project is being created to study Galactic cirrus. Galactic cirrus are clouds of Interstellar Matter (ISM) extending above and below the plane of the Milky Way. This project is being developed to utilise data from Macquarie Universities Huntsman Telescope, but can be adapted to other data.

Getting Started

To start using the Huntsman-Dust package, there are two easy steps.

1. Setup the huntsman-dust package
2. Running Code from your huntsman-dust package

Setup

Installing from Source

The project source is in a GitHub repository at https://github.com/lspitler/huntsman-dust. To install using git on the command line:

$ cd ~/Build  
$ git clone https://github.com/lspitler/huntsman-dust.git  
$ cd huntsman-dust  
$ pip install -r requirements.txt  
$ python setup.py install   

Running Code

• The functions in the Huntsman-Dust package are designed to be run from the terminal.
• Create a Symbolic Link to the ~/Build folder using the command
  ln -s ~/Build
• Scripts can now be run as follows
  python huntsman_dust/power_spectrum.py
• Use the help flag to view the arguments for each function
  python huntsman_dust/power_spectrum.py -h

Functions

This package aims to help you detect and mask sources in your image, and perform a power spectrum analysis on the masked data.

Detecting and Masking Sources

This program first aims to efficiently disentangle foreground ISM from background discrete sources. This program detects and masks sources at two levels.

1. A 2D background is determined by creating a grid of desired dimensions, sigma clipping sources within each box and iteratively determining background levels. By interpolating between these grids, a 2D background array is created. Discrete sources are identified based on two criteria:

   • Sources must be a fixed sigma above the background. By convention, a source is identified if it is 3.0 sigma above the threshold, but any other value of sigma is also acceptable.
   • A source must have a minimum number of interconnected pixels above the threshold for it to be considered a source.

2. If there is a galaxy or large source present in the image, this is separately masked. The centre of the galaxy is either determined using SESAME, for which an internet connection is required, or by supplying the Ra, Dec coordinates of the centre. The radius to be masked is supplied in arc-minutes. A circular mask is created.

Power Spectrum Analysis

Now that sources have been masked, we can begin a power spectrum analysis of the dusty data. A 2D FFT is performed of the data and the masked data. The 2D psd obtained is now azimuthally averaged to create a 1D psd, which represents the radial power spectrum of the masked data.

Fake Images

A set of simulated is generated. The fake image has gaussian sources, whose parameters can be adjusted based on the following arguments. The amplitude(flux) of the sources are distributed according to a power-law, with exponent gamma. The default value of gamma is -1.25, in accordance with the Schechter luminosity function. The standard deviations of the sources follow a 1/r^2 distribution.

A background with spatial fluctuations at various scales is created separately. This is achieved by first creating the desired 2D power spectrum, which is a radial power law in Fourier space. According to literature, the index of ISM power law distribution is -2.9. Taking the inverse FFT of this p_law array gives a background with the desired levels of spatial fluctuations in real space.

This background is added to the set of galaxies, to create an accurate simulated data set

Authors

• Aman Chokshi
• Lee Spitler