Being an astrophysicist we deal with huge amounts of raw data from the sky that is needed to be converted into meaningful realizations. In this project, a section of the sky was mapped showing line emission from the infrared dark clouds (a category of molecular clouds that appear dark in the infrared background) among the interstellar gas. The line emission is mainly restricted to N2H+ and N2D+ molecules. We determine the column density of both N2H+ and N2D+ in these clouds and the ratio of the column density of N2D+ to N2D+ gives us the Deuterium fraction which will be used to locate the early stage high mass star formation in that cloud.
Python version - 3.7.6
Packages used - aplpy, numpy, astropy, scipy, math, spectral_cube
Reference - https://academic.oup.com/mnras/article/458/2/1990/2589101
Masking - https://spectral-cube.readthedocs.io/en/latest/masking.html
- Converting the raw fits file into a 3D array.
- Slicing the data and imaging it.
- Reading the moment maps in 2D array.
- Loading the spectral cubes for visualization of flux vs velocity distribution (spectrum).
- Comparing the mean spectrum for both the molecules.
- Building contour maps for integrated intensity, column density and cloud temperature.
- Masking some values in the moment maps to show only line emission regions.
- Plotting and fitting column density, temperature and deuterium fraction.
- Calculating total column density.
After reading the fits file, I sliced the data and made a scatter plot for N2D+ vs N2H+.
I compared the velocity spectrum of both the molecules and increased the intensity of N2D+ to 5 times in order to match N2H+.
I created contour maps for integrated intensity for both the molecules, the column density and dust temperature.
I have plotted the deuterium fraction against the total column density and dust temperature and produced a linear fit for it.
