Designing a Python Module for the Calculation of Molecular Parameters and Production Rates in Comets
Abstract
Presently, there is a lack of a generalized toolbox for the analysis of cometary observations. The upcoming influx of data stemming from the development of new technology will, then, prove to be a challenge for researchers to keep up with. As a solution to this problem, there is a NASA funded project to build 'sbpy', an astropy affiliated and open source Python package for small body research. To further the development of sbpy, I have worked on various functionalities for the package. The first of these functionalities was an Astroquery module to allow the query of JPL's Molecular Spectroscopy catalog. This module provides the molecular constants needed for the production rate calculation. The second functionality is part of sbpy's spectroscopy module and it involves the calculation of molecular production rates following two specific models. This functionality was written with millimeter/sub-millimeter wavelength bands as the primary input, but is adaptable to other wavelengths. The first model I worked to recreate was a simplification of the local thermal equilibrium (LTE) Haser model, which does not include photodissociation, as described in an existing publication (Drahus 2009). This functionality has already been compared to results calculated in the peer-reviewed literature (Drahus 2012) and have shown a 0.4% error at most. The error is suspected to stem from the difference in computations of molecular parameters. Drahus has also used the CDMS catalog for some of these parameters, while we calculated them from theoretical formulas. I also worked to recreate the LTE Haser model including photodissociation rates at the comet's distance from the Sun. This model calculates the number of molecules observed for an arbitrary production rate and also calculates the number of molecules observed from the input data. The production rate is then computed through the ratio of these results. The comparison between this model and existing data (Wierzchos et al., 2018) has yielded a 2.5% error or less. In the future, more computationally intensive models for the production rate can and will be added to sbpy's repertoire.
- Publication:
-
American Astronomical Society Meeting Abstracts #233
- Pub Date:
- January 2019
- Bibcode:
- 2019AAS...23334604G