Modeling the Activity of Long Period Comets Using Bayesian Statistics

Long period comets (LPC) are the least characterized bodies in the solar system in terms of colors and activity, yet they have the most pristine volatiles, organics, and refractory materials and preserve the chemical fingerprints of the early proto-planetary disk. Thus it is critical to study this population to investigate how the solar system formed. We have a large observing program set up at telescopes CFHT and Gemini N/S that we use to track over 50 LPCs. We use this data to construct each comet’s heliocentric light curve (brightness vs. orbit) and we constrain each comet’s ice abundance/species, nucleus size, and grain size using an ice sublimation model that computes the type and active fraction of gas sublimating from an icy surface exposed to solar heating. We are currently adapting this model to run with the emcee Markov Chain Monte Carlo code. Augmenting this sublimation model to include Bayesian statistics is necessary to process such a large sample of targets in a statistically robust and efficient way, and this increases the consistency of our analysis across the data set. We present our preliminary results using this method for a sub-sample of our comets.