This week, PhD student Casey Moore describes what he has been up to in the lab (in tandem with his MSL work!) Now that he has submitted his second MSL paper and is completing his PhD studies, his focus has moved closer to home to help explain some of his measurements. (By the way, don't fault Casey for that groaner of a title - I deserve all the blame)
By Casey Moore
I do have one remaining project that has been consuming most of my time as of late. For the last four years, intermittently, between MSL work, teaching appointments, and conference preparations, myself and a long list of volunteers and summer interns have been collecting transmission spectroscopy data from an array of Martian analog regoliths.
Simplified: we have a set up that allows us to irradiate a sample of packed analog regolith from below with a high powered arc-lamp. Above the sample on a free-floating rotating breadboard is a telescope connected to an optical fibre running to a series of two spectrometers. So, light hits the sample, a large proportion of that is reflected or absorbed by the regolith, but a measurable amount is transmitted and scattered in the hemisphere above the sample. We, in a sense, record how much light is being transmitted and scattered in that hemisphere.
I have presented on this work before in a combination of oral and poster presentations at conferences, but at the time the mathematical model was still in its infancy (at least from my point of view). I knew what the end goal of this project was, but was not as familiar with the process to derive the quantities I am interested in. What was reported, however, had some very interesting implications for the survivability of microorganisms within these analog regoliths. All the samples showed an impressive amount of UV-quenching. Ultraviolet radiation is no joking matter, in addition to being responsible for some nasty sun burns in the summer time, it causes damage to DNA, which in humans can turn into cancer down the line. For microbial life, UV radiation essentially kills on contact (e.g. UV sterilization).
So, the samples blocked harmful incoming ultraviolet (UV) radiation and transmitted the beneficial visible spectrum radiation. This suggests that under certain circumstances, at a certain depth within the regolith, microorganisms can have access the visible spectrum (e.g. for energy production, like photosynthesis) while still being shielded from harmful UV radiation. This was a neat little take away, but I’m more interested in being able to model the Martian regolith in a numerical way to incorporate it into radiative transfer simulations to better understand radiative transfer at the surface/atmosphere boundary.
This means finding a way to use all the data that we have collected to produce a cohesive set of parameters that a describe the material. One such set of parameters, known as the Hapke parameters, have been developed into a beautiful theoretical framework spanning several decades of observations, experiments, and mathematical rigor by Dr. Bruce Hapke and colleagues. Over the last month or so I have become extremely familiar with Dr. Hapke’s papers and books attempting to apply his equations and knowledge to my experimental set up.
While there are still several small details and potentially a huge amount of computational time left before this work is ready to be published, I am happy with some results I have seen. To test if the mathematical model I have been working with is decent, I attempted to fit the phase function of a Thor Labs 220 grit ground glass diffuser. The phase function represents the amount of radiation that gets scattered in all directions. The phase function is typically expressed as a one-term or two-term Henyey-Greenstein function, shown below. Essentially, we are trying to find the parameters: g, b and c given our data.
For the ground glass diffuser, the model fits the one and two term Henyey-Greenstein as shown below: the two term solution has two lobes, depicting the forward and backscattering nature of the optical element:
Fitting these parameters to my dataset has proven a bit more tricky as several solutions exist. I am in the process of creating an optimization code that finds the best fit solution, while keeping computational time down. I am happy with the progress so far and while programming can sometimes cause some hair-pulling moments, finding a solution is oh so gratifying.