A new technology currently named Firecat is being developed at Redmoon. We are exploring the use of natural resources in space to further our own exploration (of space). One example coming to us by NASA during a conference at NASA Ames a few years ago was the use of asteroids orbiting the sun, just beyond the reach of Mars as a naturally occurring resource. We don’t currently know what exists in the asteroid belt, but we know that it would be helpful to learn more about the belt itself. The combination of solar and interplanetary gravitational forces (i.e. gravitational tides) generate resonances and other beautiful dynamics.
One approach to learning more about these resonances would be to observe the motions of the asteroids. Currently we have several missions undertaking this scientific objective. However NASA and the other spacefaring countries could potentially be open to new opportunities for studying the asteroids.
Two relatively new ideas are the placement of a robotic lander with infrared observing capabilities on the far side of the moon, and the same undertaking but located on the martian surface.
Our goal at Redmoon Systems is to study both of these missions and to better understand the science that could be possible.
For the Firecat proposal/mission, we have shifted our focus from LIDAR to passive infrared sensing. An active sensor generates its own light, and a passive sensor just receives light. A quick calculation indicates that there is a large amount of light due to solar glinting available to the lunar sensor and module. Here is some background info on infrared light
Infrared light is radiated by a candle which we can “feel” with our hands as heat. Here is an image of a candle flame in the infrared:
Space debris around the Earth is exposed to direct sunlight, which contains infrared radiation. Some of this radiation is absorbed by dust and debris, and some of it is reflected. Our team has experience in design and implementation of infrared telescopes and sensors from our work in the aerospace industry.
From the infrared standpoint, it is very helpful to characterize the parts of the fire which are radiating differently. This way we know how to interpret the image generated by our detector. The picture above shows that the outer flame is much hotter than the inner flame, and the melted wax also generates a heat signature. Likewise, we would like to understand and model how different parts of space debris radiate in the infrared part of the spectrum. This is called “phenomenology”, and involves physics and electromagnetic.
This article is devoted to PyRadi, which is an infrared modeling toolkit written for Python. Python is a computer language similar to MATLAB which runs on most computer systems and is completely free.
PyRadi is useful because it simplifies the process of designing and evaulating infrared vision systems. Infrared vision systems are able to detect heat signatures of life on Earth, and in other places. Additionally they can also detect many naturally occuring phenomena such as bacteria in the ocean which help our planet support life.
In my experience, infrared systems can be used for remote sensing, which means that they are deployed in space and used to observe the Earth. NASA has a mission similar to this called VIIRS which monitors ocean algae populations and tracks the rate at which they bloom.
PyRadi is completely open ended and can be played with and modified by anyone with internet access. I have been doing this for years.
Here is a summary some of the things you can do with Pyradi.
WHen you want to design a system for seeing infrared radiation, whether on the Earth or moon or both, you need to determine the performance as you generate your design. This is because the performance helps you understand how well your design will work, efficiently.
PyRadi helps with this. If you give it some information about the source of infrared light (just a rough light curve, not too hard to find for most objects), and some other basic information about your system (i.e. telescope, etc) then PyRadi will show you its guess at the performance. This is measured by the amount of signal that your system will be able to receive from it’s target or source.
Here is some output from PyRadi.
The first graph shows several curves which can be thought of as components of a transfer function, as commonly used in control theory. For anyone who doesn’t know this already, a transfer function is represented as a product of several smaller functions. It is part of the solution to a differential equation, which is the basic mathematical model of a system that evolves with respect to time (i.e. dynamical system).
Here is an example:
In this figure, the box labeled Plant is the portion of the system which we are interested in studying or modeling. The “transfer function” is basically all of the interesting aspects of the physical system which we care about. In the case of Pyradi, it includes the following:
Source basic radiant properties
Source emisivity or tendancy to illuminate debris
In summary, the PyRadi enables you to do what is called a Sensitivity Analysis of your experiment.