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Scattering And Absorption of Light in Planetary Regoliths

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Calibration of a Dust Scattering Instrument Using Tomographic Techniques and Its Application to a Dust Sensor

David Santalices^1,2, Mateo Martínez-García¹, Jesús Belmar¹

¹LIR-Infrared Laboratory, Department of Physics, Universidad Carlos III de Madrid, 28911 Leganés, Spain.

Sensors (Basel, Switzerland)

|June 10, 2023

Summary

A new method characterizes Martian dust by mapping its interaction volume using experimental data and tomography. This improves understanding of Martian climate and reduces simulation time for dust sensor instruments.

Keywords:

Martian dust angular weighting function nephelometer radon transform scattering of particles scattering sensor tomography

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Area of Science:

Planetary Science
Atmospheric Science
Optical Engineering

Background:

Understanding Martian dust is crucial for Mars climate studies.
Existing methods for dust characterization often rely on assumptions.
A Dust Sensor instrument uses scattering properties to determine dust parameters.

Purpose of the Study:

To present a novel methodology for calculating an instrumental function for a Martian Dust Sensor.
To enable solving the direct problem of predicting instrument signals based on particle distribution.
To experimentally map the dust sensor's interaction volume.

Main Methods:

Utilized experimental data from a Lambertian reflector introduced into the interaction volume.
Applied tomography techniques, specifically the inverse Radon transform.
Acquired a complete experimental mapping of the interaction volume, determining the Wf function.

Main Results:

Successfully developed and applied a novel methodology for instrumental function calculation.
The method experimentally maps the interaction volume without idealizations.
Demonstrated the method's application in a specific case study.

Conclusions:

The novel methodology accurately characterizes the Dust Sensor's interaction volume.
This approach enhances the understanding of Martian dust properties and climate.
The method reduces the need for extensive simulations and avoids prior assumptions.