Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To support these methods, we propose ten recommendations for bolstering a data-rich future in planetary science.Comment: 10 pages (expanded citations compared to 8 page submitted version for decadal survey), 3 figures, white paper submitted to the Planetary Science and Astrobiology Decadal Survey 2023-203

Planetary mass spectrometry for agnostic life detection in the Solar system

For the past fifty years of space exploration, mass spectrometry has provided unique chemical and physical insights on the characteristics of other planetary bodies in the Solar System. A variety of mass spectrometer types, including magnetic sector, quadrupole, time-of-flight, and ion trap, have and will continue to deepen our understanding of the formation and evolution of exploration targets like the surfaces and atmospheres of planets and their moons. An important impetus for the continuing exploration of Mars, Europa, Enceladus, Titan, and Venus involves assessing the habitability of solar system bodies and, ultimately, the search for life—a monumental effort that can be advanced by mass spectrometry. Modern flight-capable mass spectrometers, in combination with various sample processing, separation, and ionization techniques enable sensitive detection of chemical biosignatures. While our canonical knowledge of biosignatures is rooted in Terran-based examples, agnostic approaches in astrobiology can cast a wider net, to search for signs of life that may not be based on Terran-like biochemistry. Here, we delve into the search for extraterrestrial chemical and morphological biosignatures and examine several possible approaches to agnostic life detection using mass spectrometry. We discuss how future missions can help ensure that our search strategies are inclusive of unfamiliar life forms.https://www.frontiersin.org/articles/10.3389/fspas.2021.755100/ful

The ETNA mission concept: Assessing the habitability of an active ocean world

Enceladus is an icy world with potentially habitable conditions, as suggested by the coincident presence of a subsurface ocean, an active energy source due to water-rock interactions, and the basic chemical ingredients necessary for terrestrial life. Among all ocean worlds in our Solar System, Enceladus is the only active body that provides direct access to its ocean through the ongoing expulsion of subsurface material from erupting plumes. Here we present the Enceladus Touchdown aNalyzing Astrobiology (ETNA) mission, a concept designed during the 2019 Caltech Space Challenge. ETNA’s goals are to determine whether Enceladus provides habitable conditions and what (pre-) biotic signatures characterize Enceladus. ETNA would sample and analyze expelled plume materials at the South Polar Terrain (SPT) during plume fly-throughs and landed operations. An orbiter includes an ultraviolet imaging spectrometer, an optical camera, and radio science and a landed laboratory includes an ion microscope and mass spectrometer suite, temperature sensors, and an optical camera, plus three seismic geophones deployed during landing. The nominal mission timeline is 2 years in the Saturnian system and ∼1 year in Enceladus orbit with landed operations. The detailed exploration of Enceladus’ plumes and SPT would achieve broad and transformational Solar System science related to the building of habitable worlds and the presence of life elsewhere. The nature of such a mission is particularly timely and relevant given the recently released Origins, Worlds, and Life: A Decadal Strategy for Planetary Science and Astrobiology 2023–2032, which includes a priority recommendation for the dedicated exploration of Enceladus and its habitable potential

Exploratory data analysis (EDA) machine learning approaches for ocean world analog mass spectrometry

Many upcoming and proposed missions to ocean worlds such as Europa, Enceladus, and Titan aim to evaluate their habitability and the existence of potential life on these moons. These missions will suffer from communication challenges and technology limitations. We review and investigate the applicability of data science and unsupervised machine learning (ML) techniques on isotope ratio mass spectrometry data (IRMS) from volatile laboratory analogs of Europa and Enceladus seawaters as a case study for development of new strategies for icy ocean world missions. Our driving science goal is to determine whether the mass spectra of volatile gases could contain information about the composition of the seawater and potential biosignatures. We implement data science and ML techniques to investigate what inherent information the spectra contain and determine whether a data science pipeline could be designed to quickly analyze data from future ocean worlds missions. In this study, we focus on the exploratory data analysis (EDA) step in the analytics pipeline. This is a crucial unsupervised learning step that allows us to understand the data in depth before subsequent steps such as predictive/supervised learning. EDA identifies and characterizes recurring patterns, significant correlation structure, and helps determine which variables are redundant and which contribute to significant variation in the lower dimensional space. In addition, EDA helps to identify irregularities such as outliers that might be due to poor data quality. We compared dimensionality reduction methods Uniform Manifold Approximation and Projection (UMAP) and Principal Component Analysis (PCA) for transforming our data from a high-dimensional space to a lower dimension, and we compared clustering algorithms for identifying data-driven groups (“clusters”) in the ocean worlds analog IRMS data and mapping these clusters to experimental conditions such as seawater composition and CO2 concentration. Such data analysis and characterization efforts are the first steps toward the longer-term science autonomy goal where similar automated ML tools could be used onboard a spacecraft to prioritize data transmissions for bandwidth-limited outer Solar System missions

Concept development of control system for perspective unmanned aerial vehicles

Presented actual aspects of the development of the control system of unmanned aerial vehicles (UAVs) in the example of perspective. Because the current and future UAV oriented to implementation of a wide range of tasks, taking into account the use of several types of payload, in this paper discusses the general principles of construction of onboard control complex, in turn, a hardware implementation of the automatic control system has been implemented in the microcontroller Arduino platform and the Raspberry Pi. In addition, in the paper presents the most common and promising way to ensure the smooth and reliable communication of the command post with the UAV as well as to the ways of parry considered and abnormal situations

Concept development of control system for perspective unmanned aerial vehicles

Presented actual aspects of the development of the control system of unmanned aerial vehicles (UAVs) in the example of perspective. Because the current and future UAV oriented to implementation of a wide range of tasks, taking into account the use of several types of payload, in this paper discusses the general principles of construction of onboard control complex, in turn, a hardware implementation of the automatic control system has been implemented in the microcontroller Arduino platform and the Raspberry Pi. In addition, in the paper presents the most common and promising way to ensure the smooth and reliable communication of the command post with the UAV as well as to the ways of parry considered and abnormal situations

Detecting Molecules of Prebiotic Relevance in Titan Analog Materials in support of the Dragonfly Mass Spectrometer

International audienceTitan offers a window to an extremely complex and abundant organic chemistry, initiated in its high atmosphere by solar radiation and energetic particles from Saturn's magnetosphere, and which sediments and triggers chain reactions down to the surface. The final products are solid organic aerosols present in high concentration in the atmosphere and at the surface of Titan. Remote sensing and in situ analyses of the atmosphere, composed primarily of N2 and CH4, have indicated that nitrogen is incorporated in significant amount in both the gaseous and solid phases [1]. Laboratory experiments simulating Titan's atmospheric chemistry have yielded a wide variety of organic molecules based on C, N and H atoms [2], including pre-biotically important nitrogen-bearing molecules such as amines [3], nucleobases and possibly amino acids [4]. Once deposited at the surface, the solid organic materials may be exposed to geophysical conditions that promote chemical evolution beyond the atmospherically generated population. Notably, in certain regions organics may have been exposed to transient liquid water, such as in putative cryovolcanic regions or impact craters [5] One major goal of the Dragonfly mission, recently selected under NASA's New Frontiers Program, is to measure Titan surface materials found in sites that are representative of different environments such as dunes and icy surfaces. Chemical analyses of these different materials by the Dragonfly Mass Spectrometer (DraMS) will be performed with Laser Desorption/ionization Mass Spectrometry (LDMS) and Gas Chromatography Mass Spectrometry (GCMS). These two complementary analytical techniques combine to allow detection and identification of compounds with a wide range of mass and chemical functionalities. This dual approach is soon to be demonstrated in situ with the Mars Organic Molecule Analyzer (MOMA) experiment on the Rosalind Franklin martian rover [6]. However, as the nature of the samples and molecules to be analyzed significantly differs between Mars and Titan surface, the sample measurement approaches and the preparation techniques have to be optimized to the detection and identification of Titan's organic molecules. We present here the LDMS and GCMS techniques that will be used to analyze in situ materials collected at Titan's surface. We demonstrate the detection of chemical compounds of interest to prebiotic chemistry, with an emphasize on the amines chemical family, such molecules being of high interest for prebiotic chemistry given their ubiquity in biochemical systems and possible role in red-ox energy pathways. We also present analyses of laboratory analog materials that represent Titan's complex organics

Leveraging Open Science Machine Learning Challenges for Data Constrained Planetary Mission Instruments

International audienceWe set up two open-science machine learning (ML) challenges focusing on building models to automatically analyze massspectrometry (MS) data for Mars exploration. ML challenges provide an excellent way to engage a diverse set of experts withbenchmark training data, explore a wide range of ML and data science approaches, and identify promising models based onempirical results, as well as to get independent external analyses to compare to those of the internal team. These two challengeswere proof-of-concept projects to analyze the feasibility of combining data collected from different instruments in a singleML application. We selected mass spectrometry data from 1) commercial instruments and 2) the Sample Analysis at Mars(SAM, an instrument suite that includes a mass spectrometer subsystem onboard the Curiosity rover) testbed. These challenges,organized with DrivenData, gathered more than 1,150 unique participants from all over the world, and obtained more than 600solutions contributing powerful models to the analysis of rock and soil samples relevant to planetary science using various massspectrometry datasets. These two challenges demonstrated the suitability and value of multiple ML approaches to classifyingplanetary analog datasets from both commercial and flight-like instruments.We present the processes from the problem identification, challenge setups, and challenge results that gathered creative anddiverse solutions from worldwide participants, in some cases with no backgrounds in mass spectrometry. We also present thepotential and limitations of these solutions for ML application in future planetary missions. Our longer-term goal is to deploythese powerful methods onboard the spacecraft to autonomously guide space operations and reduce ground-in-the-loop reliance

DataSheet1_The ETNA mission concept: Assessing the habitability of an active ocean world.PDF

Enceladus is an icy world with potentially habitable conditions, as suggested by the coincident presence of a subsurface ocean, an active energy source due to water-rock interactions, and the basic chemical ingredients necessary for terrestrial life. Among all ocean worlds in our Solar System, Enceladus is the only active body that provides direct access to its ocean through the ongoing expulsion of subsurface material from erupting plumes. Here we present the Enceladus Touchdown aNalyzing Astrobiology (ETNA) mission, a concept designed during the 2019 Caltech Space Challenge. ETNA’s goals are to determine whether Enceladus provides habitable conditions and what (pre-) biotic signatures characterize Enceladus. ETNA would sample and analyze expelled plume materials at the South Polar Terrain (SPT) during plume fly-throughs and landed operations. An orbiter includes an ultraviolet imaging spectrometer, an optical camera, and radio science and a landed laboratory includes an ion microscope and mass spectrometer suite, temperature sensors, and an optical camera, plus three seismic geophones deployed during landing. The nominal mission timeline is 2 years in the Saturnian system and ∼1 year in Enceladus orbit with landed operations. The detailed exploration of Enceladus’ plumes and SPT would achieve broad and transformational Solar System science related to the building of habitable worlds and the presence of life elsewhere. The nature of such a mission is particularly timely and relevant given the recently released Origins, Worlds, and Life: A Decadal Strategy for Planetary Science and Astrobiology 2023–2032, which includes a priority recommendation for the dedicated exploration of Enceladus and its habitable potential.</p