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Download.Php? Id=28088&Type=Abstract&Section=Congressbrowser [8] Eric Horvitz SpaceCHI ’21, May 14, 2021, The CHI Workshop on Human-Computer Interaction for Space Exploration Ye and Hermann and Yildirim and Bhat et all. PIXLISE-C: Data Analysis for Mineral Identification Connie Ye∗ Dominik Moritz Scott Davidoff Lukas Hermann∗ Carnegie Mellon University Jet Propulsion Laboratory Nur Yildirim∗ Pittsburgh, Pennsylvania, USA California Institute of Technology Shravya Bhat∗ [email protected] Pasadena, California, USA [email protected] [email protected] [email protected] [email protected] [email protected] Carnegie Mellon University Pittsburgh, Pennsylvania, USA ABSTRACT in rocks to identify micron-scale evidence of biological mediation – NASA JPL scientists working on the micro x-ray fluorescence (mi- in essence, fossils that strongly suggest biogenic causes [1, 3]. croXRF) spectroscopy data collected from Mars surface perform The search for life on other planets follows a similar process, data analysis to look for signs of past microbial life on Mars. Their today with scientific instruments mounted on spacecrafts. NASA’s data analysis workflow mainly involves identifying mineral com- Perseverance Rover carries multiple astrobiology instruments[6], in- pounds through the element abundance in spatially distributed cluding a micro x-ray fluorescence (microXRF) spectrometer, called data points. Working with the NASA JPL team, we identified pain the Planetary Instrument for X-ray Lithochemistry (PIXL)[2]. PIXL points and needs to further develop their existing data visualiza- will be used by scientists to examine the concentration and the tion and analysis tool. Specifically, the team desired improvements spatial distribution of elements. This data will be used to determine for the process of creating and interpreting mineral composition the mineral composition of the rocks and to look for evidence of groups. To address this problem, we developed an interactive tool fossil activity mediated by microbes [10]. that enables scientists to (1) cluster the data using either manual An initial data visualisation tool prototype, PIXLISE, is already lasso-tool selection or through various machine learning clustering in place to support scientists in this process [2, 18]. Our preliminary algorithms, and (2) compare the clusters and individual data points discussions with the project team, therefore, focused on identifying to make informed decisions about mineral compositions. Our pre- the tasks and information needs of the scientists that might not liminary tool supports a hybrid data analysis workflow where the be supported by the current app [7]. In PIXLISE [11], the data user can manually refine the machine-generated clusters. analysis tasks are focused around a map interface, as the collected spectroscopy data are spatially localized within a sample area and CCS CONCEPTS co-registered within the associated image[2]. Our observations of the science team conclude that the data analysis workflow has two • Applied computing ! Chemistry. major stages: (1) identifying peaks in spectra and labeling them KEYWORDS as elements whose abundance is then computed; and (2) creating and interpreting mineral composition groups of the underlying Data Analysis, PIXL, Mars 2020, D3, Data Visualization rock based on spatial covariance of previously computed elemental abundance. Through a collaborative problem-defining process [14], 1 INTRODUCTION we decided to focus on mineral identification, as the science team In collaboration with NASA Jet Propulsion Laboratory Human- guided us to understand that mineral identification is a problem Centered Design Group, we developed a tool to support the ex- that affects more of the science team, and whose technical support arXiv:2103.16060v1 [cs.HC] 30 Mar 2021 ploratory analysis of astrobiological data collected via Mars 2020 in PIXLISE was the least well developed. Perseverance rover. One objective of NASA scientists is to to search In this paper, we first discuss the existing application (PIXLISE) for evidence of past microbial life on other bodies in our solar and the workflow with a focus on information needs. Next, we system, with a current focus on Mars. Geologists on Earth have introduce the improved tool we propose (PIXLISE-C), and finally examined the fine scale geochemical and morphological structures we discuss the design implications for future tool development. ∗All authors contributed equally to this research. 2 RELATED WORK Permission to make digital or hard copies of part or all of this work for personal or Across decades, instruments studying Martian geology aboard classroom use is granted without fee provided that copies are not made or distributed spacecraft from Odyssey [15], to Pathfinder [12], and Mars Sci- for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. ence Laboratory [20] have been focusing on elemental composition For all other uses, contact the owner/author(s). data to describe the mineral species. These analyses have investi- SpaceCHI ’21, May 14, 2021, The CHI Workshop on Human-Computer Interaction for gated geochemical data to elaborate the bulk composition of specific Space Exploration © 2021 Copyright held by the owner/author(s). rocks. These interpretations often examine co-varying elemental maps to infer minerals and their abundances [9, 13, 19]. PIXLISE-C: Data Analysis for Mineral Identification SpaceCHI ’21, May 14, 2021, The CHI Workshop on Human-Computer Interaction for Space Exploration The PIXL instrument on board the Perseverance rover brings un- precedented resolution to this type of mineralogical investigation. Scans from PIXL may contain as many as 15,000 spatially resolved sample points, with samples on the scale of 100 microns [2]. Mineral analyses using these data will be based several orders of magnitude richer than those from previous instruments, opening many oppor- tunities to better support the process of scientific analysis of large numbers of spatially resolved spectroscopy data. In the field of data visualization, how large amounts of data canbe visualized is an ongoing exploration; in his 1996 paper, Shneiderman performs a taxonomy of different methods for visualization and transformation, such as filtering, zooming, and more [17]. Reacting to the emergence of artificial intelligence, Horvitz suggests that human-AI interfaces can be designed to support collaboration by Figure 1: PIXLISE Interface pairing automated services with direct manipulation [8]. While the PIXLISE tool supports the process of element assign- 3 BASELINE DATASET, PIXLISE APP AND ment and quantification, our interviews with the stakeholders, (in- THE DATA ANALYSIS WORKFLOW cluding the project lead UX Manager and a co-investigator research The PIXL data set is a collection of spatially localized spectroscopy scientist), revealed that there are several pain points and needs in data as the instrument on Perseverance rover passes over the sam- terms of selection, grouping, annotation, clustering, and compari- pling area [2]. The microXRF data is sensed by the instrument and son. First, our stakeholders shared that there is a need for supporting stored as 1024 ordinal channels, each, which after energy calibration, comparison of points or multiple clusters of samples, which is a cru- encodes a count of the energy of the x-rays sensed by the instru- cial step in accurate mineral identification. Second, they shared that ment’s 2 detectors at that particular channel (in kilo electron-volts). there is a need for doing multiple selections (individual or group) at The dataset is organized into a CSV file that contains information the same time in order to compare the element distributions across on points such as coordinates (X, Y, Z) and 4000 spectral channels. different regions. Third, the scientists explained an underlying de- The current data visualization tool facilitates the data analysis sire to have auto generated clusters with various algorithm options, workflow through multiple interaction techniques (see Figure 1for so that they can semi-automatically create groups of the mapped the analysis tab). The main interaction touchpoint is the context area, instead of starting from scratch.Based on these needs and image pane (Figure 1a) that displays the discrete points in the requirements, we developed PIXLISE-C. Our tool aims to address microXRF data. Using this map display allows scientists to visually these gaps through extending the existing data analysis workflow. isolate, select, and analyze discrete geological features within an experiment site [16]. As a first step in their analysis, scientists 4 PIXLISE-C evaluate the peaks in the returned spectra, and identify peaks that We present an interactive data visualization tool, PIXLISE-C, that correspond to different elemental signatures. The spectrum pane extends the current system in place. We built our tool as a stan- (Figure 1b) visualizes the energy levels as a function of wavelength, dalone application instead of integrated development, as the current across each sample [21], and is examined individually or in bulk. architecture has several authentication layers that makes the devel- NASA has shown the instrument to be able to detect over 20 opment process difficult. However, our tool builds on the current elements at 10 ppm [10]. A spectroscopist drives the element identi- application in terms of interface layout, interaction styles, and over- fication
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