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 scott.davidoff@jpl..gov [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 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 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, , 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 process, computing the best fit for peaks using the Piquant all design to maintain familiarity. algorithm. This process relies on a physics-based model to com- pute elemental weight percents from spectral peaks [5], and disam- biguates them from diffraction, back-scatter and pile-up peaks, and background radiation. Once the elements are identified, users can look at summary statistics using the histogram tool (Figure 1c), which shows the quantified weight percentages of different elements in selected sample areas within the experiment. Scientists will then look to examine relationships between elemental abundance across the entire experiment area, and look to make associations of elemen- tal abundance with the morphological structures in the rock. The chord diagram (Figure 1d), the ternary diagram (Figure 1e), and the binary diagram (Figure 1f) support these investigations by showing the spatial covariance between quantified elements across the ex- periment. Stronger covariance suggests an underlying relationship that could point towards an underlying mineralogical cause. Figure 2: PIXLISE-C Interface 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 interface consists of four main components: Context across clusters. When initiated the Comparison Sandbox displays Image (Figure 2a), Comparison Sandbox (Figure 2b), Clustering the average element weights of all samples in the dataset on a (Figure 2c), and Parallel Coordinate Plots (Figure 2d). Below, we logarithmic or linear scale. The element symbols are displayed as describe each component in detail. columns at the top and users can hide less important elements to focus on the most relevant ones. The histogram can be sorted based 4.1 Context Image on the element abundance (using means, high to low or low to This 2D map is the main interaction touchpoint for the application. high) or the coefficient of variation (standard deviation divided by The visualization plots the collected data on the geographical con- the mean). The histogram tooltip displays more detailed statistical text image as dot per point. In the demo interface, there are 6400 information per element, including mean, standard deviation, min, data points arranged into a grid based on their spatial coordinates: max, Q1, Q3, and the median. This information is useful to help the however, in different datasets it’s possible that the number and user determine how the distribution of data looks. Min, max, Q1, arrangement of points could differ. Overlaying the datapoint over Q3 and median are especially important if the distribution is not a the real terrain view allows scientists to select and analyze data normal distribution. Additionally, variance in an element is shown in accordance with the context. There are three major actions the as an inner bar within each bar of the histogram, which informs users can take in this view. First, using the navigation icon, they whether selected data points demonstrate similar characteristics. can pan and zoom (in or out). Second, using the examining tool (eye We discussed showing variance on the bars with a line centered icon), they can hover over individual points to view related data around the mean, but due to the large dataset and the presence of in isolation, which is displayed in the Comparison Sandbox. In the the log toggle, we believe our approach is able to convey the same examining tool, they will also be able to view any user annotations information while being cleaner visually, especially in log scale. for that group, if they exist. Finally, the scientists can select and group the data points using the lasso tool and component drop- down menu. Clicking on “add item” creates a component group, and future selected data points will be displayed in that group’s color. Consequent lasso actions will add to the selection, while holding down shift will remove points from the selection. Detailed information about the groups in terms of element concentration is displayed in the Comparison Sandbox, so that the scientists can Figure 4: An example of a tooltip. iterate on grouping data into clusters based on similarity.

4.2 Comparison Sandbox As users create multiple groups using the selection and compo- nent creation tools in the Context Image view, the histograms in the Comparison Sandbox update to display multiple groups at once. Using histogram controls, users can lock and unlock a group before finalizing their selection. Additionally, they can annotate the group with insights. Once a group is annotated, hovering over the group in the Context Image view shows the annotations overlaid on top. Finally, when users want to examine how a single data point fits in a group, hovering over the data point in the Context Image view (a) In the starting state, the (b) After selecting a group of displays the element percentages of the individual data point across sandbox contains a summary blue points, the sandbox adds of all points a histogram. all histograms as overlaid ticks on bars.

(c) After selecting two more (d) Histogram data can be groups, the sandbox now has transformed using filtering Figure 5: Left: How annotations are assigned. Right: when three more histograms. and sorting tools. the examining tool view is selected, the context image dis- plays the assigned annotation and the sandbox shows the Figure 3: Showing one possible workflow for PIXLISE-C stats of the point selected overlaid on all the groups.

This view displays the element percentages in the selected data points as histograms. Instead of displaying one histogram at a time 4.3 Parallel Coordinates Plots as in the previous implementation, our tool can display up to 20 Histograms are useful in characterizing groups through element histograms for the comparison of potential mineral compositions abundance, but it can be difficult to distinguish fine grain differences PIXLISE-C: Data Analysis for Mineral Identification SpaceCHI ’21, May 14, 2021, The CHI Workshop on Human-Computer Interaction for Space Exploration among the data. To enable a more granular exploration, Parallel algorithm, we used the hovering feature in the Context Image Coordinates Plots (PCP), as seen in Figure 2d, displays percent tools for examining individual data points within clusters. Based values based on the minimum and maximum range. Therefore, on the histogram information in the Comparison Sandbox, we nuanced differences between groups can be seen per each element. were able to add or remove specific data points using the lasso tool. These groupings allow an efficient starting place for deciding 4.4 Clustering what regions to compare and explore within the dataset. Thus the researcher can use PIXLISE-C to not only compare groupings they had already decided, but can also work with the program to discover new possible groupings that provide insight when compared. 7 DISCUSSION AND FUTURE WORK Our work identifies information needs and requirements for min- (a) Hierarchi- (b) K-means (c) K-means (d) K-means eral analysis tools. We operationalize the identified requirements in cal With PCA With t-SNE No reduction our proposed tool and present interaction techniques for the com- parative analysis and clustering of microXRF spectroscopy data Figure 6: The results of different methods of clustering and in the context of the PIXL project. Our tool PIXLISE-C enabled a dimensionality reduction, all set to generate 5 clusters. No- semi-automatic data analysis workflow that enabled an augmented tably, all of them are able to identify the crater in the middle grouping of microXRF data based on element abundance. Using but have subtle variations in how they group the points. built-in statistical analyses, scientists can compare several clus- ters at once to fine tune and validate the grouping of the data. Scientists typically carry out the grouping of data points manu- Initial feedback from our stakeholders suggested that tools that sup- ally as they have extensive expertise in identifying geologic features port selection and comparison are likely to improve data analysis in images. Our tool uses various ML techniques for auto generat- workflows in the context of mineral exploration. Additional design ing clusters as starting points for a semi-automatic data analysis requirements include documentation and annotation support for workflow. The clustering tool provides three different algorithms: multi-user collaborative workflows. k-means clustering, hierarchical clustering, and minimum maxi- Our work reveals several directions for future research. For the mum. For K-means, scientists can choose to apply dimensionality overall data analysis workflow, blending human and machine in- reduction using PCA or t-SNE. The clustering tool aims to enable telligence raises major research questions. How can we combine scientists to try different clustering options and fine tune param- manually created groups with machine generated clusters to pro- eters for finding patterns within the data and for creating initial vide support scientists’ agency while removing tedious work? What groupings which they can manually edit afterwards. kind of interaction techniques (e.g. overlays) might be suited for rep- resenting human and machine generated groups? Should the system 5 TECHNICAL IMPLEMENTATION suggest potential clusters to validate human insights through mixed initiative interaction? These are some questions we would like to ex- We developed our application using React and deployed it using plore going forward. In addition, sharing annotated datasets present GitHub pages. Our code is open source [22]. While React was used an interesting direction for future research in terms of collabora- to develop the UI and coordinate the different components across tive and asynchronous workflows. Potential techniques include the application, D3.js [4], was used to create the visualizations. exporting screenshots, sharing workspaces, and sharing the dataset For the clustering implementation, we use a Python backend to be opened with a different program or tool. The stakeholders to dynamically generate cluster information based on the many expressed a need for a way to both present their findings in a visual different hyperparameters that our app allows.The client app and manner but also in a text-based format that could plug into other the Python backend communicate using Flask. The backend itself scientific software. We will also explore the database requirements is hosted in the cloud. Sklearn is used to perform all of our machine to allow saving and sharing. learning algorithms, including hierarchical clustering, t-SNE, PCA, Finally, our short term goals include the integration of PIXLISE- and k-means. Our application also enables hyperparameters such C into the current system used by NASA JPL. We will continue as linkage, variance percentage, perplexity, and number of clusters, working with our collaborators to collect feedback on our tool to be passed to the backend as well. through user testing, iterative development, and deployment. 6 RESULTS ACKNOWLEDGMENTS Our tool imports a sample dataset called “King’s Court,” which We wish to thank the PIXL science team for supporting this research. has samples from a terrain with a crater-like geological feature. The research was carried out, in part, by the Jet Propulsion Labora- Using the clustering tool, we were able to test various algorithms tory, California Institute of Technology, under a contract with the and hyperparameters for grouping the dataset into meaningful National Aeronautics and Space Administration (80NM0018D0004) subsets. 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