Planetary rovers can use onboard data analysis to adapt their measurement plan on the fly, improving the science value of data collected between commands from Earth. This paper describes the implementation of an adaptive sampling algorithm used by PIXL, the X-ray fluorescence spectrometer of the Mars 2020 Perseverance rover. PIXL is deployed using the rover arm to measure X-ray spectra of rocks with a scan density of several thousand points over an area of typically 5 x 7 mm. The adaptive sampling algorithm is programmed to recognize points of interest and to increase the signal-to-noise ratio at those locations by performing longer integrations. Two approaches are used to formulate the sampling rules based on past quantification data: 1) Expressions that isolate particular regions within a ternary compositional diagram, and 2) Machine learning rules that threshold for a high weight percent of particular compounds. The design of the rulesets are outlined and the performance of the algorithm is quantified using measurements from the surface of Mars. To our knowledge, PIXL's adaptive sampling represents the first autonomous decision-making based on real-time compositional analysis by a spacecraft on the surface of another planet.

The Mars 2020 rover, Perseverance , encountered a range of basaltic igneous rocks on the floor of Jezero crater, two of which are olivine cumulates, formed by accumulation of olivine crystals from basaltic magma. These olivine cumulates lie in a geomorphically distinct region, named Séítah, on the Jezero crater floor. To understand the origin of the olivine cumulates and their relationship with the adjacent basalts of the Máaz formation, we calculated the composition of the parent magma of one of the olivine cumulates, named Brac, based on chemical analyses and mineralogic interpretations from the Planetary Instrument for X‐ray Lithochemistry (PIXL) instrument. Acceptable Brac/Dourbes parent magmas are olivine tholeiite basalts with SiO 2 ∼ 45%, MgO ∼ 8%, FeO Tot ∼ 27%, Al 2 O 3 ∼ 6%, and total alkali oxides of ∼2.8% weight. These compositions are similar to one of the Máaz basalts, the rock Rimplas, which is stratigraphically close to Séítah, but chemically distinct from other Máaz basalts. Rimplas could (within uncertainty) be a sample of the Brac parent magma, but it is more likely that Rimplas and Brac had a common (or similar) parent magma. Geochemical similarities between Rimplas and the other Máaz basalts thus suggest that Brac (and other olivine‐rich rocks of Séítah) and the Máaz basalts could be geochemically related; they could have been cogenetic and possibly contemporaneous, or could have been derived (at different times) from similar or related mantle source(s).

The Planetary Instrument for X-ray Lithochemistry (PIXL) is a rasterable focused-beam X-ray fluorescence (XRF) spectrometer mounted on the arm of National Aeronautics and Space Administration's (NASA) Mars 2020 Perseverance rover. To ensure that PIXL would be capable of performing accurate in-flight compositional analysis of martian targets, in situ, an elemental calibration was performed pre-flight on the PIXL flight instrument in a simulated martian environment. The details of this calibration, and implications for measuring unknown materials on Mars are the subjects of this paper. The major goals of this calibration were both to align the spectrometer to perform accurate elemental analysis and, to derive a matrix of uncertainties that are applied to XRF measurements of all elements in unknown materials. A small set of pure element and pure compound targets and geologically relevant reference materials were measured with the flight hardware in a simulated martian environment. Elemental calibration and quantifications were carried out using PIXL's XRF quantification software (PIQUANT). Uncertainties generated were implemented into the PIQUANT software version employed by the PIXL's data visualization software (PIXLISE). We outline in this work, a list of factors that impact micro-XRF accuracy, the methodology and steps involved in the calibration, details on the fabrication of the uncertainty matrix, instructions on the use and interpretations of the uncertainties applied to unknowns and an assessment on the limitations and areas open to future improvement as part of subsequent calibration efforts.

A Correction to this paper has been published: https://doi.org/10.1007/s11214-021-00801-2

The Planetary Instrument for X-ray Lithochemistry (PIXL) is a micro-focus X-ray fluorescence spectrometer mounted on the robotic arm of NASA's Perseverance rover. PIXL will acquire high spatial resolution observations of rock and soil chemistry, rapidly analyzing the elemental chemistry of a target surface. In 10 seconds, PIXL can use its powerful 120 micrometer diameter X-ray beam to analyze a single, sand-sized grain with enough sensitivity to detect major and minor rock-forming elements, as well as many trace elements. Over a period of several hours, PIXL can autonomously scan an area of the rock surface and acquire a hyperspectral map comprised of several thousand individual measured points.

The Orbiting Carbon Observatory-3 (OCO-3) was launched on 04 May 2019 and provides a new perspective to the important task of studying atmospheric carbon dioxide (CO2) as well as solar-induced chlorophyll fluorescence (SIF), a bonus product, from space. The flight spare three-channel grating spectrometer instrument built for OCO-2 has been adapted for use on the International Space Station (ISS) as OCO-3 by modifying the entrance optics, using a new calibrator assembly, incorporating a two-axis pointing mirror assembly (PMA), and adding two context cameras. The ISS' recessing orbit allows measurements to be collected from dawn to dusk in the equatorial to northern & southern mid-latitude regions and the PMA enables a new snapshot area mapping mode where ~80 km x ~80 km areas can be examined in more detail. The OCO-3 payload underwent an extensive ground test and calibration program in a 3 m diameter thermal vacuum chamber. The chamber has a port/window that allowed optical ground support equipment, including a heliostat, to illuminate the instrument under operating environmental conditions. The payload's performance in space during the in-orbit checkout (IOC) period compares favorably with ground test results. Initial and not-yet-fully-calibrated retrieved estimates of the column-averaged dry air mole fraction of CO2 (XCO2) are reasonable when compared to ground-based measurements. SIF estimates show clear contrast between areas of high and low vegetation. There is high confidence that the three-year prime mission will deliver the data needed for science/research, data applications, and informed decision-making.

The current system for evaluating prostate cancer architecture is the Gleason grading system which divides the morphology of cancer into five distinct architectural patterns, labeled 1 to 5 in increasing levels of cancer aggressiveness, and generates a score by summing the labels of the two most dominant patterns. The Gleason score is currently the most powerful prognostic predictor of patient outcomes; however, it suffers from problems in reproducibility and consistency due to the high intra-observer and inter-observer variability amongst pathologists. In addition, the Gleason system lacks the granularity to address potentially prognostic architectural features beyond Gleason patterns. We evaluate prostate cancer for architectural subtypes using techniques from topological data analysis applied to prostate cancer glandular architecture. In this work we demonstrate the use of persistent homology to capture architectural features independently of Gleason patterns. Specifically, using persistent homology, we compute topological representations of purely graded prostate cancer histopathology images of Gleason patterns 3,4 and 5, and show that persistent homology is capable of clustering prostate cancer histology into architectural groups through a ranked persistence vector. Our results indicate the ability of persistent homology to cluster prostate cancer histopathology images into unique groups with dominant architectural patterns consistent with the continuum of Gleason patterns. In addition, of particular interest, is the sensitivity of persistent homology to identify specific sub-architectural groups within single Gleason patterns, suggesting that persistent homology could represent a robust quantification method for prostate cancer architecture with higher granularity than the existing semi-quantitative measures. The capability of these topological representations to segregate prostate cancer by architecture makes them an ideal candidate for use as inputs to future machine learning approaches with the intent of augmenting traditional approaches with topological features for improved diagnosis and prognosis.

In this study, we present an automated approach to classify prostate cancer (PCa) whole slide images (WSIs) as high or low cancer aggressiveness using features derived from persistent homology, a tool of topological data analysis (TDA). This extends previous work on the use of these features for representing the characteristics of prostate cancer architecture in region of interest (ROI) images, and demonstrates the value of features derived from persistent homology to predict cancer aggressiveness of WSIs on an ROI basis. We compute persistence on ROI images and summarize persistence as a persistence image. Using this summary we construct a random forest classifier to predict cancer aggressiveness. We demonstrate the potential of persistent homology to capture the architectural differences between low and high grade prostate cancers in a feature representation that lends itself well to machine learning approaches.

Diagnoses performed on the basis of histopathological evaluation depend on the premise that information derived from a small number of samples is valid for the entire tissue volume. By insufficiently sampling a biopsy volume the ability of pathologists to draw meaningful inferences from the sample is impeded. This work attempts to apply an information theoretic approach to biopsy sampling rates informed by variation in tissue morphology identified by persistent homology. By quantifying the diagnostic information present in a sample may be possible to prevent under sampling by the clinician by creating a "Nyquist limit" for histopathological sampling given the frequency of morphologically distinct regions in a single biopsy.

Prostate cancer comprises the second most common cancer in men. One of the most powerful and established prognostic indicators of adenocarcinoma of the prostate is the Gleason score, a subjective assessment of the pattern of tumor growth and extent of glandular differentiation in H&E stained histology slides. Despite being the most dominant prostate grading method in use, the Gleason score suffers from high variability between grading pathologists, and due to its 2D nature, fails to effectively capture potentially prognostic information contained in 3D glandular growth patterns. We have previously demonstrated that persistent homology, a subset of topological data analysis (TDA), is effective in generating a quantitative morphological descriptor capable of differentiating Gleason grade in 2D. By capturing glands as loops in 2D, and voids in 3D, persistent homology lends itself naturally to the assessment of 3D glandular growth patterns while maintaining a correspondence to their 2D analogue. Dual-view inverted selective plane illumination microscopy (diSPIM) with a fluorescent H&E analogue was leveraged for volumetric imaging of optically-cleared prostate biopsies. The two orthogonal views of the diSPIM system yielded isotropic resolution in all dimensions, facilitating reconstruction of tissue histology in 3D for quantitative morphological assessment by persistent homology. The use of a nuclei specific hematoxylin analog (DRAQ5), in addition to the isotropic resolution of the system, enabled accurate 3D nuclei segmentation, thereby facilitating application of persistent homology to the corresponding nuclei 3D point clouds. Through TDA a quantitative, reproducible descriptor for 3D prostate cancer morphology will be demonstrated.

The Chlorophyll Fluorescence Imaging Spectrometer (CFIS) is an airborne high resolution imaging spectrometer built at NASA's Jet Propulsion Laboratory (JPL) for evaluating solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2). OCO-2 is a NASA mission designed to measure atmospheric CO2 but one of the novel data products is SIF, retrieved using reductions in the optical depth of Fraunhofer lines in OCO-2's O-2 Aband, covering 757-775 nm at 0.042 nm spectral resolution. CFIS was specifically designed to retrieve SIF within the wavelength range of OCO-2, but extends further down to 737 nm, nearly maintaining the high spectral resolution of the OCO-2 instrument (0.07 vs. 0.042 nm). Here, we provide an overview of the instrument calibration and performance as well as the retrieval strategy based on non-linear weighted least-squares. To illustrate the retrieval performance using actual flight data, we focus on data acquired over agricultural fields in Mead, Nebraska from an unpressurized Twin Otter (DHC-6) aircraft at a flight altitude of 3000 m above ground level (AGL). Spectral residuals are consistent with expected detector noise, which enables us to compute realistic 1-alpha precision errors of 0.5-0.7 W/m(2)/sr/mu m for typical SIF retrievals, which can be reduced to < 0.2 W/m(2)/sr/mu m when individual data is gridded at 30 m spatial resolution. The 30 m resolution also enabled direct comparison with the Crop Data Layer from the National Agricultural Statistics Service as well as Landsat imagery (NDVI, EVI, T-sk(in)), taken just a day prior to the CFIS overflights. Results show consistently higher vegetation indices and SIF values over soy fields compared to corn, likely due to the respective phenological stage, which might already have affected chlorophyll content and canopy structure (August 15, 2016). While this work is intended to highlight the technical capabilities and performance of CFIS, the comparisons against Landsat and crop types provide insights into how CFIS can be used to study mechanisms related to photosynthesis at fine spatial scales, with the fidelity needed to obtain un-biased SIF retrievals void of atmospheric correction.

This paper provides an overview of technology development for the Terrestrial Planet Finder Interferometer (TPF-I). TPF-I is a mid-infrared space interferometer being designed with the capability of detecting Earth-like planets in the habitable zones around nearby stars.

This paper provides a survey of the state-of-the-art in coronagraph and starshade technologies and highlights areas where advances are needed to enable future NASA exoplanet missions. An analysis is provided of the remaining technology gaps and the relative priorities of technology investments leading to a mission that could follow JWST. This work is being conducted in support of NASAs Astrophysics Division and the NASA Exoplanet Exploration Program (ExEP), who are in the process of assessing options for future missions. ExEP has funded Science and Technology Definition Teams to study coronagraphs and starshade mission concepts having a lifecycle cost cap of less than $1B. This paper provides a technology gap analysis for these concepts.

In 2009 the Astrophysics Division at NASA Headquarters established the Strategic Astrophysics Technology (SAT) solicitation as a new technology maturation program to fill the needed gap for mid-Technology Readiness Level (TRL) levels (3≤ TRL <6). In three full proposal selection cycles since the inception of this program, more than 40 investigations have been selected, many meritorious milestones have been met and advances have been achieved. In this paper, we review the process of establishing technology priorities, the management of technology advancements and milestones, and the incipient success of some of these investigations in light of the need of future space missions.

We present and compare experimental results in high contrast imaging representing the state of the art in coronagraph and starshade technology. These experiments have been undertaken with the goal of demonstrating the capability of detecting Earth-like planets around nearby Sun-like stars. The contrast of an Earth seen in reflected light around a Sun-like star would be about 1.2 x 10(-10). Several of the current candidate technologies now yield raw contrasts of 1.0 x 10(-9) or better, and so should enable the detection of Earths, assuming a gain in sensitivity in post-processing of a factor of 10. We present results of coronagraph and starshade experiments conducted at visible and infrared wavelengths. Cross-sections of dark fields are directly compared as a function of field angle and bandwidth. The strength and differences of the techniques are compared.

We present the results of the fourth Optical/IR Interferometry Imaging Beauty Contest. The contest consists of blind imaging of test data sets derived from model sources and distributed in the OI-FITS format. The test data consists of spectral data sets on an object "observed" in the infrared with spectral resolution. There were 4 different algorithms competing this time: BSMEM the Bispectrum Maximum Entropy Method by Young, Baron & Buscher; RPR the Recursive Phase Reconstruction by Rengaswamy; SQUEEZE a Markov Chain Monte Carlo algorithm by Baron, Monnier & Kloppenborg; and, WISARD theWeak-phase Interferometric Sample Alternating Reconstruction Device by Vannier & Mugnier. The contest model image, the data delivered to the contestants and the rules are described as well as the results of the image reconstruction obtained by each method. These results are discussed as well as the strengths and limitations of each algorithm.

Small-angle coronagraphy is technically and scientifically appealing because it enables the use of smaller telescopes, allows covering wider wavelength ranges, and potentially increases the yield and completeness of circumstellar environment - exoplanets and disks - detection and characterization campaigns. However, opening up this new parameter space is challenging. Here we will review the four posts of high contrast imaging and their intricate interactions at very small angles (within the first 4 resolution elements from the star). The four posts are: choice of coronagraph, optimized wavefront control, observing strategy, and post-processing methods. After detailing each of the four foundations, we will present the lessons learned from the 10+ years of operations of zeroth and first-generation adaptive optics systems. We will then tentatively show how informative the current integration of second-generation adaptive optics system is, and which lessons can already be drawn from this fresh experience. Then, we will review the current state of the art, by presenting world record contrasts obtained in the framework of technological demonstrations for space-based exoplanet imaging and characterization mission concepts. Finally, we will conclude by emphasizing the importance of the cross-breeding between techniques developed for both ground-based and space-based projects, which is relevant for future high contrast imaging instruments and facilities in space or on the ground.