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All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

X-ray emission from supernova remnants MSH 14 - 63 and RCW 103

Electron-ion collisions, heavy element enrichment of emissions from reverse-shocked ejecta, abundances in the interstellar medium (ISM), the correspondence between the projected and measured age, ISM particle density, and distance, and classification factors for Type I or II supernovae are examined using a nonequilibrium ionization (NEI) model. The NEI model accounts for the absence of collisional ionization equilibrium and possibly thermal equilibrium between electrons and ions behind the shock fronts, and describes the emission spectra from ejecta and the shocked ISM. When applied to data from the SNR MSH 14 - 63 a non-Coulomb, ion-electron interaction is obtained that is consistent with a shocked ISM. For the SNR RCW 103, however, the processes behind the shock front are not discernable, although the elemental abundances will have solar values if the interactions are non-Coulomb. The age of RCW 103 must be approximated from physical data, as Chinese astronomers would not have been able to see the event from their position on earth two millenia ago.

Nugent, J. J.↗
NASA NTRS2018

Igneous Rocks Formed by Hypervelocity Impact

Igneous rocks are the primary building blocks of planetary crusts. Most igneous rocks originate via decompression melting and/or wet melting of protolith lithologies within planetary interiors and their classification and compositional, petrographic, and textural characteristics, arewell-studied. As our exploration of the Solar System continues, so too does the inventory of intrusive and extrusive igneous rocks, settings, and processes. The results of planetary exploration have also clearly demonstrated that impact cratering is a ubiquitous geological process that has affected, andwill continue to affect, all planetary objectswith a solid surface,whether that be rock or ice. It is nowrecognized that the production of igneous rocks is a fundamental outcome of hypervelocity impact. The goal of this review is to provide an up-to-date synthesis of our knowledge and understanding of igneous rocks formed by hypervelocity impact. Following a brief overview of the basics of the impact process, we describe howand whymelts are generated during impact events and howimpactmelting differs fromendogenic igneous processes.While the processmay differ,we showthat the products of hypervelocity impact can share close similarities with volcanic and shallow intrusive igneous rocks of endogenic origin. Such impact melt rocks, as they are termed, can display lobate margins and cooling cracks, columnar joints and at the hand specimen andmicroscopic scale, such rocks can displaymineral textures that are typical of volcanic rocks, such as quench crystallites, ophitic, porphyritic, aswell as features such as vesicles, flowtextures, and so on.Historically, these similarities led to themisidentification of some igneous rocks now known to be impact melt rocks as being of endogenic origin. This raises the question as to how to distinguish between an impact versus an endogenic origin for igneous-like rocks on other planetary bodies where fieldwork and sample analysis may not be possible and all that may be available is remote sensing data.While the interpretation of some impact melt rocks may be relatively straightforward (e.g., for clast-rich varieties and those with clear projectile contamination) we conclude that distinguishing between impact and endogenic igneous rocks is a non-trivial task that ultimately may require sample investigation and analysis to be conducted. Caution is, therefore, urged in the interpretation of igneous rocks on planetary surfaces.

Osinski, Gordon R.↗
DOE OSTI2020

Time Series Classification for Locating Forced Oscillation Sources

Here, this article presents a machine learning based time-series classification method for using synchrophasor measurements to locate the source of forced oscillation (FO) for fast disturbance removal. First, multivariate time series (MTS) matrices are constructed by the most informative measurements selected by sequential feature selection from each power plant. Then, the Mahalanobis matrix is trained such that the Mahalanobis distance between the MTSs from the same class (i.e., with the same FO source location) are minimized and from different classes (i.e., with different FO source locations) are maximized. This allows MTSs to be classified by classifiers with class membership corresponding to the location of each FO source. To meet the runtime requirements of online matching, class templates are constructed to reduce data size and improve matching efficiency. To account for uncertainty in identifying the exact beginning of an FO event, dynamic time warping is used to align the out-of-sync MTSs. IEEE 39bus and WECC 179bus systems are used for algorithm development and validation. Simulation results demonstrate that the algorithm meets online operation runtime requirement with high accuracy using misaligned data sets.

42 ENGINEERING↗
NASA NTRS2009

Small Negative Cloud-to-Ground Lightning Reports at the KSC-ER

'1he NASA Kennedy Space Center (KSC) and Air Force Eastern Range (ER) use data from two cloud-to-ground (CG) lightning detection networks, the CGLSS and the NLDN, and a volumetric lightning mapping array, LDAR, to monitor and characterize lightning that is potentially hazardous to ground or launch operations. Data obtained from these systems during June-August 2006 have been examined to check the classification of small, negative CGLSS reports that have an estimated peak current, [I(sup p)] less than 7 kA, and to determine the smallest values of I(sup p), that are produced by first strokes, by subsequent strokes that create a new ground contact (NGC), and by subsequent strokes that remain in a pre-existing channel (PEC). The results show that within 20 km of the KSC-ER, 21% of the low-amplitude negative CGLSS reports were produced by first strokes, with a minimum I(sup p) of-2.9 kA; 31% were by NGCs, with a minimum I(sup p) of-2.0 kA; and 14% were by PECs, with a minimum I(sup p) of -2.2 kA. The remaining 34% were produced by cloud pulses or lightning events that we were not able to classify.

Wilson, Jennifer G.↗
DOE OSTI2025

Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Graph theory inspired anomaly detection at the LHC

Designing model-independent anomaly detection algorithms for analyzing LHC data remains a central challenge in the search for new physics, due to the high dimensionality of collider events. In this work, we develop a graph autoencoder as an unsupervised, model-agnostic tool for anomaly detection, using the LHC Olympics dataset as a benchmark. By representing jet constituents as a graph, we introduce a method to systematically control the information available to the model through sparse graph constructions that serve as physically motivated inductive biases. Specifically, (1) we construct graph autoencoders based on locally rigid Laman graphs and globally rigid unique graphs, and (2) we explore the clustering of jet constituents into subjets to interpolate between high- and low-level input representations. We obtain the best performance, measured in terms of the Significance Improvement Characteristic curve for an intermediate level of subjet clustering and certain sparse unique graph constructions. We further investigate the role of graph connectivity in jet classification tasks. Our results demonstrate the potential of leveraging graph-theoretic insights to refine and increase the interpretability of machine learning tools for collider experiments.

Automation↗
DOE OSTI2020

Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning

Abstract Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal‐to‐noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake‐like signals originating from nontectonic sources.

Johnson, Christopher W.↗
DOE OSTI2023

What Machine Learning Can and Cannot Do for Inertial Confinement Fusion

Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals and metrics. The methodologies are effective in image analysis, classification, and systems without long chains of logic. Recently, machine-learning methodologies have been widely applied to inertial confinement fusion (ICF) capsules and the design optimization of OMEGA (Omega Laser Facility) capsule implosion and NIF (National Ignition Facility) ignition capsules, leading to significant progress. As machine learning is being increasingly applied, concerns arise regarding its capabilities and limitations in the context of ICF. ICF is a complicated physical system that relies on physics knowledge and human judgment to guide machine learning. Additionally, the experimental database for ICF ignition is not large enough to provide credible training data. Most researchers in the field of ICF use simulations, or a mix of simulations and experimental results, instead of real data to train machine learning models and related tools. They then use the trained learning model to predict future events. This methodology can be successful, subject to a careful choice of data and simulations. However, because of the extreme sensitivity of the neutron yield to the input implosion parameters, physics-guided machine learning for ICF is extremely important and necessary, especially when the database is small, the uncertain-domain knowledge is large, and the physical capabilities of the learning models are still being developed. In this work, we identify problems in ICF that are suitable for machine learning and circumstances where machine learning is less likely to be successful. This study investigates the applications of machine learning and highlights fundamental research challenges and directions associated with machine learning in ICF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Phylogenomics and the first higher taxonomy of Placozoa, an ancient and enigmatic animal phylum

Placozoa is an ancient phylum of extraordinarily unusual animals: miniscule, ameboid creatures that lack most fundamental animal features. Despite high genetic diversity, only recently have the second and third species been named. While prior genomic studies suffer from incomplete placozoan taxon sampling, we more than double the count with protein sequences from seven key genomes and produce the first nuclear phylogenomic reconstruction of all major placozoan lineages. This leads us to the first complete Linnaean taxonomic classification of Placozoa, over a century after its discovery: This may be the only time in the 21st century when an entire higher taxonomy for a whole animal phylum is formalized. Our classification establishes 2 new classes, 4 new orders, 3 new families, 1 new genus, and 1 new species, namely classes Polyplacotomia and Uniplacotomia; orders Polyplacotomea, Trichoplacea, Cladhexea, and Hoilungea; families Polyplacotomidae, Cladtertiidae, and Hoilungidae; and genus Cladtertia with species Cladtertia collaboinventa, nov. Our likelihood and gene content tree topologies refine the relationships determined in previous studies. Adding morphological data into our phylogenomic matrices suggests sponges (Porifera) as the sister to other animals, indicating that modest data addition shifts this node away from comb jellies (Ctenophora). Furthermore, by adding the first genomic protein data of the exceptionally distinct and branching Polyplacotoma mediterranea , we solidify its position as sister to all other placozoans; a divergence we estimate to be over 400 million years old. Yet even this deep split sits on a long branch to other animals, suggesting a bottleneck event followed by diversification. Ancestral state reconstructions indicate large shifts in gene content within Placozoa, with Hoilungia hongkongensis and its closest relatives having the most unique genetics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

What Are Space Exposure Histories Telling Us about CM Carbonaceous Chondrites?

Chondrites are chemically primitive and carbonaceous (C) chondrites are potentially the most primitive among them because they mostly escaped thermal metamor-phism that affected the other chondrite groups and ratios of their major, non-volatile and most of the volatile elements are similar to those of the Sun. Therefore, C chondrites are ex-pected to retain a good record of the origin and early history of the solar system. Carbonaceous chondrites are chemically differentiated from other chondrites by their high Mg/Si ratios and refractory elements, and have experienced various degrees of aqueous alteration. They are subdivided into eight subgroups (CI, CM, CO, CV, CK, CR, CB and CH) based on major element and oxygen isotopic ratios. Their elemental ratios spread over a wide range though those of ordinary and enstatite chondrites are relatively uniform. It is critical to know how many sepa-rate bodies are represented by the C chondrites. In this study, CM chondrites, the most abundant carbona-ceous chondrites, are examined. They are water-rich, chon-drule- and CAI-bearing meteorites and most of them are brec-cias. High-temperature components such as chondrules, iso-lated olivine and CAIs in CMs are frequently altered and some of them are replaced by clay minerals and surrounded by sul-fides whose Fe was derived from mafic silicates. On the basis of degrees of aqueous alteration, CMs have been classified into subtypes from 1 to 2, although Rubin et al. [1] assigned subtype 1 to subtype 2 and subtype 2 to subtype 2.6 using various petrologic properties. The classification is based on petrographic and mineralogic properties. For example, though tochilinite (2[(Fe, Mg, Cu, Ni[])S] 1.57-1.85 [(Mg, Fe, Ni, Al, Ca)(HH)2]) clumps are produced during aqueous alteration, they disappear and sulfide appears with increasing degrees of aqueous alteration. Cosmic-ray exposure (CRE) age measurements of CM chondrites reveal an unusual feature. Though CRE ages of other chondrite groups range from several Myr to tens of Myr, CMs exposure ages are not longer than 7 Myr with one-third of the CM having less than 1 Myr CRE age. For those CM chondrites that have CRE ages <1 Myr, there are two discern-able CRE peaks. Because a CRE age reflects how long a me-teorite is present as a separate body in space, the peaks pre-sumably represent collisional events on the parent body (ies) [2]. In this study we defined 4 distinct CRE age groups of CMs and systematically characterized the petrography in each of the 4 CRE age groups to determine whether the groups have significant petrographic differences, with such differences probably reflecting different parent body (asteroid) geological processing, or multiple original bodies.

Takenouchi, A.↗
DOE OSTI2022

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

Abstract Fault detection and classification in photovoltaic (PV) systems through real‐time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend‐based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change‐point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Flood Detection with Synthetic Aperture Radar: A Case Study of Houston, Texas Following Hurricane Harvey (2017) using C- and X-Band Observations

Hurricane Harvey produced record-breaking rainfall of up to 60 inches resulting in extensive flooding in Houston, Texas, in late August and early September of 2017. The slow forward motion of the storm following landfall left much of the area unobservable to optical remote sensing instruments for several days due to cloud cover. The active nature of Synthetic Aperture Radar (SAR) instruments allows for observations through clouds, which can supplement efforts to estimate hurricane-induced flood impacts. A growing fleet of SAR constellations has helped lower the latency of imagery following a hurricane, allowing for more timely detections of flooding to help support emergency response efforts. In this study, we leverage publicly available C-band SAR observations from the European Space Agency’s Sentinel-1B (S1B) satellite, collected on 30 August, and X-band SAR imagery collected on 1 September by the Airbus TanDEM-X (TDX) satellite made available through the NASA Commercial Smallsat Data Acquisition (CSDA) program. For each dataset, one co-polarized, StripMap, Radiometric Terrain Corrected (RTC) image was used to create a binary water/no water classification map by referencing permanent water in the Cropland Data Layer (CDL) dataset to determine thresholds. A validation dataset of randomly distributed “ground truth” points was generated using optical imagery from PlanetScope on 31 August, where the domain had relatively little cloud cover. We found that the S1B- and TDX-derived open water maps achieved overall accuracies of 94.17% and 91.57%, respectively. The variation in performance is attributed to both the penetrative abilities of C- and X-band SAR wavelengths in vegetated areas and the increased spatial resolution of the commercial SAR (~3 m) over Sentinel-1 (30 m). While publicly available Sentinel-1 observations are commonly relied upon in response and recovery efforts, these results suggest that including commercial X-band SAR imagery could be beneficial by providing both increased spatial resolution and more frequent revisits when deriving post-event flood mapping products.

Alexander M Melancon↗
DOE OSTI2024

Recovered supernova Ia rate from simulated LSST images

Aims.TheVera C. RubinObservatory’s Legacy Survey of Space and Time (LSST) will revolutionize time-domain astronomy by detecting millions of different transients. In particular, it is expected to increase the number of known type Ia supernovae (SN Ia) by a factor of 100 compared to existing samples up to redshift ∼1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of the properties and rates of these objects. However, the impact of all other sources of uncertainty on the measurement of the SN Ia rate must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SN Ia rate can put constraints on the evolutionary scenarios of different SN Ia progenitors. Methods.We used simulated data from the Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) and LSST Data Preview 0 to measure the SN Ia rate on a 15 deg 2 region of the “wide-fast-deep” area. We selected a sample of SN candidates detected in difference images, associated them to the host galaxy with a specially developed algorithm, and retrieved their photometric redshifts. We then tested different light-curve classification methods, with and without redshift priors (albeit ignoring contamination from other transients, as DC2 contains only SN Ia). We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. Results.We measured the SN Ia rate, analyzing the impact of uncertainties due to photometric redshift, host-galaxy association and classification on the distribution in redshift of the starting sample. We find that we are missing 17% of the SN Ia, on average, with respect to the simulated sample. As 10% of the mismatch is due to the uncertainty on the photometric redshift alone (which also affects classification when used as a prior), we conclude that this parameter is the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SN Ia rate, including synergies with other surveys, which may help us to use the rate to discriminate different progenitor models.

Astronomy & Astrophysics↗
NASA NTRS2019

GROWTH on S190510g: DECam Observation Planning and Follow-up of a Distant Binary Neutron Star Merger Candidate

The first two months of the third Advanced LIGO and Virgo observing run (2019 April–May) showed that distant gravitational-wave (GW) events can now be readily detected. Three candidate mergers containing neutron stars (NS) were reported in a span of 15 days, all likely located more than 100 Mpc away. However, distant events such as the three new NS mergers are likely to be coarsely localized, which highlights the importance of facilities and scheduling systems that enable deep observations over hundreds to thousands of square degrees to detect the electromagnetic counterparts. On 2019 May 10 02:59:39.292 UT the GW candidate S190510g was discovered and initially classified as a binary neutron star (BNS) merger with 98% probability. The GW event was localized within an area of 3462 deg2, later refined to 1166 deg2 (90%) at a distance of 227 ± 92 Mpc. We triggered Target-of-Opportunity observations with the Dark Energy Camera (DECam), a wide-field optical imager mounted at the prime focus of the 4 m Blanco Telescope at Cerro Tololo Inter-American Observatory in Chile. This Letter describes our DECam observations and our real-time analysis results, focusing in particular on the design and implementation of the observing strategy. Within 24 hr of the merger time, we observed 65% of the total enclosed probability of the final skymap with an observing efficiency of 94%. We identified and publicly announced 13 candidate counterparts. S190510g was reclassified 1.7 days after the merger, after our observations were completed, with a "BNS merger" probability reduced from 98% to 42% in favor of a "terrestrial classification.

Igor Andreoni↗
NASA NTRS1973

Investigation of use of space data in watershed hydrology

The author has identified the following significant results. Ground truth data have been compiled and 256 rainfall runoff events have been used to detemine coefficients for two storm runoff equations that apply to the 20 study watersheds. Two scenes have been processed using CCT data. Data pertinent to each of the study watersheds have been excerpted from the tapes. A computer program and display technique has been developed to retrieve the data from irregular shaped areas and store it in a format that allows future display of only the area desired. Means, standard deviations, and distributions have been determined for extreme dry and extreme wet conditions in the dormant season. Mean digital values show some variation between watersheds when dry, but no significant differences when wet. Standard deviations of digital values diminish drastically under wet dormant conditions. Thus the standard deviations may be a good indicator of influence of moisture. Distributions of seven bit values show that odd numbers are prevalent and irregular distribution may influence some classification schemes now in use.

Blanchard, B.↗
NASA NTRS2002

How Sample Completeness Affects Gamma-Ray Burst Classification

Unsupervised pattern recognition algorithms support the existence of three gamma-ray burst classes; class I (long, large fluence bursts of intermediate spectral hardness), Class II (short, small fluence, hard bursts), and class III (soft bursts of intermediate durations and fluences). The algorithms surprisingly assign larger membership to class III than to either of the other two classes. A known systematic bias has been previously used to explain the existence of class III in terms of class I; this bias allows the fluences and durations of some bursts to be underestimated. We show that this bias primarily affects only the longest bursts and cannot explain the bulk of the class III properties. We resolve the question of class III existence by demonstrating how samples obtained using standard trigger mechanisms fail to preserve the duration characteristics of small peak flux bursts: (Sample incompleteness is thus primarily responsible for the existence of class III.) In order to avoid this incompleteness, we show how a new dual timescale peak flux can be defined in terms of peak flux and fluence. The dual timescale peak flux reserves the duration distribution of faint bursts and correlates either with spectral hardness (and presumably redshift) than either peak flux or fluence. The techniques presented here are generic and have applicability to the studies of other transient events. The results also indicate that pattern recognition algorithms are sensitive to sample completeness; this can influence the study of large astronomical databases such as those found in a Virtual Observatory.

Hakkila, Jon↗
DOE OSTI2026

AT2025ulz and S250818k: Zooming in with the Hubble Space Telescope

AT2025ulz is an optical/near-infrared transient discovered during follow-up of the candidate gravitational wave (GW) event S250818k. Its young age (≲1 day), rapid decline, and strong color evolution over the first 48 hr classify it as a potential kilonova candidate. In this work, we present the results of our observing campaign, carried out with the Gran Telescopio Canarias and the Hubble Space Telescope (HST). Although the early time evolution of AT2025ulz resembles some aspects of a kilonova, its rapid onset (∼3 hr after the GW trigger) and luminosity (a factor of ∼5 brighter than AT2017gfo in g band) are difficult to reproduce. Only a small subset of our kilonova models matches its multicolor light curve, and the inferred ejecta mass is uncomfortably large given the low chirp mass (≲0.87 M ⊙ ) of the GW candidate. HST observations place the transient within a nearby (z = 0.08489) spiral galaxy with ongoing star formation and measure a color (F336W − F160W ≈ 1.4 mag) that is too blue to match with a kilonova. Our data support the classification of AT2025ulz as a supernova, initially undergoing a shock-cooling phase and later entering its photospheric phase and spectroscopically identified via its broad absorption features.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Occultation of a Large Star by the Large Plutino (28978) Ixion on 2020 October 13 UTC

We observed the occultation of the star Gaia DR2 4056440205544338944 by (28978) Ixion. The event was observed from two Lowell Observatory sites, using the 4.3 m Lowell Discovery Telescope (LDT), near Happy Jack, AZ, USA, and a 0.32 m telescope co-mounted with the Titan Monitoring telescope on Lowell’s Mars Hill campus in Flagstaff, AZ. The LDT chord, at 44.86 s, was roughly 30% longer than the longest predicted possible chord. Under the assumption of a spherical body, Ixion’s fitted diameter D = 709.6 ± 0.2 km. The LDT light-curve profile was used to place an upper limit on the surface pressure P < 2 μbar on any possible atmosphere of Ixion. At the distance of Ixion, the occulted star had a fitted projected diameter of 19.25 ± 0.3 km assuming uniform disk illumination, giving a stellar angular diameter of 0.675 ± 0.010 mas. Using the Gaia EDR3 parallax of 0.565 mas, the stellar radius is 130{sub −17}{sup +20} R{sub ⊙}. The measured size is consistent with prior spectral classification of this star as a reddened mid-M giant. This is one of only a modest number of M5 III stars to have a directly measured diameter, and is more distant than most.

79 ASTRONOMY AND ASTROPHYSICS↗