Results for “space computing” — Page 28

NASA NTRS2014

Results from Testing Crew-Controlled Surface Telerobotics on the International Space Station

During Summer 2013, the Intelligent Robotics Group at NASA Ames Research Center conducted a series of tests to examine how astronauts in the International Space Station (ISS) can remotely operate a planetary rover. The tests simulated portions of a proposed lunar mission, in which an astronaut in lunar orbit would remotely operate a planetary rover to deploy a radio telescope on the lunar far side. Over the course of Expedition 36, three ISS astronauts remotely operated the NASA "K10" planetary rover in an analogue lunar terrain located at the NASA Ames Research Center in California. The astronauts used a "Space Station Computer" (crew laptop), a combination of supervisory control (command sequencing) and manual control (discrete commanding), and Ku-band data communications to command and monitor K10 for 11 hours. In this paper, we present and analyze test results, summarize user feedback, and describe directions for future research.

robotics↗

NASA NTRS2010

Evolutionary Scheduler for the Deep Space Network

A computer program assists human schedulers in satisfying, to the maximum extent possible, competing demands from multiple spacecraft missions for utilization of the transmitting/receiving Earth stations of NASA s Deep Space Network. The program embodies a concept of optimal scheduling to attain multiple objectives in the presence of multiple constraints.

Guillaume, Alexandre↗

DOE OSTI2023

Coupled cluster theory on modern heterogeneous supercomputers

This study examines the computational challenges in elucidating intricate chemical systems, particularly through ab-initio methodologies. This work highlights the Divide-Expand-Consolidate (DEC) approach for coupled cluster (CC) theory—a linear-scaling, massively parallel framework—as a viable solution. Detailed scrutiny of the DEC framework reveals its extensive applicability for large chemical systems, yet it also acknowledges inherent limitations. To mitigate these constraints, the cluster perturbation theory is presented as an effective remedy. Attention is then directed towards the CPS (D-3) model, explicitly derived from a CC singles parent and a doubles auxiliary excitation space, for computing excitation energies. The reviewed new algorithms for the CPS (D-3) method efficiently capitalize on multiple nodes and graphical processing units, expediting heavy tensor contractions. As a result, CPS (D-3) emerges as a scalable, rapid, and precise solution for computing molecular properties in large molecular systems, marking it an efficient contender to conventional CC models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2005

Experimental Feedback Control of Flow Induced Cavity Tones

Discrete-time, linear quadratic methods were used to design feedback controllers for reducing tones generated by flow over a cavity. The dynamics of a synthetic jet actuator mounted at the leading edge of the cavity as observed by two microphones in the cavity were modeled over a broad frequency range using state space models computed from experimental data. Variations in closed loop performance as a function of model order, control order, control bandwidth, and state estimator design were studied using a cavity in the Probe Calibration Tunnel at NASA Langley. The controller successfully reduced the levels of multiple cavity tones at the tested flow speeds of Mach 0.275, 0.35, and 0.45. In some cases, the closed loop results were limited by excitation of sidebands of the cavity tones, or the creation of new tones at frequencies away from the cavity tones. Nonetheless, the results validate the combination of optimal control and experimentally-generated state space models, and suggest this approach may be useful for other flow control problems. The models were not able to account for non-linear dynamics, such as interactions between tones at different frequencies.

Cabell, Randolph H.↗

NASA NTRS1986

A computer modeling methodology and tool for assessing design concepts for the Space Station Data Management System

A computer modeling tool is being developed to assess candidate designs for the Space Station Data Management System (DMS). The DMS is to be a complex distributed computer system including the processor, storage devices, local area networks, and software that will support all processing functions onboard the Space Station. The modeling tool will allow a candidate design for the DMS, or for other subsystems that use the DMS, to be evaluated in terms of parameters. The tool and its associated modeling methodology are intended for use by DMS and subsystem designers to perform tradeoff analyses between design concepts using varied architectures and technologies.

Jones, W. R.↗

DOE OSTI2020

A space-time parallel algorithm with adaptive mesh refinement for computational fluid dynamics

This work describes a space-time parallel algorithm with space-time adaptive mesh refinement (AMR). AMR with subcycling is added to multigrid reduction-in-time (MGRIT) in order to provide solution efficient adaptive grids with a reduction in work performed on coarser grids. This algorithm is achieved by integrating two software libraries: XBraid (Parallel time integration with multigrid. https://computation.llnl.gov/projects/parallel-timeintegration-multigrid) and Chombo (Chombo software package for AMR applications—design document, 2014). The former is a parallel time integration library using multigrid and the latter is a massively parallel structured AMR library. Employing this adaptive space-time parallel algorithm is Chord (Comput Fluids 123:202–217, 2015), a computational fluid dynamics (CFD) application code for solving compressible fluid dynamics problems. For the same solution accuracy, speedups are demonstrated from the use of space-time parallelization over the time-sequential integration on Couette flow and Stokes’ second problem. On a transient Couette flow case, at least a 1.5× speedup is achieved, and with a time periodic problem, a speedup of up to 13.7× over the time-sequential case is obtained. In both cases, the speedup is achieved by adding processors and exploring additional parallelization in time. The numerical experiments show the algorithm is promising for CFD applications that can take advantage of the time parallelism. Future work will focus on improving the parallel performance and providing more tests with complex fluid dynamics to demonstrate the full potential of the algorithm.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Freely scalable and reconfigurable optical hardware for deep learning

Abstract As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values. The path-length-independence of optical energy consumption enables information locality between a transmitter and a large number of arbitrarily arranged receivers, which allows greater flexibility in architecture design to circumvent scaling limitations. In a proof-of-concept experiment, we demonstrate optical multicast in the classification of 500 MNIST images with a 3-layer, fully-connected network. We also analyze the energy consumption of the DONN and find that digital optical data transfer is beneficial over electronics when the spacing of computational units is on the order of $$>10\,\upmu $$ > 10 μ m.

42 ENGINEERING↗

DOE OSTI2022

FutureTense

Protective vaccines and reliable diagnostics are essential tools for controlling viral diseases. However, the efficacy of these tools can be diminished by mutations in viral genomes. The delay between the emergence of new viral strains and the redesign of vaccines and diagnostics allows for continued viral transmission. Is it possible to address this challenge by computationally predicting viral genome sequence evolution? Can we “future-proof” vaccines and diagnostics by targeting both current and anticipated future sequence variants? While predicting viral evolution is still an unsolved, “grand challenge” problem in biology, the large, and rapidly growing, number of SARS-CoV-2 genome sequences provide an opportunity to quantify the ability of machine learning to predict viral genome sequence evolution. Towards this end, we have developed a simple computational model for predicting viral evolution at the level of individual nucleotides. The key metric for quantifying the per-base, prediction accuracy for viral evolution is the Mann-Whitney U statistic (or, equivalently, the area under the receiver operator curve). Since the Mann-Whitney U statistic is not a differentiable function, existing deep leaning packages (like Pytorch and Keras/TensorFlow) are not useful, as they require that the accuracy metric/objective function be analytically differentiable with respect to the model parameters. To overcome this challenge, we have implemented custom software, “FutureTense”, that can train a machine learning model by maximizing the non-differentiable Mann-Whitney U statistic. This software trains a machine learning model by exploring along the direction of the discrete gradient of the Mann-Whitney U statistic in the model parameter space. Parallel computing and genome sequence-specific optimizations are used to accelerate model training. The resulting machine learning model learns the observed high C->U mutation rates in the SARS-CoV-2 genome (which are potentially induced by host defenses) and provides prediction accuracies that are significantly better than one would expect from random chance. While predicting viral evolution is still quite far from a solved problem, the surprising performance of this simple model gives hope that the accuracy of predicting viral genome evolution can be further increased by more sophisticated approaches.

Gans, Jason↗

NASA NTRS1994

PI in the sky: The astronaut science advisor on SLS-2

The Astronaut Science Advisor (ASA, also known as Principal-Investigator-in-a-Box) is an advanced engineering effort to apply expert systems technology to experiment monitoring and control. Its goal is to increase the scientific value of information returned from experiments on manned space missions. The first in-space test of the system will be in conjunction with Professor Larry Young's (MIT) vestibulo-ocular 'Rotating Dome' experiment on the Spacelab Life Sciences 2 mission (STS-58) in the Fall of 1993. In a cost-saving effort, off-the-shelf equipment was employed wherever possible. Several modifications were necessary in order to make the system flight-worthy. The software consists of three interlocking modules. A real-time data acquisition system digitizes and stores all experiment data and then characterizes the signals in symbolic form; a rule-based expert system uses the symbolic signal characteristics to make decisions concerning the experiment; and a highly graphic user interface requiring a minimum of user intervention presents information to the astronaut operator. Much has been learned about the design of software and user interfaces for interactive computing in space. In addition, we gained a great deal of knowledge about building relatively inexpensive hardware and software for use in space. New technologies are being assessed to make the system a much more powerful ally in future scientific research in space and on the ground.

Hazelton, Lyman R.↗

NASA NTRS1965

Propulsion and Space Vehicle Systems Analysis Program Tensor I, volume I Technical report no. 341

Computer program for space vehicle and propulsion systems analysis

SYSTEMS ANALYSIS↗

NASA NTRS2001

Hubble Space Telescope Servicing Mission 3A Rendezvous Operations

The Hubble Space Telescope (HST) hardware complement includes six gas bearing, pulse rebalanced rate integrating gyros, any three of which are sufficient to conduct the science mission. After the loss of three gyros between April 1997 and April 1999 due to a known corrosion mechanism, NASA decided to split the third HST servicing mission into SM3A, accelerated to October 1999, and SM3B, scheduled for November 2001. SM3A was developed as a quick turnaround 'Launch on Need' mission to replace all six gyros. Loss of a fourth gyro in November 1999 caused HST to enter Zero Gyro Sunpoint (ZGSP) safemode, which uses sun sensors and magnetometers for attitude determination and momentum bias to maintain attitude stability during orbit night. Several instances of large attitude excursions during orbit night were observed, but ZGSP performance was adequate to provide power-positive sun pointing and to support low gain antenna communications. Body rates in ZGSP were estimated to exceed the nominal 0.1 deg/sec rendezvous limit, so rendezvous operations were restructured to utilize coarse, limited life, Retrieval Mode Gyros (RMGs) under Hardware Sunpoint (HWSP) safemode. Contingency procedures were developed to conduct the rendezvous in ZGSP in the event of RMGA or HWSP computer failure. Space Shuttle Mission STS-103 launched on December 19, 1999 after a series of weather and Shuttle-related delays. After successful rendezvous and grapple under HWSP/RMGA, the crew changed out all six gyros. Following deploy and systems checkout, HST returned to full science operations.

Lee, S.↗

NASA NTRSDate not supplied

Clinical Decision Support - Concepts of Operation

We are entering a new era in space exploration to return to the moon and explore Mars. These ambitious goals will require significant changes to in-flight and habitat medical care due to constraints on mass, volume, power, crew time and medical evacuation capabilities. These constraints make it absolutely necessary to develop transformative solutions using new technologies. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) pushes the boundary of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses the gap Medical-701 within the Inflight Medical Conditions risk: Enhance medical capabilities within an exploration medical system. For long-duration, deep space missions, computational and data resources will play an important role in maintaining crew health, wellness and performance where the crew will need to be more self-reliant. The aim of the CDS project is to develop and provide recommended requirements for an in-vehicle CDSS that acts as an assistant for delivering optimal health and performance and medical care during exploration missions. The CDSS is envisioned as a software-based tool that will augment a crewmembers’ knowledge, skills and abilities to assist in decision-making and crew health and performance (CHP) management thus increasing CHP systems capabilities. The human interface will be context aware and lessen the cognitive load to assimilate and use information as well as combine large disparate data sets in such a manner that provides the crew with actionable insight to decisions related to crew medical, health and performance management. Crew autonomy will be provided through a CDS that presents knowledge and data in a context aware manner to augment a crew members’ knowledge, skills and abilities during the process of observation, orientation, decisions and action. The CDS project addresses the need for crew members to operate independently during long duration space exploration missions that require medical Levels of Care (LoC) V, the highest level specified by NASA-STD-3001 and described in more detail by the ExMC interpretation of LoC document (NASA/TM-2017-219290), where significant changes to in-flight and habitat medical care necessitate increasing crew autonomy in decision making and task performance. The CDS project will develop and test a series of iterative and increasingly more complex system prototypes. These annual demonstrations of the data system integration with the crew health and performance domain will inform exploration medical system requirements for an on-board Clinical Decision Support System (CDSS) through a series of use cases that guide CDS prototype functionality. CDS concepts are based on ExMC Concept of Operations documents (presented separately) and will highlight architecture extensibility to other more complex analyses and tests using core crew health and performance integrated data management, processing and visualization capabilities. This approach also establishes how externally developed analyses and approaches could be added to expand a clinical decision support system and thus highlight how a comprehensive system can be commercially and/or globally developed. The CDS project will build upon the concept of an integrated data management approach based on the Medical Data Architecture (MDA) project to more fully address challenges associated with in-flight and habitat medical, health and performance care due to constraints on mass, volume, power, crew time and medical evacuation capabilities required for medical LoC V. These requirements will be derived through systems engineering approaches and software prototype developments over the course of the multi-year CDS project to address crew health and performance decision-making and task performance, often autonomously executed by the crew, in a manner that is consistent with the appropriate medical level of care for the mission. This presentation will provide an overview of the vision for the CDS project and highlight the initial accomplishments in project planning, implementation and requirements identification in fiscal year 2020.

clinical decision support↗

NASA NTRSDate not supplied

Clinical Decision Support - Overview and Update

We are entering a new era in space exploration to return to the moon and explore Mars. These ambitious goals will require significant changes to in-flight and habitat medical care due to constraints on mass, volume, power, crew time and medical evacuation capabilities. These constraints make it absolutely necessary to develop transformative solutions using new technologies. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) pushes the boundary of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses the gap Medical-701 within the Inflight Medical Conditions risk: Enhance medical capabilities within an exploration medical system. For long-duration, deep space missions, computational and data resources will play an important role in maintaining crew health, wellness and performance where the crew will need to be more self-reliant. The aim of the CDS project is to develop and provide recommended requirements for an in-vehicle CDSS that acts as an assistant for delivering optimal health and performance and medical care during exploration missions. The CDSS is envisioned as a software-based tool that will augment a crewmembers’ knowledge, skills and abilities to assist in decision-making and crew health and performance (CHP) management thus increasing CHP systems capabilities. The human interface will be context aware and lessen the cognitive load to assimilate and use information as well as combine large disparate data sets in such a manner that provides the crew with actionable insight to decisions related to crew medical, health and performance management. Crew autonomy will be provided through a CDS that presents knowledge and data in a context aware manner to augment a crew members’ knowledge, skills and abilities during the process of observation, orientation, decisions and action. The CDS project addresses the need for crew members to operate independently during long duration space exploration missions that require medical Levels of Care (LoC) V, the highest level specified by NASA-STD-3001 and described in more detail by the ExMC interpretation of LoC document (NASA/TM-2017-219290), where significant changes to in-flight and habitat medical care necessitate increasing crew autonomy in decision making and task performance. The CDS project will develop and test a series of iterative and increasingly more complex system prototypes. These annual demonstrations of the data system integration with the crew health and performance domain will inform exploration medical system requirements for an on-board Clinical Decision Support System (CDSS) through a series of use cases that guide CDS prototype functionality. CDS concepts are based on ExMC Concept of Operations documents (presented separately) and will highlight architecture extensibility to other more complex analyses and tests using core crew health and performance integrated data management, processing and visualization capabilities. This approach also establishes how externally developed analyses and approaches could be added to expand a clinical decision support system and thus highlight how a comprehensive system can be commercially and/or globally developed. The CDS project will build upon the concept of an integrated data management approach based on the Medical Data Architecture (MDA) project to more fully address challenges associated with in-flight and habitat medical, health and performance care due to constraints on mass, volume, power, crew time and medical evacuation capabilities required for medical LoC V. These requirements will be derived through systems engineering approaches and software prototype developments over the course of the multi-year CDS project to address crew health and performance decision-making and task performance, often autonomously executed by the crew, in a manner that is consistent with the appropriate medical level of care for the mission. This presentation will provide an overview of the vision for the CDS project and highlight the initial accomplishments in project planning, implementation and requirements identification in fiscal year 2020.

clinical decision support system↗

NASA NTRS2024

Historical Aerospace Software Errors Categorized to Influence Fault Tolerance

Since the first use of computers in space and aircraft, software errors have occurred. These errors can manifest as loss-of-life or less catastrophically. As the demand for automation increases, software in mission or safety-critical systems should be designed to be tolerant to the most likely software faults. This paper categorizes a set of 55 historic aerospace software error incidents from 1962 to 2023 to determine trends of how and where automation is most likely to fail, behaving unexpectedly. A distinction between software producing unexpected (erroneous) output versus no output (failsilent) is introduced. Of the historical incidents analyzed, 85% were from software producing wrong output rather than simply stopping. Rebooting was found to be ineffective to clear erroneous behavior, and not reliable to recover from silent failures. Error origin was within the code/logic itself in 58% of cases, 16% from configurable data, 15% from unexpected sensor input, and 11% from command/operator input. A substantial forty percent (40%) of unexpected software behavior was indicated by the absence of code, arising from unanticipated situations and missing requirements, and 16% of incidents were subjectively deemed “unknown-unknowns”. No incidents were found to be the result of programming language, compiler, tool, or operating system; and only sixteen percent (16%) of all incidents were considered errors traditional computer science/programming in nature. These findings indicate that for fault tolerance, erroneous automation behavior must be a primary consideration especially at critical moments, and reboot recoverability may not be viable. Special care should be taken to validate configurable data and commands prior to use. “Test-like-you-fly”, including hardware-in-the-loop combined with robust off-nominal testing should be used to uncover missing logic arising from unanticipated situations not covered by requirements alone. This study uniquely focuses on manifestations of unexpected flight software behavior, independent of ultimate root cause. We characterize software error behavior and origin to improve software design, test, and operations for resilience to the most common manifestations, and provide a rich dataset for further study.

Aerospace↗

NASA NTRS2023

Computer Human Interface Challenges in Space Exploration

NASA’s plans to return humans to the Lunar surface require overcoming a variety of challenging technical and operational obstacles. In 2022, NASA formed the Extravehicular Activity (EVA) and Human Surface Mobility (HSM) Program (EHP) at the Johnson Space Center with responsibilities including development of space suits and surface mobility systems for Lunar missions. This program includes a Technology Development and Partnerships office chartered to identify high priority gaps in capabilities for Lunar surface mobility and to coordinate resources to close those gaps. This presentation details the EHP technology roadmap for “Informatics and Decision Support,” a subset of spacecraft avionics focused on effective and autonomous crew interaction with spaceflight systems. The gaps, grouped into displays, audio systems, and information technology infrastructure, are largely driven by the unique interaction requirements for human spacecraft and the severe radiation environments beyond low earth orbit. The roadmap identifies ongoing activities and paths to technology infusion into Lunar spacecraft. NASA is seeking input on the content and ideas for alternative paths to gap closure. Closing these gaps is important to successful human operations on the Lunar surface and vital to NASA’s long-term goal of human missions to Mars.

Spacecraft Displays↗

NASA NTRS2017

The Astromaterials X-Ray Computed Tomography Laboratory at Johnson Space Center

The Astromaterials Acquisition and Curation Office at NASA's Johnson Space Center (hereafter JSC curation) is the past, present, and future home of all of NASA's astromaterials sample collections. JSC curation currently houses all or part of nine different sample collections: (1) Apollo samples (1969), (2) Lunar samples (1972), (3) Antarctic meteorites (1976), (4) Cosmic Dust particles (1981), (5) Microparticle Impact Collection (1985), (6) Genesis solar wind atoms (2004); (7) Stardust comet Wild-2 particles (2006), (8) Stardust interstellar particles (2006), and (9) Hayabusa asteroid Itokawa particles (2010). Each sample collection is housed in a dedicated clean room, or suite of clean rooms, that is tailored to the requirements of that sample collection. Our primary goals are to maintain the long-term integrity of the samples and ensure that the samples are distributed for scientific study in a fair, timely, and responsible manner, thus maximizing the return on each sample. Part of the curation process is planning for the future, and we also perform fundamental research in advanced curation initiatives. Advanced Curation is tasked with developing procedures, technology, and data sets necessary for curating new types of sample collections, or getting new results from existing sample collections [2]. We are (and have been) planning for future curation, including cold curation, extended curation of ices and volatiles, curation of samples with special chemical considerations such as perchlorate-rich samples, and curation of organically- and biologically-sensitive samples. As part of these advanced curation efforts we are augmenting our analytical facilities as well. A micro X-Ray computed tomography (micro-XCT) laboratory dedicated to the study of astromaterials will be coming online this spring within the JSC Curation office, and we plan to add additional facilities that will enable nondestructive (or minimally-destructive) analyses of astromaterials in the near future (micro-XRF, confocal imaging Raman Spectroscopy). These facilities will be available to: (1) develop sample handling and storage techniques for future sample return missions; (2) be utilized by PET for future sample return missions; (3) be used for retroactive PET (Positron Emission Tomography)-style analyses of our existing collections; and (4) for periodic assessments of the existing sample collections. Here we describe the new micro-XCT system, as well as some of the ongoing or anticipated applications of the instrument.

Zeigler, R. A.↗

NASA NTRS2017

The Astromaterials X-Ray Computed Tomography Laboratory at Johnson Space Center

The Astromaterials Acquisition and Curation Office at NASA's Johnson Space Center (hereafter JSC curation) is the past, present, and future home of all of NASA's astromaterials sample collections. JSC curation currently houses all or part of nine different sample collections: (1) Apollo samples (1969), (2) Lunar samples (1972), (3) Antarctic meteorites (1976), (4) Cosmic Dust particles (1981), (5) Microparticle Impact Collection (1985), (6) Genesis solar wind atoms (2004); (7) Stardust comet Wild-2 particles (2006), (8) Stardust interstellar particles (2006), and (9) Hayabusa asteroid Itokawa particles (2010). Each sample collection is housed in a dedicated clean room, or suite of clean rooms, that is tailored to the requirements of that sample collection. Our primary goals are to maintain the long-term integrity of the samples and ensure that the samples are distributed for scientific study in a fair, timely, and responsible manner, thus maximizing the return on each sample. Part of the curation process is planning for the future, and we also perform fundamental research in advanced curation initiatives. Advanced Curation is tasked with developing procedures, technology, and data sets necessary for curating new types of sample collections, or getting new results from existing sample collections [2]. We are (and have been) planning for future curation, including cold curation, extended curation of ices and volatiles, curation of samples with special chemical considerations such as perchlorate-rich samples, and curation of organically- and biologically-sensitive samples. As part of these advanced curation efforts we are augmenting our analytical facilities as well. A micro X-Ray computed tomography (micro-XCT) laboratory dedicated to the study of astromaterials will be coming online this spring within the JSC Curation office, and we plan to add additional facilities that will enable nondestructive (or minimally-destructive) analyses of astromaterials in the near future (micro-XRF, confocal imaging Raman Spectroscopy). These facilities will be available to: (1) develop sample handling and storage techniques for future sample return missions; (2) be utilized by PET for future sample return missions; (3) be used for retroactive PET (Positron Emission Tomography)-style analyses of our existing collections; and (4) for periodic assessments of the existing sample collections. Here we describe the new micro-XCT system, as well as some of the ongoing or anticipated applications of the instrument.

Zeigler, R. A.↗

DOE OSTI2022

$t\overline{t}t\overline{t}$ signatures through the lens of color-octet scalars

We reinterpret two recent LHC searches for events containing four top quarks ($t\overline{t}t\overline{t}$) in the context of supersymmetric models with Dirac gauginos and color-octet scalars (sgluons). We explore whether sgluon contributions to the four-top production cross section σ ($pp$ → $t\overline{t}t\overline{t}$) can accommodate an excess of four-top events recently reported by the ATLAS collaboration. We also study constraints on these models from an ATLAS search for new phenomena with high jet multiplicity and significant missing transverse energy ($E^{miss}_{T}$) sensitive to signals with four top quarks. We find that these two analyses provide complementary constraints, with the jets + $E^{miss}_{T}$ search exceeding the four-top cross section measurement in sensitivity for sgluons heavier than about 800 GeV. We ultimately find that either a scalar or a pseudoscalar sgluon can currently fit the ATLAS excess in a range of reasonable benchmark scenarios, though a pseudoscalar in minimal Dirac gaugino models is ruled out. We finally offer sensitivity projections for these analyses at the HL-LHC, mapping the 5σ discovery potential in sgluon parameter space and computing exclusion limits at 95% CL in scenarios where no excess is found.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗