Search NASA⌕ Search

SEARCH · Search NASA

Results for “Human Performance Modeling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Comparison of two weighted integration models for the cueing task: linear and likelihood

In a task in which the observer must detect a signal at two locations, presenting a precue that predicts the location of a signal leads to improved performance with a valid cue (signal location matches the cue), compared to an invalid cue (signal location does not match the cue). The cue validity effect has often been explained with a limited capacity attentional mechanism improving the perceptual quality at the cued location. Alternatively, the cueing effect can also be explained by unlimited capacity models that assume a weighted combination of noisy responses across the two locations. We compare two weighted integration models, a linear model and a sum of weighted likelihoods model based on a Bayesian observer. While qualitatively these models are similar, quantitatively they predict different cue validity effects as the signal-to-noise ratios (SNR) increase. To test these models, 3 observers performed in a cued discrimination task of Gaussian targets with an 80% valid precue across a broad range of SNR's. Analysis of a limited capacity attentional switching model was also included and rejected. The sum of weighted likelihoods model best described the psychophysical results, suggesting that human observers approximate a weighted combination of likelihoods, and not a weighted linear combination.

NASA Program Biomedical Research and Countermeasur↗
NASA NTRS2010

Human System Drivers for Exploration Missions

Evaluation of DRM4 in terms of the human system includes the ability to meet NASA standards, the inclusion of the human system in the design trade space, preparation for future missions and consideration of a robotic precursor mission. Ensuring both the safety and the performance capability of the human system depends upon satisfying NASA Space Flight Human System Standards.1 These standards in turn drive the development of program-specific requirements for Near-earth Object (NEO) missions. In evaluating DRM4 in terms of these human system standards, the currently existing risk models, technologies and biological countermeasures were used. A summary of this evaluation is provided below in a structure that supports a mission architecture planning activities. 1. Unacceptable Level of Risk The duration of the DRM4 mission leads to an unacceptable level of risk for two aspects of human system health: A. The permissible exposure limit for space flight radiation exposure (a human system standard) would be exceeded by DRM4. B. The risk of visual alterations and abnormally high intracranial pressure would be too high. 1

Kundrot, Craig E.↗
NASA NTRS2018

Swarm Size Planning Tool for Multi-Job Type Missions

As part of swarm search and service (SSS) missions, swarms are tasked with searching an area while simultaneously servicing jobs as they are encountered. Jobs must be immediately serviced and can be one of multiple types. Each type requires that vehicle(s) break off from the swarm and travel to the job site for a specified amount of time. The number of vehicles needed and the service time for each job type are known. Once a job has been successfully serviced, vehicles return to the swarm and are available for reallocation. When planning SSS missions, human operators are tasked with determining the required number of vehicles needed to handle the expected job demand. The complex relationship between job type parameters makes this choice challenging. This work presents a prediction model used to estimate the swarm size necessary to achieve a given performance. User studies were conducted to determine the usefulness and ease of use of such a prediction model as an aid during mission planning. Results show that using the planning tool leads to 7 times less missed area and a 50 percent cost reduction.

Chandarana, Meghan↗
NASA NTRS1989

The use of vestibular models for design and evaluation of flight simulator motion

Quantitative models for the dynamics of the human vestibular system are applied to the design and evaluation of flight simulator platform motion. An optimal simulator motion control algorithm is generated to minimize the vector difference between perceived spatial orientation estimated in flight and in simulation. The motion controller has been implemented on the Vertical Motion Simulator at NASA Ames Research Center and evaluated experimentally through measurement of pilot performance and subjective rating during VTOL aircraft simulation. In general, pilot performance in a longitudinal tracking task (formation flight) did not appear to be sensitive to variations in platform motion condition as long as motion was present. However, pilot assessment of motion fidelity by means of a rating scale designed for this purpose, were sensitive to motion controller design. Platform motion generated with the optimal motion controller was found to be generally equivalent to that generated by conventional linear crossfeed washout. The vestibular models are used to evaluate the motion fidelity of transport category aircraft (Boeing 727) simulation in a pilot performance and simulator acceptability study at the Man-Vehicle Systems Research Facility at NASA Ames Research Center. Eighteen airline pilots, currently flying B-727, were given a series of flight scenarios in the simulator under various conditions of simulator motion. The scenarios were chosen to reflect the flight maneuvers that these pilots might expect to be given during a routine pilot proficiency check. Pilot performance and subjective rating of simulator fidelity was relatively insensitive to the motion condition, despite large differences in the amplitude of motion provided. This lack of sensitivity may be explained by means of the vestibular models, which predict little difference in the modeled motion sensations of the pilots when different motion conditions are imposed.

Bussolari, Steven R.↗
DOE OSTI2019

Evaluating Safety with Automated Vehicles at Signalized Intersections: Application of Adaptive Cruise Control in Mixed Traffic

Automated Vehicles (AV) can potentially improve the performance of transportation system by reducing human errors and enhancing mobility and safety. In lower levels of automation, humans are still controlling the vehicle and receiving some advisory information regarding their surrounding environment. This paper investigates the safety impact of low and high levels of AVs, and their interaction with conventional human-driven vehicles at intersections. In order to enhance our understanding of the future interactions between conventional vehicles with AVs, we developed a framework to simulate the mixed traffic environment. Different market penetration scenarios are simulated using VENTOS (VEhicular NeTwork Open Simulator) software. A modified Wiedemann car-following model was calibrated to represent the behavior of human drivers of both conventional and low-level AVs, while an Adaptive Cruise Control model was used to represent high-level AVs. Notably, this study investigates purely the automation improvement in the system. We modified the acceleration and deceleration regimes of the Wiedemann model and tuned it by harnessing real-world connected vehicle data. Next, the simulation was calibrated utilizing two measures of driving volatility to ensure it closely represents the volatility measures of the real-world data. To evaluate the safety performance of a representative intersection under different scenarios, the number of longitudinal conflicts and driving volatility are used as surrogate safety indicators. The results reveal that increase in market penetration rate of LAVs and HAVs substantially improves intersection safety performance by reducing the number of conflicts and driving volatility.

Arvin, Ramin↗
NASA NTRS2004

Effects of heavy ions on visual function and electrophysiology of rodents: the ALTEA-MICE project

ALTEA-MICE will supplement the ALTEA project on astronauts and provide information on the functional visual impairment possibly induced by heavy ions during prolonged operations in microgravity. Goals of ALTEA-MICE are: (1) to investigate the effects of heavy ions on the visual system of normal and mutant mice with retinal defects; (2) to define reliable experimental conditions for space research; and (3) to develop animal models to study the physiological consequences of space travels on humans. Remotely controlled mouse setup, applied electrophysiological recording methods, remote particle monitoring, and experimental procedures were developed and tested. The project has proved feasible under laboratory-controlled conditions comparable in important aspects to those of astronauts' exposure to particle in space. Experiments are performed at the Brookhaven National Laboratories [BNL] (Upton, NY, USA) and the Gesellschaft fur Schwerionenforschung mbH [GSI]/Biophysik (Darmstadt, FRG) to identify possible electrophysiological changes and/or activation of protective mechanisms in response to pulsed radiation. Offline data analyses are in progress and observations are still anecdotal. Electrophysiological changes after pulsed radiation are within the limits of spontaneous variability under anesthesia, with only indirect evidence of possible retinal/cortical responses. Immunostaining showed changes (e.g. increased expression of FGF2 protein in the outer nuclear layer) suggesting a retinal stress reaction to high-energy particles of potential relevance in space. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

NASA Center JSC↗
DOE OSTI2025

GRUMDN: A Multi-Task Model for Predicting Human Patterns-of-Life from Stay Transition Data

Understanding human patterns-of-life (PoL) is essential towards ensuring safe and secure indoor facility environment as well as outdoor urban environment. Prediction of human movement in between places of interest is vital in understanding human PoL. Movement between spaces maybe represented and detected in one of the two forms: 1) trajectories: locations measured at regular time intervals by mobile sensors, bluetooth or GPS sensors; or 2) stay transitions: semantic PoI (points of interest) and stay duration data measurable by eventbased sensors that collect data when a check-in or check-out event is detected. Stay transition data provides a more compressed data format compared to trajectories data, especially in situations with longer stay durations, while preserving the information necessary for PoL analysis. Now as introduced briefly in the paper, our deployed end application (Digital Twin of a facility with non-player characters, besides the interactive user in virtual reality) needed a well-performing and validated AI/ML model for simulating high quality stay transitions behavior. In this study we thus primarily present our findings with developing and validating that model, which is a multi-task neural network for stay transition prediction. The neural network consists of two heads, for corresponding two tasks of stay category prediction and stay duration prediction. We evaluated gated recurrent units and multi-layer perceptrons of varying network sizes for stay category prediction; while mixture density networks, noisy generator-only networks, and generative adversarial networks of varying network sizes for stay duration prediction. We have then evaluated four multi-task models, constructed by combining these specialized models, on their ability to predict stay transition data. We tested our models on datasets from two different cases: 1) a simulation-generated dataset of indoor movement within the HFIR (high flux isotope reactor) nuclear reactor facility at Oak Ridge National Laboratory (ORNL); and 2) the GeoLife human mobility dataset of outdoor urban movement available in literature. Our results indicate that GRUMDN, which combines gated recurrent units (GRU) for stay category prediction task, and mixture density networks (MDN) for stay duration prediction task, did overall outperform other multitask models and the current state-of-the-art.

Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗
DOE OSTI2024

Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance

Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. Here, we finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1988

High level intelligent control of telerobotics systems

A high level robot command language is proposed for the autonomous mode of an advanced telerobotics system and a predictive display mechanism for the teleoperational model. It is believed that any such system will involve some mixture of these two modes, since, although artificial intelligence can facilitate significant autonomy, a system that can resort to teleoperation will always have the advantage. The high level command language will allow humans to give the robot instructions in a very natural manner. The robot will then analyze these instructions to infer meaning so that is can translate the task into lower level executable primitives. If, however, the robot is unable to perform the task autonomously, it will switch to the teleoperational mode. The time delay between control movement and actual robot movement has always been a problem in teleoperations. The remote operator may not actually see (via a monitor) the results of high actions for several seconds. A computer generated predictive display system is proposed whereby the operator can see a real-time model of the robot's environment and the delayed video picture on the monitor at the same time.

Mckee, James↗
NASA NTRSDate not supplied

A Theoretical Model of Static and Dynamic Field-of-View (FOV) using NASA's Neutral Buoyancy Laboratory (NBL)

Developing an objective field-of-view methodology for studying vehicle window placement has been somewhat difficult. It is not surprising that considerable effort has been directed at specific problems of visibility from vehicles. Aeronautical design engineers know when designing an aircraft cockpit, the pilot must have an adequate view for approaching and landing on an airfield. However, compromise between the ergonomic preference for the largest possible viewing envelope and the practicalities of arranging a structure that is lightweight and strong will always plague the cockpit’s transparent area’s size and shape. The human’s visual sense, when operating any kind of vehicle, is the main source of the vast majority of information needed to drive or fly safely. However, vehicle design can affect a driver’s visual performance for satisfying their need for information. For any vehicle, be it an automobile or a spacecraft, the visibility quantity is the sum total of the visual access to the environment as defined by the window placement of that vehicle. The Alternate Multiple Mission Space Exploration Vehicle (AMMSEV) configuration is much like a large cargo truck where there is no direct visual access to the rear of the vehicle or the rear sides of the vehicle; thus, making the forward and side views of greater importance to the situational awareness of the crew if obstructions are creating visual issues. The National Aeronautics and Space Administration (NASA) tested a new theoretical model for Field-of-View (FOV) of spacecraft windows in NASA’s own Neutral Buoyancy Laboratory (NBL). The NBL is an essential tool for design, testing and development of future space exploration hardware for upcoming NASA operational missions. Using water, with its natural propensity for giving a human the perception of dynamic body motion under a weightless or partial gravity conditions, gives human factors investigators a vigorous space-like environment to study new methods of collecting dynamic field-of-view for future exploration vehicle designs.

Human Factors↗
NASA NTRSDate not supplied

Simulation of Radiation-Induced DNA Damage With the Code RITRACKS

INTRODUCTION DNA damage is one of the most physiologically important effects of ionizing radiation. Clustered DNA damage events, like double-strand breaks (DSBs), have the most notable biological consequences. DNA damage types depend on both the track structure of the radiation and the spatial organization of the DNA. High linear energy transfer (LET) charged nuclei, found in galactic cosmic rays (GCR), are known to produce large numbers of complex DNA damage events. The human genome is packaged into chromatin, which can take on locus-dependent and cell type-dependent spatial conformations that correspond to epigenetic states, such as more open, extended structures in transcriptionally active chromatin. These epigenetic differences can affect DNA break patterns in response to ionizing radiation, potentially creating distinct DNA repair and signaling outcomes across the genome in different cells. MATERIAL AND METHODS The code RITRACKS (Relativistic Ion Tracks), which simulates stochastic radiation track structures and radiation chemistry, was used to model damage on isolated and histone-bound DNA by various types of ions and photons. The changes made to the code to perform radiation-induced DNA damage, and simulation results on single nucleosomes are given in our recent paper. In this work, the DNA building capabilities of RITRACKS have been extended to simulate more complex DNA structures build on the coarse-grain simulation framework meso-WLCsim. This code can sample generic chromatin fiber conformation ensembles based on the geometry of nucleosomes and mechanical properties of DNA. Using RITRACKS, we simulated the fragment length distributions (FLD) of irradiated DNA structures built using the chromatin conformations of WLCsim and obtained results representative of those obtained with Radiation-Induced Correlated Cleavage with sequencing (RICC-Seq) experiments [6]. We have also performed Fe ion and photon irradiations of K562, IMR90, BJ and RPE-1 cells at NSRL to experimentally validate results. Sample processing and data analysis are in progress and any available preliminary results will be discussed. DISCUSSION The recent updates in the code RITRACKS allow the calculation of several quantities such as the DNA damage yield and the FLD. This approach can be used to model epigenetic state-specific chromatin structure parameters to leverage the epigenetic state data available for many human cell types to infer relative DNA damage sensitivity among genomic loci.

I Plante↗
DOE OSTI2021

Vadose and Saturated Zone Flow and Transport Calculations for the Active Trenches of the Low-Level Burial Grounds, Hanford Site, Washington

The purpose of the fate and transport modeling described in this environmental calculation file (ECF) is to evaluate the impacts to groundwater associated with waste disposal operations at Mixed Waste Low-Level Burial Ground (LLBG) Trenches 31 and 34 to satisfy requirements in DOE O 435.1, Radioactive Waste Management. The model integrates the flow and transport in the vadose zone beneath the active trenches with the saturated zone downgradient of the trenches to predict the radionuclide concentration at the point of assessment (POA). DOE M 435.1-1, Radioactive Waste Management Manual, defines the POA as the point of highest projected dose or concentration beyond a 100 m (328 ft) buffer zone surrounding the disposed waste. The modeling is conducted in accordance with the DOE G 435.1, Implementation Guide for Use with DOE M 435.1-1, performance assessment (PA) guidelines. The modeling involves evaluation of the groundwater concentrations and radionuclide arrival times during the 1,000-year compliance and 10,000-year sensitivity-uncertainty periods per DOE O 435.1 and DOE M 435.1. This analysis does not consider radionuclide release during facility operations, only the post-closure impacts of the radionuclides to the environment. The evaluation of potential radiological dose to groundwater receptors caused by releases from a closed facility containing radioactive waste typically includes the following: (1) Release of radionuclides from that facility (2) Transport of those radionuclides through the environment, and (3) Exposure to humans to environmental concentration levels of those radionuclides The fate and transport three-dimensional (3D) model analysis involves the post-closure impacts to the environment of the technetium-99, iodone-129, and uranium (all isotopes in the waste). The residual inventory estimates include several radionuclides, but technetium-99 is typically responsible for almost all of the beta-gamma dose equivalent associated with groundwater (water resources) protection per 40 CFR 141, “National Primary Drinking Water Regulations” (e.g., see the results in WCH-520, Performance Assessment of Environmental Restoration Disposal Facility, Hanford Site, Washington; hereinafter referred to as the ERDF PA), and iodone-129 can also be a significant dose contributor for some waste (e.g., RPP-RPT-59958, Performance Assessment for the Integrated Disposal Facility, Hanford Site, Washington; hereinafter referred to as the IDF PA). Uranium does not typically factor significantly into the impacts to groundwater, even during the 10,000-year sensitivity-uncertainty period, but always remains of interest as a contaminant. This ECF does not address vadose and saturated zone modeling for Trench 94 of the 200 East Area LLBG. Current information confirms the validity of the low corrosion rate of the naval reactor plant carbon steel (HY-80), and the even lower corrosion rate of the nickel-iron-chromium alloy reactor vessel (Inconel Alloy 600) presented in DOE/EIS-0259, Final Environmental Impact Statement on the Disposal of Decommissioned, Defueled Cruiser, Ohio Class, and Los Angeles Class Naval Reactor Plants. Based on these low corrosion rates, the time to breach the reactor vessel to allow release of radionuclides from the activated metal of the reactor vessel internal structure is at least 10,000 years. This time to breach precludes the need to evaluate the vadose and saturated zone transport of contaminants released from the reactor compartment disposal packages in the 200 East Area LLBGs PA (CP-63826, Waste Release Model Package Report for the Active Trenches of the Low-Level Burial Grounds, Hanford Site, Washington).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2024

Presound: UAV Diagnostic System Enabled by Vibration-Based Machine Learning

A low-weight, inexpensive small unmanned aerial system (sUAS) that takes off, performs a mission, lands, and safely stows and recharges itself has myriad future applications ranging from agricultural imaging to last-mile package delivery. Likewise, Urban Air Mobility (UAM) systems will enable people to take air taxis from point to point in cities, rapidly moving commuters long distances without concern for road traffic and congestion. Fully electric aviation systems will be cleaner and quieter than ground transport. Cities could eliminate cars and buses, and convert roads to higher capacity bike and pedestrian throughways. Yet, for sUAS as well as UAM, system reliability and assurance is a limiting factor to deploying affordable autonomous flight systems. For this bright future of aviation to be realized, aircraft must be able to autonomously and accurately self-diagnose health issues both before takeoff and during flight. The GreenSight PreSound system is designed to identify defects on aircraft through intelligent analysis of vibration. It accomplishes this by measuring structural vibrations induced by the vehicle’s own propellers, and analyzing that data using a machine learning model that determines whether a defect is present. The PreSound system is designed to require no human oversight, and to operate across a wide array of vehicles through re-training of the model for each target aircraft. PreSound has been developed and seen limited early success using data collected from the GreenSight Dreamer sUAS, a 5lb quadrotor vehicle designed for aerial imaging applications. The final detection model, trained on data with props spinning at 50% throttle, achieves excellent performance with over 99% average accuracy in detecting blade damage using a single FFT vector input. It demonstrates the ability to generalize to new types of blade damage, correctly classifying a different type of blade damage with 98% accuracy. Full test pulses were classified with 100% accuracy, and in live testing, all sets of data during blade movement were classified accurately with over 95% confidence. When trained on in-flight data, the same model achieves an average accuracy of 85% in distinguishing between undamaged and blade-damaged states in flight. The authors believe that these accuracies show significant potential of this approach to expand unmanned flight safety, with significant potential benefits in accelerating Advanced Aerial Mobility (AAM) and UAM aviation applications.

UAS↗
NASA NTRS2002

Machine Learning for Biological Trajectory Classification Applications

Machine-learning techniques, including clustering algorithms, support vector machines and hidden Markov models, are applied to the task of classifying trajectories of moving keratocyte cells. The different algorithms axe compared to each other as well as to expert and non-expert test persons, using concepts from signal-detection theory. The algorithms performed very well as compared to humans, suggesting a robust tool for trajectory classification in biological applications.

Sbalzarini, Ivo F.↗
NASA NTRS2001

Soluble TNF-alpha receptor 1 and IL-6 plasma levels in humans subjected to the sleep deprivation model of spaceflight

BACKGROUND: The extent to which sleep loss may predispose astronauts to a state of altered immunity during extended space travel prompts evaluation with ground-based models. OBJECTIVE: We sought to measure plasma levels of selected cytokines and their receptors, including the putative sleep-regulation proteins soluble TNF-alpha receptor (sTNF-alpha R) I and IL-6, in human subjects undergoing 2 types of sleep deprivation during environmental confinement with performance demands. METHODS: Healthy adult men (n = 42) were randomized to schedules that varied in severity of sleep loss: 4 days (88 hours) of partial sleep deprivation (PSD) involving two 2-hour naps per day or 4 days of total sleep deprivation (TSD). Plasma samples were obtained every 6 hours across 5 days and analyzed by using enzyme-linked immunoassays for sTNF-alpha RI, sTNF-alpha RII, IL-6, soluble IL-2 receptor, IL-10, and TNF-alpha. RESULTS: Interactions between the effects of time and sleep deprivation level were detected for sTNF-alpha RI and IL-6 but not for sTNF-alpha RII, soluble IL-2 receptor, IL-10, and TNF-alpha. Relative to the PSD condition, subjects in the TSD condition had elevated plasma levels of sTNF-alpha RI on day 2 (P =.04), day 3 (P =.01), and across days 2 to 4 of sleep loss (P =.01) and elevated levels of IL-6 on day 4 (P =.04). CONCLUSIONS: Total sleep loss produced significant increases in plasma levels of sTNF-alpha RI and IL-6, messengers that connect the nervous, endocrine, and immune systems. These changes appeared to reflect elevations of the homeostatic drive for sleep because they occurred in TSD but not PSD, suggesting that naps may serve as the basis for a countermeasures approach to prolonged spaceflight.

Non-NASA Center↗
NASA NTRS2004

Human Motion Tracking at Marshall Space Flight Center's Collaborative Engineering Center ANVIL

The installation and use of electromagnetic human motion trackers requires a specially designed and metal-free environment for optimal performance. Such an area is not readily available at the Marshall Space Flight Center Collaborative Engineering Center ANVIL. Our paper details a systems engineering approach to installing and operating Ascension Technologies Ethernet MotionStar tracking system in a sub-optimal environment, used with the JACK human computer model's motion capture capabilities. We also discuss how this system is integrated into the Marshall Space Flight Center's Human Engineering process.

Henderson, Steven J.↗
NASA NTRS2015

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Accurate taxi time prediction can be used for more efficient runway scheduling to increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. This paper describes two different approaches to predicting taxi times, which are a data-driven analytical method using machine learning techniques and a fast-time simulation-based approach. These two taxi time prediction methods are applied to realistic flight data at Charlotte Douglas International Airport (CLT) and assessed with actual taxi time data from the human-in-the-loop simulation for CLT airport operations using various performance measurement metrics. Based on the preliminary results, we discuss how the taxi time prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast-time simulation model for implementing it with an airport scheduling algorithm in real-time operational environment.

Lee, Hanbong↗
NASA NTRS2021

Identifying Planetary Transit Candidates in TESS Full-frame Image Light Curves via Convolutional Neural Networks

The Transiting Exoplanet Survey Satellite(TESS)mission measured light from stars in∼75% of the sky throughout its 2 yr primary mission, resulting in millions of TESS 30-minute-cadence light curves to analyze in the search for transiting exoplanets. To search this vast data trove for transit signals, we aim to provide an approach that both is computationally efficient and produces highly performant predictions. This approach minimizes the required human search effort. We present a convolutional neural network, which we train to identify planetary transit signals and dismiss false positives. To make a prediction for a given light curve, our network requires no prior transit parameters identified using other methods. Our network performs inference on a TESS 30-minute-cadence light curve in∼5 ms on a single GPU, enabling large-scale archival searches. We present 181 new planet candidates identified by our network, which pass subsequent human vetting designed to rule out false positives.Our neural network model is additionally provided as open-source code for public use and extension

Gregory Olmschenk↗