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NASA NTRS2023

Extensive Global Wetland Loss Over the Past Three Centuries

Wetlands have long been drained for human use, thereby strongly affecting greenhouse gas fluxes, flood control, nutrient cycling and biodiversity. Nevertheless, the global extent of natural wetland loss remains remarkably uncertain. Here, we reconstruct the spatial distribution and timing of wetland loss through conversion to seven human land uses between 1700 and 2020, by combining national and subnational records of drainage and conversion with land-use maps and simulated wetland extents. We estimate that 3.4 million km 2 (confidence interval 2.9–3.8) of inland wetlands have been lost since 1700, primarily for conversion to croplands. This net loss of 21% (confidence interval 16–23%) of global wetland area is lower than that suggested previously by extrapolations of data disproportionately from high-loss regions. Wetland loss has been concentrated in Europe, the United States and China, and rapidly expanded during the mid-twentieth century. Our reconstruction elucidates the timing and land-use drivers of global wetland losses, providing an improved historical baseline to guide assessment of wetland loss impact on Earth system processes, conservation planning to protect remaining wetlands and prioritization of sites for wetland restoration.

Etienne Fluet-Chouinard↗
NASA NTRS2023

Deciphering Solar Magnetic Activity: The (Solar) Hale Cycle Terminator of 2021

McIntosh and colleagues identified an event in the solar timeline that appeared to play a role in how Sunspot Cycle 23 (SC23) transitioned into Sunspot Cycle 24 (SC24). The timeframe for this transition was rapid, taking place in as short as time as a solar rotation. M2014 inferred that the transition observed was a critical episode for the Sun’s global-scale magnetic field that was being manifest in the spatially and temporally overlapping and magnetic systems belonging to the Sun’s 22-year (Hale) magnetic cycle. These events have been dubbed as Hale Cycle terminations, or ‘terminators’ for short. Further exploration revealed a relationship between terminator separation (as a measure of overlap in the Hale Cycles) and the upcoming sunspot cycle amplitude. McIntosh and colleagues extrapolated upon this relationship to identify the termination of the SC24 carrying Hale Cycle band in Mid-2020 and inferred that this would result in a very large Sunspot Cycle 25 (SC25). This paper presents observational analysis of the end of SC24 and the initial months of SC25 growth following a terminator that occurred in mid-December 2021 (approximately 12/13/2021). We use the December 2021 terminator to finalize the forecast of SC25 amplitude 184 (±17 with 95% confidence, and ±63 with 68% confidence). Finally, we use other terminator-related superposed epoch analyses to project the timing of SC25 maxima in late 2023 to mid 2024.

Sun↗
NASA NTRSDate not supplied

End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben D Phillips↗
NASA NTRSDate not supplied

Setting the Bar for the Replacement of the Probability of Collision Metric

To date, satellite conjunction assessment (CA) risk analysis has largely embraced the probability of collision (Pc) as the omnibus metric to evaluate collision likelihood, and its use in such assessments has mostly been straightforward: at the point at which a conjunction mitigation decision is required, the calculated Pc is compared to a threshold; and if the calculated Pc exceeds that threshold, then a mitigation action is warranted. With only minor variation, this approach is employed by major CA risk assessment centers (e.g., NASA, EUSST, CNES, JAXA) and is advanced as the preferred method in the published CA best practices handbooks. Despite this near unanimity of operational practice, there is a major strain of secondary literature critical of the Pc and willing to propose alternatives. Alfano (2005) pointed out the ability of the Pc to underrepresent the risk in certain situations and counselled a maximum Pc construct. Carpenter (2017, 2019) reiterated this criticism and proposed using instead a confidence interval on the miss distance. Balch et al. (2019) identified what they argued was a defect in the entire Bayesian Pc construct and believed that the use of a more conservative methodology based on covariance ellipsoid overlap was necessary. Delande (2022) introduced the framework of collision “plausibility” to the risk assessment process and sketched out how this might be used operationally. Elkantassi (2022) published a full development of the miss distance confidence interval approach and applied it to several worked examples. While these different approaches to collision risk assessment do differ in their details, they all converge on two central points: first, the Pc’s failure to give an adequate expression of the risk in dilution region situations is a fatal flaw; and second, a conjunction should be presumed risky and in need of mitigation until the evidence of the situation can establish otherwise. These criticisms, if correct, would counsel a number of modifications to current CA operational practice; as such, they force a re-examination of fundamental aspects of the CA problem, including the following: 1. Is the CA risk assessment a probability problem, a statistics problem, or something else? 2. If it is a statistics problem, does it lend itself naturally to a hypothesis test construction? 3. If it can be construed as a hypothesis test, what form should the null hypothesis take, to wit: what constraints exist on the choice of the null hypothesis, what selections are in best alignment with all of the attendant parameters of the problem, and what is implied philosophically by different choices? 4. What are the implications of using the different proposed risk assessment parameters for CA? This question should be answered both in determining how frequently the dilution region situation cited by the critics of the Pc actually appears in an operationally significant manner and the missed detection and false alarm rates of all of the proposed risk assessment metrics, compared both to the Pc and to each other. This paper explores and offers preliminary answers to the above questions, presenting a researched treatment of the philosophical nature of the CA problem and the null hypothesis choice that achieves the greatest consistency with all of the different aspects of operational CA conduct. It then profiles all of the different proposed risk assessment metrics enumerated in the earlier paragraph against an extremely large database of conjunction events at both the 550km and 700km altitudes. The combination of the philosophical exploration of the CA problem and the results of the profiling activity articulates what a risk assessment metric will need to demonstrate, in terms of both innate construction and performance, in order to be a true competitor to the Pc.

conjunction assessment↗
NASA NTRSDate not supplied

End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben Phillips↗
NASA NTRS2024

Computing Proximity to Threat Along Uncertain Trajectory to Support Urban Air Mobility

Many airspace threats affect the selection of a flight route, such as terrain, physical obstacles, adverse weather, and special-use airspace, among others. Threat avoidance during Urban Air Mobility (UAM) and Low Altitude Mobility (LAM) flights is especially challenging due to their lower cruising altitudes. These operations may be exposed to buildings, towers, trees, terrain undulations, etc., along much of their flight route. Moreover, these low-altitude flights in urban environments are expected to encounter very busy airspace, increasing the workload associated with threat avoidance. The Proximity to Threat (PtT) function aims to support onboard or remote pilots by computing the risk from geospatial threats in the airspace along an aircraft’s flight path. The threats are both static entities and dynamic airspace restrictions that have been modeled and stored in geo-referenced mapping databases. The flight path may be specified by waypoints, airways, or similar discrete route elements or by a time series of closely spaced position and altitude points. Flight path uncertainty can also be taken into account by assuming both the position and altitude to be sampled from two normal distributions, each being specified by a mean and variance. PtT uses this additional information to verify that the flight path remains clear of threats along a wider path or if a position is reached earlier or later than anticipated, assuring the pilot or operator that there will be an available safety margin even if the aircraft deviates due to one or more occurrences of unexpected wind gusts, mechanical failures, airspeed changes, collision avoidance maneuvers, etc. The client can specify an uncertainty confidence level to bound the set of trajectories evaluated for threats so that the confidence level corresponds to the client’s risk tolerance under the expected conditions. PtT can be used as a pre-flight planning tool to determine a safe route through known threats and in-flight to avoid emerging or changing threats. In this report, we present the PtT function, discuss how the trajectory uncertainty is incorporated into the PtT function, and describe the use of this PtT function in a representative scenario.

urban air mobility↗
NASA NTRSDate not supplied

Blind Validation Study of PRICE TruePlanning and SEER-H

Two of the primary parametric costing tools used to estimate the development and production cost of future spacecraft hardware are PRICE TruePlanning by PRICE Systems, and System Estimation and Evaluation of Resources-Hardware (SEER-H) by Galorath. These are standard tools used by NASA and industry to estimate the cost of new aerospace hardware. This study is an independent verification of the accuracy of these tools and was originally published in 2018. The purpose of this presentation is to circulate the findings of that study among the NASA cost community. Both PRICE Systems and Galorath have completed internal validation studies of their parametric cost estimating tools; however, they only provided the results of the studies and did not detail the exact methods used to perform the validation. In the present study, cost estimators used PRICE TruePlanning and SEER-H to estimate the cost of twelve different past NASA science missions. The estimators were prevented from knowing the actual cost of the missions in an effort to minimize cognitive biases. In the present study, SEER-H had an average error of 23%, median error of -0.3%, with a standard deviation of 43%. PRICE had an average error of 52%, median error of 50%, and standard deviation of 45%. Nine of the twelve mission's actual costs fell within the 80% confidence interval of SEER's probabilistic estimates; however, none of the missions fell within PRICE’s confidence intervals. There were several factors independent of PRICE and SEER-H which may have affected the accuracy of the results in the present study including: uncertainty in the technical data used for the estimates, the methods used to estimate uncertainty in spacecraft component mass and numbers of prototypes, and the experience of the estimators.

SEER↗
NASA NTRSDate not supplied

A Science-Focused Artificial Intelligence (AI) Responding in Real-Time to New Information: Capability Demonstration for Ocean World Missions

Introduction: Artificial intelligence (AI) has long been considered a potential mechanism to explore increasingly challenging environments, including those with extreme temperatures and pressures, limited communication capabilities, or those with demanding terrain. We posit that missions in extreme environments could deploy an onboard AI focused on science observations and goals in order to augment a traditional concept(s) of operations (ConOps). An onboard AI capability could perform functions such as data analysis in order to make high-level decisions, including prioritized data transmission for analysis by ground-based teams or autonomously-guided follow-on analyses that maximize science return. Such a capability would empower missions to respond to scientific data of interest in real-time; a mission could make observations and perform a preliminary analysis to alert ground-based scientists to an observation of interest, enabling an informed, rapid response from Earth-based teams. Enceladus Case Study for Onboard AI: We are developing an onboard AI capability for real-time telemetry response that formulates and carries-out informed decisions in service to established mission goals, enabling increased science return of a mission. We focus our AI development for use on a constellation of SmallSats orbiting Enceladus. Our Enceladus case study tests autonomous decision-making capabilities in scenarios with complex orbital dynamics, plume ejecta, extreme cold environments, power restrictions, and a requirement to maximize science return for a potential positive detection of life, while critically evaluating the potential for false positives. Telemetry includes simulated scientific data, spacecraft onboard operational data (e.g., position, velocity, and rotation), and engineering hardware performance data. Enceladus SmallSat Constellation. Our constellation includes eight SmallSat spacecraft in an 8:35 resonant orbit-based formation, leveraging Saturn’s gravitational forces to maintain stable orbits with global coverage around Enceladus. To our knowledge, we simulate the first stable configuration of multiple spacecraft in closed orbits around Enceladus, using a full ephemeris force model (Russell and Lara, 2009). Each spacecraft’s orbit will precess, causing an eastward ground track shift (from an orbiter’s perspective) of each spacecraft for each orbit. However, all spacecraft return to their original positions relative to Enceladus after eight Enceladus revolutions around Saturn. We model communication pathways between SmallSats to understand how information would need to be transmitted across the constellation to enable AI-driven decision-making and resource allocation across the fleet. Capability Demonstration. Our simulated capability demonstration inputs position, velocity, and rotation telemetry from our Enceladus-focused constellation simulations, and mass spectrometry data collected from abiotic and biotic laboratory-analog ocean world experiments (Theiling et al., 2018; Theiling, 2021; Da Poian et al., 2023). Data from these experiments are used to simulate MS measurements and different scenarios of science observations for onboard analysis performed on each of the eight spacecraft. For these demonstrations, we integrate 24 machine learning (ML) algorithms into an onboard intelligence as a ‘knowledge base’, including algorithms evaluating data quality and those predicting (with % confidence) gas composition, ocean aqueous chemistry, and whether the sample was influenced by microbial life. The onboard AI capability is designed to use the knowledge base to come to a consensus-based decision in the interpretation of the observed data in order to request additional action outside of a pre-defined ConOps. Requested actions could include e.g., prioritized downlink to Earth (for analysis by ground-based teams) or follow-on analyses performed across the constellation. The spacecraft’s intelligent onboard planner must then determine whether sufficient resources (e.g., time, power, etc.) are available and weigh the request with mission priorities. In our simulation, the constellation is able to identify potential biosignatures using onboard ML algorithms, evaluate the confidence of that prediction, and perform follow-on analyses across the fleet to confirm the detection, in order to best prepare a transmission of these data to Earth-based teams.

astrobiology↗
NASA NTRS2023

X-Ray Polarimetry of the Accreting Pulsar GX 301-2

The phase- and energy-resolved polarization measurements of accreting X-ray pulsars (XRPs) allow us to test different theoretical models of their emission, and they also provide an avenue to determine the emission region geometry. We present the results of the observations of the XRPGX 301−2 performed with the Imaging X-ray Polarimetry Explorer (IXPE). A persistent XRP, GX 301−2 has one of the longest spin periods known:∼680 s. A massive hyper-giant companion star Wray 977 supplies mass to the neutron star via powerful stellar winds. We did not detect significant polarization in the phase-averaged data when using spectropolarimetric analysis, with the upper limit on the polarization degree (PD)of 2.3% (99% confidence level). Using the phase-resolved spectropolarimetric analysis, we obtained a significant detection of polarization (above99% confidence level) in two out of nine phase bins and a marginal detection in three bins, with a PD ranging between∼3% and∼10% and a polarization angle varying in a very wide range from∼0◦to∼160◦. Using the rotating vector model, we obtained constraints on the pulsar geometry using both phase-binned and unbinned analyses, finding excellent agreement. Finally, we discuss possible reasons for a low observed polarization in GX 301−2.

GX 301-2, polarization, X-ray, X-ray binary, pulsa↗
NASA NTRS2023

The First X-Ray Polarimetric Observation of the Black Hole Binary LMC X-1

We report on an X-ray polarimetric observation of the high-mass X-ray binary LMC X-1 in the high/soft state, obtained by the Imaging X-ray Polarimetry Explorer ( IXPE ) in 2022 October. The measured polarization is below the minimum detectable polarization of 1.1 per cent (at the 99 per cent confidence level). Simultaneously, the source was observed with the Neutron Star Interior Composition Explorer (NICER), Nuclear Spectroscopic Telescope Array ( NuSTAR ), and Spectrum-Rontgen-Gamma ( SRG )/Astronomical Roentgen Telescope – X-ray Concentrator (ART-XC) instruments, which enabled spectral decomposition into a dominant thermal component and a Comptonized one. The low 2–8 keV polarization of the source did not allow for strong constraints on the black hole spin and inclination of the accretion disc. However, if the orbital inclination of about 36◦ is assumed, then the upper limit is consistent with predictions for pure thermal emission from geometrically thin and optically thick discs. Assuming the polarization degree of the Comptonization component to be 0, 4, or 10 per cent, and oriented perpendicular to the polarization of the disc emission (in turn assumed to be perpendicular to the large-scale ionization cone orientation detected in the optical band), an upper limit to the polarization of the disc emission of 1.0, 0.9, or 0.9 per cent, respectively, is found (at the 99 per cent confidence level).

LMC X-1↗
NASA NTRS2024

Highly Significant Detection of X-Ray Polarization from the Brightest Accreting Neutron Star Sco X-1

The Imaging X-ray Polarimetry Explorer measured with high significance the X-ray polarization of the brightest Z-source, Sco X-1, resulting in the nominal 2–8 keV energy band in a polarization degree of 1.0% ± 0.2% and a polarization angle of 8° ± 6° at a 90% confidence level. This observation was strictly simultaneous with observations performed by NICER, NuSTAR, and Insight-HXMT, which allowed for a precise characterization of its broadband spectrum from soft to hard X-rays. The source has been observed mainly in its soft state, with short periods of flaring. We also observed low-frequency quasiperiodic oscillations. From a spectropolarimetric analysis, we associate a polarization to the accretion disk at <3.2% at 90% confidence level, compatible with expectations for an electron scattering dominated optically thick atmosphere at the Sco X-1 inclination of ∼44°; for the higher-energy Comptonized component, we obtain a polarization of 1.3% ± 0.4%, in agreement with expectations for a slab of Thomson optical depth of ∼7 and an electron temperature of ∼3 keV. A polarization rotation with respect to previous observations by OSO-8 and PolarLight, and also with respect to the radio-jet position angle, is observed. This result may indicate a variation of the polarization with the source state that can be related to relativistic precession or a change in the corona geometry with the accretion flow.

Fabio La Monaca↗
NASA NTRS2024

Recommendations on Evidence and Process for Certification of Learning-enabled Components in Aerospace Systems

This report primarily identifies a collection of relevant and necessary evidence for assurance of machine learnt components (MLCs)—also known as learning-enabled components—integrated into aircraft systems, and gives preliminary suggestions on the elements of a certification process that invoke the identified evidence. The main focus is on feedforward neural networks that are static and trained offline through supervised learning. A brief background on the generic elements of the lifecycle of an MLC is given to contextualize the assurance considerations and, consequently, the evidence that is relevant and necessary to support certification. At the level of an MLC, those considerations relate to: (i) the consistency and correctness of MLC contributions to system functions in the context of a validated functional intent; and (ii) the absence of MLC contributions to aircraft-level failure conditions. At an ML model level, confidence in model and data properties contribute to assurance of the containing MLC, in particular: (a) generalizability and robustness of models, in the presence of inputs not previously seen during training, disturbances to inputs, and unexpected inputs; and (b) valid data, i.e., data that are at least representative, relevant, complete, and accurate. Evidence for the above span the elements of the ML lifecycle, and includes, at a minimum, lifecycle artifacts that pertain to: (1) properties of requirements capturing functional intent, safety constraints, and aspects of the intended use and operating environment; (2) model performance, model complexity and design, and algorithm choice; (3) achievement of required performance at the levels of a trained model during model development, a trained model after model development is complete, and a trained model that is transformed into an executable equivalent; (4) model implementation aspects necessary for transforming a trained model into the executable equivalent; (5) integration of the executable trained model into the containing MLC, and eventually the larger system; and, (6) lastly, the verification and validation (V&V) of each of the above. Such V&V lifecycle artifacts themselves include: aspects of coverage, e.g., of various levels of requirements by the input space of the model and the data; traceability (where applicable); application of formal methods for property specification, analysis, and checking. Examples of evidence generation methods and tools further ground the discussion on what constitutes evidence, and the contribution to assurance during certification. The identified assurance considerations and supporting evidence is not a comprehensive set. Additionally, neither what should be considered as sufficient evidence relative to the assigned criticality of an MLC, nor how criticality ought to be determined and adjusted, have been considered in this report. However, suggestions are made for potential activities of the ML lifecycle that are aimed at providing confidence that an MLC can be relied upon when integrated into its containing (aircraft) system. Those activities are proposed as candidate elements of a certification process for MLCs. The main purpose of this report to inform regulatory guidance and consensus standards that may be used to meet the safety intent of the applicable regulations.

Aviation safety↗
NASA NTRS2024

Building Identity and Community for Early Career Professionals in the Emerging Field of Astrobiology

To support training and foster retention in the emerging field of astrobiology, NASA has funded opportunities for graduate students and early career scientists to develop a community, foster interdisciplinarity, increase confidence, and showcase career options. The design of these opportunities builds on research on factors that increase retention, including feeling competent, having autonomy and a sense of purpose, having a sense of identity, and being connected to others in the field. Findings are reported from retrospective studies of two NASA career-building opportunities, the Astrobiology Graduate Conference and the International Astrobiology Summer School held in Santander, Spain. We present evidence that attendees gain confidence by presenting to, and working with, their peers, and feel competent to express their ideas and interests and build relationships in the field that continue after the experiences. Many say that they feel less isolated and go on to present or publish with colleagues they meet. Their career options also expand by meeting potential colleagues from different disciplines. Based on the findings, participating in either of these long-running programs shows clear positive impact on early career astrobiology professionals.

Early Career Researchers↗
NASA NTRSDate not supplied

Metrics on Predictive Probability of Collision Assessments Reliant on Object State Updates

The probability of collision (Pc) metric is the dominant method of risk charac-terization for evaluating satellite close approaches, and has become of particular interest following major debris production incidents. However, confidence in the use of Pc for actionability has been questioned due in part due to its non-intuitive and often significant corrections following messaging updates. This paper assesses predictively computed probabilities and confidence bounds on Pc values corresponding to orbit determination updates by comparing these predic-tive values to observed operational values. This paper also presents use cases for this methodology in assessing event actionability.

conjunction assessment↗
DOE OSTI2020

Multi-Channel Entity Alignment via Name Uniqueness Estimation

When searching for adversarial activity within multiple networks, one of the greatest challenges is how to accurately align entities across different channels of information. This task becomes increasingly difficult when minimal additional information is known about each individual besides a name. Within this study, we analyze name rarity and how it can be used to align people on three distinct data channels: Venmo financial transactions, Reddit online discussions, and a bibliographic data source of academic writings. We explore how the uniqueness of a name can be used to decide if a person is likely the same as another across networks, in the absence of any additional ground truth. While 100 percent confidence cannot be gained, we can use this information to clarify when a possible alignment is more or less likely to be the same individual, increasing our confidence of accurately detecting adversarial behavioral patterns. From the data collected, we found that 0.1% of people had the same name across data sets, and 22.5% of those names are considered rare by our threshold. In our study, we also examine the accuracy of our method and show how real names can be extracted from account usernames, and compared in a similar manner.

Orren, Miquette J.↗
DOE OSTI2022

Higgs Self Couplings Measurements at Future proton-proton Colliders: a Snowmass White Paper

The Higgs boson trilinear and quartic self-couplings are directly related to the shape of the Higgs potential; measuring them with precision is extremely important, as they provide invaluable information on the electroweak symmetry breaking and the electroweak phase transition. In this paper, we perform a detailed analysis of double Higgs boson production, through the gluon gluon fusion process, in the most promising decay channels di-bottom-quark di-photons, di-bottom-quark di-tau, and four-bottom-quark for several future colliders: the HL-LHC at 14 TeV and the FCC-hh at 100 TeV, assuming respectively 3 inverse ab and 30 inverse ab of integrated luminosity. In the HL LHC scenario, we expect an upper limit on the di Higgs cross section production of 0.76 at 95% confidence level, corresponding to a significance of 2.8 sigma. In the FCC-hh scenario, depending on the assumed detector performance and systematic uncertainties, we expect that the Higgs self-coupling will be measured with a precision in the range 4.8-8.5% at 95% confidence level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Paving the Way for Net Zero Energy Codes through Performance Based Approaches

The prescriptive path is the most widely used approach for commercial code compliance in the United States. However, to achieve near-term net zero energy performance that many stakeholders in the buildings industry desire, it is clear that energy codes will need to transition from prescriptive to performance-based approaches. However, that transition is not without potential pitfalls. Among the concerns expressed by stakeholders are added complexity and cost, lack of confidence in energy modeling results, gamesmanship on the part of applicants, lack or qualified reviewers, challenges in promoting de-carbonization, and the inequity of trading long lived envelope efficiency for short lived measures such as building controls. This paper will discuss multiple approaches being evaluated and implemented by Pacific Northwest National Laboratory to improve the usability of the performance-based approach for both code compliance and beyond code programs to meet aggressive energy savings targets. A 'system performance' approach provides a simpler approach compared to whole building performance, while keeping tradeoffs limited to specific building systems. A simplified whole-building performance-based approach for small commercial buildings can cost effectively achieve deeper savings for buildings that typically follow the prescriptive compliance approach. Improved reporting and verification processes applied to the traditional performance path can provide greater confidence in simulation results and facilitate adoption of performance-based approaches. Prescriptive packages provide prescriptive solutions while targeting specific performance thresholds. This paper discusses the progress being made with each of these approaches and provides examples of their implementation in energy codes and beyond code programs.

Performance Based Codes, Appendix G, PRM, TSPR, Sy↗
DOE OSTI2022

A Search for Sterile-Neutrino-Based Muon Neutrino Disappearance Using the MicroBooNe Deep Learning Analysis

We describe a search for νµ disappearance using the MicroBooNE Deep Learning analysis 1µ1p selection. Presently, the unexplained MiniBooNE and LSND anomalies could be explained by a sterile neutrino impacting neutrino oscillations. Our analysis searches for the allowed parameter space that could describe such a sterile neutrino. We determine the allowed and excluded region of a 3+1 sterile-based muon neutrino disappearance model in MicroBooNE at 90% confidence. Our allowed region includes both the null model, and current global best fit model. Context for the underlying Deep Learning analysis is provided and several validation studies surrounding both the disappearance search, and originating 1µ1p selection are performed to strengthen confidence in the result. In addition, a next-generation deep learning tool for cosmic-ray-muon event discrimination is proposed and evaluated, demonstrating a removal of 70% of the remaining event background when added to current methods, under the cut criteria used.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗