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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Ellicott City Disasters II - Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗
NASA NTRS2020

Ellicott City Disasters III: Building a Real-Time Statistical Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗
NASA NTRSDate not supplied

Uranus Probe Entry and Descent Mission Concept

Introduction: Uranus was identified as the third highest priority flagship mission in the 2012-2022 Planetary Science Decadal Survey. This latest concept study was requested by the Decadal Survey panel to determine NASA’s planetary science priorities from 2022-2032. This study focused on the probe’s entry and descent aspects and associated trades for viable trajectory options. Uranus Mission and Descent Probe: The proposed Uranus Orbiter and Probe (UOP) Flagship mission will investigate Uranus and its surrounding moons using an orbiting spacecraft with a Uranus descent probe. Unlike previous studies the probe release will occur after orbit insertion allowing sufficient separation of critical events during the orbit insertion burn. The probe will be released at an altitude that allows one hour of in situ atmospheric readings that will be relayed to the orbiter. Afterwards the orbiter will transition to the moon tour phase of the mission. The configuration chosen for the entry aeroshell was a 45° sphere-cone. This shape has been used in the past in the Pioneer Venus Galileo missions. However, the nose radius considered in the present study differed from the values used in either of the previous configurations, primarily to reduce the heat flux at the stagnation point. A two-step approach was used in the development of flight trajectories for the chosen configuration. In the first step, the trajectory code POST2 was used to screen the thousands of entry states provided by interplanetary trajectory simulations, which were terminated at an altitude of 2000 km from the reference surface (1 bar) of Uranus. The screening criteria were: (i) optimization of the communication geometry between the entry probe and orbiter to ensure at least 1 hour of science measurements, (ii) peak stagnation point pressures to be less than six bar, (iii) peak heat fluxes to be less than 5 kW/cm2; the latter two constraints being the limits of ground-test capabilities of the arc jets at NASA Ames Research Center. The entry team investigated two trajectories that met the criteria above, a shallow entry (high heat load ~44 kJ/cm2) and a steeper entry (high heat rate ~1950 W/cm2). In the second step, the two bounding candidate entry states from the POST2 screening process were used in developing flight trajectories using TRAJ coupled with FIAT (a materials thermal response and sizing code) and a margins policy to determine a margined uniform thickness (hence mass) of the forward heatshield material based on the aerothermal environments at the stagnation point. Since the combination of TRAJ and FIAT size the TPS based on stagnation point environments only, flow field computations using DPLR were necessary to determine turbulent aerothermal environments on the conical flank, and the augmentation of these environments due to surface roughness. The environments at select locations on the forward heatshield were then used to size the thermal protection material, with the largest thickness value then used to estimate the mass. The newly developed woven thermal protection material –HEEET (Heatshield for Extreme Entry Environments Technology) –was considered for the forward heatshield and PICA (Phenolic-Impregnated Carbon Ablator) was considered for the backshell. These NASA-developed materials are at TRL 6 and TRL 9, respectively. Furthermore, two options were considered for the HEEET material: (i) a dual-layer option with a denser recession layer on top and an insulative layer underneath it, and (ii) a single layer option consisting of the insulative layer alone. Results: It is clear that probe entry states are feasible and the selected TPS options are able to perform in the predicted aerothermal environments thus enabling the mission to meet of the descent probe portion of this flagship mission

Entry Descent and Landing↗
DOE OSTI2022

Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference

Inferring the values and uncertainties of cosmological parameters in a cosmology model is of paramount importance for modern cosmic observations. In this paper, we use the simulation-based inference (SBI) approach to estimate cosmological constraints from a simplified galaxy cluster observation analysis. Using data generated from the Quijote simulation suite and analytical models, we train a machine learning algorithm to learn the probability function between cosmological parameters and the possible galaxy cluster observables. The posterior distribution of the cosmological parameters at a given observation is then obtained by sampling the predictions from the trained algorithm. Our results show that the SBI method can successfully recover the truth values of the cosmological parameters within the 2σ limit for this simplified galaxy cluster analysis, and acquires similar posterior constraints obtained with a likelihood-based Markov Chain Monte Carlo method, the current state-of the-art method used in similar cosmological studies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Early calendar life and health prediction of silicon batteries via machine learning with uncertainty quantification

Lithium-ion batteries with silicon anodes promise high energy density but are limited by calendar lifetime. Reducing the long iteration time to obtain experimental results requires predicting calendar lifetime early in a cell's life. In this study, we demonstrate that lightweight machine learning models with feature engineering can provide calendar lifetime estimates from early electrochemical signals. After 1 month of electrochemical aging, the best models achieve 10% error in calendar-life prediction and can separate "bad" from "good" lifetime cells with a mean F1 score of 0.857. As battery systems exhibit inherent variability, four methods for uncertainty quantification are compared, and confidence intervals are demonstrated with an uncertainty of +-3.6 months in lifetime prediction. A feature importance analysis indicates that early patterns in voltage decay are the strongest indicators of calendar lifetime. Finally, this modeling approach has high error when generalizing to new electrode chemistries or testing conditions but with appropriately low confidence.

25 ENERGY STORAGE↗
DOE OSTI2024

Soil Carbon and Nitrogen Stocks Across Hillslopes Underlain by Continuous Permafrost in the Northern Arctic Foothills, Alaska, United States

Constraining the variability of soil organic carbon (SOC) and total nitrogen (TN) stocks across hillslopes in Low Arctic permafrost-affected landscapes remains a significant challenge for improving global estimates of permafrost SOC stocks. We investigated SOC and TN stocks across hillslopes at two sites in the Arctic Foothills of Alaska, United States (Happy Valley and Sagwon Hills). Average SOC and TN stocks for the 0–1-m depth interval were high (52.0 ± 15.1 kg C m −2 and 2.74 ± 0.82 kg N m −2 ) and linearly related ( R 2 = 0.74, p < 0.0001). Unlike soils of other permafrost and nonpermafrost landscapes, variability was greatest within rather than between hillslope positions. Furthermore, SOC and TN stocks in the surface 1 m did not exhibit strong patterns by hillslope position and were only weakly associated with major geomorphic parameters that typically predict SOC and TN stocks well in other landscapes. Although sampling at upper hillslope positions was largely limited to depths of less than 1.5 m due to the presence of coarse fragments in reworked glacial till, deeper observations at lower hillslope positions (footslopes, toeslopes, and basins) revealed significantly larger SOC stocks (92.0 ± 18.0 kg C m −2 at 2 m; 117.1 ± 10.4 kg C m −2 at 3 m). The unique small-scale variability in ice content, cryoturbation, patterned ground, and organic layer thickness on these broad, Low Arctic sites contributes to the relatively homogeneous distribution of SOC and TN stocks across hillslope positions in the top 1 m, but a future focus on deeper sampling may reveal greater differences in SOC and TN stocks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Shear strength measurements and hydrostatic compression of rhenium diboride under high pressures

Shear strength measurements have been carried out on rhenium diboride, ReB 2 , to a pressure of 74 GPa using a Radial X-ray Diffraction (R-XRD) technique in a diamond anvil cell using platinum as an internal x-ray pressure standard. The R-XRD result has provided a unique insight into the deformation of hexagonal crystal lattice under non-hydrostatic compression and variation of shear strength with increasing pressure. From R-XRD data, we have estimated hydrostatic component of compression to determine an equation of state of rhenium diboride yielding a bulk modulus of K 0 = 366 ± 25 GPa with a pressure derivative $K^{'}_{0}$ = 4.3 ± 0.5 in good agreement with hydrostatic density functional theory calculations. Here, the average lower bound of shear strength (τ) from various diffraction planes was then calculated using the measured interplanar d-spacing (d m ) and hydrostatic component of d-spacing (d p ) to be shown to approach 6.7 ± 0.4 GPa at 70 GPa. Our results show that the anisotropic compression effects observed in ReB 2 under hydrostatic compression are correlated to electronic structure changes under compression as predicted by theoretical calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Temperature distribution in a laser-heated diamond anvil cell as described by finite element analysis

Finite element analysis (FEA) is a powerful tool for numerically solving partial differential equations over complex geometries and is thus useful for analyzing heat transport in laser-heated diamond anvil cell (LHDAC) experiments. Our models expand on previously published simulations by calculating the volume-averaged temperatures of both the sample and insulation/pressure media under steady-state heating to determine the thermal pressure of the hot sample. Our goal is to produce an accurate relationship between the measured surface temperature of the absorbing sample and the temperature of the transparent insulating media, which is used to determine thermal pressure but susceptible to steep temperature gradients. We find that in doing so, our FEA models of temperature within the pressure/insulation media can differ from simplified estimates of temperature gradients by more than a factor of 2. We also explore temperature-dependent and temperature-independent thermal conductivity models and find that the volume-averaged temperatures differ by up to a factor of 1.3, forcing the predicted thermal pressures determined to also differ by up to a factor of 1.5 at a temperature of 2000 K at 50 GPa for neon. Higher temperatures exacerbate this difference. We also find that unintentional asymmetric sample insertion and sample heating, which are common in LHDAC experiments, do not have a first-order effect on volume-averaged temperatures. The FEA models, available in both Python and FlexPDE, are versatile across different sample geometries, materials, and heat source laser shapes.

Farah, Frederick↗
DOE OSTI2024

Repulsive Tomonaga-Luttinger liquid in the quasi-one-dimensional alternating spin-$\frac{1}{2}$ antiferromagnet NaVOPO 4

Here, we probe the magnetic field-induced Tomonaga-Luttinger liquid (TLL) state in the bond-alternating spin1/2 antiferromagnetic (AFM) chain compound NaVOPO 4 using thermodynamic as well as local μSR and 31 P NMR probes down to mK temperatures in magnetic fields up to 14 T. The μSR and NMR relaxation rates in the gapless TLL regime decay slowly following characteristic power-law behavior, enabling us to directly determine the interaction parameter K as a function of the magnetic field. These estimates are crosschecked using magnetization and specific heat data. The field-dependent K lies in the range of 0.4 < K < 1 and indicates the repulsive nature of interactions between the spinless fermions, in line with the theoretical predictions. This renders NaVOPO 4 the first experimental realization of TLL with repulsive fermionic interactions in hitherto studied S = 1/2 bond-alternating AFM-AFM chain compounds.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Calibration of CDM-Based Creep Constitutive Model Using Accelerated Creep Test (ACT) Data

In conventional creep testing (CCT) a specimen is subject to constant load and temperature for a long-duration until creep rupture occurs. Conventional testing can be costly when considering the number of experiments needed to characterize the creep response of a material over a range of stress and temperature. To predict long-term creep-rupture properties, the time-temperature-stress superposition principle (TTSSP) approach has been employed where stress and/or temperature is applied at an elevated level; the result of which are extrapolated down to low stress and/or temperature conditions. These methods have been successful in predicting minimum-creep-strain-rate (MCSR) and stress-rupture (SR) but suffer from an inability to predict the creep deformation curve or account for changes in deformation mechanisms or aging that occurs at long-duration. An accelerated technique, termed the Stepped isostress method (SSM) allows the accelerated testing of materials to determine their creep deformation response. Unlike TTSSP tests, the SSM test employs a single specimen where the stress is periodically step increased until rupture. The SSM creep deformation curve is processed (time and strain shifted) to produce an accelerated creep deformation curve that represent the creep deformation curve at the initial stress level in SSM. A processing procedure for metals has yet to be developed. The research objective of this study is to develop a processing procedure for SSM test data using a creep-damage constitutive model. Triplicate SSM tests were conducted on Ni-based superalloy Inconel 718 at 650°C with stress being periodically increased until rupture. Triplicate CCT tests were conducted at the initial stress level of the SSM tests. The Sine-hyperbolic (Sinh) creep-damage model was employed in this study. The Sinh creep-damage constitutive model is based on coupled creep strain rate and damage evolution equations; where both rates are dependent on the current state of damage. Calibration is two-step: analytical and numerical optimization. Each stepped creep deformation curve is tackled quasi-analytically to determine MCSR and SR related material constants and accumulated damage. The damage accumulated at the end of each step was then passed onto subsequent steps to calibrate the MCSR, rupture prediction, and damage evolution. Numerical optimization was applied to optimize model constants involved in the creep strain constitutive equations in order to generate best-fitted Sinh creep deformation curves. The Sinh model predictions were compared to the SSM and CCT data. The Sinh model satisfactorily predicts the SSM data and thus the calibrated material constants provides a good estimate of rupture found in the CCT data. Calibration using SSM data reduces the number of tests needed to calibrate a model; significantly reducing costs. A single SSM test replaces numerous creep tests at different stresses.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Uncertainty quantification for competing failure mechanisms in unidirectionally reinforced carbon–carbon composites

Microstructure-informed finite element models play a key role in the carbon–carbon composite design process. Variability in manufacturing process parameters and experimental limitations introduce model parameter uncertainty. This study quantifies the effect of model parameter uncertainty on transverse tensile fracture behavior and proposes a methodology to predict the failure mode based on competing microscale damage mechanisms. Finite element simulations incorporate fiber–matrix interface debonding with cohesive zones and matrix damage with a smeared crack band approach in a unidirectional carbon–carbon composite. Results from a variance-based global sensitivity analysis identifies interfacial and matrix damage parameters as the primary source of variability in fracture behavior. Sobol’ indices indicate that matrix and cohesive zone strengths contribute 94% of the variance in the effective ultimate stress. A local analysis elucidates the relationship between these constituent strength parameters and failure mode by estimating the probability of cohesive, matrix, and mixed-mode dominated failure. Based on the results for 4000 simulations, 93% exhibit mixed-mode or interfacial dominated failure, which underscores the crucial role of fiber–matrix interface debonding in the transverse tensile failure of carbon–carbon composites. These uncertainty quantification results facilitate more efficient model calibration and provide a framework for microstructure-informed failure predictions in the face of manufacturing-induced uncertainty.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Neural Network‐Based Methods for Ocean Surface Wave Measurement Using Submarine Distributed Acoustic Sensing (DAS)

Two new data-driven models for estimating ocean surface waves from distributed acoustic sensing (DAS) submarine cable strain rate are developed using supervised machine learning on a 10-day data set collected offshore of Oliktok Point, Alaska. The new models were trained on target data from seafloor pressure moorings at three sites spaced evenly along 27.1 km of cable and were benchmarked against an empirical transfer function method previously used to estimate waves from DAS. A model which uses convolutional neural networks to transform 2-km frequency-wavenumber strain spectra to seafloor pressure spectra outperforms the benchmark in wave height prediction (RMSE of 0.15 vs. 0.41 m) and period prediction (0.29 vs. 0.37 s) when evaluated on a held-out test data set. When applied to a DAS data set collected on the same cable 2 years prior, the CNN-based model maintained similar significant wave height performance (RMSE = 0.23 m) relative to available satellite altimetry data. A two-hidden-layer, fully connected neural network which transforms 1-D strain spectra to seafloor pressure spectra also outperforms the benchmark in wave height prediction (RMSE of 0.19 vs. 0.41 m), but does not generalize as well to the prior data. Regression-based machine learning is useful for estimating waves from DAS data when the pressure-strain relationship varies temporally and spatially across different wave conditions. Models can be applied to DAS data to measure waves with higher spatial resolution and longer temporal coverage than traditional methods, which often measure waves only at a single point.

Davis, Jacob R. [Univ. of Washington, Seattle, WA ↗
DOE OSTI2026

Reliability Assessment of Cooling Fans for PV Inverters: Testing, Modeling, and Case Studies

The reliability of photovoltaic (PV) inverters is critical for long-term solar system performance, with cooling fan failures frequently leading to costly downtime. While much research exists on general cooling fan reliability, little attention has been given to fans operating within PV inverters and their unique environmental challenges. Here, this article proposes a comprehensive methodology to address this gap. First, a failure mode and effects analysis is performed on fans to identify the key failure mechanisms in PV applications, their corresponding stressors, and the models necessary for lifetime prediction. Second, an accelerated life test is designed and conducted to collect valuable experimental data for PV inverter fans in a reasonable amount of time. Third, a mathematical conversion of dynamic mission profiles into effective constant stress levels is derived. Fourth, case studies are given, showcasing lifetime estimates that account for geographic variations in mission profile data. The results demonstrate that this integrated approach leads to an accurate reliability assessment for PV inverter cooling fans.

accelerated life testing (ALT)↗
DOE OSTI2025

On the Efficacy of Repeat Voltage Holds for Conditioning and Calendar Life Testing of Graphite and Silicon Cells

Voltage-hold (V-hold) protocols have shown promise toward calendar lifetime analysis of cells with graphite (Gr) and silicon (Si) anodes. In this work, repeat V-holds are performed on Gr and Si cells paired with lithium iron phosphate cathodes to delineate their beneficial role in formation and conditioning. We find that V-hold at the top of charge supplements constant current cycling in conditioning the cell to higher capacities for both Gr and Si cells after the first V-hold. A reduced order model provides the irreversible capacity proportions of each V-hold. With each repeat V-hold, parasitic loss of lithium to the solid electrolyte interphase (SEI) decreases on both Gr and Si cells. Gr cells show the square-root-of-time capacity loss behavior within 200 h of V-hold, indicative of its fast relaxation and low impact of reference performance test cycles on the SEI growth. Lifetime estimates from repeat V-holds on Gr can reach years. Si exhibits longer transition times from kinetic to diffusion-limited SEI growth, evidenced by the 400 h and 200 h holds showing square-root-of-time and linear behavior, respectively. Lifetime predictions from repeat V-holds on Si only reach 1–2 months, highlighting its limitations. Recommended duration of V-holds for Si cells should be ≥400 h.

25 ENERGY STORAGE↗
DOE OSTI2025

The Principle of Maximum Conformality Correctly Resolves the Renormalization-Scheme-Dependence Problem

In this paper, we clarify a serious misinterpretation and consequent misuse of the Principle of Maximum Conformality (PMC), which also can serve as a mini-review of PMC. In a recently published article, P. M. Stevenson has claimed that “the PMC is ineffective and does nothing to resolve the renormalization-scheme-dependence problem”, concluding incorrectly that the success of PMC predictions is due to the PMC being a “laborious, ad hoc, and back-door” version of the Principle of Minimal Sensitivity (PMS). We show that such conclusions are incorrect, deriving from a misinterpretation of the PMC and an overestimation of the applicability of the PMS. The purpose of the PMC is to achieve precise fixed-order pQCD predictions, free from conventional renormalization schemes and scale ambiguities. We demonstrate that the PMC predictions satisfy all the self-consistency conditions of the renormalization group and standard renormalization-group invariance; the PMC predictions are thus independent of any initial choice of renormalization scheme and scale. The scheme independence of the PMC is also ensured by commensurate scale relations, which relate different observables to each other. Moreover, in the Abelian limit, the PMC dovetails into the well-known Gell-Mann–Low framework, a method universally revered for its precision in QED calculations. Due to the elimination of factorially divergent renormalon terms, the PMC series not only attains a convergence behavior far superior to that of its conventional counterparts but also deftly curtails any residual scale dependence caused by the unknown higher-order terms. This refined convergence, coupled with its robust suppression of residual uncertainties, furnishes a sound and reliable foundation for estimating the contributions from unknown higher-order terms. Anchored in the bedrock of standard renormalization-group invariance, the PMC simultaneously eradicates the factorial divergences and eliminates superfluous systematic errors, which inversely provides a good foundation for achieving high-precision pQCD predictions. Consequently, owing to its rigorous theoretical underpinnings, the PMC is eminently applicable to virtually all high-energy hadronic processes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Estimating Flexibility Envelopes for Residential Customers From Utility Smart Meter Data: Preprint

Demand response from residential customers has significant potential to support power system operations, but accurate flexibility estimation is challenging due to the limited resolution of advanced metering infrastructure (AMI) data. Most utility AMI measurements are recorded at hourly intervals, with only a small portion at higher resolutions, and even fewer households have appliance-level energy usage data. To address this issue, this paper proposes a two-stage long short-term memory (LSTM) framework for estimating household flexibility envelopes from low-resolution AMI data. In the first stage, the heating, ventilating, and air-conditioning (HVAC) load and non-HVAC loads are estimated by using a model trained on a small set of households with appliance-level profiles. These estimated data are then used to compute the upper- and lower-flexibility bounds, which are subsequently down-sampled to lower-resolution data. In the second stage, these flexibility bounds serve as training inputs for another LSTM model, enabling direct prediction of flexibility envelopes for households with only hourly AMI data. This method is validated using Pecan Street data from two different areas, and the results demonstrate its applicability and effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2013

Estimated Probability of Traumatic Abdominal Injury During an International Space Station Mission

The Integrated Medical Model (IMM) is a decision support tool that is useful to spaceflight mission planners and medical system designers when assessing risks and optimizing medical systems. The IMM project maintains a database of medical conditions that could occur during a spaceflight. The IMM project is in the process of assigning an incidence rate, the associated functional impairment, and a best and a worst case end state for each condition. The purpose of this work was to develop the IMM Abdominal Injury Module (AIM). The AIM calculates an incidence rate of traumatic abdominal injury per person-year of spaceflight on the International Space Station (ISS). The AIM was built so that the probability of traumatic abdominal injury during one year on ISS could be predicted. This result will be incorporated into the IMM Abdominal Injury Clinical Finding Form and used within the parent IMM model.

Lewandowski, Beth E.↗
DOE OSTI2020

The Higgs trilinear coupling and the scale of new physics

We consider modifications of the Higgs potential due to new physics at high energy scales. These upset delicate cancellations predicted by the Standard Model for processes involving Higgs bosons and longitudinal gauge bosons, and lead to a breakdown of the theory at high energies. We focus on modifications of the Higgs trilinear coupling and use the violation of tree-level unitarity as an estimate of the scale where the theory breaks down. We obtain a completely model-independent bound of $ \underset{\sim }{<} $ 13 TeV for an order-1 modification of the trilinear. We argue that this bound can be saturated only in fine-tuned models, and the scale of new physics is likely to be much lower. The most stringent bounds are obtained from amplitudes involving multiparticle states that are not conventional scattering states. Our results show that a future determination of the Higgs cubic coupling can point to a well-defined scale of new physics that can be targeted and explored at future colliders.

43 PARTICLE ACCELERATORS↗