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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

First Measurements of Differential Cross Sections In Kinematic Imbalance Variables With The MicroBooNE Detector

Making high-precision measurements of neutrino oscillation parameters requires an unprecedented understanding of neutrino-nucleus scattering. In this presentation, we present the first muon neutrino charged current double-differential cross sections in kinematic imbalance variables. These variables characterize the imbalance in the plane transverse to an incoming neutrino. We use events with a single muon above 100 MeV/c, a single final state proton above 300 MeV/c, and no recorded final state pions. Thus, these variables act as a direct probe of nuclear effects such as final state interactions, Fermi motion, and multi-nucleon processes. We also present a complementary ongoing analysis using electron neutrinos. This channel is of the utmost importance for the extraction of neutrino oscillation parameters by making high-precision measurements. Our measurements allow us to constrain systematic uncertainties associated with neutrino oscillation results performed by near-future experiments of the Short Baseline Neutrino (SBN) program, as well as by future large-scale experiments like DUNE.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

First Measurement of Differential Charged Current Quasielastic-like $\nu_\mu$–Argon Scattering Cross Sections in Kinematic Imbalance Variables with the MicroBooNE Detector

We report the first measurement of flux-integrated multi-differential cross sections for charged-current events with muon neutrinos scattering on argon with solely a muon and a single proton in the final state as a function of kinematic imbalance variables. The measurement was carried out using the Booster Neutrino Beam at Fermi National Accelerator Laboratory within the MicroBooNE Liquid Argon Time Projection Chamber detector with an exposure of 6.79 × 10 20 protons on target. Events were selected to enhance the contribution of charged-current mesonless interactions with one proton detected in the final state. The data discussed here are reported in terms of multi-differential cross sections in kinematic imbalance variables, which are generally sensitive to nuclear effects. The double-differential results in these variables can provide an excellent handle to disentagle specific nuclear aspects not easily isolated via single differential cross sections. Our results pave a path towards identifying regions of the phase-space where future interaction modeling development and Monte Carlo neutrino generator tuning efforts should concentrate.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

A Review of Variables Impacting the Indoor Inhalation Radon Equilibrium Factor (FEQ)

With radon and its daughter products estimated as the second leading cause of lung cancer in the United States, it is imperative to understand their relative equilibrium inside commercial, community, and residential dwellings. The radon indoor inhalation fractional equilibrium factor (F eq ) quantifies the disequilibrium between radon and its progeny in indoor air, and recent advances have shown how air exchange rates (ACH) influence F eq . These numerically derived ACH-dependent F eq values are incorporated into the U.S. EPA's Radon Vapor Intrusion Screening Level (RVISL) calculator, which assists risk assessors in evaluating radon exposure. To advance the risk assessment science of actinon (Rn-219), thoron (Rn-220), and radon (Rn-222), the impact of variables such as indoor aerosol concentration and composition, outdoor air quality, household-specific characteristics, and environmental/meteorological conditions on the F eq must be examined. The primary objective of this research is to analyze these additional variables to determine the usefulness of incorporating such adjustment factors into the RVISL calculator and to identify areas of future research. Studies regarding the influence of these parameters are presented along with recommendations regarding the adjustment of the numerically derived F eq value. For example, elevated indoor aerosol concentrations, such as those originating from outdoor PM 2.5 or cigarette smoke, increase the abundance of accumulation- mode particles indoors, which in turn raises F eq values by facilitating the attachment of radon progeny to these aerosols. Smoking increases both the bronchial dose and F eq , while regions with high smog levels demonstrate the impact of regional air quality on F eq . In contrast, air cleaning systems and purifiers have been shown to reduce the F eq , suggesting that these systems could help mitigate radon exposure. Additionally, higher F eq values are typically observed during winter when ventilation rates are lower. This paper presents adjustment factors that may be applied to the RVISL F eq , emphasizing the need for further research to refine these variables and ensure accurate risk assessments in diverse environments. Applying these adjustment factors will minimize calculator over- and underestimations, providing a more accurate representation of the real-world risk associated with radon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Arctic Impact Identification with Less Data Using Variable Relationships: An Exploratory Express LDRD project.

Regional impacts from sea ice loss can be challenging to separate from internal climate variability, potentially requiring thousands of ensemble members. East Asian wintertime cooling has been linked to sea ice loss from present day conditions in the Polar Amplification Model Intercomparison Project with these large ensemble counts. This cooling is theorized to arise from a strengthened Siberian High and East Asian Jet response. The strengthened Siberian High can be detected with one fifth the ensemble members needed for the East Asian wintertime cooling in a single model. We thus hypothesize that leveraging relationships between multiple variables in a conditional pathways-based approach would reduce the number of required ensemble members to conclusively attribute East Asian wintertime cooling to future sea ice concentrations. In all analyzed cases, confidence was increased when evaluating sea ice loss’s responsibility for the joint effects of East Asian cooling, East Asian Jet strengthening, and Siberian High strengthening over just East Asian cooling. However, we were not able to confidently attribute future East Asian wintertime cooling to sea ice loss in a single model. We found that significant intra-ensemble variability within single Earth System Models (ESMs) produced highly uncertain forcing response models upon which attribution results were undermined. We were able to show that ensemble mean seasonally averaged metrics from multiple ESMs greatly improved the accuracy of the forcing response linear models and exposed the necessity of all three steps in the pathway (sea ice area, Siberian High pressure, and East Asian Jet speed) for accurate prediction of East Asian wintertime cooling. Although all three steps were necessary, East Asian wintertime cooling possesses a large dependence on the Siberian High pressure, which weakens the confidence associated with overall strong joint-attribution comparing present day and future scenarios. We believe transitioning the pathway nodes to relative changes between the Siberian High and Aleutian Low as well as between the midlatitude westerlies and subtropical jet in the East Asianj Jet region may be able to produce significant attribution more fully dependent upon all three steps. Ultimately, this research demonstrates the simple extensibility of conditional pathways-based attribution to sea ice loss forcing on the Earth system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Variable Curvature Pultruded Vertical Axis Wind Blades

XFlow Energy is developing a floating offshore vertical-axis wind turbine (VAWT) enabling a 70% reduction in the levelized cost of energy compared to floating horizontal-axis wind turbines (HAWTs). While the wind industry has historically been successful at lowering the cost of terrestrial wind systems, there are no scalable, economically viable floating wind solutions on the market with the levelized cost of energy (LCOE) of current floating offshore solutions is $200/MWh (Musial 2022). Low-cost blade production is one of the key technologies for realizing this target LCOE reduction. The proposed design uses a constant cross-section blade, enabling the use of pultrusion, a continuous, automated, mold-free production method. By employing blades with variable curvature, XFlow can minimize the bending stresses in the blades, allowing for light-weighting. Constant curvature pultrusion has been demonstrated on hollow sections. Variable curvature pultrusion has been demonstrated on solid sections. This project aims to develop a method of variable curvature pultrusion for hollow sections, as required for mass-optimal VAWT blades.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Variable Curvature Pultruded Vertical Axis Wind Blades

XFlow Energy is developing a floating offshore vertical-axis wind turbine (VAWT) enabling a 70% reduction in the levelized cost of energy compared to floating horizontal-axis wind turbines (HAWTs). While the wind industry has historically been successful at lowering the cost of terrestrial wind systems, there are no scalable, economically viable floating wind solutions on the market with the levelized cost of energy (LCOE) of current floating offshore solutions is $\$$200/MWh (Musial 2022). Low-cost blade production is one of the key technologies for realizing this target LCOE reduction. The proposed design uses a constant cross-section blade, enabling the use of pultrusion, a continuous, automated, mold-free production method. By employing blades with variable curvature, XFlow can minimize the bending stresses in the blades, allowing for light-weighting. Constant curvature pultrusion has been demonstrated on hollow sections. Variable curvature pultrusion has been demonstrated on solid sections. This project aims to develop a method of variable curvature pultrusion for hollow sections, as required for mass-optimal VAWT blades.

17 WIND ENERGY↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

Danovo Energy Solution's presented its paper named: Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events at the 2026 Georgia Tech Fault & Disturbance Analysis Conference. The full paper can be found at OSTI ID# 3169150 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danova Energy Solutions]↗
DOE OSTI2020

FACETS Dynamical Downscaling Simulations over North America by the CAM-MPAS Variable-Resolution Model

Comprehensive assessment of climate datasets created by statistical or dynamical models is important for effectively communicating model projection and associated uncertainty to stakeholders and decision-makers. The Department of Energy FACETS project aims to foster such communication through development of metrics and their demonstration on a hierarchy of downscaled climate datasets to quantify aspects of climate change projections that are credible, particularly for supporting decisions related to the energy-water-land nexus. As a part of this effort, we have produced a regional climate dataset using the Model for Prediction Across Scales coupled to the Community Atmosphere Model (CAM-MPAS). This global modeling framework is configured with variable-resolution meshes featuring higher resolutions over North America, as well as quasi-uniform resolution meshes across the globe. The variable-resolution configurations allow fine-scale features to be better resolved inside the refinement and interact with global-scale circulations. The dataset includes multiple uniform- (240km and 120km) and variable-resolution (200-50km, 100-25km, and 46-12km) simulations that are designed to be compatible with other regional climate simulations that contribute to the hierarchy of downscaled climate datasets of the project. Furthermore, the dataset consists of simulations for both the present-day (1989-2010) and future (2079-2100) climate and post-processing of the model output has been coordinated across the project for consistency to facilitate common analysis across the hierarchy of datasets. Altogether, this CAM-MPAS model dataset provides a unique opportunity to assess the influence of resolutions and modeling framework on model credibility and climate change projection.

Sakaguchi, Koichi↗
DOE OSTI2020

Integration of Soft Data Into Geostatistical Simulation of Categorical Variables

Uncertain or indirect “soft” data, such as geologic interpretation, driller’s logs, geophysical logs or imaging, offer potential constraints or “soft conditioning” to stochastic models of discrete categorical subsurface variables in hydrogeology such as hydrofacies. Previous bivariate geostatistical simulation algorithms have not fully addressed the impact of data uncertainty in formulation of the (co) kriging equations and the objective function in simulated annealing (or quenching). This paper introduces the geostatistical simulation code tsim-s, which accounts for categorical data uncertainty through a data “hardness” parameter. In generating geostatistical realizations with tsim-s, the uncertainty inherent to soft conditioning is factored into both 1) the data declustering and spatial correlation functions in cokriging and 2) the acceptance probability for change of category in simulated quenching. The degree or sensitivity to which soft data conditions a realization as a function of hardness can be quantified by mapping category probabilities derived from multiple realizations. In addition to point or borehole data, arrays of data (e.g., as derived from a depth-dependency function, probability map, or “prior realization”) can be used as soft conditioning. The tsim-s algorithm provides a theoretically sound and general framework for integrating datasets of variable location, resolution, and uncertainty into geostatistical simulation of categorical variables. A practical example shows how tsim-s is capable of generating a large-scale three-dimensional simulation including curvilinear features.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Effect of Feedstock Variability, Feedstock Blends, and Pretreatment Conditions on Sugar Yield and Production Costs

Mass production of sustainable biofuels replacing fossil fuels will significantly reduce global greenhouse gas emissions. However, biomass feedstock variability, availability, and affordability pose significant risks for the sustainable production of biofuels in biorefinery plants. To derisk the sustainable production of biofuels, this work investigates the sugar yields and production costs of four different feedstocks as well as their blends, using different pretreatment methods: deacetylation and dilute acid pretreatment (DDA) and deacetylation and mechanical refining pretreatment (DMR). We found that not only feedstock species variability, but also feedstock harvesting techniques have large impacts on both sugar yields and production costs. Single-pass harvested corn stover (SPCS) showed higher sugar yields and lower production costs than multipass harvested corn stover, followed by sorghum and switchgrass. The highest glucose yields of 91% and 95.3%, which corresponds to the lowest sugar production costs of $0.2286/lb. and $0.2490/lb., were observed for SPCS using DDA and DMR, respectively. The difference in sugar yields and production costs due to feedstock variability can be largely mitigated by increasing deacetylation severity, especially in DMR. Higher deacetylation severity resulted in decreased sugar production costs in the case of DMR in contrast to increased costs in the case of DDA. Blending switchgrass and sorghum into corn stover, which can significantly increase the feedstock availability and sustainability, resulted in sugar yields and production costs of roughly the weighted average of those derived from constituent individual feedstocks, respectively. Thus, blending feedstocks of different biomass species is a potential successful strategy for sustainable production of biofuels to replace fossil-derived fuels.

09 BIOMASS FUELS↗
DOE OSTI2024

Reliability, biological variability, and accuracy of multi-frequency bioelectrical impedance analysis for measuring body composition components

Introduction Bioelectrical impedance analysis (BIA) systems are gaining popularity for use in research and fitness assessments as the technology improves and becomes more affordable and easier to use. Multifrequency BIA (MF-BIA) may improve accuracy and precision using octopolar contacts for segmental analyses. Purpose Evaluate reliability, biological variability, and accuracy of component measures (total body water, mass, and composition) of commercially available MF-BIA system (InBody 770, Cerritos, California, USA). Methods Fourteen healthy military-age adults were assessed by MF-BIA in duplicate on five laboratory visits across 3 weeks (10 measures each). Participants were evaluated at the same time of day after refraining from strenuous exercise (> 48 h), alcohol consumption (> 24 h), and caffeine, nicotine, and food (> 10 h). Systematic error (test–retest reliability) and biological variability (day-to-day reliability) were summarized by intraclass correlation coefficient (ICC) values determined for body mass (fat, fat-free, total) and body water (extracellular, intracellular, total). Body composition measurements derived from BIA on the second visit were also tested for accuracy compared to dual-energy x-ray absorptiometry (DXA). Results Test–retest reliability was very high for all measurements of whole-body water and mass (ICC ≥ 0.999) and high for regional body water and mass (ICC 0.973–1.000). Biological variability was observable with very minor differences between tests (same day) for total and regional body water (0.0–0.2 L) and total and regional body mass measurements (0.0–0.2 kg); while between day differences were slightly higher (0.0–0.5 L and 0.1–0.7 kg). Compared to DXA, the MF-BIA whole-body measures showed an offset in %BF (Bias −4.0 ± 2.8%; Standard error of the estimate (SEE), 2.6%), an overprediction for total body fat-free mass (Bias 2.8 ± 2.1 kg; SEE 2.2 kg) and an underprediction of total body fat mass (Bias −2.9 ± 2.0 kg; SEE 1.9 kg). Conclusion Under controlled conditions with fit and healthy men and women, this MF-BIA system has high methodological reliability and demonstrates stable day-to-day measurements of major body composition components. Previously reported ~3% body fat offset compared to criterion methods was again confirmed. Precision of the InBody 770 shows consistency and supports further testing of this specific device as a new military standards method and suitability across a wider range of %BF.

Nutrition & Dietetics↗
DOE OSTI2021

Highlights of Discoveries for δ Scuti Variable Stars from the Kepler Era

The NASA Kepler and follow-on K2 mission (2009–2018) left a legacy of data and discoveries, finding thousands of exoplanets, and also obtaining high-precision long time-series data for hundreds of thousands of stars, including many types of pulsating variables. Here we highlight a few of the ongoing discoveries from Kepler data on δ Scuti pulsating variables, which are core hydrogen-burning stars of about twice the mass of the Sun. We discuss many unsolved problems surrounding the properties of the variability in these stars, and the progress enabled by Kepler data in using pulsations to infer their interior structure, a field of research known as asteroseismology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Variability in Proto-planetary Nebulae. VI. Multitelescope Light Curve Studies of Several Medium-bright (V = 13–15), Carbon-rich Objects

We present 10 years of new photometric monitoring of the light variability of five evolved stars with strong mid-infrared emission from surrounding dust. Three are known carbon-rich proto-planetary nebulae (PPNe) with F−G spectral types; the nature of the other two was previously unknown. For the three PPNe, we determine or refine the pulsation periods of IRAS 04296+3429 (71 days), 06530−0213 (80 days), and 23304+6147 (84 days). A secondary period was found for each, with a period ratio P {sub 2}/P {sub 1} of 0.9. The light variations are small, 0.1–0.2 mag. These are similar to values found in other PPNe. The other two are found to be giant stars. IRAS 09296+1159 pulsates with a period of only 47 days but reaches pulsational light variations of 0.5 mag. Supplemental spectroscopy reveals the spectrum of a CH carbon star. IRAS 08359−1644 is a G1 III star that does not display pulsational variability; rather, it shows nonperiodic decreases of brightness of up to 0.5 mag over this 10 year interval. These drops in brightness are reminiscent of the light curves of R Corona Borealis variables, but with much smaller decreases in brightness and are likely due to transient dust obscuration. Its spectral energy distribution is very similar to that of the unusual oxygen-rich giant star HDE 233517, which possesses mid-infrared hydrocarbon emission features. These two non-PPNe turn out to be members of the rare group of giant stars with large mid-infrared excesses due to dust, objects which presumably have interesting evolutionary histories.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters

The ability to generate physically plausible ensembles of variable sources is critical to the optimization of time domain survey cadences and the training of classification models on data sets with few to no labels. Traditional data augmentation techniques expand training sets by reenvisioning observed exemplars, seeking to simulate observations of specific training sources under different (exogenous) conditions. Unlike fully theory-driven models, these approaches do not typically allow principled interpolation nor extrapolation. Moreover, the principal drawback of theory-driven models lies in the prohibitive computational cost of simulating source observables from ab initio parameters. In this work, we propose a computationally tractable machine learning approach to generate realistic light curves of periodic variables capable of integrating physical parameters and variability classes as inputs. Our deep generative model, inspired by the transparent latent space generative adversarial networks, uses a variational autoencoder (VAE) architecture with temporal convolutional network layers, trained using the OGLE-III optical light curves and physical characteristics (e.g., effective temperature and absolute magnitude) from Gaia DR2. A test using the temperature–shape relationship of RR Lyrae demonstrates the efficacy of our generative “physics-enhanced latent space VAE” (PELS-VAE) model. Such deep generative models, serving as nonlinear nonparametric emulators, present a novel tool for astronomers to create synthetic time series over arbitrary cadences.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Detecting Long-period Variability in the SDSS Stripe 82 Standards Catalog

We report the results of a search for long-period (100 < P < 600 days) periodic variability in the SDSS Stripe 82 standards catalog. The SDSS coverage of Stripe 82 enables such a search because there are on average 20 observations per band in ugriz bands for about one million sources, collected over about 6 yr, with a faint limit of r ~ 22 mag and precisely calibrated 1%–2% photometry. We calculated the periods of variable source candidates in this sample using the Lomb–Scargle periodogram and considered the three highest periodogram peaks in each of the gri filters as relevant. Only those sources with gri periods consistent within 0.1% were later studied. We use the Kuiper statistic to ensure uniform distribution of data points in phased light curves. We present five sources with the spectra consistent with quasar spectra and plausible periodic variability. This SDSS-based search bodes well for future sensitive large-area surveys, such as the Rubin Observatory Legacy Survey of Space and Time, which, due to its larger sky coverage (about a factor of 60) and improved sensitivity (~2 mag), will be more powerful for finding such sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

X-Ray Emission of Ultraviolet Variable Active Galactic Nucleus Candidates

We analyze the X-ray properties of a sample of 23 high-probability active galactic nucleus (AGN) candidates with ultraviolet variability identified in Wasleske et al. Using data from the Chandra X-ray Observatory and XMM-Newton Observatory, we that find 11/23 nuclei are X-ray detected. We use spectral energy distribution modeling to compute star formation rates and show that the X-ray luminosities are typically in excess of the X-ray emission expected from star formation by at least an order of magnitude. Interestingly, this sample shows a diversity of optical spectroscopic properties. We explore possible reasons for why some objects lack optical spectroscopic signatures of black hole activity while still being UV-variable and X-ray bright. We find that host galaxy stellar emission and obscuration from gas and dust are potential factors. We study where this sample falls on relationships such as α OX - L 2500 and L X - L IR and find that some of the sample falls outside the typical scatter for these relations, indicating that they differ from the standard quasar population. With the diversity of optical spectroscopic signatures and varying impacts of dust and stellar emissions on our sample, these results emphasize the strength of variability in selecting the most complete set of AGN, regardless of other host galaxy properties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Extreme Variability Quasars in Their Various States. I. The Sample Selection and Composite SDSS Spectra

Extremely variable quasars (EVQs) are a population of sources showing large optical photometric variability revealed by time-domain surveys. The physical origin of such extreme variability is yet unclear. In this first paper of a series, we construct the largest-ever sample of 14,012 EVQs using more than 15 yr of photometric data from Sloan Digital Sky Survey (SDSS) and Pan-STARRS1. We divide the EVQs into five subsamples according to the relative brightness of each EVQ during SDSS spectroscopic observation compared with the mean brightness from photometric observations. Corresponding control samples of normal quasars are built with matched redshift, bolometric luminosity, and supermassive black hole mass. We obtain the composite SDSS spectra of EVQs in various states and their corresponding control samples. We find EVQs exhibit clearly bluer SDSS spectra during bright states and clearly redder spectra during dim states, consistent with the "bluer-when-brighter" trend widely seen in normal quasars. We further find that the line equivalent widths (EWs) of broad Mg II, C IV and [O III] (but not broad Hβ, which is yet puzzling) gradually decreases from the dim state to the bright state, similar to the so-called intrinsic Baldwin effect commonly seen in normal active galactic nuclei. In addition, EVQs have consistently larger line EWs compared with the control samples. We also see that EVQs show slight excess in the very broad line component compared with control samples. Possible explanations for the discoveries are discussed. Our findings support the hypothesis that EVQs are in the tail of a broad distribution of quasar properties but are not a distinct population.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Examining AGN UV/Optical Variability beyond the Simple Damped Random Walk

We present damped harmonic oscillator (DHO) light-curve modeling for a sample of 12,714 spectroscopically confirmed quasars in the Sloan Digital Sky Survey Stripe 82 region. DHO is a second-order continuous-time autoregressive moving-average process, which can be fully described using four independent parameters: a natural oscillation frequency (ω 0 ), a damping ratio (ξ), a characteristic perturbation timescale (τ perturb ), and an amplitude for the perturbing white noise (σ ϵ ). The asymptotic variability amplitude of a DHO process is quantified by σ DHO —a function of ω 0 , ξ, τ perturb , and σ ϵ . We find that both τ perturb and σ ϵ follow different dependencies with rest-frame wavelength (λ RF ) on either side of 2500 Å, whereas σ DHO follows a single power-law relation with λ RF . After correcting for wavelength dependence, σ DHO exhibits anticorrelations with both the Eddington ratio and the black hole mass, while τ perturb —with a typical value of days in the rest frame—shows an anticorrelation with the bolometric luminosity. Modeling active galactic nuclei (AGN) variability as a DHO offers more insight into the workings of accretion disks close to the supermassive black holes at the center of AGN. The newly discovered short-term variability (characterized by τ perturb and σ ϵ ) and its correlation with bolometric luminosity pave the way for new algorithms that will derive fundamental properties (e.g., Eddington ratio) of AGN using photometric data alone.

79 ASTRONOMY AND ASTROPHYSICS↗