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At least 397 records · Page 22

Mitigating the optical depth degeneracy using the kinematic Sunyaev-Zel’dovich effect with CMB-S4 data

The epoch of reionization is one of the major phase transitions in the history of the universe, and is a focus of ongoing and upcoming cosmic microwave background (CMB) experiments with improved sensitivity to small-scale fluctuations. Reionization also represents a significant contaminant to CMB-derived cosmological parameter constraints, due to the degeneracy between the Thomson-scattering optical depth, $\tau$, and the amplitude of scalar perturbations, $A_s$. This degeneracy subsequently hinders the ability of large-scale structure data to constrain the sum of the neutrino masses, a major target for cosmology in the 2020s. In this work, we explore the kinematic Sunyaev-Zel'dovich (kSZ) effect as a probe of reionization, and show that it can be used to mitigate the optical depth degeneracy with high-sensitivity, high-resolution data from the upcoming CMB-S4 experiment. We discuss the dependence of the kSZ power spectrum on physical reionization model parameters, as well as on empirical reionization parameters, namely $\tau$ and the duration of reionization, $\Delta z$. We show that by combining the kSZ two-point function and the reconstructed kSZ four-point function, degeneracies between $\tau$ and $\Delta z$ can be strongly broken, yielding tight constraints on both parameters. We forecast $\sigma(\tau) = 0.003$ and $\sigma(\Delta z) = 0.25$ for a combination of CMB-S4 and Planck data, including detailed treatment of foregrounds and atmospheric noise. The constraint on $\tau$ is nearly identical to the cosmic-variance limit that can be achieved from large-angle CMB polarization data. The kSZ effect thus promises to yield not only detailed information about the reionization epoch, but also to enable high-precision cosmological constraints on the neutrino mass.

79 ASTRONOMY AND ASTROPHYSICS↗

The Morphology and Structure of Stellar Populations in the Fornax Dwarf Spheroidal Galaxy from Dark Energy Survey Data

Using deep wide-field photometry 3 yr data (Y3) from the Dark Energy Survey (DES), we introduce a panoramic study of the Fornax dwarf spheroidal galaxy. The data discussed here—a small subset of the full survey—uniformly cover a region of 25 deg 2 centered on the galaxy to a depth of g ~ 23.5. We use these data to study the structural properties of Fornax, overall stellar population, and its member stars in different evolutionary phases. We also search for possible signs of tidal disturbance. Fornax is found to be significantly more spatially extended than what early studies suggested. No statistically significant distortions or signs of tidal disturbances were found down to a surface brightness limit of ~32.1 mag arcsec -2 . Yet, there are hints of shell-like features located ~20'–40' from the center of Fornax that may be stellar debris from past merger events. We also find that intermediate-age and young main-sequence populations show different orientation at the galaxy center and have many substructures. The deep DES Y3 data allow us to characterize the age of those young stellar substructures with great accuracy, both those previously known and those newly identified as possible overdensities in this work, on the basis of their color–magnitude diagram morphology. We find that the youngest overdensities are all found on the eastern side of Fornax, where the Fornax field population itself is slightly younger than in the west. In summary, the high-quality DES Y3 data reveal that Fornax has many rich structures and provide insights into its complex formation history.

79 ASTRONOMY AND ASTROPHYSICS↗

The Global Magneto-ionic Medium Survey: A Faraday Depth Survey of the Northern Sky Covering 1280–1750 MHz

The Galactic interstellar medium hosts a significant magnetic field, which can be probed through the synchrotron emission produced from its interaction with relativistic electrons. Linearly polarized synchrotron emission is generated throughout the Galaxy and, at longer wavelengths, modified along nearly every path by Faraday rotation in the intervening magneto-ionic medium. Full characterization of the polarized emission requires wideband observations with many frequency channels. We have surveyed polarized radio emission from the Northern sky over the range 1280–1750 MHz, with channel width 236.8 kHz, using the John A. Galt Telescope (diameter 25.6 m) at the Dominion Radio Astrophysical Observatory, as part of the Global Magneto-Ionic Medium Survey. The survey covered 72% of the sky, decl. −30° to +87° at all R.A. The intensity scale was absolutely calibrated, based on the flux density and spectral index of Cygnus A. Polarization angle was calibrated using the extended polarized emission of the Fan Region. Data are presented as brightness temperatures with angular resolution 40′. Sensitivity in Stokes Q and U is 45 mK rms in a 1.18 MHz band. We have applied rotation measure synthesis to the data to obtain a Faraday depth cube of resolution 150 rad m{sup −2} and sensitivity 3 mK rms of polarized intensity. Features in Faraday depth up to a width of 110 rad m{sup −2} are represented. The maximum detectable Faraday depth is ±2 × 10{sup 4} rad m{sup −2}. The survey data are available at the Canadian Astronomy Data Centre.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

A search for fast radio bursts associated with gamma-ray bursts

The detection of seven fast radio bursts (FRBs) has recently been reported. FRBs are short duration (∼1 ms), highly dispersed radio pulses from astronomical sources. The physical interpretation for the FRBs remains unclear but is thought to involve highly compact objects at cosmological distance. It has been suggested that a fraction of FRBs could be physically associated with gamma-ray bursts (GRBs). Recent radio observations of GRBs have reported the detection of two highly dispersed short duration radio pulses using a 12 m radio telescope at 1.4 GHz. Motivated by this result, we have performed a systematic and sensitive search for FRBs associated with GRBs. We have observed five GRBs at 2.3 GHz using a 26 m radio telescope located at the Mount Pleasant Radio Observatory, Hobart. The radio telescope was automated to rapidly respond to Gamma-ray Coordination Network notifications from the Swift satellite and slew to the GRB position within ∼140 s. The data were searched for pulses up to 5000 pc cm{sup –3} in dispersion measure and pulse widths ranging from 640 μs to 25.60 ms. We did not detect any events ≥6σ. An in depth statistical analysis of our data shows that events detected above 5σ are consistent with thermal noise fluctuations only. A joint analysis of our data with previous experiments shows that previously claimed detections of FRBs from GRBs are unlikely to be astrophysical. Our results are in line with the lack of consistency noted between the recently presented FRB event rates and GRB event rates.

79 ASTRONOMY AND ASTROPHYSICS↗

Constraining the Magmatic System at Mount St. Helens (2004-2008) Using Bayesian Inversion With Physics-Based Models Including Gas Escape and Crystallization

Physics-based models of volcanic eruptions track conduit processes as functions of depth and time. When used in inversions, these models permit integration of diverse geological and geophysical data sets to constrain important parameters of magmatic systems. We develop a 1-D steady state conduit model for effusive eruptions including equilibrium crystallization and gas transport through the conduit and compare with the quasi-steady dome growth phase of Mount St. Helens in 2005. Viscosity increase resulting from pressure-dependent crystallization leads to a natural transition from viscous flow to frictional sliding on the conduit margin. Erupted mass flux depends strongly on wall rock and magma permeabilities due to their impact on magma density. Including both lateral and vertical gas transport reveals competing effects that produce nonmonotonic behavior in the mass flux when increasing magma permeability. Using this physics-based model in a Bayesian inversion, we link data sets from Mount St. Helens such as extrusion flux and earthquake depths with petrological data to estimate unknown model parameters, including magma chamber pressure and water content, magma permeability constants, conduit radius, and friction along the conduit walls. Even with this relatively simple model and limited data, we obtain improved constraints on important model parameters. We find that the magma chamber had low (<5 wt %) total volatiles and that the magma permeability scale is well constrained at ~10 –11.4 m 2 to reproduce observed dome rock porosities. Here, compared with previous results, higher magma overpressure and lower wall friction are required to compensate for increased viscous resistance while keeping extrusion rate at the observed value.

58 GEOSCIENCES↗

Depth-resolved carbon dioxide and methane concentrations in 522 lakes, ponds, and reservoirs worldwide

Lakes, ponds, and reservoirs (hereafter: “lakes”) are important sources of the greenhouse gases carbon dioxide (CO 2 ) and methane (CH 4 ). Emissions of CO 2 and CH 4 from lakes are regulated in part by in-lake processes, including the production and storage of gases in the lower parts of the water column (bottom waters). However, while substantial efforts have been made to improve estimates of greenhouse gas emissions from lakes, limited data on gas concentrations along depth profiles have prevented the incorporation of bottom-water processes in global emission estimates. Here, we present GHG-depths: the largest existing dataset of depth-profile CO 2 and CH 4 measurements worldwide, including 522 lakes across 38 countries and all seven continents. These data include contributions from 45 research teams and 56 published studies, totaling 2558 discrete sampling events. As global change continues to alter biogeochemical cycling in lakes, these data can help improve mechanistic models to better predict greenhouse gas production and emission from lakes worldwide.

Rabaey, Joseph S. [University of Minnesota, Duluth↗

Estimating Surface Solar Irradiance Using Meteosat-8 Satellite for India and Surrounding Regions (2017-2019)

Using the Physical Solar Model, Version 3 (PSM V3), the National Renewable Energy Laboratory—in collaboration with the University of Wisconsin, the National Oceanic and Atmospheric Administration, and Solar Consulting Services—produced modeled surface solar irradiance based on Meteosat-8 satellite cloud information. PSM V3 implements a physics-based radiative transfer approach to model surface solar irradiance using cloud properties processed from raw satellite data and ancillary data such as aerosol optical depth and precipitable water vapor from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Further, the modeled surface solar irradiance was validated using 10 high-quality ground measurement data from the Baseline Surface Radiation Network. The modeled data showed good correlation with the ground measurement, and the overall uncertainty is less than 35% for all validation locations. Because of the satellite viewing geometry, the Indian and European locations demonstrated higher uncertainties than the African locations, where the Meteosat-8 satellite has a more nadir viewing geometry. Further, instruction on how to access the data is provided.

14 SOLAR ENERGY↗

End-of-Winter Snow Depth, Temperature, Density and SWE Measurements at Teller Road Site, Seward Peninsula, Alaska, 2016-2018

Measurements of end-of-winter snow properties were collected at the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) for three consecutive years - 2016, 2017, and 2018. This dataset contains one *.pdf user guide and four *.csv data files spatially distributed values of snow depth, snow water equivalent (SWE), temperature at the snow surface and at the bottom of the snowpack, and snow density as a function of depth from top of snowpack. Data was collected toward the end of the winter season, typically in late March or early April, when the snowpack would be near its maximum. In 2016, snow depth measurements were taken using either a thaw probe or avalanche probe. In 2017 and 2018 a Snow-Hydro MagnaProbe http://www.snowhydro.com/products/column2.html was used to improve collection efficiency and enhance spatial coverage. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

The Snow Albedo Evolution (SALVO) Campaign at the North Slope of Alaska Field Campaign Report

The springtime surface-albedo transition in the Alaskan Arctic, along with the forces that determine its duration and nature, was the focus of the Snow ALbedo eVOlution (SALVO) campaign. The SALVO team investigated the reasons and durations of the stages of spring melt, during which albedo values decrease from 0.8 to 0.1, signifying the year’s largest and most significant radiative energy change. SALVO II (2022, 2024) built on findings from the successful SALVO I (2019) melt season campaign and previous research on the melt transition period conducted by Grenfell and Perovich (2004). SALVO used the ARM NSA observatory, thus aligning with a core principle of the ARM Decadal Vision to “provide comprehensive and impactful field measurements to support scientific advancement of atmospheric process understanding.” SALVO fieldwork was conducted in the spring near the ARM NSA Central Facility in Utqiagvik, Alaska (Figure 1). Comprehensive data sets were collected in 2019, 2022, and 2024, which included spectral and broadband albedos, snow and meltwater depths, snow stratigraphy, snow grain size data, and aerial imagery. In April of each project year, before the snow began to melt, we established survey lines at three or four locations: inland tundra at NSA E12, coastal tundra at the NSA C1 site, Elson Lagoon, and offshore on the Chukchi Sea sea ice (2022 only). We set up a 200-meter-long line, marked every five meters, to enable repeated measurements of surface albedo and snow depth at each location. During most site visits, the SALVO II team dug at least one snow pit to assess the characteristics of the vertical snow layers, including the types and sizes of snow grains, snow density, and the distribution of liquid water. We evaluated the characteristics of flowing or pooled water at the bottom of the snowpack. Watching snow melt in the Arctic has been compared by some to being as exciting as watching paint dry, but it is far more exciting and dynamic than that. Initially, changes happen slowly while the snow cover remains above 80%, but then reductions in the albedo of the landscape, from about 0.8 to 0.4, combined with rising spring temperatures, begin to accelerate the melt. It happens so quickly that no matter how hard the field team works, they cannot keep up with documenting the changes. At first, liquid water is scarce—only a few wet layers of snow in the snowpack. But then, water becomes ubiquitous, transporting melt energy and creating an extremely heterogeneous albedo and melt landscape. At the end of each field season, the SALVO team is exhausted, relieved to see the snow gone, and joyful to play in the melt ponds on the sea ice under the midnight sun. Overall, the 2024 SALVO field campaign was a highlight, reflecting numerous lessons learned from 2019 and 2022. Daily measurements were taken more than 10 times at each site, resulting in a comprehensive time series of snow conditions and albedo. The team deployed using snowmachines and then on foot when snowmachine travel was no longer permitted on the melting tundra. Instrument mounts and packaging were optimized for quick deployment, regardless of the mode of transportation.

54 ENVIRONMENTAL SCIENCES↗

Data for Post-Access Report: Tidal Energy Resource Characterization and Model Validation via the Assessment of Cost-effective Spatially Distributed Current Measurements from microFloat Swarms

This project evaluated how high-resolution, spatially distributed field data can be used to refine and validate site-scale hydrodynamic simulations of tidal channels. Use of such spatially-distributed field observations or site-scale hydrodynamic simulations will be needed for producing accurate predictions of tidal energy production over larger arrays of tidal turbines. The data contained in this archive includes field observations of water velocity from a series of short surveys in Agate Pass taken on August 20th, 2020 using station-keeping ADCP (acoustic doppler current profiler) measurements, drifting downward-looking ADCPs, and microFloats, gathered by researchers at APL-UW under a previous project. It also includes high-resolution hydrodynamic model runs for the site during that time generated under TEAMER support. An explanatory file (.pdf format) describes the survey and field data structure. Processed data is stored in .mat format. Survey summary: start time, number of floats, water level. microFloat data: - Cleaned data for each float in survey, - Time series of position (lat., lon.), depth (relative to surface), and horizontal velocity (u, v) Drifting ADCP data: - Cleaned data for each drifting ADCP in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) Stationary ADCP data: - Cleaned data for each station-keeping data set in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) FVCOM (Finite Volume Coastal Ocean Model) model outputs: Water level and velocity, as well as derived variables (e.g., power density) from the final refined model simulations for the Agate Pass subdomain. Contained in NetCDF and ASCII files for the duration of August 18th and 20th 2020 field surveys. Additional scripts for processing the data and generating the figures in the report are included.

16 TIDAL AND WAVE POWER↗

SPRUCE S1 Bog Peat Depth Determined by Push Probe and GPR: 2009-2010

This data set reports the thickness of peat for the S1 Bog measured directly with manual push probes on September 21 and 22, 2009, and measured by ground penetrating radar (GPR) across transects from January 26 to February 2, 2010 and June 2 to June 9, 2010. Also included are interpolated values of peat depth from both probe and GPR data for the S1 Bog at 1 meter square horizontal resolution.The key objectives of these geophysical field surveys were to define the S1 peat depth, basin geometry, and to provide additional subsurface information for selecting the optimal locations for experimental plots (i.e., rows of chambers). GPR was specifically used to obtain better estimates of peat depths to refine the existing 3-D model of the S1 basin geometry and to provide high-density data on peat stratigraphy and examine variations in mineral soil near the proposed experimental plots.

54 ENVIRONMENTAL SCIENCES↗

Depth-resolved ultra-violet spectroscopic photo current-voltage measurements for the analysis of AlGaN/GaN high electron mobility transistor epilayer deposited on Si

We have demonstrated that the depth-dependent defect distribution of the deep level traps in the AlGaN/GaN high electron mobility transistor (HEMT) epi-structures can be analyzed by using the depth-resolved ultra-violet (UV) spectroscopic photo current-voltage (IV) (DR-UV-SPIV). It is of great importance to analyze deep level defects in the AlGaN/GaN HEMT structure, since it is recognized that deep level defects are the main source for causing current collapse phenomena leading to reduced device reliability. The AlGaN/GaN HEMT epi-layers were grown on a 6 in. Si wafer by metal-organic chemical vapor deposition. The DR-UV-SPIV measurement was performed using a monochromatized UV light illumination from a Xe lamp. The key strength of the DR-UV-SPIV is its ability to provide information on the depth-dependent electrically active defect distribution along the epi-layer growth direction. The DR-UV-SPIV data showed variations in the depth-dependent defect distribution across the wafer. As a result, rapid feedback on the depth-dependent electrical homogeneity of the electrically active defect distribution in the AlGaN/GaN HEMT epi-structure grown on a Si wafer with minimal sample preparation can be elucidated from the DR-UV-SPIV in combination with our previously demonstrated spectroscopic photo-IV measurement with the sub-bandgap excitation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Effect of layer thickness on device response of silicon heavily supersaturated with sulfur

We report on a simple experiment in which the thickness of a hyperdoped silicon layer, supersaturated with sulfur by ion implantation followed by pulsed laser melting and rapid solidification, is systematically varied at constant average sulfur concentration, by varying the implantation energy, dose, and laser fluence. Contacts are deposited and the external quantum efficiency (EQE) is measured for visible wavelengths. We posit that the sulfur layer primarily absorbs light but contributes negligible photocurrent, and we seek to support this by analyzing the EQE data for the different layer thicknesses in two interlocking ways. In the first, we use the measured concentration depth profiles to obtain the approximate layer thicknesses, and, for each wavelength, fit the EQE vs. layer thickness curve to obtain the absorption coefficient of hyperdoped silicon for that wavelength. Comparison to literature values for the hyperdoped silicon absorption coefficients [S.H. Pan et al. Applied Physics Letters 98, 121913 (2011)] shows good agreement. Next, we essentially run this process in reverse; we fit with Beer’s law the curves of EQE vs. hyperdoped silicon absorption coefficient for those wavelengths that are primarily absorbed in the hyperdoped silicon layer, and find that the layer thicknesses obtained from the fit are in good agreement with the original values obtained from the depth profiles. We conclude that the data support our interpretation of the hyperdoped silicon layer as providing negligible photocurrent at high S concentrations. This work validates the absorption data of Pan et al. [Applied Physics Letters 98, 121913 (2011)], and is consistent with reports of short mobility-lifetime products in hyperdoped layers. It suggests that for optoelectronic devices containing hyperdoped layers, the most important contribution to the above band gap photoresponse may be due to photons absorbed below the hyperdoped layer.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Digging deeper into the Southern skies: A compact Milky Way companion discovered in first-year Dark Energy Survey data

Here, the Dark Energy Survey (DES) is a 5000 sq. degree survey in the southern hemisphere, which is rapidly reducing the existing north-south asymmetry in the census of MW satellites and other stellar substructure. We use the first-year DES data down to previously unprobed photometric depths to search for stellar systems in the Galactic halo, therefore complementing the previous analysis of the same data carried out by our group earlier this year. Our search is based on a matched filter algorithm that produces stellar density maps consistent with stellar population models of various ages, metallicities, and distances over the survey area. The most conspicuous density peaks in these maps have been identified automatically and ranked according to their significance and recurrence for different input models. We report the discovery of one additional stellar system besides those previously found by several authors using the same first-year DES data. The object is compact, and consistent with being dominated by an old and metal-poor population. DES J0034-4902 is found at high significance and appears in the DES images as a compact concentration of faint blue point sources at ~ 87 {kpc}.

79 ASTRONOMY AND ASTROPHYSICS↗

Ross Filter Diode Array Analysis for Red Sage

A Ross Filter Diode Array (RFDA) diagnostic was fielded on the Cygnus x-ray source during the final stages of the Red Sage campaign. This report briefly describes the motivation for the diagnostic, its construction and operating principles, and the raw data that it produces followed by an in-depth look at the procedures used to analyze this data. RFDA signals from eight Cygnus shots conducted after the conclusion of Nightshade C are analyzed and those results are discussed. Finally, recommendations are made to improve the diagnostic for future experiments.

42 ENGINEERING↗

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗

LOGISTIC FUNCTION PROFILE FIT: A least-squares program for fitting interface profiles to an extended logistic function

The extended logistic function provides a physically reasonable description of interfaces such as depth profiles or line scans of surface topological or compositional features. It describes these interfaces with the minimum number of parameters, namely, position, width, and asymmetry. Logistic Function Profile Fit (LFPF) is a robust, least-squares fitting program in which the nonlinear extended logistic function is linearized by a Taylor series expansion (equivalent to a Newton-Raphson approach) with no apparent introduction of bias in the analysis. The program provides reliable confidence limits for the parameters when systematic errors are minimal and provides a display of the residuals from the fit for the detection of systematic errors. The program will aid researchers in applying ASTM E1636-10, 'Standard practice for analytically describing sputter-depth-profile and linescan-profile data by an extended logistic function,' and may also prove useful in applying ISO 18516: 2006, 'Surface chemical analysis-Auger electron spectroscopy and x-ray photoelectron spectroscopy-determination of lateral resolution.' Examples are given of LFPF fits to a secondary ion mass spectrometry depth profile, an Auger surface line scan, and synthetic data generated to exhibit known systematic errors for examining the significance of such errors to the extrapolation of partial profiles.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Learning planar Ising models

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the best planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.

97 MATHEMATICS AND COMPUTING↗