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

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Neutrino charge radius and additional one-loop radiative corrections at ultranear reactor experiments

We scrutinize the potential of upcoming ultranear reactor neutrino experiments to detect radiative corrections in the elastic neutrino-electron scattering channel, focusing on the JUNO-TAO and CLOUD detectors, which employ advanced scintillator detection technologies. Previous reactor experiments have already constrained the electron neutrino charge radius, which is a neutrino property associated with a certain subset of the total radiative corrections, and have achieved limits that are only about an order of magnitude away from the Standard Model prediction. Our study demonstrates that JUNO-TAO and CLOUD could discover the neutrino charge radius in the near future, considering the established treatment of the charge radius. However, we show that it is necessary to go beyond this standard treatment. By including the complete set of one-loop level radiative corrections, we find a partial cancellation with the charge radius effect, reducing the experimental sensitivity to this quantity. Nevertheless, JUNO-TAO and CLOUD still have the potential to achieve a 5 σ discovery but over longer timescales within a reasonable operational time frame. Published by the American Physical Society 2024

Brdar, Vedran (ORCID:0000000170275104)↗
DOE OSTI2022

Influence of Entrainment on Centimeter-Scale Cloud Microphysics in Marine Stratocumulus Clouds Observed during CSET

Abstract Cloud microphysical relationships observed during the Cloud System Evolution in the Trades (CSET) campaign held between Northern California and Hawaii were analyzed to study the effects of entrainment and subsequent mixing of free-tropospheric and cloudy air on cloud microphysical properties of marine stratocumulus clouds. The data measured by Holographic Detector for Clouds (HOLODEC) were extensively used because they could provide the 3D positions and sizes of droplets within sample volume on the centimeter scale, making it possible to explore the 3D spatial distribution of droplets, which has not been possible for conventional cloud probes. This study focused on analyzing the 3D spatial distribution of droplets and visual traits of inhomogeneous mixing and on quantifying the relationship between 3D spatial distributions and traits of inhomogeneous mixing. Two types of spatial distributions are compared. The first is measured droplet spatial distribution and the second type is generated randomly distributed droplets using the Monte Carlo approach, that is, to analyze whether or not clustering is strong enough to classify as a clustered distribution for a hologram. The difference between the two types of spatial distributions depends on whether they are affected by entrainment and mixing. The holograms observed near the cloud top, where the effects of entrainment and mixing would be immediate, showed relatively high confidence in the significance test for spatially clustered populations of droplets. Moreover, spatially clustered holograms appeared to exhibit stronger visual traits of inhomogeneous mixing than perfectly randomly distributed holograms only when observed near the cloud top. On the other hand, these characteristics did not appear for holograms observed deeper into the cloud where the effects of entrainment and mixing would be reduced. Such 3D structural characteristics of droplet distributions seem to be consistent with vertical circulation mixing.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Entrainment and aerosol effects on marine boundary-layer clouds: An investigation using ACE-ENA data from HOLODEC, G1, Pico and ACTOS (Final Report on Project Activities)

Marine boundary-layer clouds cover large regions of the globe and are known to strongly influence radiative balances. The microphysical properties and persistence of these clouds are tightly coupled with cloud-top entrainment and aerosol properties within both the boundary layer and the overlying free troposphere. This work addressed the microphysical response to entrainment and aerosol properties in marine stratocumulus clouds, using data from the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) project. Specifically, emphasis was placed on 1. extensive in situ measurements taken with the Atmospheric Radiation Measurement (ARM) G1 aircraft using the Holographic Detector for Clouds (Holodec) instrument; 2. airborne measurements obtained with the helicopter-borne Airborne Cloud Turbulence Observation System (ACTOS) operated by the Leibniz Institute for Tropospheric Research (TROPOS) during the first phase of ACE-ENA; and 3. above-boundary-layer, mountain-top measurements taken at the Pico Mountain Observatory (OMP) by the research group of the co-investigator and scientists from TROPOS during the first phase of ACE-ENA. The work is aligned with the topic “Warm Boundary-Layer Atmospheric Processes.” Key results include the following. Data from the Holographic Detector for Clouds (Holodec) were reprocessed, refined, and validated through a careful instrument intercomparison, resulting in a high-quality dataset available on the ARM archive for ACE-ENA. High-resolution ACTOS measurements of thermodynamics, microphysical, and turbulence properties were analyzed to explore the behavior of the entrainment velocity at cloud top. Analysis of the vertical variability of cloud droplet size distribution shape suggests that mixing is more inhomogeneous near cloud top, and more homogeneous deeper into the cloud. Analysis of aerosol measurements from OMP explored the mixing state and cloud condensation nucleus properties, as well as the implications for radiative forcing. Finally, a machine learning algorithm was developed for identifying characteristic cloud droplet size distributions, and was employed to explore microphysical regimes in stratocumulus clouds observed during ACE-ENA. The characteristic size distributions are narrow, and only when spatially averaged do they produce the broad “gamma” distributions typically assumed in models. This implies that precipitation development should account for variability and correlations in the distribution shape as well as the number concentration of cloud droplets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Automated Identification of Characteristic Droplet Size Distributions in Stratocumulus Clouds Utilizing a Data Clustering Algorithm

Abstract Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these average distributions? This paper describes an algorithm to search and classify characteristic size distributions within a cloud. The approach combines hypothesis testing, specifically, the Kolmogorov–Smirnov (KS) test, and a widely used class of machine learning algorithms for identifying clusters of samples with similar properties: density-based spatial clustering of applications with noise (DBSCAN) is used as the specific example for illustration. The two-sample KS test does not presume any specific distribution, is parameter free, and avoids biases from binning. Importantly, the number of clusters is not an input parameter of the DBSCAN-type algorithms but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the KS test results, and hence spatial correlation is not required for a cluster. The method is explored using data obtained from the Holographic Detector for Clouds (HOLODEC) deployed during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The algorithm identifies evidence of the existence of clusters of nearly identical local size distributions. It is found that cloud segments have as few as one and as many as seven characteristic size distributions. To validate the algorithm’s robustness, it is tested on a synthetic dataset and successfully identifies the predefined distributions at plausible noise levels. The algorithm is general and is expected to be useful in other applications, such as remote sensing of cloud and rain properties. Significance Statement A typical cloud can have billions of drops spread over tens or hundreds of kilometers in space. Keeping track of the sizes, positions, and interactions of all of these droplets is impractical, and, as such, information about the relative abundance of large and small drops is typically quantified with a “size distribution.” Droplets in a cloud interact locally, however, so this work is motivated by the question of whether the cloud droplet size distribution is different in different parts of a cloud. A new method, based on hypothesis testing and machine learning, determines how many different size distributions are contained in a given cloud. This is important because the size distribution describes processes such as cloud droplet growth and light transmission through clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Accurate contrast determination for X-ray speckle visibility spectroscopy

X-ray speckle visibility spectroscopy using X-ray free-electron lasers has long been proposed as a probe of fast dynamics in noncrystalline materials. In this paper, numerical modeling is presented to show how the data interpretation of visibility spectroscopy can be impacted by the nonidealities of real-life X-ray detectors. Using simulated detector data, this work provides a detailed analysis of the systematic errors of several contrast extraction algorithms in the context of low-count-rate X-ray speckle visibility spectroscopy and their origins are discussed. Here, it was found that the finite detector charge cloud and pixel size lead to an unavoidable `degeneracy' in photon position determination, and that the contrasts extracted using different algorithms can all be corrected by a simple linear model. The results suggest that experimental calibration of the correction coefficient at the count rate of interest is possible and essential. This allows computationally lightweight algorithms to be implemented for on-the-fly analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction

Detector simulation and reconstruction are a significant computational bottleneck in particle physics. Here, we develop particle-flow neural-assisted simulations (parnassus) to address this challenge. Our deep learning model takes as input a point cloud (particles impinging on a detector) and produces a point cloud (reconstructed particles). By combining detector simulations and reconstruction into one step, we aim to minimize resource utilization and enable fast surrogate models suitable for application both inside and outside large collaborations. We demonstrate this approach using a publicly available dataset of jets passed through the full simulation and reconstruction pipeline of the Compact Muon Solenoid (CMS) experiment. We show that parnassus accurately mimics the CMS particle flow algorithm on the (statistically) same events it was trained on and can generalize to jet momentum and type outside of the training distribution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Absolute contrast estimation for soft X-ray photon fluctuation spectroscopy using a variational droplet model

Abstract X-ray photon fluctuation spectroscopy using a two-pulse mode at the Linac Coherent Light Source has great potential for the study of quantum fluctuations in materials as it allows for exploration of low-energy physics. However, the complexity of the data analysis and interpretation still prevent recovering real-time results during an experiment, and can even complicate post-analysis processes. This is particularly true for high-spatial resolution applications using CCDs with small pixels, which can decrease the photon mapping accuracy resulting from the large electron cloud generation at the detector. Droplet algorithms endeavor to restore accurate photon maps, but the results can be altered by their hyper-parameters. We present numerical modeling tools through extensive simulations that mimic previous x-ray photon fluctuation spectroscopy experiments. By modification of a fast droplet algorithm, our results demonstrate how to optimize the precise parameters that lift the intrinsic counting degeneracy impeding accuracy in extracting the speckle contrast. These results allow for an absolute determination of the summed contrast from multi-pulse x-ray speckle diffraction, the modus operandi by which the correlation time for spontaneous fluctuations can be measured.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

A Contrast Calibration Protocol for X-ray Speckle Visibility Spectroscopy

X-ray free electron lasers, with their ultrashort highly coherent pulses, opened up the opportunity of probing ultrafast nano- and atomic-scale dynamics in amorphous and disordered material systems via speckle visibility spectroscopy. However, the anticipated count rate in a typical experiment is usually low. Therefore, visibility needs to be extracted via photon statistics analysis, i.e., by estimating the probabilities of multiple photons per pixel events using pixelated detectors. Considering the realistic X-ray detector responses including charge cloud sharing between pixels, pixel readout noise, and gain non-uniformity, speckle visibility extraction relying on photon assignment algorithms are often computationally demanding and suffer from systematic errors. In this paper, we present a systematic study of the commonly-used algorithms by applying them to an experimental data set containing small-angle coherent scattering with visibility levels ranging from below 1% to ∼60%. We also propose a contrast calibration protocol and show that a computationally lightweight algorithm can be implemented for high-speed correlation evaluation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Prong Segmentation using Point Set Transformers in Multiple View Neutrino Detectors

NOvA is a long-baseline neutrino experiment studying neutrino oscillations by detecting neutrinos from the NuMI beam at Fermilab. Its physics analysis relies on accurate prong segmentation, which involves matching each hit to its source particle and identifying the particle type. This task has commonly been addressed using a combination of traditional clustering algorithms and convolutional neural networks (CNNs). However, NOvA’s detector design presents data as two sparse and decoupled 2D images (XZ and YZ views) rather than a native 3D representation, posing a significant challenge for traditional CNN-based models. In this talk, we propose a novel neural network based on the Point Set Transformer. By treating detector hits as sparse point clouds and implementing a cross-view attention mechanism, our model enables efficient information mixing between both views. Evaluated on NOvA simulated data, our model achieves superior accuracy while requiring significantly fewer computational resources compared to other models. Furthermore, the model demonstrates great performance when applied to Liquid Argon Time Projection Chamber (LArTPC) data, which shows its potential as a universal prong segmentation algorithm for multiple view neutrino detectors.

Liu, Jiaxi [UC, Irvine]↗
DOE OSTI2024

AEPF: Attention-Enabled Point Fusion for 3D Object Detection

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class’s easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

Chemistry↗
DOE OSTI2025

Enhancing direct detection of Higgsino dark matter

While much supersymmetric weakly interacting massive particle (WIMP) parameter space has been ruled out, one remaining important candidate is Higgsino dark matter. The Higgsino can naturally realize the “inelastic dark matter” scenario, where the scattering off a nucleus occurs between two nearly-degenerate states, making it invisible to WIMP direct detection experiments if the splitting is too large to be excited. It was realized that a “luminous dark matter” detection process, where the Higgsino upscatters in the Earth and subsequently decays into a photon in a large neutrino detector, offers the best sensitivity to such a scenario. We consider the possibility of adding a large volume of a heavy element, such as Pb or U, around the detector. We also consider the presence of U and Th in the Earth itself, and the effect of an enhanced high-velocity tail of the dark matter distribution due to the presence of the Large Magellanic Cloud. These effects can significantly improve the sensitivity of detectors such as JUNO, SNO + , KamLAND, and Borexino, potentially making it possible in the future to cover much of the remaining parameter space for this classic supersymmetric WIMP dark matter. Published by the American Physical Society 2025

Graham, Peter W. (ORCID:0000000216001601)↗
DOE OSTI2020

ElectroMon Geometry Considerations: Simulations of Electron Transport to a Diamond-Based Detector

Particle-in-Cell simulations were performed to investigate the effects of electrode bias and pitch on the expected number of electrons arriving at a diamond-based detector at a set of relevant incident electron energies for electron cloud monitoring. Results of these simulations indicate that the pitch of the electrodes directly impacts the number of incident electrons based on complete opacity of the electrodes to the electrons; image charge does not play a role at any relevant electron energy. Positive bias on the incident electrodes directly increases the energy of the incident electrons, while negative bias reduces the energy and completely repels incoming electrons of energies lower than the applied bias. These results are intended to be used as input parameters for a solid-state simulation which will determine the collection efficiency of the electron-hole pairs produced within the diamond as a function of position and energy.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

US ATLAS and US CMS HPC and Cloud Blueprint

The Large Hadron Collider (LHC) at CERN houses two general purpose detectors - ATLAS and CMS - which conduct physics programs over multi-year runs to generate increasingly precise and extensive datasets. The efforts of the CMS and ATLAS collaborations lead to the discovery of the Higgs boson, a fundamental particle that gives mass to other particles, representing a monumental achievement in the field of particle physics that was recognized with the awarding of the Nobel Prize in Physics in 2013 to François Englert and Peter Higgs. These collaborations continue to analyze data from the LHC and are preparing for the high luminosity data taking phase at the end of the decade. The computing models of these detectors rely on a distributed processing grid hosted by more than 150 associated universities and laboratories worldwide. However, such new data will require a significant expansion of the existing computing infrastructure. To address this, both collaborations have been working for years on integrating High Performance Computers (HPC) and commercial cloud resources into their infrastructure and continue to assess the potential role of such resources in order to cope with the demands of the new high luminosity era. US ATLAS and US CMS computing management have charged the authors to provide a blueprint document looking at current and possibly future use of HPC and Cloud resources, outlining integration models, possibilities, challenges and costs. The document will address key questions such as the optimal use of resources for the experiments and funding agencies, the main obstacles that need to be overcome for resource adoption, and areas that require more attention.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

The optimal use of segmentation for sampling calorimeters

One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation. Here, to inform this choice, we study the impact of calorimeter segmentation on energy reconstruction. To ensure that the trends are due entirely to hardware and not to a sub-optimal use of segmentation, we deploy deep neural networks to perform the reconstruction. These networks make use of all available information by representing the calorimeter as a point cloud. To demonstrate our approach, we simulate a detector similar to the forward calorimeter system intended for use in the ePIC detector, which will operate at the upcoming Electron Ion Collider. We find that for the energy estimation of isolated charged pion showers, relatively fine longitudinal segmentation is key to achieving an energy resolution that is better than 10% across the full phase space. These results provide a valuable benchmark for ongoing EIC detector optimizations and may also inform future studies involving high-granularity calorimeters in other experiments at various facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

EPiC-GAN: Equivariant point cloud generation for particle jets

With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.

Buhmann, Erik↗
DOE OSTI2022

FORTE Measurements of Global Optical Lightning Waveforms and Implications for Optical Lightning Detection

Lightning processes generate a diverse collection of optical pulses from current traversing the lightning channels. These signals are then broadened spatially and temporally via scattering in the clouds. The resulting waveforms measured from space with instruments like the photodiode detector (PDD) on the Fast On-orbit Recording of Transient Events (FORTE) satellite have a variety of shapes. In this study, we use coincident optical and Radio-Frequency measurements to document the properties of optical PDD waveforms associated with different types of lightning, estimate delays from scattering in the clouds, and comment on how pulse shape impacts optical lightning detection. We find that the attributes of optical pulses recorded by the PDD are consistent with prior studies, but vary globally and with event amplitude. The most powerful lightning tends to be single-peaked with faster rise times (median: ~100 µs) and shorter effective widths (median: ~400 µs) than normal lightning. Particularly dim events, meanwhile, include cases of broad optical waveforms with sustained optical emission throughout the PDD record, which the pixelated FORTE Lightning Location System (LLS) instrument has difficulty detecting. We propose that this is due to the optical signal being divided between individual LLS pixels that are each, individually, not bright enough to trigger. We use PDD waveforms and Monte Carlo radiative transfer modeling to demonstrate that increasing the temporal and spatial resolution of a pixelated lightning imager will make it more difficult to detect these broad/dim pulses as their energy becomes divided between additional pixels/integration frames.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Texas A&M University Mobile Facility Measurements during TRACER (Field Campaign Report)

One of the main goals of the U.S Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Tracking Aerosol Convection Interactions Experiment (TRACER) near Houston, Texas is to improve understanding of how meteorology and aerosols impact storm dynamical and microphysical processes in deep convection to better constrain and improve their model representation. The Houston area is strongly influenced by sea- and bay-breeze circulations that generate convergence and help to initiate and organize deep convection. To properly isolate and understand the roles of varied meteorological conditions and cloud condensation nuclei (CCN) and ice nucleation particles (INP) distributions in different air masses, co-located thermodynamic, kinematic, and aerosol vertical profile observations are needed. The focus of this campaign was to provide key measurements in air masses both in front of and behind sea/bay breeze fronts moving through the greater Houston area to sample the airmass heterogeneity. The overarching scientific goal of this campaign is to understand how the vertical distributions of both CCN and INP correspond to the inflow layer of deep convection in maritime, background continental, and polluted continental air masses, and how these variations influence deep convection. To fully sample the heterogeneity in both meteorological conditions and aerosols across the sea-breeze front (SBF), Texas A&M University (TAMU) deployed a InterMet 3050A 403 MHz mobile unit launching iMet-4 radiosondes and the new Rapid Onsite Atmospheric Measurement Van (ROAM-V) for aerosol sampling during the TRACER intensive operational period (IOP) from June to September 2022. The suite of instruments deployed on ROAM-V included a condensation particle counter (CPC; GRIMM Model 5.403 CPC), scanning mobility particle sizer (SMPS; TSI 3750 detector, TSI 3082 classifier, TSI 3088 neutralizer, TSI 3081A differential mobility analyzer), cloud condensation nuclei counter (Droplet Measurement Technologies CCN counter), micropulse lidar (Droplet Measurement Technologies micropulse lidar [miniMPL]), and a Davis Rotating Uniform size-cut Monitor (DRUM; DRUMAir 4-DRUM). Before sampling at each location, the latitude and longitude were recorded using the Global Positioning System (GPS) on the phone application “My Altitude”. The DRUM data were collected as part of a closely related ARM field campaign and also supported by DOE Atmospheric System Research grant DE-SC0021047. The TAMU team sampled these airmass heterogeneities by strategically choosing deployment sites in a different airmass than the ARM fixed sites in La Porte and Guy, Texas. On days when the sea/bay breeze boundary was pushing inland, the TAMU team would usually sample the airmass on the maritime side of the SBF at a coastal site in Galveston, Texas in the early afternoon (1730-1900 UTC) and then move inland ahead of the SBF to sample the airmass on the continental side during late afternoon (2030-2230). Figure 1 shows the Galveston maritime site and the array of sites for the late afternoon continental measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

CPC data from TAMU TRACER campaign in the Houston, TX region from July to September 2022

During TRACER, the Texas A&M Rapid Onsite Atmospheric Measurements Van (ROAM-V) was deployed to capture airmasses behind (maritime) and ahead (continental) of the passage of the sea-breeze front through Houston. On select sampling days, ROAM-V sampled in the morning/mid-day on the coast and then transited to a second inland site for the afternoon/evening. The suite of instruments deployed on ROAM-V included a Condensation Particle Counter (CPC; GRIMM Model 5.403 CPC), Scanning Mobility Particle Sizer (SMPS; TSI 3750 detector, TSI 3082 classifier, TSI 3088 neutralizer, TSI 3081A Differential Mobility Analyzer), Cloud Condensation Nuclei counter (Droplet Measurement Technologies CCN Counter), micro pulse lidar (Droplet Measurement Technologies Micro Pulse LiDAR (miniMPL)), and a Davis Rotating Uniform size-cut Monitor (DRUM; DRUMAir 4-DRUM). Before sampling at each location, the latitude and longitude were recorded using the GPS on the phone application “My Altitude”. Onboard the ROAM-V, aerosol samples are drawn through a shared isokinetic inlet at a flow rate ranging from 3.5 to 7.0 LPM. A portion of this flow is directed through a cyclone impactor (Brechtel, Inc. Model SCC 0.732) and 0.3 LPM is directed to the CPC. To correct the data for particle losses, we used a two-step method. First, the measured SMPS size distributions were used to calculate total particle loss through the inlet for every SMPS scan at a single deployment location. Second, the CPC data was corrected for particle losses using the average of the total losses per scan. This calculation was done separately for each deployment location due to changes in the measured size distributions between locations. Particle loss from diffusion (based on Kesten, 1991 and Gormley, 1949), inertial impaction in 90-degree bends (based on Aerosol Measurement, 2011 and Crane, 1977), and cyclone impactor efficiency (based on Dirgo, 1985) were included in the loss calculation. When the SMPS was not sampling at a location (in the case of an instrument malfunction or operator error), the reported CPC data was corrected with an average of the total losses for the entire campaign at the specified deployment location (e.g., if we needed to correct Galveston data, then the average of all calculated losses at Galveston was taken). These flatline corrections were used for all data on 22/07/13, 22/07/20, 22/07/22, and the data from Galveston on 22/08/09. An additional flatline correction of 14% was applied to all CPC data based on laboratory calibration prior to and after the campaign. In laboratory calibrations, we identified that for the same sample of air the field CPC (GRIMM Model 5.403 CPC) undercounted the total concentration of particles by 14% compared to the ground truth laboratory CPC (TSI Model 3750). This data was collected for ARM Field Campaign AFC07055 and supported by DOE ASR grant DE-SC0021047. For any further questions, please feel free to contact the instrument PI, Sarah D. Brooks, sbrooks@tamu.edu. Kesten et. al. Calibration of a TSI Model 3025 Ultrafine Condensation Particle Counter. Aerosol Science and Technology, 15:2, 107-111, 1991. Gormley et. al. Diffusion from a Stream Flowing through a Cylindrical Tube. Proceedings of the Royal Irish Academy, Vol 52, 163-169, 1948. Aerosol Measurement: Principles, Techniques, and Applications, Third Edition. John Wiley & Sons, Inc, 2011. Crane et. al. Inertial Deposition of Particles in a Bent Pipe. Journal of Aerosol Science, Vol 8, 161-170, 1977. Dirgo et. al. Cyclone Collection Efficiency: Comparison of Experimental Results with Theoretical Predictions. Aerosol Science and Technology, 4:4, 401-415, 1985.

54 ENVIRONMENTAL SCIENCES↗