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At least 1,063 records · Page 59

Aircraft Engine Run-To-Failure Data Set Under Real Flight Conditions

The generation of data-driven prognostics models requires the availability of datasets with run-to-failure trajectories. In order to contribute to the development of these methods, the dataset provides a new realistic dataset of run-to-failure trajectories for a small fleet of aircraft engines under realistic flight conditions. The damage propagation modelling used for the generation of this synthetic dataset builds on the modelling strategy from previous work [1] and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet [2]. Secondly, it extends the degradation modelling by relating the degradation process to the operation history. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model [3]. More details about the generation process can be found in [4].

CMAPSS↗

DOME: Directional medical embedding vectors from Electronic Health Records

Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHRembedding.

60 APPLIED LIFE SCIENCES↗

OzDES reverberation mapping program: Lag recovery reliability for 6-yr C iv analysis

ABSTRACT We present the statistical methods that have been developed to analyse the OzDES reverberation mapping sample. To perform this statistical analysis we have created a suite of customizable simulations that mimic the characteristics of each source in the OzDES sample. These characteristics include: the variability in the photometric and spectroscopic light curves, the measurement uncertainties, and the observational cadence. By simulating the sources in the OzDES sample that contain the C iv emission line, we developed a set of criteria that rank the reliability of a recovered time-lag depending on the agreement between different recovery methods, the magnitude of the uncertainties, and the rate at which false positives were found in the simulations. These criteria were applied to simulated light curves and these results used to estimate the quality of the resulting Radius–Luminosity relation. We grade the results using three quality levels (gold, silver, and bronze). The input slope of the R–L relation was recovered within 1σ for each of the three quality samples, with the gold standard having the lowest dispersion with a recovered a R–L relation slope of 0.454 ± 0.016 with an input slope of 0.47. Future work will apply these methods to the entire OzDES sample of 771 AGN.

79 ASTRONOMY AND ASTROPHYSICS↗

The impact of tomographic redshift bin width errors on cosmological probes

ABSTRACT Systematic errors in the galaxy redshift distribution n(z) can propagate to systematic errors in the derived cosmology. We characterize how the degenerate effects in tomographic bin widths and galaxy bias impart systematic errors on cosmology inference using observational data from the Deep Lens Survey. For this we use a combination of galaxy clustering and galaxy–galaxy lensing. We present two end-to-end analyses from the catalogue level to parameter estimation. We produce an initial cosmological inference using fiducial tomographic redshift bins derived from photometric redshifts, then compare this with a result where the redshift bins are empirically corrected using a set of spectroscopic redshifts. We find that the derived parameter S8 ≡ σ8(Ωm/.3)1/2 decreases from $0.841^{+0.062}_{-0.061}$ to $0.781^{0.061}_{0.054}$ upon correcting the n(z) errors in the second method.

79 ASTRONOMY AND ASTROPHYSICS↗

An innovative approach to the development of a portable unit for analytical flame characterization in a microgravity environment

The availability of manned laboratory facilities in space offers wonderful opportunities and challenges in microgravity combustion science and technology. In turn, the fundamentals of microgravity combustion science can be studied via spectroscopic characterization of free radicals generated in flames. The laser-induced fluorescence (LIF) technique is a noninvasive method of considerable utility in combustion physics and chemistry suitable for monitoring not only specific species and their kinetics, but it is also important for imaging of flames. This makes LIF one of the most important tools for microgravity combustion science. Flame characterization under microgravity conditions using LIF is expected to be more informative than other methods aimed at searching for effects like pumping phenomenon that can be modeled via ground level experiments. A primary goal of our work consisted in working out an innovative approach to devising an LIF-based analytical unit suitable for in-space flame characterization. It was decided to follow two approaches in tandem: (1) use the existing laboratory (non-portable) equipment and determine the optimal set of parameters for flames that can be used as analytical criteria for flame characterization under microgravity conditions; and (2) use state-of-the-art developments in laser technology and concentrate some effort in devising a layout for the portable analytical equipment. This paper presents an up-to-date summary of the results of our experiments aimed at the creation of the portable device for combustion studies in a microgravity environment, which is based on a portable UV tunable solid-state laser for excitation of free radicals normally present in flames in detectable amounts. A systematic approach has allowed us to make a convenient choice of species under investigation, as well as the proper tunable laser system, and also enabled us to carry out LIF experiments on free radicals using a solid-state laser tunable in the UV.

Dubinskiy, Mark A.↗

A New Clinical Instrument for The Early Detection of Cataract Using Dynamic Light Scattering and Corneal Topography

A growing cataract can be detected at the molecular level using the technique of dynamic light scattering (DLS). However, the success of this method in clinical use depends upon the precise control of the scattering volume inside a patient's eye and especially during patient's repeat visits. This is important because the scattering volume (cross-over region between the scattered fight and incident light) inside the eye in a high-quality DLS set-up is very small (few microns in dimension). This precise control holds the key for success in the longitudinal studies of cataract and during anti-cataract drug screening. We have circumvented these problems by fabricating a new DLS fiber optic probe with a working distance of 40 mm and by mounting it inside a cone of a corneal analyzer. This analyzer is frequently used in mapping the corneal topography during PRK (photorefractive keratectomy) and LASIK (laser in situ keratomileusis) procedures in shaping of the cornea to correct myopia. This new instrument and some preliminary clinical tests on one of us (RRA) showing the data reproducibility are described.

Ansari, Rafat R.↗

Deep Space Network Scheduling Using Multi-Objective Optimization with Uncertainty

We have developed a novel technique to incorporate uncertainty modeling within an evolutionary algorithm approach to multi-objective scheduling, with the goal of identifying a Pareto frontier (tradeoff curve) that recognizes the likelihood of events that can impact the schedule outcome. Our approach is particularly applicable to the generation of multiobjective optimized robust schedules, where objectives are assigned a service level, for example that we require an objective value to be greater than or equal to X with Y% confidence. We have demonstrated that such an approach can, for example, minimize scheduling on less reliable resources, based solely on a resource reliability model and not on any ad hoc heuristics. We have also investigated an alternative method of optimizing for robustness, in which we add to the set of objectives a failure risk objective to minimize. We compare the advantages and disadvantages of these two approaches. Future plans for further developing this technology include its application to space-based observatory scheduling problems.

Johnston, Mark D.↗

Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set

Abstract This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases ofd-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupieddstates near the Fermi level, we determine that the large, sharpddensity of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.

Chemistry↗

Exploring Diverse Data Sets and Developing New Theories and Ideas With Project Integration Architecture

The development of new ideas is the essence of scientific research. This is frequently done by developing models of physical processes and comparing model predictions with results from experiments. With models becoming ever more complex and data acquisition systems becoming more powerful, the researcher is burdened with wading through data ranging in volume up to a level of many terabytes and beyond. These data often come from multiple, heterogeneous sources and usually the methods for searching through it are at or near the manual level. In addition, current documentation methods are generally limited to researchers pen-and-paper style notebooks. Researchers may want to form constraint-based queries on a body of existing knowledge that is, itself, distributed over many different machines and environments and from the results of such queries then spawn additional queries, simulations, and data analyses in order to discover new insights into the problem being investigated. Currently, researchers are restricted to working within the boundaries of tools that are inefficient at probing current and legacy data to extend the knowledge of the problem at hand and reveal innovative and efficient solutions. A framework called the Project Integration Architecture is discussed that can address these desired functionalities.

Benyo, Theresa L.↗

Generative adversarial networks for scintillation signal simulation in EXO-200

Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network — a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw scintillation waveforms as input. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach and, importantly, generalize from the training sample and discern salient high-level features of the data. In particular, the network correctly deduces position dependency of scintillation light response in the detector and correctly recognizes dead photodetector channels. Furthermore, the network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy distributions comparable to that of real waveforms. Finally, the remaining discrepancies and potential ways to improve the approach further are highlighted.

47 OTHER INSTRUMENTATION↗

Molecular Processes in Comets

The research focused on molecular hydrogen and its response to ultraviolet radiation, photoelectron impact excitation and X-ray radiation and on the interpretation of the ultraviolet spectra of the Jupiter dayglow and auroras. A systematic effort was made to obtain reliable rate coefficients for rovibrational energy transfer of H2, particularly in collisions with hydrogen atoms. We carried out elaborate quantum-mechanical calculations of the scattering of H and H2 on what had been characterized as a reliable potential energy surface of the H3 molecular system. These calculations took into account reactive channels and rate coefficients for ortho-para transitions were obtained. Quantal calculations are too complex to be applied to all the possible rovibrational transitions and we turned to a semi-classical method. With it we calculated rate coefficients for transitions, reactive and non-reactive, for all the rovibrational levels. We carried out the calculations for three of the available H3 potential energy surfaces. We discovered an unexpected sensitivity of the rate coefficients for the non-reactive channels to the potential energy surface. This discovery stimulated more thorough investigations of the potential energy surface elsewhere and to the construction of a new surface. We have used it in further semi-classical calculations in work that will shortly be 2 completed and which, together with new quantum-mechanical calculations should comprise a set of reliable rate coefficients that can be used in discussions of H2 on the Jovian planets. We carried out a detailed study of the Jovian ultraviolet dayglow. There has been a long-running argument about the dayglow on Jupiter. There are two sources of excitation: fluorescence and photoelectron impact excitation. It had been argued that a third source "the electron glow" was needed to bring theory and observation into agreement. We believe we have shown conclusively that the third source is unnecessary. We have achieved a close quantitative agreement between the predicted spectrum arising from fluorescence and photoelectrons and the measured spectrum. We also demonstrated a method by which the presence of HD could be established observationary. Similar calculations were carried out of the ultraviolet spectra of the Jovian auroras. We again found close agreement. Indeed, the agreement was so detailed we were able to derive the temperature of the atmosphere. We found, contrary to the standard model of the time, a high temperature between 400 K and 600 K and established the presence of a significant temperature gradient. A large temperature gradient was indeed found by direct sampling with the Jupiter probe.

Dalgarno, A.↗

RT-EOM-CCSD Calculations of Inner and Outer Valence Ionization Energies and Spectral Functions

Photoelectron spectroscopy (PES) is a standard experimental method for material characterization, but its interpretation can be hampered by its reliance on standard materials. To facilitate the study of unknown systems, theoretical methods are desirable. Here we present a real-time equation-of-motion coupled cluster (RT-EOM-CC) approach for valence PES, extending our core-level development. Here, we demonstrate that RT-EOM-CC yields ionization energies and spectral functions in good agreement with experiment and CI-based methods, even for some more correlated cases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Residential Building Energy Efficiency Field Studies: Low-Rise Multifamily

In recent years, the U.S. Department of Energy (DOE) has conducted a series of research studies to validate energy efficient building technologies in the field. Much of the work has focused on single-family construction, and some has also addressed commercial energy codes. The work detailed in this DOE-funded study (EE0007616) focuses on low-rise multifamily buildings (three stories or fewer above grade) in various regions of the United States, and reports on how state-level building codes are being implemented, both in terms of observed characteristics and also in terms of estimated energy impacts. Nearly 100 buildings across four states—Illinois, Minnesota, Oregon, and Washington—were sampled, which represent a range of climate types from mild temperature to very cold continental. Both common entry and outdoor entry buildings were included, and a parallel research project evaluated envelope air tightness and current still-evolving air tightness testing methods. Finally, a set of structured interviews of building designers and other relevant professionals was carried to out to gain more insight into this market. To the greatest extent possible, the methodology developed under the project for low-rise multifamily buildings mirrored the approach established by Pacific Northwest National Laboratory (PNNL) for single-family residential buildings (https://www.energy.gov/eere/buildings/downloads/residential-building-energy-code-field-study). This included the general approach to sampling, recruitment, and data collection, as well as data analysis and presentation. The range of permitting dates for the sites encompassed two energy code cycles in most regions. All states in the study had adopted a variation of the International Energy Conservation Code (IECC) for the structure of their state code. The low-rise multifamily occupancy presents a hybrid building type: most of the building’s conditioned floor area was covered by the residential chapter of the code while portions of the building (such as corridors and common spaces) fell under the commercial code chapter. The key items assessed in this work were: Building Shell—exterior wall insulation, ceiling insulation, foundation insulation, windows. Common Areas—HVAC and lighting. Living Units—lighting, ventilation. A few items were not assessed in detail, given their relative paucity in this occupancy type; these included duct leakage, pipe insulation, and hot water circulation controls. Building characteristics were collected via a combination of architectural, mechanical, electrical, and plumbing plan reviews and field inspections, and entered into a spreadsheet-based tool that was later queried to build a database. Data went through quality control both upon arrival and via a later semi-automated review and assurance process. Most of the data are presented graphically so that the reader can quickly assess compliance with the applicable energy codes (both by state and by code year). As a final step, EnergyPlus™ simulations were created for all buildings in the study to estimate both the as-found energy use intensity (EUI) and the energy and CO 2 that could be saved if features that were found to not meet code minimums were brought up to code. The savings estimates were tabulated for each of the four states in the study. The research team found that the single-family approach was largely applicable to low-rise multifamily buildings. This applies to both the data collection and the prototype EUI analysis. Most of the occupied space is living units and falls under residential energy codes, and many characteristics use similar envelope construction and relatively straightforward mechanical systems and lighting. One of the most challenging aspects of this work was to build an effective spreadsheet-based data collection instrument that could allow efficient collection of both building plan and field data. The research team is of the view that other methods could be equally effective if the work is done carefully with diligent quality control. The primary findings for the work center around the thermal envelope and mechanical systems and lighting at the sites: For thermal envelope components, the majority of buildings met or were better than the prescriptive code.This suggests that building designers and builders are aware of code requirements. In some cases, surveyed buildings were designed to qualify for energy efficiency certification programs. These buildings made up at least 20% of sampled buildings in each state. Almost all buildings met mechanical system efficiency requirements (for both living units and common areas). In some cases, sites employed systems that were considerably more efficient than required by the applicable energy code. Dwelling units had a majority of high-efficacy lighting, often in excess of the state’s residential code requirements. While high-efficacy fixtures were also typical in common areas (corridors and stairwells), lighting power densities (LPDs) in these areas were sometimes higher than levels dictated by the applicable part of the state commercial energy code. The simulation models run on a series of low-rise multifamily prototypes, informed by a composite of the field data collected, calculated annual EUIs of between 20 and 50 kBtu/ft2-yr, with the range representing the effects of both building characteristics and building location (climate zone). A detailed process (based on simulations of prototype buildings) was used to estimate the amount of avoided energy use that would occur if 100% adherence to energy codes were attained. The results indicated modest savings are attainable for items such as window thermal performance and common area lighting. The result is overall only a modest potential for additional energy savings, averaging about 10% of EUI.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Nontargeted virus sequence discovery pipeline and virus clustering for metagenomic data

The analysis of large microbiome data sets holds great promise for the delineation of the biological and metabolic functioning of living organisms and their role in the environment. In the midst of this genomic puzzle, viruses, especially those that infect microbial communities, represent a major reservoir of genetic diversity with great impact on biogeochemical cycles and organismal health. Overcoming the limitations associated with virus detection directly from microbiomes can provide key insights into how ecosystem dynamics are modulated. Here, we present a computational protocol for accurate detection and grouping of viral sequences from microbiome samples. Our approach relies on an expanded and curated set of viral protein families used as bait to identify viral sequences directly from metagenomic assemblies. This protocol describes how to use the viral protein families catalog (~7 h) and recommended filters for the detection of viral contigs in metagenomic samples (~6 h), and it describes the specific parameters for a nucleotide-sequence-identity-based method of organizing the viral sequences into quasi-species taxonomic-level groups (~10 min).

Paez-Espino, David↗

Exact and efficient hybrid Monte Carlo algorithm for accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts

Single cells exhibit a significant amount of variability in transcript levels, which arises from slow, stochastic transitions between gene expression states. Elucidating the nature of these states and understanding how transition rates are affected by different regulatory mechanisms require state-of-the-art methods to infer underlying models of gene expression from single cell data. A Bayesian approach to statistical inference is the most suitable method for model selection and uncertainty quantification of kinetic parameters using small data sets. However, this approach is impractical because current algorithms are too slow to handle typical models of gene expression. To solve this problem, we first show that time-dependent mRNA distributions of discrete-state models of gene expression are dynamic Poisson mixtures, whose mixing kernels are characterized by a piecewise deterministic Markov process. Here, we combined this analytical result with a kinetic Monte Carlo algorithm to create a hybrid numerical method that accelerates the calculation of time-dependent mRNA distributions by 1000-fold compared to current methods. We then integrated the hybrid algorithm into an existing Monte Carlo sampler to estimate the Bayesian posterior distribution of many different, competing models in a reasonable amount of time. We demonstrate that kinetic parameters can be reasonably constrained for modestly sampled data sets if the model is known a priori. If there are many competing models, Bayesian evidence can rigorously quantify the likelihood of a model relative to other models from the data. We demonstrate that Bayesian evidence selects the true model and outperforms approximate metrics typically used for model selection.

59 BASIC BIOLOGICAL SCIENCES↗

Size Effect of Local Current-Voltage Characteristics of MX 2 Nanoflakes: Local Density of States Reconstruction from Scanning Tunneling Microscopy Experiments

Local current-voltage characteristics for low-dimensional transition-metal dichalcogenides (LDTMD), as well as the reconstruction of their local density of states (LDOS) from scanning tunneling microscopy (STM) experiments, are of fundamental interest and can be useful for advanced applications. Most of the existing models either have limited applicability for complex-shaped LDTMDs (e.g., those based on the Simmons approach) or require solving of an ill-defined integral equation to deconvolute the unknown LDOS (e.g., those based on the Tersoff approach). Using a serial expansion of the Tersoff formulas, we propose a flexible method to reconstruct the LDOS from local current-voltage characteristics measured in STM experiments. We establish a set of key physical parameters, which characterize the tunneling current of a STM-probe–sample contact and the sample LDOS expanded in Gaussian functions. Using a direct variational method coupled with probabilistic analysis, we determine these parameters from the STM experiments for MoS2 nanoflakes with different numbers of layers. The main result is the reconstruction of the LDOS in a relatively wide energy range around the Fermi level, which allows us to gain insight into the local band structure of LDTMDs. The reconstructed LDOS reveal pronounced size effects for the single-layer, two-layer, and three-layer MoS 2 nanoflakes, which we relate to the low dimensionality and strong bending or corrugation of the nanoflakes. We hope that the proposed elaboration of the Tersoff approach, allowing LDOS reconstruction, will be of critical interest for the quantitative description of STM experiments and also of use to better understand the microscopic physical aspects of the surface, strain, and bending contributions to the LDTMDs’ electronic properties.

42 ENGINEERING↗

A Heuristic Approach to Value-Driven Evaluation of Visualizations

Recently, an approach for determining the value of a visualization was proposed, one moving beyond simple measurements of task accuracy and speed. The value equation contains components for the time savings a visualization provides, the insights and insightful questions it spurs, the overall essence of the data it conveys, and the confidence about the data and its domain it inspires. This articulation of value is purely descriptive, however, providing no actionable method of assessing a visualization's value. In this work, we create a heuristic-based evaluation methodology to accompany the value equation for assessing interactive visualizations. We refer to the methodology colloquially as ICE-T, based on an anagram of the four value components. Our approach breaks the four components down into guidelines, each of which is made up of a small set of low-level heuristics. Evaluators who have knowledge of visualization design principles then assess the visualization with respect to the heuristics. We conducted an initial trial of the methodology on three interactive visualizations of the same data set, each evaluated by 15 visualization experts. As a result, we found that the methodology showed promise, obtaining consistent ratings across the three visualizations and mirroring judgments of the utility of the visualizations by instructors of the course in which they were developed.

97 MATHEMATICS AND COMPUTING↗

Theoretical and Experimental Studies in Accelerator Physics

This report describes research supported by the US Dept. of Energy Office of High Energy Physics (OHEP), performed by the UCLA Particle Beam Physics Laboratory (PBPL). The UCLA PBPL has, over the last two decades-plus, played a critical role in the development of advanced accelerators, fundamental beam physics, and new applications enabled by these thrusts, such as new types of accelerator-based light sources. As the PBPL mission is broad it is natural that it has been grown within the context of the accelerator science and technology stewardship of the OHEP. Indeed, steady OHEP support for the program has always been central to the success of the PBPL; it has provided stability, and above all has set the over-arching themes for our research directions, which have producing over 500 publications (>120 in high level journals). While other agency support has grown notably in recent years, permitting more vigorous pursuit of the program, it is transient by comparison. Beyond permitting program growth in a time of flat OHEP budgets, the influence of other agency missions is found in push to adapt advanced accelerator methods to applications, in light of the success the field has had in proof-of-principle experiments supported first by the DoE OHEP. This three-pronged PBPL program — advanced accelerators, fundamental beam physics and technology, and revolutionary applications — has produced a generation of students that have had a profound affect on the US accelerator physics community. PBPL graduates, numbering 28 in total, form a significant population group in the accelerator community, playing key roles as university faculty, scientific leaders in national labs (two have been named Panofsky Fellows at SLAC), and vigorous proponents of industrial application of accelerators. Indeed, the development of advanced RF, optical and magnet technology at the PBPL has led directly to the spin-off company, RadiaBeam Technologies, now a leading industrial accelerator firm. We note also that PBPL graduates remain as close elaborators for the program after leaving UCLA. The UCLA PBPL program is a foremost developer of on-campus facilities, such as the Neptune and Pegasus Laboratories, providing a uniquely strong environment for student-based research. In addition, the PBPL is a strong user of off-campus national lab facilities, such as SLAC FACET and NLCTA, and the BNL ATF. UCLA has also vigorously participated in the development of these facilities. The dual emphases on off- and on-campus opportunities permit the PBPL to address in an agile way a wide selection of cutting-edge research topics. The topics embraced by this proposal illustrate this program aspect well. These include: GV/m dielectric wakefield acceleration/coherent Cerenkov radiation experiments at FACET (E-201) and the ATF; synergistic laser-excited dielectric accelerator and light source development; plasma wakefield (PWFA) experiments on “Trojan horse” ionization injection (FACET E-210), quasi-nonlinear PWFA at BNL and the production at Neptune high transformer ratio plasma wakes; the inauguration of a new type of RF photoinjector termed “hybrid” at UCLA, and application to PWFA; space-charge dominated beam and cathode/near cathode physics; the study of advanced IFEL systems, for very high energy gain and utilization of novel OAM modes; the physcis of inverse Compton scattering (ICS), with applications to e+ production and γγ colliders; electron diffraction; and advanced beam diagnostics using coherent imaging techniques. These subjects are addressed under the leadership of PBPL director Prof. James Rosenzweig in Task A, and Prof. Pietro Musumeci in Task J, which was initiated following his OHEP Outstanding Junior Investigator award.

43 PARTICLE ACCELERATORS↗