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At least 379 records · Page 21

The Role of Quantum Science Concepts in Enhancing Sensing and Imaging Technologies: Applications for Biology: Proceedings of a Workshop

Quantum concepts hold the potential to enable significant advances in sensing and imaging technologies that could be vital to the study of biological systems. The workshop Quantum Science Concepts in Enhancing Sensing and Imaging Technologies: Applications for Biology, held online March 8–10, 2021, was organized to examine the research and development needs to advance biological applications of quantum technology. Hosted by the National Academies of Sciences, Engineering, and Medicine, the event brought together experts working on state-of-the-art, quantum-enabled technologies and scientists who are interested in applying these technologies to biological systems. Through talks, panels, and discussions, the workshop facilitated a better understanding of the current and future biological applications of quantum-enabled technologies in fields such as microbiology, molecular biology, cell biology, plant science, mycology, and many others. The workshop was organized around three main themes. The first, quantum in biology, examined quantum concepts that are hypothesized to be important for life processes and that researchers are working to observe through biological imaging and sensing. The second, quantum for biology, addressed ways to use quantum concepts to enhance technologies for biological imaging and sensing. The third, biology for quantum, offered a wider discussion of how the frontiers of biological imaging and sensing could enable future study using quantum concepts, tools, or technologies. Throughout the workshop, participants identified a wide range of emerging approaches and opportunities at the intersection of quantum physics and biological sensing and imaging. During the workshop, there were some differences in how each speaker defined the term quantum. During one of the panels, Prem Kumar offered thoughts on what phenomena are classical versus quantum, explaining that techniques get progressively more quantum as you move from just having superposition to having superposition with measurement and entanglement. Another explanation from Clarice Aiello delineates the definition into several levels. This includes a base level of “quantum-ness,” which reflects that all matter is made of atoms, and when these particles are isolated they behave based on quantum mechanical principles. A second level is related to quantum coherence, where a single quantum object might be found in a coherent superposition state. A final level, which she described as the quantum-entangled level, involves multiple quantum systems which are entangled among themselves. Overall, the workshop touched on concepts such as superposition, entanglement, and squeezing and their potential implications for communication, computing, and simulation, in addition to the workshop’s main focal area, biological sensing and imaging. At the opening of the workshop, Thorsten Ritz of the University of California, Irvine, identified two questions at the heart of quantum biology: Is the machinery of life quantum mechanical, and can quantum mechanics be used to study the machinery of life in new ways? Participants highlighted systems in which researchers have explored these questions, from the vast array of molecular interactions involved in biological processes such as photosynthesis, to the mechanics involved in cellular functions such as differentiation and aggregation, to the role of oscillating magnetic fields in flight orientation among birds. Sensing and imaging technologies are crucial to biological research; these technologies could both enhance the study of quantum effects and be enhanced by quantum concepts. A critical challenge in biological research is to develop imaging and sensing tools that do not damage or interfere with the often fragile and fleeting systems being studied. Attendees discussed a variety of established and emerging technologies that could enhance noninvasive biological imaging, including single- and two-photon spectroscopy, single-molecule spectroscopy, quantum illumination, ghost imaging, and cryo-electron microscopy. One example came from Marlan Scully who gave a keynote address on the first day of the workshop. Scully emphasized the use of different laser technologies, which exhibit coherence and other quantum properties, in moving toward real-world biological applications, such as the detection of SARS-CoV-2. Both tools and theory will play an important role in advancing quantum biology research and applications. Several participants suggested theorists and experimentalists should work in tandem to understand and model biological processes. While physics often reduces systems to their simplest forms for fundamental insights, participants also noted the value of observing and understanding biological systems in all their “messiness,” capturing both the inner workings of biological systems and the complex interactions that occur within and between organisms. In discussions among participants, several attendees stressed the need to match emerging tools with the right scientific questions. Rather than developing quantum technologies as “a hammer looking for a nail,” participants emphasized a focus on exploring the problems these technologies are best suited to address. For example, it is important to consider the size of the phenomenon being studied, the timescales that are important in answering the scientific question, and other relevant considerations. Every tool along the spectrum from classical to quantum involves its own set of trade-offs. For example, Ted Laurence of the Lawrence Livermore National Laboratory said that quantum measurements, such as single photon counting, fluorescence transitions, and lasers, can take longer to produce the same results as classical measurements. These quantum measurements, however, do not require calibration and can enable new research questions to be answered. Prem Kumar, Northwestern University, noted that, despite their promise, quantum approaches should not be used simply for the sake of using quantum, especially in situations where classical approaches better meet the needs of the researcher. Understanding and applying quantum concepts could enable advances in a wide range of application areas including energy, synthetic biology, medicine, and sustainability. For example, Michelle O’Malley, University of California, Santa Barbara, described how improved noninvasive imaging approaches could help capture the complex interactions and functions involved in the breakdown of organic matter by microbial communities and lead to new technologies for capturing valuable products from plant waste. Several other participants discussed needs in tracking the movement of metabolites and molecules in microbial communities for insights into nutrient cycling in environments such as soil. Margaret Ahmad, Sorbonne University, discussed potential opportunities to leverage the magnetic properties of cryptochromes to advance new treatment approaches for diseases such as COVID-19 and cancer. Looking toward the future development of the field, participants discussed challenges to advancing quantum biology that arise from disciplinary disconnects between physicists and biologists. The siloing of academic research disciplines represents a significant barrier to progress. Disconnects in terminology, motivations and priorities, and structural barriers to collaborative work underscore the need for concerted efforts to bridge these divides. Attendees and speakers offered suggestions for resolving these divergences, establishing a shared language, moving the field forward, and fostering meaningful feedback between disciplines. Overall, participants stressed a need for balance, open communication, collaboration, unity, and clear dialogue on trade-offs between quantum and classical approaches. Keiko Torii, The University of Texas at Austin, said that the best collaborations happen when the project provides mutual advantages that can show off everyone’s talents, each team finds the work interesting, and partners develop a camaraderie to pursue new knowledge. To enable near- and long-term opportunities in this space, participants suggested that exploratory, high-risk funding could improve existing instrumentation to explore quantum enhancement collaboratively. They also emphasized the need for collaboration between quantum physicists and sensing/imaging scientists, which could be advanced through a dedicated quantum biology investigator program. People, even more than technology, will be crucial to the future of quantum biology. Participants explored training, education, and workforce needs to further develop this burgeoning field and cultivate the next generation of scientists. While many programs are still in their nascent stages, participants highlighted examples of approaches and programs being developed at various types of institutions to engage students and professional scientists in quantum biology research. Several participants stressed the need for an inclusive approach, spanning disciplines as well as communities to foster a diverse field fueled by the intellectual contributions of a wide range of people, including historically under-resourced schools and students. To increase awareness and excitement about quantum physics and related areas of biology, attendees suggested capitalizing on the “buzz” around quantum. Several participants emphasized the need to start early, introducing students to quantum concepts and their appealing “weirdness” in K–12 education. Engaging students early—before they become entrenched in traditional disciplinary siloes as typically happens in graduate school—could help to galvanize interest in the area and foster a generation of scientists with the interdisciplinary mindset and skills needed to advance this interdisciplinary field. While these efforts could be advanced at many levels and across multiple sectors, several participants suggested a national quantum biology center could be a valuable hub to coordinate and support quantum biology education and workforce development across academia, industry, and government.

59 BASIC BIOLOGICAL SCIENCES↗

Overview of the forthcoming AAPM TG-158 Report, 'Measurements and calculations of doses outside the treatment volume from External Beam Radiation Therapy' - Paper 85

The evolution of techniques and technology in radiotherapy has greatly improved our ability to deliver higher tumor doses while minimizing the dose to the adjacent organs at risk. However, this improved conformality has not removed the problem of doses to normal tissues outside the target volume. Whether these out-of-field doses arise from photons, electrons, protons or neutrons, there are unique challenges that many medical physicists may have limited knowledge about or experience with. With modern treatment planning systems, high dose regions and areas within the primary beam path are typically well described. However, their accuracy beyond a few centimeters outside the treatment field edge are usually poor. In these cases, alternate methods are required to assess the out-of-field dose. Such assessments require additional care to avoid potentially large errors associated with a variety of unique complications in performing dosimetry outside the treatment field. Furthermore, physicists will not always have an a priori estimate of the expected dose, and, as a result, large dosimetric errors are more easily missed. Low radiation doses outside the treatment volume are a concern because they can cause deleterious effects to the patient. The risk of late effects from the secondary radiation may be more evident today because of the success of cancer screening and modern therapies that have increased the number of cancer patients who survive and live long enough for the adverse radiation effects on healthy tissues to manifest. Simultaneously, radiotherapy procedures have become more complex, making the estimation of low out-of-field doses even more challenging. Over the past 30 years, numerous studies have been published detailing non-target doses from various radiotherapy treatments (for a detailed review see Xu et al. However, dosimetry data were obtained using a variety of measurement or computational techniques. An overview of dosimetry techniques for measuring low-level non-target doses is not available. Improved, or at least valid, dosimetry is important for several reasons. In clinical care (e.g. fetal dose estimates), large dose errors can lead to inappropriate or unnecessary treatment decisions, with potentially serious consequences to the patient. In a broader sense of somatic effects, more accurate dosimetry will improve patient risk assessment, understanding of dose responses, treatment planning, and design of the radiation delivery machines. Once reliable dosimetry data are available, it is then the clinician's responsibility to assess the risk vs. benefit of the radiotherapy treatment to make a sound and informed assessment of all possible treatment outcomes. (author)

61 RADIATION PROTECTION AND DOSIMETRY↗

Alvin William Trivelpiece

An outstanding leader, physicist, and administrator, Alvin William Trivelpiece died on 7 August 2022 in Rancho Santa Margarita, California.

99 GENERAL AND MISCELLANEOUS↗

Radiation biology workforce in the United States

In recent decades, the principal goals of participants in the field of radiation biologists have included defining dose thresholds for cancer and non-cancer endpoints to be used by regulators, clinicians and industry, as well as informing on best practice radiation utilization and protection applications. Importantly, much of this work has required an intimate relationship between “bench” radiation biology scientists and their target audiences (such as physicists, medical practitioners and epidemiologists) in order to ensure that the requisite gaps in knowledge are adequately addressed. However, despite the growing risk for public exposure to higher-than-background levels of radiation, e.g. from long-distance travel, the increasing use of ionizing radiation during medical procedures, the threat from geopolitical instability, and so forth, there has been a dramatic decline in the number of qualified radiation biologists in the U.S. Contributing factors are thought to include the loss of applicable training programs, loss of jobs, and declining opportunities for advancement. This report was undertaken in order to begin addressing this situation since inaction may threaten the viability of radiation biology as a scientific discipline.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Digital radiography exposure indices: A review

Digital radiography (DR) technologies have the advantage of a wide dynamic range compared to their film-screen predecessors, however, this poses a potential for increased patient exposure if left unchecked. Manufacturers have developed the exposure index (EI) to counter this, which provides radiographers with feedback on the exposure reaching the detector. As these EIs were manufacturer-specific, a wide variety of EIs existed. To offset this, the international standardised EI has been developed by the International Electrotechnical Commission (IEC) and the American Association of Physicists in Medicine (AAPM). The purpose of this article is to explore the current literature relating to EIs, beginning with the historical development of the EI, the development of the standardised EI and an exploration of common themes and studies as evidenced in the research literature. It is anticipated that this review will provide radiographers with a useful guide to understanding EIs, their application in clinical practice, limitations and suggestions for further research.

60 APPLIED LIFE SCIENCES↗

Picturing the future of food

Abstract High‐throughput phenotyping (HTP) has emerged as one of the most exciting and rapidly evolving spaces within plant science. The successful application of phenotyping technologies will facilitate increases in agricultural productivity. High‐throughput phenotyping research is interdisciplinary and may involve biologists, engineers, mathematicians, physicists, and computer scientists. Here we describe the need for additional interest in HTP and offer a primer for those looking to engage with the HTP community. This is a high‐level overview of HTP technologies and analysis methodologies, which highlights recent progress in applying HTP to foundational research, identification of biotic and abiotic stress, breeding and crop improvement, and commercial and production processes. We also point to the opportunities and challenges associated with incorporating HTP across food production to sustainably meet the current and future global food supply requirements.

59 BASIC BIOLOGICAL SCIENCES↗

Nb$_{3}$Sn Accelerator Magnets. Designs, Technologies and Performance

The goal of this book is to summarize and review the vast experience with Nb 3 Sn accelerator dipole magnets accumulated in the United States, Europe, and Asia since the discovery and production of Nb 3 Sn composite conductors. Interest in Nb 3 Sn accelerator magnets is soaring, and their further development is rapidly gaining momentum worldwide, thanks to the growing maturity of this technology and its great potential for particle accelerators used in high-energy physics. This book is intended to contribute to the transfer of the accumulated experience with the design, technology, and performance of such magnets in view of the challenging requirements set by the needs for ever-higher collision energies in future colliders. Engineers and physicists working in the field of particle accelerators, as well as students studying courses in particle accelerator physics and technologies, may find it an indispensable source of information on Nb 3 Sn accelerator magnets. Readers with a general interest in the history of science and technology may also find useful information that was obtained over a long period of time, from the late 1960s to the present day.

43 PARTICLE ACCELERATORS↗

Deep learning in color: towards automated quark/gluon jet discrimination

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. Here, to establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

LHC SUSY and WIMP dark matter searches confront the string theory landscape

The string theory landscape of vacua solutions provides physicists with some understanding as to the magnitude of the cosmological constant. Similar reasoning can be applied to the magnitude of the soft SUSY breaking terms in supersymmetric models of particle physics: there appears to be a statistical draw towards large soft terms which is tempered by the anthropic requirement of the weak scale lying not too far from ~100 GeV. For a mild statistical draw of m softn with n = 1 (as expected from SUSY breaking due to a single F term) then the light Higgs mass is preferred at ~125 GeV while sparticles are all pulled beyond LHC bounds. We confront a variety of LHC and WIMP dark matter search limits with the statistical expectations from a fertile patch of string theory landscape. The end result is that LHC and WIMP dark matter detectors see exactly that which is expected from the landscape: a Standard Model-like Higgs boson of mass 125 GeV but as yet no sign of sparticles or WIMP dark matter. SUSY from the n = 1 landscape is most likely to emerge at LHC in the soft opposite-sign dilepton plus jet plus MET channel. Multi-ton noble liquid WIMP detectors should be able to completely explore the n = 1 landscape parameter space.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Verification of the Rigidity of the Coulomb Field in Motion

Laplace, analyzing the stability of the Solar System, was the first to calculate that the velocity of the motion of force fields can significantly exceed the velocity of light waves. In electrodynamics, the Coulomb field should rigidly accompany its source for instantaneous force action in distant regions. Such rigid motion was recently inferred from experiments at the Frascati Beam Test Facility with short beams of relativistic electrons. The comments of the authors on their observations are at odds with the comments of theoreticians on retarded potentials, which motivates a detailed study of the positions of both sides. Predictions of measurements, based on the Lienard–Wiechert potentials, are used to propose an unambiguous scheme for testing the rigidity of the Coulomb field. Realization of the proposed experimental scheme could independently refute or support the assertions of the Italian physicists regarding the rigid motion of Coulomb fields and likewise the nondual field approach to macroscopic reality.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Potential of the Julia Programming Language for High Energy Physics Computing

Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed and resource usage. To meet these requirements, a compiled high-performance language is typically used; while for physicists, who focus on the application when developing the code, better research productivity pleads for a high-level programming language. A popular approach consists of combining Python, used for the high-level interface, and C++, used for the computing intensive part of the code. A more convenient and efficient approach would be to use a language that provides both high-level programming and high-performance. The Julia programming language, developed at MIT especially to allow the use of a single language in research activities, has followed this path. In this paper the applicability of using the Julia language for HEP research is explored, covering the different aspects that are important for HEP code development: runtime performance, handling of large projects, interface with legacy code, distributed computing, training, and ease of programming. The study shows that the HEP community would benefit from a large scale adoption of this programming language. The HEP-specific foundation libraries that would need to be consolidated are identified.

97 MATHEMATICS AND COMPUTING↗

Optimizing High-Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server

Abstract With machine learning applications now spanning a variety of computational tasks, multi-user shared computing facilities are devoting a rapidly increasing proportion of their resources to such algorithms. Graph neural networks (GNNs), for example, have provided astounding improvements in extracting complex signatures from data and are now widely used in a variety of applications, such as particle jet classification in high energy physics (HEP). However, GNNs also come with an enormous computational penalty that requires the use of GPUs to maintain reasonable throughput. At shared computing facilities, such as those used by physicists at Fermi National Accelerator Laboratory (Fermilab), methodical resource allocation and high throughput at the many-user scale are key to ensuring that resources are being used as efficiently as possible. These facilities, however, primarily provide CPU-only nodes, which proves detrimental to time-to-insight and computational throughput for workflows that include machine learning inference. In this work, we describe how a shared computing facility can use the NVIDIA Triton Inference Server to optimize its resource allocation and computing structure, recovering high throughput while scaling out to multiple users by massively parallelizing their machine learning inference. To demonstrate the effectiveness of this system in a realistic multi-user environment, we use the Fermilab Elastic Analysis Facility augmented with the Triton Inference Server to provide scalable and high-throughput access to a HEP-specific GNN and report on the outcome.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Experiments at Jefferson Lab

This chapter presents experiments conducted at Thomas Jefferson National Accelerator Facility (Jefferson Lab), a U.S. Department of Energy national laboratory in Newport News, Virginia. There, physicists exploring the nature of matter make use of the Continuous Electron Beam Accelerator Facility (CEBAF), a DOE Office of Science user facility that enables the research of more than 1,650 scientists worldwide. CEBAF’s precise electron beams can reach energies up to 12 billion electron-volts and exhibit high degrees of polarization. Jefferson Lab’s first experiment began taking data in 1995. Since then, the facility has become a world leader in the study of quantum chromodynamics. Today, experiments are carried out simultaneously in four experimental halls, each with specialized capabilities. The primary instruments in use are focusing or large-acceptance magnetic spectrometers, many of which feature superconducting elements. Jefferson Lab’s physics program provides unprecedented insight into the particles and forces that shape the visible universe.

Achenbach, Patrick [Thomas Jefferson National Acce↗

A systematic analysis of phase stability in refractory high entropy alloys utilizing linear and non-linear cluster expansion models

We report that obtaining surrogate models for various types of materials has long been a goal of computational material scientists and physicists, as they allow the calculation of thermodynamic quantities and properties of interest far more efficiently than DFT. Many surrogate models, including interatomic potentials generated by machine learning algorthims and cluster expansions, rely on incorporating many-body interactions such as three-body or four-body interactions. When utilizing machine learning algorithms for interatomic potentials, it is common to start with two-body interactions and systematically include three- and four-body clusters to improve the fit, stopping when including the additional clusters does not measurably change the predictive power of the model. In cluster expansions for FCC and related lattices, it is often enough to include mostly two-body interactions and a handful of three-body interactions. However, it remains to be seen whether those will be enough in the case of lattices with a lower packing fraction, such as BCC.

36 MATERIALS SCIENCE↗

In search of truth: In memory of Balraj Singh

Born in Punjab (India) in December 1941, Balraj Singh is not only the single most prolific nuclear data evaluator and disseminator of nuclear structure and decay data with 148 evaluations in Nuclear Data Sheets — 85 as the first and often only author — plus other journals, but his upmost curiosity and dedication brought him to be one of the finest nuclear physicists, with an everlasting influence on many of us. Furthermore, Balraj passed away about a year ago on 9 October 2023 in Ottawa, Ontario (Canada) at the age of 81, and at Atomic Data and Nuclear Data Tables we would like to commemorate some of his scientific achievements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Vectorial approach of determining the wave propagation at metasurfaces

Vector approach often benefits optical engineers and physicists, and a vector formulation of the laws of reflection and refraction has been studied (Tkaczyk, 2012). However, the conventional reflection and refraction laws may be violated in the presence of a metasurface, and reflection and refraction at the metasurface obey generalized laws of reflection and refraction (Yu et al., 2011). In this letter, the vectorial laws of reflection and refraction at the metasurface were derived, and the matrix formulation of these vectorial laws are also obtained. These results enable highly efficient and unambiguous computations in ray-tracing problems that involve a metasurface.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

JuTrack: A Julia package for auto-differentiable accelerator modeling and particle tracking

Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerator modeling package developed in the Julia programming language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JuTrack enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. Here we demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization.

43 PARTICLE ACCELERATORS↗

TPCpp-10M: Simulated proton-proton collisions in a time projection chamber for AI foundation models

Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physics data pose significant barriers to interdisciplinary collaboration with the broader machine learning community. This work introduces a large, openly accessible dataset of 10 million simulated proton-proton collisions, designed to support self-supervised training of foundation models. To facilitate ease of use, the dataset is provided in a common NumPy format. In addition, it includes 70,000 labeled examples spanning three well defined downstream tasks: track finding, particle identification, and noise tagging, to enable systematic evaluation of the foundation model's adaptability. The simulated data are generated using the Pythia Monte Carlo event generator at a center of mass energy of $\sqrt{s}$ = 200 GeV and processed with Geant4 to include realistic detector conditions and signal emulation in the sPHENIX Time Projection Chamber at the Relativistic Heavy Ion Collider, located at Brookhaven National Laboratory. This dataset resource establishes a common ground for interdisciplinary research, enabling machine learning scientists and physicists alike to explore scaling behaviors, assess transferability, and accelerate progress toward foundation models in nuclear and high energy physics. The complete simulation and reconstruction chain is reproducible with the sPHENIX software stack. All data and code locations are provided under Data Accessibility.

Data Analysis, Statistics and Probability (physics↗