Collaborative Research in Accelerator Physics: Acknowledgments and DOE Funding
Estimated Reading Time: 6 minutes
- Collaboration is Paramount: Advanced accelerator physics thrives on diverse teams pooling expertise and resources, transcending institutional boundaries.
- Critical DOE Funding: The U.S. Department of Energy (DOE) provides indispensable financial support, enabling long-term, large-scale research projects essential for scientific progress.
- Public Access Mandate: DOE funding ensures public access to research outcomes, fostering innovation and broad societal benefits, as outlined in the DOE Public Access Plan.
- Detailed Acknowledgments: Scientific papers meticulously acknowledge all contributions, from informal discussions and shared code to formal institutional support and funding.
- Driving Innovation: Sophisticated methodologies, often incorporating machine learning, are essential for characterizing and controlling accelerator beams, pushing scientific frontiers in diverse applications.
- Collaborative Research in Accelerator Physics: Acknowledgments and DOE Funding
- Key Takeaways
- Table of Contents
- The Power of Collaboration in Advanced Accelerator Research
- The Critical Role of DOE Funding and Public Access
- Real-World Impact of Publicly Funded Research
- Driving Innovation: Methodologies and Future Directions
- Actionable Steps for Progress
- Conclusion
- Frequently Asked Questions
Accelerator physics stands at the forefront of scientific discovery, powering breakthroughs from medical diagnostics and cancer therapy to fundamental explorations of matter and energy. The sheer scale and complexity of these endeavors mean that progress is rarely the work of isolated individuals. Instead, it thrives on the synergistic efforts of diverse teams, cutting-edge facilities, and sustained financial support.
This article delves into the indispensable role of collaboration and the critical mechanisms of acknowledgment and funding, particularly from entities like the U.S. Department of Energy (DOE). Understanding how these elements intertwine provides a clearer picture of the ecosystem that fuels innovation in accelerator science.
The Power of Collaboration in Advanced Accelerator Research
Scientific collaboration transcends geographical and institutional boundaries, bringing together specialized expertise, shared resources, and varied perspectives that are crucial for tackling the grand challenges in accelerator physics. From designing next-generation particle colliders to optimizing beam dynamics, researchers pool their knowledge to push the limits of what’s possible.
Discussions among peers often serve as the fertile ground where new ideas germinate and complex problems find innovative solutions. The exchange of data, methodologies, and even preliminary code significantly accelerates research timelines and enhances the robustness of results. This spirit of open scientific dialogue is a hallmark of high-impact research environments.
To illustrate the profound importance of these connections and the structure of scientific contributions, let’s examine an excerpt from the acknowledgments section of a recent paper by Austin Hoover (Oak Ridge National Laboratory) and Jonathan C. Wong (Institute of Modern Physics, Chinese Academy of Sciences), highlighting direct impacts of individual discussions, shared resources, and crucial governmental funding. This specific section outlines the paper’s structure, acknowledges key contributors, and details its funding and public access agreements:
I. Introduction
II. Maximum Entropy Tomography
A. Ment
B. Ment-Flow
III. Numerical Experiments
A. 2D reconstructions from 1D projections
B. 6D reconstructions from 1D projections
IV. Conclusion and Extensions
V. Acknowledgments and References
V. ACKNOWLEDGEMENTS
We are grateful to Ryan Roussel (SLAC National Accelerator Laboratory), Juan Pablo Gonzalez-Aguilera (University of Chicago), and Auralee Edelen (SLAC National Accelerator Laboratory) for discussions that seeded the idea for this work and for sharing their differentiable kernel density estimation code.
\
This manuscript has been authored by UT Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
\ [1] B. Cathey, S. Cousineau, A. Aleksandrov, and A. Zhukov, First six-dimensional phase space measurement of an accelerator beam, Phys. Rev. Lett. 121, 064804 (2018). \
[2] K. Ruisard, A. Aleksandrov, S. Cousineau, V. Tzoganis, and A. Zhukov, High dimensional characterization of the longitudinal phase space formed in a radio frequency quadrupole, Phys. Rev. Accel. Beams 23, 124201 (2020). \
[3] A. Hoover, K. Ruisard, A. Aleksandrov, A. Zhukov, and S. Cousineau, Analysis of a hadron beam in fivedimensional phase space, Phys. Rev. Accel. Beams 26, 064202 (2023). \
[4] Y. Liu, C. Long, and A. Aleksandrov, Nonintrusive measurement of time-resolved emittances of 1-gev operational hydrogen ion beam using a laser comb, Phys. Rev. Accel. Beams 23, 102806 (2020). \
[5] A. Wolski, D. C. Christie, B. L. Militsyn, D. J. Scott, and H. Kockelbergh, Transverse phase space characterization in an accelerator test facility, Phys. Rev. Accel. Beams 23, 032804 (2020). \
[6] K. Hock and A. Wolski, Tomographic reconstruction of the full 4D transverse phase space, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 726, 8 (2013). \
[7] M. Wang, Z. Wang, D. Wang, W. Liu, B. Wang, M. Wang, M. Qiu, X. Guan, X. Wang, W. Huang, and S. Zheng, Four-dimensional phase space measurement using multiple two-dimensional profiles, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 943, 162438 (2019). \
[8] B. Marchetti, A. Grudiev, P. Craievich, R. Assmann, H.- H. Braun, N. Catalan Lasheras, F. Christie, R. D’Arcy, R. Fortunati, R. Ganter, et al., Experimental demonstration of novel beam characterization using a polarizable x-band transverse deflection structure, Scientific reports 11, 3560 (2021). \
[9] A. Wolski, M. A. Johnson, M. King, B. L. Militsyn, and P. H. Williams, Transverse phase space tomography in an accelerator test facility using image compression and machine learning, Phys. Rev. Accel. Beams 25, 122803 (2022). \
[10] R. Roussel, A. Edelen, C. Mayes, D. Ratner, J. P. Gonzalez-Aguilera, S. Kim, E. Wisniewski, and J. Power, Phase space reconstruction from accelerator beam measurements using neural networks and differentiable simulations, Physical Review Letters 130, 145001 (2023). \
[11] A. Scheinker, F. Cropp, and D. Filippetto, Adaptive autoencoder latent space tuning for more robust machine learning beyond the training set for six-dimensional phase space diagnostics of a time-varying ultrafast electron-diffraction compact accelerator, Phys. Rev. E 107, 045302 (2023). \
[12] S. Jaster-Merz, R. W. Assmann, R. Brinkmann, F. Burkart, W. Hillert, M. Stanitzki, and T. Vinatier, 5D tomographic phase-space reconstruction of particle bunches, Phys. Rev. Accel. Beams 27, 072801 (2024). \
[13] R. Roussel, J. P. Gonzalez-Aguilera, A. Edelen, E. Wisniewski, A. Ody, W. Liu, Y.-K. Kim, and J. Power, Efficient 6-dimensional phase space reconstruction from experimental measurements using generative machine learning (2024). \
[14] S. Press´e, K. Ghosh, J. Lee, and K. A. Dill, Principles of maximum entropy and maximum caliber in statistical physics, Rev. Mod. Phys. 85, 1115 (2013). \
[15] J. Skilling and S. F. Gull, Bayesian maximum entropy image reconstruction, Lecture Notes-Monograph Series , 341 (1991). \
[16] R. D. Rosenkrantz, ET Jaynes: Papers on probability, statistics and statistical physics, Vol. 158 (Springer Science & Business Media, 2012). \
[17] A. Giffin, Maximum Entropy: The Universal Method for Inference, Ph.D. thesis, University at Albany, State University of New York, Albany, NY, USA (2008). \
[18] G. Loaiza-Ganem, Y. Gao, and J. P. Cunningham, Maximum entropy flow networks, in International Conference on Learning Representations (2016). \
[19] J. C. Wong, A. Shishlo, A. Aleksandrov, Y. Liu, and C. Long, 4D transverse phase space tomography of an operational hydrogen ion beam via noninvasive 2d measurements using laser wires, Physical Review Accelerators and Beams 25, 10.1103/PhysRevAccelBeams.25.042801 (2022). \
[20] C. Mottershead, Maximum entropy tomography, in Proceedings of the Fifteenth International Workshop on Maximum Entropy and Bayesian Methods, Santa Fe, New Mexico, USA (Springer, 1996) pp. 425–430. \
[21] G. Minerbo, MENT: A maximum entropy algorithm for reconstructing a source from projection data, Computer Graphics and Image Processing 10, 48 (1979). \
[22] N. J. Dusaussoy and I. E. Abdou, The extended MENT algorithm: a maximum entropy type algorithm using prior knowledge for computerized tomography, IEEE Transactions on Signal Processing 39, 1164 (1991). \
[23] A. Tran and Y. Hao, Beam tomography with coupling using maximum entropy technique, in Proc. 14th International Particle Accelerator Conference, 14 (JACoW Publishing, Geneva, Switzerland, 2023) pp. 3944–3947. \
[24] S. Bond-Taylor, A. Leach, Y. Long, and C. G. Willcocks, Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models, IEEE transactions on pattern analysis and machine intelligence 44, 7327 (2021). \
[25] J. Kaiser, C. Xu, A. Eichler, and A. Santamaria Garcia, Bridging the gap between machine learning and particle accelerator physics with high-speed, differentiable simulations, Phys. Rev. Accel. Beams 27, 054601 (2024). \
[26] Z. Ao and J. Li, Entropy estimation via normalizing flow, in Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36 (2022) pp. 9990–9998. \
[27] G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, Normalizing flows for probabilistic modeling and inference, The Journal of Machine Learning Research 22, 2617 (2021). \
[28] V. Stimper, B. Sch¨olkopf, and J. M. Hern´andez-Lobato, Resampling base distributions of normalizing flows, in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), Proceedings of Machine Learning Research, Vol. 151 (PMLR, 2022) pp. 4915–4936. \
[29] G. M. Green, Y.-S. Ting, and H. Kamdar, Deep potential: Recovering the gravitational potential from a snapshot of phase space, The Astrophysical Journal 942, 26 (2023). \
[30] C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, Neural spline flows, Advances in neural information processing systems 32 (2019). \
[31] G. Papamakarios, T. Pavlakou, and I. Murray, Masked autoregressive flow for density estimation, Advances in neural information processing systems 30 (2017). \
[32] A. Hoover, MENT-Flow: maximum-entropy phase space tomography using normalizing flows, 10.5281/zenodo.11110801 (2024). \
[33] A. Aleksandrov, S. Cousineau, and K. Ruisard, Understanding beam distributions in hadron linacs in the presence of space charge, Journal of Instrumentation 15 (7), P07025. \
[34] M. Laszkiewicz, J. Lederer, and A. Fischer, Marginal tailadaptive normalizing flows, in Proceedings of the 39th International Conference on Machine Learning, Proceedings of Machine Learning Research, Vol. 162, edited by K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, and S. Sabato (PMLR, 2022) pp. 12020–12048. \
[35] S. Basir and I. Senocak, An adaptive augmented lagrangian method for training physics and equality constrained artificial neural networks, arXiv preprint arXiv:2306.04904 (2023). \
[36] B. Dai and U. Seljak, Sliced iterative normalizing flows, in Proceedings of the 38th International Conference on Machine Learning, Proceedings of Machine Learning Research, Vol. 139, edited by M. Meila and T. Zhang (PMLR, 2021) pp. 2352–2364. \
[37] B. Klartag, A central limit theorem for convex sets, Inventiones mathematicae 168, 91 (2007). \
[38] J. Qiang, Differentiable self-consistent space-charge simulation for accelerator design, Phys. Rev. Accel. Beams 26, 024601 (2023). \
[39] M. Mardani, J. Song, J. Kautz, and A. Vahdat, A variational perspective on solving inverse problems with diffusion models, arXiv preprint arXiv:2305.04391 (2023).
\
(1) Austin Hoover, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, USA (hooveram@ornl.gov);
(2) Jonathan C. Wong, Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China.
This detailed acknowledgment section underscores several key aspects. Firstly, the gratitude extended to Ryan Roussel, Juan Pablo Gonzalez-Aguilera, and Auralee Edelen for their discussions and sharing of code exemplifies how individual contributions, even informal ones, can “seed the idea” for significant work. This highlights the value of knowledge exchange and the open-source ethos within the scientific community.
The Critical Role of DOE Funding and Public Access
Beyond individual collaborations, large-scale scientific endeavors, particularly in fields as capital-intensive as accelerator physics, depend heavily on institutional and governmental funding. The U.S. Department of Energy (DOE) plays an unparalleled role in supporting fundamental research and developing advanced technologies that benefit the nation and the world.
The excerpt above explicitly states: “This manuscript has been authored by UT Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy.” This contractual relationship is typical for research conducted at national laboratories managed by private entities on behalf of the DOE. It signifies a long-term commitment to scientific progress, providing the stable foundation required for multi-year projects and the maintenance of complex facilities.
Crucially, the acknowledgment also details the government’s retention of rights: “The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.” This ensures that the public, who ultimately funds this research through taxes, has broad access to its outcomes. This is further reinforced by the DOE Public Access Plan, which mandates that results of federally sponsored research be made publicly available.
Real-World Impact of Publicly Funded Research
Consider the development of proton therapy, a highly precise form of radiation treatment for cancer. The fundamental physics and accelerator technologies underpinning proton therapy were largely developed through decades of federally funded basic research at national laboratories and universities. Because this knowledge was publicly accessible, medical researchers and commercial enterprises could leverage these discoveries, leading to advanced treatments now available in hospitals worldwide. This trajectory from fundamental research to societal benefit showcases the immense return on investment from government funding and open access policies, transforming abstract science into tangible human impact.
Driving Innovation: Methodologies and Future Directions
The research described in the acknowledgments—focused on “Maximum Entropy Tomography” and its variants like MENT and MENT-Flow, applied to “2D and 6D reconstructions from 1D projections”—points to highly sophisticated techniques. These methodologies leverage advanced mathematics and computational power, often incorporating machine learning and neural networks (as evidenced by the numerous references to these topics), to characterize and understand the intricate behavior of accelerator beams.
Such work is fundamental to improving accelerator performance, ensuring beam stability, and enabling new experimental capabilities. The ability to precisely measure and control beam phase space dimensions (like the ambitious 6D reconstructions mentioned) is critical for pushing the frontiers of high-energy physics, materials science, and medical applications. The commitment to sharing code and fostering discussions directly contributes to the rapid evolution and adoption of these cutting-edge diagnostic tools.
The very structure of the paper, as indicated by its “Table of Links,” reflects a rigorous scientific process, moving from theoretical frameworks (Maximum Entropy Tomography) to numerical experiments and ultimately conclusions that pave the way for future extensions. This systematic approach, bolstered by collaborative insights and consistent funding, defines the path of scientific progress.
Actionable Steps for Progress
- For Researchers: Actively seek collaborative opportunities, foster inter-institutional discussions, and ensure transparent acknowledgment of all contributions, including shared code and foundational ideas.
- For Institutions and Funding Bodies: Prioritize robust funding mechanisms for basic research, establish clear public access policies for federally sponsored work, and facilitate platforms for open scientific exchange.
- For the Public and Policymakers: Recognize and champion the long-term societal benefits derived from sustained investment in fundamental scientific research, understanding that today’s basic science is tomorrow’s transformative technology.
Conclusion
The advancement of accelerator physics, a field critical to countless scientific and technological applications, is a testament to the power of human ingenuity. It is a process deeply interwoven with collaborative partnerships, meticulous acknowledgment of contributions, and the indispensable financial backing of organizations like the U.S. Department of Energy. These elements collectively create an environment where complex problems can be tackled, knowledge is shared freely, and scientific breakthroughs can truly benefit all of humanity.
The commitment to open science, exemplified by detailed acknowledgments and robust public access plans, ensures that the fruits of federally funded research are not confined to academic silos but are instead made available to inspire further innovation and address global challenges.
Explore the DOE Public Access Plan Today
Frequently Asked Questions
Why is collaboration so important in accelerator physics?
Accelerator physics projects are often immense in scale and complexity, requiring diverse specialized expertise, significant shared resources, and varied perspectives. Collaboration allows researchers to pool knowledge, share data and methodologies, and accelerate research timelines, leading to more robust and innovative solutions that would be impossible for individuals or isolated teams to achieve alone.
What role does the U.S. Department of Energy (DOE) play in this research?
The DOE is a critical funding body for fundamental research in accelerator physics. It provides the necessary financial stability and long-term commitment required for large-scale, capital-intensive projects, often through contracts with national laboratories. This funding is essential for developing advanced technologies and ensuring sustained scientific progress.
What is the DOE Public Access Plan?
The DOE Public Access Plan is a policy that mandates that results of federally sponsored research be made publicly available. This ensures that the scientific knowledge generated through taxpayer funding is accessible to a broad audience, fostering further innovation, education, and ultimately, greater societal benefit from scientific breakthroughs.
How do acknowledgments in scientific papers contribute to progress?
Acknowledgments are crucial for formally recognizing all contributions, both formal and informal, that led to a research outcome. This includes specific discussions that “seeded ideas,” shared code, and institutional support. By transparently detailing these contributions, acknowledgments foster a culture of open science, encourage knowledge exchange, and provide a clear lineage of intellectual development, which can inspire future work.
