Posted on 11/25/2024 5:31:19 AM PST by Red Badger
The Central Neutron Detector installed in Experimental Hall B. Silvia Niccolai and her team at the Laboratory of the Physics of the two Infinities Irène Joliot-Curie (IJCLab), a joint research unit of CNRS in Orsay, France, Paris-Saclay University, and Paris-City University, began constructing the detector in 2011 with funding from the French National Institute of Nuclear and Particle Physics. Credit: Silvia Niccolai
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Recent advancements at the Thomas Jefferson National Accelerator Facility have enabled physicists to explore the internal structure of neutrons in unprecedented detail.
Using a new detector, researchers have achieved a deeper understanding of how quarks and gluons contribute to the nucleon’s overall spin, making significant progress in nuclear physics.
Protons and neutrons, collectively known as nucleons, are the fundamental building blocks of matter. However, when it comes to nuclear physics experiments, protons have historically received more attention—until now.
For the first time, scientists have gained a glimpse into the internal structure of the neutron. This breakthrough, published in Physical Review Letters, was made possible by a decade-long effort to develop a specialized detector, now installed in Experimental Hall B at the U.S. Department of Energy’s Thomas Jefferson National Accelerator Facility.
“We detected the neutron for the first time in this type of reaction, and it’s a quite important result for the study of nucleons,” said Silvia Niccolai, a research director at the French National Centre for Scientific Research (CNRS).
Niccolai, who proposed the experiment, explained that this milestone will provide valuable insights into the structure and spin of both neutrons and protons, advancing our understanding of nucleon behavior.
Insights Into Nucleon Structure
Nucleons are made up of smaller particles called quarks and gluons. Physicists don’t yet fully understand how these constituent particles are distributed inside nucleons, or how they contribute to overall nucleon spin. Experimenters use the Continuous Electron Beam Accelerator Facility (CEBAF), a DOE Office of Science user facility, to probe these particles, scattering electrons off nucleon targets and detecting the final products of these reactions.
One reaction is called deeply virtual Compton scattering (DVCS). In DVCS, an electron interacts with a nucleon target. The nucleon absorbs some of the electron’s energy and emits a photon, but doesn’t break. In the end, three particles can be detected: the impinged nucleon, the photon it emitted, and the electron that interacted with the nucleon.
Researchers have studied DVCS extensively using the CLAS12 detector, which stands for the CEBAF Large Acceptance Spectrometer at 12 GeV beam energy, as well as its predecessor, CLAS. However, the CLAS and CLAS12 detectors in Hall B have mostly been used to explore DVCS on the proton, which is easier to measure than DVCS on the neutron.
Neutrons involved in DVCS are more difficult to detect because they tend to scatter 40 degrees up from the beamline, an area CLAS12 cannot access.
Development of the Central Neutron Detector
“In the standard configuration, there was no detection for neutrons possible in these angles,” Niccolai said. In 2007, she started thinking about how the CLAS collaboration of nuclear physicists could measure these neutrons. Her solution? The Central Neutron Detector.
Niccolai and her team at the Laboratory of the Physics of the two Infinities Irène Joliot-Curie (IJCLab), a joint research unit of CNRS in Orsay, France, Paris-Saclay University, and Paris-City University, began constructing the detector in 2011 with funding from the French National Institute of Nuclear and Particle Physics.
The team completed the detector in 2015. Two years later, it was installed in CLAS12. Pierre Chatagnon, a Ph.D. student at Paris-Saclay University at the time, joined the IJCLab team at Jefferson Lab to install the detector. He also wrote software to calibrate it. Today, he has returned to Jefferson Lab as a postdoc in Hall B.
Overcoming Technical Challenges
The Central Neutron Detector collected data between 2019 and 2020. While it was able to cover the necessary angles to detect neutrons, Niccolai and her team encountered an unexpected problem during data analysis: proton contamination.
The detector was designed to discard charged, non-neutron signals. However, they found that the part of the detector responsible for vetoing protons had dead zones, allowing protons to sneak in and contaminate the neutron measurements.
Fortunately, Adam Hobart, a researcher at IJCLab who led the data analysis for this experiment, was able to clean the data.
“This was solved thanks to Adam’s experience using machine learning techniques,” Niccolai said. “He developed a ML-based tool to discriminate fake signals from real neutrons, and this was vital to achieve our final results.”
Using these ML techniques with the Central Neutron Detector permitted the first measurements of DVCS on the neutron that directly detect the neutron participating in the reaction. Many processes can occur between a beam electron and a nucleon target; directly detecting the neutron gives the researchers confidence that they are indeed detecting DVCS.
“If you’re not detecting the neutron, you have a certain range of possibilities of the process that is happening, and then you have less precision in the observables that you would measure later,” Hobart said.
Advancing Nuclear Physics
The theoretical framework known as generalized parton distributions (GPDs) transforms measurements from scattering experiments into information about the distribution of partons, the collective name for quarks and gluons, inside nucleons. There are four types of GPDs. The neutron measurements from this experiment allowed the researchers to access one of the least known types, denoted as GPD E.
During the experiment, CEBAF’s beam was polarized, meaning the spins of its electrons were pointed in the same direction. This allowed the researchers to extract an observable, known as an asymmetry, that depends on the spin of the beam. With this asymmetry, they were able to extract GPD E with unprecedented precision.
“The GPD E is very important because it can give us information into the spin structure of nucleons,” Niccolai said.
When combined with other GPDs, GPD E can be used to quantify how much constituent quarks contribute to the total spin of the nucleon, which is currently unknown. Though that calculation will be carried out in future work, in this work the researchers took another step toward solving the so-called “nucleon spin crisis.”
Nucleons contain two types, or flavors, of quarks: up and down. A proton has two up quarks and one down quark; a neutron has two down quarks and one up quark. GPDs can be split up by quark flavor.
Combining measurements of DVCS on the neutron with previous measurements of DVCS on the proton allowed the researchers to separate the imaginary parts of the GPDs E and H by quark flavor for the first time. Separating the distributions for up and down quarks will help physicists understand how different flavors of quark contribute to nucleon spin.
Theorists Maria Čuić and Krešimir Kumerički also contributed to this first-time flavor separation, but the work of the entire CLAS collaboration was crucial to achieving these pioneering results.
“We must give gratitude to the whole CLAS Collaboration,” Hobart said. “Taking and processing the data is collaborative work.”
Future Directions and Achievements
Proof of principle in hand, the researchers plan next to collect more data with CLAS12 and the Central Neutron Detector to make even more precise measurements.
“But this first result is major,” Niccolai said. “It feels like the completion of a cycle and a lifetime achievement because this is the first project that I took full responsibility for in my career. Finally arriving at a physically meaningful result and having it published feels like I’ve had another baby.”
Reference:
“First Measurement of Deeply Virtual Compton Scattering on the Neutron with Detection of the Active Neutron”
by CLAS Collaboration, A. Hobart, S. Niccolai, M. Čuić, K. Kumerički, P. Achenbach, J. S. Alvarado, W. R. Armstrong, H. Atac, H. Avakian, L. Baashen, N. A. Baltzell, L. Barion, M. Bashkanov, M. Battaglieri, B. Benkel, F. Benmokhtar, A. Bianconi, A. S. Biselli, S. Boiarinov, M. Bondi, W. A. Booth, F. Bossù, K.-Th. Brinkmann, W. J. Briscoe, W. K. Brooks, S. Bueltmann, V. D. Burkert, T. Cao, R. Capobianco, D. S. Carman, P. Chatagnon, G. Ciullo, P. L. Cole, M. Contalbrigo, A. D’Angelo, N. Dashyan, R. De Vita, M. Defurne, A. Deur, S. Diehl, C. Dilks, C. Djalali, R. Dupre, H. Egiyan, A. El Alaoui, L. El Fassi, L. Elouadrhiri, S. Fegan, A. Filippi, C. Fogler, K. Gates, G. Gavalian, G. P. Gilfoyle, D. Glazier, R. W. Gothe, Y. Gotra, M. Guidal, K. Hafidi, H. Hakobyan, M. Hattawy, F. Hauenstein, D. Heddle, M. Holtrop, Y. Ilieva, D. G. Ireland, E. L. Isupov, H. Jiang, H. S. Jo, K. Joo, T. Kageya, A. Kim, W. Kim, V. Klimenko, A. Kripko, V. Kubarovsky, S. E. Kuhn, L. Lanza, M. Leali, S. Lee, P. Lenisa, X. Li, I. J. D. MacGregor, D. Marchand, V. Mascagna, M. Maynes, B. McKinnon, Z. E. Meziani, S. Migliorati, R. G. Milner, T. Mineeva, M. Mirazita, V. Mokeev, C. Muñoz Camacho, P. Nadel-Turonski, P. Naidoo, K. Neupane, G. Niculescu, M. Osipenko, P. Pandey, M. Paolone, L. L. Pappalardo, R. Paremuzyan, E. Pasyuk, S. J. Paul, W. Phelps, N. Pilleux, M. Pokhrel, S. Polcher Rafael, J. Poudel, J. W. Price, Y. Prok, T. Reed, J. Richards, M. Ripani, J. Ritman, P. Rossi, A. A. Golubenko, C. Salgado, S. Schadmand, A. Schmidt, Marshall B. C. Scott, E. M. Seroka, Y. G. Sharabian, E. V. Shirokov, U. Shrestha, N. Sparveris, M. Spreafico, S. Stepanyan, I. I. Strakovsky, S. Strauch, J. A. Tan, N. Trotta, R. Tyson, M. Ungaro, S. Vallarino, L. Venturelli, V. Tommaso, H. Voskanyan, E. Voutier, D. P. Watts, X. Wei, R. Williams, M. H. Wood, L. Xu, N. Zachariou, J. Zhang, Z. W. Zhao and M. Zurek, 20 November 2024, Physical Review Letters.
DOI: 10.1103/PhysRevLett.133.211903
It's no secret.
When I look at that picture, it makes me wonder who drew up the plans to build that thing.
Hang on, guys. My cat Mr. Whiskers almost has it worked out. He claims that all he needs is a few more cat treats to finish the job.
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A quantum of Ping!.................
A quantum of ping!...............
Well she does live near Oak Ridge.............
He discovered ‘cations’?...................
How truly awesome God must be to set up all of those, and make them interact with each other just right. He's the Chief Designer at both the macro and the micro level.
This has been a bad year. First I bought a radar detector that didn't detect radar, then I bought a neutron detector that didn't detect neutrons. I have to go to court on those next month.
“I just threw a book at it and WHOOSH, she opened!” from the ghost and Mr. chicken. and that’s how it happened.
How do they know how many particles there are in the Universe if they can’t even see them all to count them?............
How do they know how many particles there are in the Universe if they can’t even see them all to count them?............
> He discovered ‘cations’?................... <
👍🏻
As a retired chemistry teacher, I can appreciate that joke. However, Mr. Whiskers thinks you’re not taking him seriously.
Sigh. I guess you can’t please everyone.
Maybe reversing the polarity of the neutron flow can happen?
https://youtu.be/QDaCMhKPGys?si=TzVtr3H1aLpID5G1
Way back, when the Internet was new, I discovered ‘morons’..............
Automated Neutron Scattering:
Researchers at Oak Ridge National Laboratory (ORNL) are developing an AI-driven platform called Hyperspectral Computed Tomography (HyperCT) for neutron scattering. This system automates sample rotation and optimizes measurement angles, leading to faster and more accurate data collection without human intervention, significantly reducing experiment times and improving data quality Source.
Pattern Recognition in Neutron Scattering Data:
A team at ORNL has trained an artificial neural network (ANN) to analyze neutron scattering data, helping to uncover the microscopic properties of quantum materials. This AI approach aids in interpreting complex data from neutron scattering experiments, which can be challenging for researchers Source.
Addressing the Nuclear Many-Body Problem:
The STREAMLINE project aims to use machine learning to tackle the nuclear many-body problem, which involves understanding nucleon interactions within atomic nuclei. AI techniques are expected to simplify complex calculations and improve predictions about nucleon behavior, which is crucial for various branches of nuclear physics Source.
Improving Nuclear Reactor Performance: At Purdue University, researchers have developed a machine learning algorithm that predicts neutron flux levels in reactors with high accuracy. This AI application enhances monitoring and control of small modular reactors (SMRs), potentially reducing operational costs and improving reactor performance Source.
Enhancing Experimental Nuclear Physics:
Projects at William & Mary involve using AI to optimize experiments related to the strong nuclear force, enhancing the understanding of quark and gluon interactions within nucleons. These projects focus on improving experimental techniques and data analysis through AI-assisted methods Source.
AI-Assisted Neutron Spectroscopy:
A probabilistic active learning approach is being developed for neutron spectroscopy, allowing for autonomous detection of interesting measurement regions in experiments. This method optimizes beam time by focusing on areas with significant signals rather than background noise Source.
Overall, these advancements illustrate how AI is transforming neutron research by increasing efficiency, improving data analysis, and enabling new insights into fundamental nuclear physics questions.
Paging Ant Man!
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