CERN Accelerating science
Beamline for Schools
The Beamline for Schools competition offers high-school students the chance to conduct experiments at a real, functioning physics beamline at CERN. More about the competition .
The Beamline for Schools winners experimented at CERN and DESY
From 14 to 28 September, the three winning teams of the 2023 edition of the Beamline for Schools competition had the chance to use the test-beam facilities at CERN and DESY to perform their experiments
The Beamline for Schools winners experimented...
Join us in celebrating the tenth edition of the beamline for schools competition.
The hybrid event is open to all and will showcase presentations from different perspectives of the competition, including current and former winning students
Join us in celebrating the tenth edition of ...
Three teams of secondary school pupils from the netherlands, pakistan and the united states have been selected to carry out their own experiments using accelerator beams from cern and desy, three teams of secondary school pupils from t..., 2022 beamline for schools winners at cern and desy.
On 21 September, the winners of the 2022 Beamline for Schools competition arrived at CERN and DESY to start their experiments
2022 Beamline for Schools winners at CERN and...
Three teams of high-school students from egypt, spain and france win the cern beamline for schools competition.
Three teams of high-school students from the Club de Física Enrico Fermi (Vigo, Spain), the Elsewedy Technical Academy (STA) (Cairo, Egypt), and the École du Sacré-Coeur (Reims, France) have won the 2022 edition of the Beamline for Schools competition
Three teams of high-school students from Egyp...
Register now for beamline for schools 2022.
Are you a high-school student or teacher anywhere in the world? Register now for a unique opportunity to create and perform a scientific experiment at CERN
Beamline for Schools: Inspiring the next generation of scientists
The winning teams of Beamline for Schools 2021 spent two full weeks at DESY working on their experiments
Beamline for Schools: Inspiring the next gene...
Two high-school teams from italy and mexico win the cern beamline for schools competition.
Two teams of high-school students from the Liceo Scientifico Statale “A. Scacchi” (Bari, Italy) and the Escuela Nacional Preparatoria “Plantel 2” (Mexico City, Mexico) have won the 2021 edition of the Beamline for Schools competition
Two high-school teams from Italy and Mexico w...
Registration is open for beamline for schools 2021.
In this unique competition, high-school students anywhere in the world team up to create and perform their scientific experiment at a leading particle physics laboratory
Registration is open for Beamline for Schools...
Beamline for schools 2020: it’s a wrap.
The two winning teams of this year’s competition tested their projects on real-life particle beams in September
High-school teams from Switzerland and Germany win CERN Beamline for Schools competition
Two teams of high-school students from Switzerland and Germany, have won the 2020 Beamline for Schools competition
High-school teams from Switzerland and German...
- Current Openings
- Summer Student & Faculty Employment
- Fellowships
- Quantum Information Science and Technology
- Machine Learning and Artificial Intelligence
- Modeling & Simulation
- Software Enabling Science
- Advanced Computing Technologies
- Data Science
- Frontiers of Computing
- Applied Mathematics
- Autonomous Scientific Discovery
- Alvarez Seminar Series
- Postdoc Symposium 2024
- CSA Summer Program
- IDEA in Computing Sciences
- CSA Mentoring Program
- Policy & Procedures
- Safety Committee Website
- Safety Walk Arounds
- News Archive
SMART Algorithm Makes Beamline Data Collection Smarter
August 28, 2019
Contact: Kathy Kincade, kkincade@lbl.gov , +1 510 495 2124
Typical error functions, where high regions indicate goods spots to perform a next measurement. (Credit: Marcus Noack, Berkeley Lab)
We hear a lot these days about the overwhelming “data deluge” in scientific research, stemming in large part from the growing sophistication of experimental instrumentation. As a result, there has been an emphasis on developing or optimizing tools – often using machine- and deep-learning methods – to analyze these increasingly large data sets. But what is equally important for improving scientific productivity is the optimization of data collection – aka “data taking” – methods.
Toward this end, Marcus Noack, a postdoctoral scholar at Lawrence Berkeley National Laboratory in the Center for Advanced Mathematics for Energy Research Applications (CAMERA), and James Sethian, director of CAMERA and Professor of Mathematics at UC Berkeley, have been working with beamline scientists at Brookhaven National Laboratory to develop and test SMART (Surrogate Model Autonomous expeRimenT), a mathematical method that enables autonomous experimental decision making without human interaction. A paper describing SMART and its application in experiments at Brookhaven’s National Synchrotron Light Source II (NSLS-II) was published August 14, 2019 in Nature Scientific Reports .
“Modern scientific instruments are acquiring data at ever-increasing rates, leading to an exponential increase in the size of data sets,” said Noack, lead author on that paper. “Taking full advantage of these acquisition rates requires corresponding advancements in the speed and efficiency not just of data analytics but also experimental control.”
The goal of many experiments is to gain knowledge about the material that is studied, and scientists have a well-tested way to do this: they take a sample of the material and measure how it reacts to changes in its environment. User facilities such as Brookhaven’s NSLS-II and the Center for Functional Nanomaterials (CFN) offer access to high-end materials characterization tools. The associated experiments are often lengthy, and complicated procedures and measurement time is precious. A research team might only have a few days to measure their materials, so they need to make the most of each step in each measurement.
“A standard approach for users at light sources like the NSLS-II is to manually and exhaustively scan through a sample,” Noack said. “But if you assume the data set is 3D or higher dimensional, at some point this exercise becomes intractable. So what is needed is something that can automatically tell me where I should take my next measurement.”
Using Gaussian Process Regression for Intelligent Data Collection
Noack joined Berkeley Lab two years ago to bring mathematics into the design and optimization of experiments, with the ultimate goal of enabling autonomous experiments. The result is SMART, a Python-based algorithm that automatically selects measurements from an experiment and exploits Gaussian process regression (aka Kriging ) to construct a surrogate model and an error function based on the available experimental data. Mathematical function optimization is then used to explore the error function to find the maximum error and suggest the location for the next measurement. The result is a mathematically rigorous and compact approach to systematically perform optimally efficient experiments.
“People have been doing intelligent data collection for a long time, but for beamline scientists this is the first application of the most sophisticated generation of Gaussian processes,” said Sethian, a co-author on the Nature Scientific Reports paper. “By exploiting Gaussian processes, approximation theory, and optimization, Marcus has designed a framework to bring autonomous optimized modeling and AI to beamline science.”
In practice, before starting an experiment, the scientists provide SMART with a set of goals they want to get out of the experiment. The raw data is sent to an automated-analysis software, usually available at beamlines, and then handed to the SMART decision-making algorithm. To determine the next measurement, the algorithm creates a surrogate model of the data, which is comparable to an educated guess on how the material will behave in the next possible steps and calculates the uncertainty – basically how confident it is in its guess – for each possible next step. Based on this, it then selects the most uncertain option to measure next. The trick here is that by picking the most uncertain step to measure next, it maximizes the amount of knowledge it gains by measuring it. The algorithm also defines when to end the experiment by figuring out the moment when any additional measurements would yield no further new knowledge about the material.
“The basic idea is, given a bunch of experiments, how can you automatically pick the next best one?” said Sethian. “Marcus has built a world which builds an approximate surrogate model on the basis of your previous experiments and suggests the best or most appropriate experiment to try next.”
“The final goal is not only to take data faster but also to improve the quality of the data we collect,” said Kevin Yager, co-author and CFN scientist. “I think of it as experimentalists switching from micromanaging their experiment to managing at a higher level. Instead of having to decide where to measure next on the sample, the scientists can instead think about the big picture, which is ultimately what we as scientists are trying to do.”
Enabling Autonomous X-Ray Scattering Experiments at NSLS-II
In experiments run at NSLS-II, the collaborators used SMART to demonstrate autonomous experiments using x-ray scattering. The first experimental setup was on NSLS-II’s Complex Materials Scattering (CMS) beamline, which offers ultrabright x-rays to study the nanostructure of different materials. For their first fully autonomous experiment, the team imaged the thickness of a droplet of nanoparticles using a technique called small-angle x-ray scattering at the CMS beamline. After their initial success, they reached out to other users and proposed having them test SMART on their scientific problems. Since then they have measured a number of samples, Yager noted.
“This is an exciting part of this collaboration,” said Masafumi Fukuto, co-author of the study and scientist at NSLS-II. “We all provided an essential piece for it: the CAMERA team worked on the decision-making algorithm, Kevin from CFN worked on the real-time data analysis, and we at NSLS-II provided the automation for the measurements.”
While the code has been shown to be stable and working well, Noack is making improvements to SMART to make it more powerful, run faster on more measurements, and be less computationally expensive. In the meantime, scientists at other beamlines are expressing interest in using SMART, and new experiments are already scheduled at Brookhaven and Berkeley Lab’s Advanced Light Source.
SMART isn’t intended just for beamline experiments, however. “SMART is implemented in a way that has nothing to do with a beamline,” Noack said. “If you want to explore a space where your data lives, all you need to know is how big that space is, that it has a beginning and an end in every dimension, and you can press SMART and it will give you everything. As long as you put numbers on it and a number on how much you like it, generally any experiment will work with SMART.”
The National Synchrotron Light Source II and the Center for Functional Nanomaterials are DOE Office of Science User Facilities.
About Computing Sciences at Berkeley Lab
High performance computing plays a critical role in scientific discovery. Researchers increasingly rely on advances in computer science, mathematics, computational science, data science, and large-scale computing and networking to increase our understanding of ourselves, our planet, and our universe. Berkeley Lab’s Computing Sciences Area researches, develops, and deploys new foundations, tools, and technologies to meet these needs and to advance research across a broad range of scientific disciplines.
- • Berkeley Lab
A U.S. Department of Energy National Laboratory Operated by the University of California
You are using an outdated browser. Please upgrade your browser or activate Google Chrome Frame to improve your experience.
Neutrino Physics
Neutrinos fill the whole universe, with about 10 million of them per cubic foot, and most of them zip straight through Earth, and through particle detectors, without leaving a trace. Because they almost never interact with matter, only massive and sophisticated experiments can catch and measure the properties of neutrinos.
Studying Neutrinos
The subatomic particles called neutrinos are among the most elusive in the particle kingdom. Scientists have built detectors underground, underwater, and at the South Pole to measure these ghostly particles that come from the sun, from supernovae and from many other celestial objects.
In addition to measuring neutrinos from the sky, physicists on Earth use powerful accelerators to produce neutrino beams containing billions of neutrinos, of which a tiny fraction can be measured by detectors placed in the beam line. At Fermilab, the DONUT accelerator-based neutrino experiment led in 2000 to the discovery of the tau neutrino , the third of the three known types of neutrinos.
The NuMI beamline and the Booster Neutrino beamline deliver high intensity neutrino beams to Fermilab experiments such as NOvA , ICARUS , and ANNIE with MicroBooNE having recently completed operations and SBND currently under construction.
Why Neutrinos are Important
Particle Physics has made great progress in the last half century probing the quark half of the fundamental particles. We are now in a position to propose doing similar for the neutrinos. The mixing between the 3 neutrino generations is starting to look very different to its quark counterpart. We don’t know why but it is probably important. Neutrinos may hold the key to understanding why the fundamental particles exist in 3 generations.
Neutrinos are the real oddities of the fundamental particles (only interact weakly, ultra small, but non-zero masses). Science often advances when studying the oddities, such as understanding life processes in general by studying life around deep sea vents.
Neutrinos may only interact weakly, but they are the most abundant particle in the universe with a pivotal role in the evolution of our universe.
A difference between how the neutrino types mix and how the antineutrino types mix is postulated to be the reason why matter dominates over anti-matter in our universe (i.e. why we exist).
PIP-II and Producing Neutrino Beams
The PIP-II project will enable a large increase in the power of Fermilab’s proton beams. This, in turn will produce more powerful neutrino beams. See the animation below to see how this happens.
CERN Accelerating science
Beamline for schools competition.
CERN’s Beamline for Schools Competition (BL4S) is a science competition open to high school students from all over the world. The prize, fabulous by any standards, is the opportunity to conduct an experiment for 10 days on-site at CERN, in Geneva. Now entering its tenth year, BL4S has enabled CERN to engage almost 22 teams and more than 17,000 students from across the globe to experience particle physics research first-hand.
Through BL4S, students test themselves as innovators, problem solvers and collaborators, and are motivated to embark in science, technology, engineering and mathematics (STEM) careers to meet the growing demand for a stronger workforce with STEM skills. In order to participate, students prepare experimental proposals, often with the help of members of the International Particle Physics Outreach Group (IPPOG) and other physicists around the world.
The shortlisted teams win a cosmic-ray detector for their school (introduced for the first time in 2016), a BL4S t-shirt for each team member, and for some, the chance to visit a nearby physics laboratory. All participants in BL4S will receive a BL4S certificate.
The winning teams selected will then travel together to CERN to perform their experiment. So far, more than 22 teams have seized this thrilling once-in-a-lifetime opportunity! In 2014, the winning teams came from Greece and the Netherlands; in 2015, Italy and South Africa; in 2016, Poland and the U.K.; in 2017, Italy and Canada; in 2018, India and Philippines; in 2019, Netherlands and USA; in 2020, Switzerland and Germany; in 2021 Italy and Mexico; in 2022 Egypt, France and Spain; in 2023, Pakistan, The Netherlands and the US .
For more information about the competition and the latest updates, please visit the BL4S website .
ALS Computing Group Brings Machine Learning Models to Beamtimes around the World
June 21, 2024
From the types of samples to the techniques used to study them, user experiences at beamlines around the world can vary, but one commonality connects them: beamtime is precious. At different facilities, users encounter different beamline controls, and varying availability of compute infrastructure to process their data. Beyond needing to familiarize themselves with different equipment and software setups, they also need to ensure that they’re collecting meaningful, consistent data no matter where they are. For the past several months, the ALS Computing group has been traveling around the world for beamtime. Their firsthand experience is informing the development of a suite of tools aimed at lowering the barriers of access to advanced data processing for all users.
Today’s beamtime experience
As a beamline scientist at the ALS, Dula Parkinson has helped numerous users with microtomography, a technique that can yield ten gigabytes of data in two seconds. “In many cases, users won’t have done this kind of experiment or analysis before, and they won’t have the computing infrastructure or software needed to analyze the huge amounts of complex data being produced,” he said.
Computational tools and machine-learning models can help the users, from adjusting their experimental setup in real time to processing the data after the experiment has concluded. Eliminating these bottlenecks can make the limited beamtime more efficient and help users glean scientific insights more quickly.
As a former beamline scientist himself, Computing Program Lead Alex Hexemer has first-hand knowledge of the user experience. He was instrumental in the creation of a dedicated computing group at the ALS in 2018, which continues to grow in both staff numbers and diversity of expertise. A current focus for the group is to advance the user experience with intuitive interfaces.
Computing approach to beamtime
Recently, Hexemer and two of his group members, Wiebke Koepp and Dylan McReynolds, traveled to Diamond Light Source, where they worked with Beamline Scientist Sharif Ahmed to test some of their tools during a beamline experiment. “It is always useful to see other facilities from the user’s perspective,” McReynolds said. “We want our software to be usable at many facilities, so getting to test in other environments was very valuable.”
The computational infrastructure is an essential complement to the beamline instrumentation. To standardize their experiments across different microtomography beamlines, the team performed measurements on a reference material—sand with standardized size distributions. Each scan captures a “slice” from the sample; the slices then need to be reconstructed into three-dimensional images that contain 50 to 200 gigabytes of data.
Within that data, the researchers need to glean meaningful information. “We need to segment the data,” explained Hexemer. “This is sand. This is the vial holding the sand. This is air in between.” Identifying the segments allows researchers to more easily decide where to take the next scan—in essence where to move the beam to detect more sand and less vial. But, this type of analysis has traditionally happened after an experiment. That means that researchers might take more scans than necessary, because some scan parameters yield less insightful measurements.
Here, the computing group saw a need for users to assess the quality of their data in near-real-time. “The goal is to be able, at the moment when a scan comes in, to do some immediate analysis to inform the experiment further,” Koepp said. “Our goal is that, algorithmically, you’ll be able to greatly reduce the number of scans you need to take to get the same amount of meaningful data,” McReynolds added.
The seed for this idea has been planted; Berkeley Lab Staff Scientist Peter Zwart and his collaborators in CAMERA developed machine learning algorithms for segmentation. Through beamtime at Diamond and the ALS, the computing group is expanding the functionality and testing the robustness of the algorithms. “We’re replicating the experimental setup at different facilities as closely as possible,” Koepp said, “because different data processing steps, different exposure times, etc., could all potentially affect model performance.”
But, to take advantage of these algorithms, synchrotron users need to be able to access and use powerful computational infrastructure that can parse the many gigabytes, and even terabytes, of data.
User-friendly (and user-facility-friendly) computing for the future
The ALS can facilitate user access to computational infrastructure, like the NERSC supercomputing facility. But, the users still need a portal into NERSC and a simple interface that doesn’t require a background in coding.
The Computing group is addressing this need by developing a web interface for users as part of the MLExchange project . “We’re trying to give users access to fantastic hardware in web interfaces that are easy to use,” said Hexemer. “When they come for such a short beamtime, they won’t have to write code just to use the computational infrastructure,” he added.
McReynolds expanded upon the goals for the user experience. “We want to make it easy for the algorithm to interface with different beamline hardware,” he said. And so, after testing their tools at the DIAD beamline at Diamond Light Source with Ahmed, the Computing group returned to the ALS to perform the same scans on the same samples at Beamline 8.3.2 with Parkinson to test the robustness of their machine-learning model.
The machine-learning models hold great potential to facilitate cross-facility learning, enabling more-efficient experiments. “If somebody scans sand and trains the network, somebody at another facility could use the same model to segment their sand, or maybe just fine tune their analysis instead of starting from scratch,” Hexemer explained.
The cross-facility learning is not limited to the machine learning models. In fact, these advances are made possible by people around the world all contributing different insights and experiences. The ALS Computing team, including Hexemer, Koepp, and McReynolds, as well as Tanny Chavez, Tibbers Hao, Raja Vyshnavi Sriramoju, and Xiaoya Chong, has been collaborating with Zwart at Berkeley Lab, Tim Snow and Jacob Filik at Diamond, and beamline scientists at ALS Beamlines 8.3.2 and 7.3.3, Diamond, and DESY. Much of their work is part of the MLExchange, which is a collaboration Hexemer leads with user facilities at SLAC, Oak Ridge, Argonne, and Brookhaven National Laboratories. This type of cross-facility learning delivers cross-facility results. “We want to make sure that all the pipelines we build can be easily taken somewhere else and used at other beamlines,” said Hexemer.
The ALS Computing group’s web interface will provide synchrotron users with real-time feedback and analysis capabilities at different beamlines around the world. Starting as a tool to resolve experimental bottlenecks, computing is evolving to become an essential building block for the experimental framework itself. In fact, Parkinson can already envision applications on a grand scale. “They’re taking a serious step toward providing a ‘digital twin’ of the sample, allowing users to really understand and simulate their experiments,” he said. With synchrotron data feeding into a machine-learning model, and a machine-learning model guiding data collection, all accessible to the end user, the future of synchrotron science is poised to answer questions at the limits of our imagination.
- Click to share on Facebook (Opens in new window)
- Click to share on Twitter (Opens in new window)
- Click to share on LinkedIn (Opens in new window)
Breadcrumbs Section. Click here to navigate to respective pages.
Live Autonomous Beamline Experiments: Physics In the Loop
DOI link for Live Autonomous Beamline Experiments: Physics In the Loop
Click here to navigate to parent product.
In an APS, machine learning (ML) controls automated laboratory equipment, allowing for ML-driven experiment design, execution, and analysis in a closed loop. The ML-driven closed-loop experiment cycle of APS promises to allow researchers to perform the minimum number of experiments necessary to explore the search space and identify improved technology-relevant materials. The pipeline begins with data collection from the experimentfollowed by preprocessing the data to increase its utility for the experiment. Based on closed-loop results of the APS system, the pipeline is re-engineered to improve performance. Designing the machine learning pipeline requires the selection of multiple algorithms. A common first step is to identify easy-to-use, off-the-shelf machine learning tools that can be assembled into a preliminary ML pipeline.
- Privacy Policy
- Terms & Conditions
- Cookie Policy
- Taylor & Francis Online
- Taylor & Francis Group
- Students/Researchers
- Librarians/Institutions
Connect with us
Registered in England & Wales No. 3099067 5 Howick Place | London | SW1P 1WG © 2024 Informa UK Limited
CERN Accelerating science
The interconnecting system used to transport antimatter between ALPHA 2 and ALPHA-g.
ALPHA produces antihydrogen by slowly merging clouds of positrons and antiprotons inside specially designed Penning traps, found at the centres of the ALPHA 2 and ALPHA-g experiments. During each experimental cycle, positrons and antiprotons must be transferred into these parts of the machine from the positron accumulator and catching trap at opposite ends of the apparatus . Charged particles can be ejected from these traps as a “pulsed beam” by quickly changing the voltages that are applied to the Penning trap electrodes, allowing the particles to escape together in one direction. The various Penning traps are connected together by a beamline, which uses magnets to continually steer and focus these beams as they make their way around the experiment.
In most beamlines, particles are transported at relatively high energies of millions (MeV) or even billions (GeV) of electron-volts. In contrast , ALPHA’s Penning traps can only produce slow-moving beams with energies of 10 – 100 eV, which are easily affected by the strong magnetic fields of the traps themselves. To prevent this, the ALPHA beamlines are designed so that particles always move along the direction of the magnetic field . In strong magnetic fields, the beam is focused down to a small point, and in weaker fields the beam can expand to a larger diameter.
A special part of the beamline, known as the ' interconnect' , is used to steer positrons and antiprotons between ALPHA’s spectroscopy and gravity experiments . The interconnect uses an arrangement of seven independent magnets to create a curved magnetic field. Beams that pass through the interconnect will follow the direction of this magnetic field, allowing us to steer them in various directions. We use diagnostic tools such as Micro-Channel Plates (MCP) to image the beam at different points along the beamline, and check that it is following the correct path.
What's more?
- For more about Antimatter in general , have a look at the CERN Antimatter page
- Good descriptions of 'How ALPHA works' in detail can be found in the publication section on our website
- Particularly appropriate for students and non-physicists are Cold Antihydrogen: a new frontier in fundamental physics , Antihydrogen for precision tests in physics and Antihydrogen in a bottle
- For experts , a general overview of many of the important aspects can be found in Search for Trapped Antihydrogen
Not found what you were looking for?
Local Navigation
Site Navigation
General Lab Information
Pioneering the Cellular Frontier
Brookhaven researchers explore a single cell using advanced x-ray imaging techniques.
July 23, 2024
These superimposed tomography images show the nucleus (red) and cytosol (gray) with correlative X-ray fluorescence images of calcium distribution (green) in a human embryonic kidney cell. (Brookhaven National Laboratory/ Nature Communications Biology )
Every plant, animal, and person is a rich microcosm of tiny, specialized cells. These cells are worlds unto themselves, each with their own unique parts and processes that elude the naked eye. Being able to see the inner workings of these microscopic building blocks at nanometer resolution without harming their delicate organelles has been a challenge, but scientists from different disciplines across the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory have found an effective way to image a single cell using multiple techniques. The fascinating process to capture these images was published in Communications Biology .
Being able to understand the inner structures of cells, the way chemicals and proteins interact within them, and how those interactions signal certain biological processes at nanometer resolution can have significant implications in medicine, agriculture, and many other important fields. This work is also paving the way for better biological imaging techniques and new instruments to optimize biological imaging.
“Studying human cells and the organelles inside of them is exciting,” said Qun Liu, a structural biologist at Brookhaven Lab, “but there are so many opportunities to benefit from our multimodal approach that combines hard X-ray computed tomography and X-ray fluorescence imaging. We can study pathogenic fungi or beneficial bacteria. We’re able to not only see the structure of these microorganisms but also the chemical processes that happen when cells interact in different ways.”
Pulling out one of life’s building blocks
Before the researchers even began imaging, one of their biggest challenges was preparing the sample itself. The team decided to use a cell from the human embryonic kidney (HEK) 293 line. These cells are known for being easy to grow but difficult to take multiple X-ray measurements of. Even though they are very small, cells are quite susceptible to X-ray-induced damage.
The scientists went through a careful, multistep process to make the sample more robust. They used paraformaldehyde to chemically preserve the structure of the cell, then had a robot rapidly freeze the samples by plunging them into liquid ethane, transferred them to liquid nitrogen, and finally freeze dried them to remove water but maintain the cellular structure. Once this process was complete, the researchers placed the freeze-dried cells under a microscope to locate and label them for targeted imaging.
At only about 12-15 microns in diameter (the average human hair is 150 microns thick), setting up the sample for measurements was not easy, especially for measurements on different beamlines. The team needed to ensure that the cell’s structure could survive multiple measurements with high energy X-rays without significant damage and that the cell could be reliably held in one place for multiple measurements. To overcome these hurdles, the scientists created standardized sample holders to be used on multiple pieces of equipment and implemented optical microscopes to quickly find and image the cell and minimize prolonged X-ray exposure that could damage it.
The overall workflow of single-cell correlative X-ray imaging (Brookhaven National Laboratory/ Nature Communications Biology )
Multimodal measurements
The team used two imaging techniques found at the National Synchrotron Light Source II (NSLS-II) — a DOE Office of Science user facility at Brookhaven — X-ray computed tomography (XCT) and X-ray fluorescence (XRF) microscopy.
The researchers collected XCT data, which uses X-rays to tell scientists about the cell’s physical structure, on the Full Field X-ray Imaging (FXI) beamline. Tomography uses X-rays to show a cross-section of a solid sample. A familiar example of this is the CT scan, which medical practitioners use to image cross sections of any part of the body.
The researchers collected XRF microscopy data, which provides more clues about the distribution of chemical elements within the cell, on the Submicron Resolution X-ray Spectroscopy (SRX) beamline. In this technique, the researchers direct high energy X-rays at a sample, exciting the material and causing it to emit X-ray fluorescence. The X-ray emission has its own unique signature, letting scientists know exactly what elements the sample is composed of and how they are distributed to fulfill their biological functions.
“We were motivated to combine XCT and XRF imaging based on the unique, complementary information each provides,” said Xianghui Xiao, FXI lead beamline scientist. “Fluorescence gives us a lot of useful information about the trace elements inside of cells and how they are distributed. This is very critical information to biologists. Getting a high-resolution fluorescence map on many cells can be very time consuming, though. Even just for a 2D image, it may take quite a few hours.”
This is where getting a 3D image of the cell using XCT is helpful. This information can help guide the fluorescence measurements to specific locations of interest. It saves time for the scientists, increasing throughput, and it also ensures that the sample doesn’t need to be exposed to the X-rays for as long, mitigating potential damage to the fragile cell.
“This correlative approach provides useful, complementary information that could advance several practical applications,” remarked Yang Yang, a beamline scientist at SRX. “For something like drug delivery, specific subsets of organelles can be identified, and then specific elements can be traced as they are redistributed during treatment, giving us a clearer picture of how these pharmaceuticals work on a cellular level.”
While these advances in imaging have provided a better view into the cellular world, there are still challenges to be met and ways to improve imaging even further. As part of the NSLS-II Experimental Tools III project — a plan to build out new beamlines to provide the user community with new capabilities — Yang is science lead of the team working on the upcoming Quantitative Cellular Tomography (QCT) beamline, which will be dedicated to bio-imaging. QCT is a full-field soft X-ray tomography beamline for imaging frozen cells with nanoscale resolution without the need for chemical fixation. This cryo-soft X-ray tomography beamline will be complementary to current methods, providing even more detail into cellular structure and functions.
Future findings
While being able to peer into the cells that make up the systems in human bodies is fascinating, being able to understand the pathogens that attack and disrupt those systems can give scientists an edge in fighting infectious disease.
“This technology allows us to study the interaction between a pathogen and its host,” explained Liu. “We can look at the pathogen and a healthy cell before infection and then image them both during and after the infection. We will notice structural changes in both the pathogen and the host and gain a better understanding of the process. We can also study the interaction between beneficial bacteria in the human microbiome or fungi that have a symbiotic relationship with plants.”
Liu is currently working with scientists from other national laboratories and universities for DOE’s Biological and Environmental Research Program to study the molecular interactions between sorghum and Colletotrichum sublineola, the pathogenic fungus that causes anthracnose, which can harm the leaves of plants. Sorghum is a major DOE bioenergy crop and is the fifth most important cereal crop in the world, so humanity would have a lot to gain by understanding the tactics of this devastating fungus and how sorghum’s defenses operate at the cellular and molecular levels.
Being able to see at this scale can give scientists insight into the wars being waged by pathogens on crops, the environment, and even human bodies. This information can help develop the right tools to fight these invaders or fix systems that aren’t working optimally at a fundamental level. The first step is being able to see a world that human eyes aren’t able to see, and advances in synchrotron science have proven to be a powerful tool in uncovering it.
This work was supported by Brookhaven’s Laboratory Directed Research and Development funding and the DOE Office of Science.
Brookhaven National Laboratory is supported by the Office of Science of the U.S. Department of Energy. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit science.energy.gov .
Follow @BrookhavenLab on social media. Find us on Instagram , LinkedIn , X , and Facebook .
2024-21843 | INT/EXT | Newsroom
Other Articles...
Using Fire Management to See How Ticks…Tick
Thursday, July 25, 2024
536th Brookhaven Lecture on Wednesday, 7/24: Slow Electrons and 2D Materials
Tuesday, July 23, 2024
Zhiwan Xu Wins 2024 Gertrude Scharff-Goldhaber Prize
Monday, July 22, 2024
Oleg Gang Named a 2024 Vannevar Bush Fellow
Friday, July 19, 2024
Visit Brookhaven Lab for "Dazzling Discoveries," 7/21
Thursday, July 18, 2024
Brookhaven National Laboratory
Brookhaven National Laboratory is a multipurpose research institution funded by the U.S. Department of Energy. Located on Long Island, NY, Brookhaven operates large-scale facilities for studies in physics, chemistry, biology, medicine, applied science, and advanced technology. The Laboratory's almost 3,000 scientists, engineers, and support staff are joined each year by more than 5,000 visiting researchers from around the world.
PO Box 5000 Upton, NY 11973-5000 (631) 344-8000
- Our Science
- Visiting the Lab
- Staff Directory
- Guest Center
- Partnerships
- For Vendors
- Departments
- Public Events
- Diversity, Equity & Inclusion
- Technology Licensing
- Stakeholder Relations
- Students & Educators
- Sustainability
- Privacy and Security Notice
- Vulnerability Disclosure Program
Brookhaven Science Associates
Brookhaven Science Associates manages and operates Brookhaven National Laboratory on behalf of the U.S. Department of Energy's Office of Science. BSA is a partnership between Battelle and The Research Foundation for the State University of New York on behalf of Stony Brook University. | More
| Journal of Synchrotron Radiation Journal of Synchrotron Radiation |
1. Introduction
2. beamline design and instrumentation, 3. data acquisition, processing and storage, 4. results and beamline performance, 5. applications, 6. summary and outlook, supporting information.
| Format | BIBTeX | |
| EndNote | ||
| RefMan | ||
| Refer | ||
| Medline | ||
| CIF | ||
| SGML | ||
| Plain Text | ||
| Text | ||
beamlines \(\def\hfill{\hskip 5em}\def\hfil{\hskip 3em}\def\eqno#1{\hfil {#1}}\)
| JOURNAL OF SYNCHROTRON RADIATION |
BEATS: BEAmline for synchrotron X-ray microTomography at SESAME
a SESAME – Synchrotron-light for Experimental Science and Applications in the Middle East, Allan, Jordan, b ESRF – The European Synchrotron, Grenoble, France, c Elettra-Sincrotrone Trieste SCpA, Basovizza, Trieste, Italy, d ALBA Synchrotron, Cerdanyola, Catalonia, Spain, e The Cyprus Institute, Nicosia, Cyprus, f Solaris National Synchrotron Radiation Centre, Jagiellonian University, Krakow, Poland, g Laboratori Nazionali di Frascati dell'INFN, INFN, Frascati, Rome, Italy, h Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany, and i Swiss Light Source, Paul Scherrer Institut, Villigen, Switzerland * Correspondence e-mail: [email protected] , [email protected]
The ID10 beamline of the SESAME (Synchrotron-light for Experimental Science and Applications in the Middle East) synchrotron light source in Jordan was inaugurated in June 2023 and is now open to scientific users. The beamline, which was designed and installed within the European Horizon 2020 project BEAmline for Tomography at SESAME (BEATS), provides full-field X-ray radiography and microtomography imaging with monochromatic or polychromatic X-rays up to photon energies of 100 keV. The photon source generated by a 2.9 T wavelength shifter with variable gap, and a double-multilayer monochromator system allow versatile application for experiments requiring either an X-ray beam with high intensity and flux, and/or a partially spatial coherent beam for phase-contrast applications. Sample manipulation and X-ray detection systems are designed to allow scanning samples with different size, weight and material, providing image voxel sizes from 13 µm down to 0.33 µm. A state-of-the-art computing infrastructure for data collection, three-dimensional (3D) image reconstruction and data analysis allows the visualization and exploration of results online within a few seconds from the completion of a scan. Insights from 3D X-ray imaging are key to the investigation of specimens from archaeology and cultural heritage, biology and health sciences, materials science and engineering, earth, environmental sciences and more. Microtomography scans and preliminary results obtained at the beamline demonstrate that the new beamline ID10-BEATS expands significantly the range of scientific applications that can be targeted at SESAME.
Keywords: X-ray imaging ; computed tomography ; X-ray phase-contrast ; microscopy ; radiography ; synchrotron radiation ; cultural heritage ; materials science ; biomedical imaging .
X-ray tomography is a widely non-destructive three-dimensional (3D) imaging technique which generates volume images of a specimen by using penetrating radiation. The method involves the acquisition of multiple projection images (radiographs) of angular views covering 180° or 360° of an object, and a mathematical procedure of 3D tomographic reconstruction. Its scientific and technological applications are vast, ranging from life and materials science to archaeology as well as earth and environmental research.
The project Beamline for Tomography at SESAME (BEATS) was funded by the EU via the H2020 programme, and brought together a consortium of research facilities in the Middle East (SESAME, Jordan and the Cyprus Institute, Cyprus), and European synchrotron radiation facilities and high-energy laboratories [ALBA, Spain; DESY, Germany; Elettra, Italy; European Synchrotron Radiation Facility (ESRF), France; INFN, Italy; Paul Scherrer Institut (PSI), Switzerland; and SOLARIS, Poland] with the goal to design, install and commission a beamline for hard X-ray full-field tomography at SESAME, fostering in this way the user community of the facility. The project was coordinated by the ESRF.
This communication describes the instrumentation and performance of BEATS, which is fully commissioned for scientific user operation. First SXCT scans performed at the beamline are presented, demonstrating the huge scientific potential of the instrument for a broad range of disciplines.
| Schematic layout of the ID10-BEATS beamline of SESAME. Only optical and beam-defining elements are shown. |
2.1. X-ray source: three-pole wiggler
| ( ) BEATS three-pole wiggler insertion device installed in the SESAME storage ring. ( ) Simulated and measured profiles of the vertical component of the magnetic field along the longitudinal axis of the ID, at minimum gap of 11.15 mm. Simulations were performed in (Elleaume , 1997 ). ( ) X-ray emitted by the BEATS 3PW at different magnetic gaps. |
(i) Provide an X-ray point source with broadband energy spectrum and photon flux at the sample position of at least 1 × 10 10 photons mm −2 s −1 at 50 keV.
(ii) Minimize the multipolar effects on the SESAME storage ring optics.
(iii) Reduce the attractive forces between the magnetic structures leading to minor mechanical constraints.
2.2. Front-endThe beamline front-end comprises: (i) A fixed mask defining a useful beamline aperture of 1.8 mrad × 0.36 mrad (H × V). (ii) Motorized, in-vacuum slits, used to adjust the beam size and reduce the heat load on downstream components. (iii) A 0.5 mm-thick chemical vapour deposition (CVD) diamond window separating accelerator and beamline vacuum sections. (iv) A white beam attenuator system composed of five motorized actuators with four cooled filters each. (v) Radiation safety and vacuum shutters designed to absorb the beam heat load and high-energy X-rays, and to protect from contamination of the storage ring vacuum environment in case of outgassing or leak. Available X-ray filters range from 5 mm glassy carbon (HTW, Germany) to polished plates of high- Z metals (0.5 mm W and Au), and can be used in succession to tune the intensity and average energy of the white beam, and to modulate the heat load on the first mirror of the monochromator. The front-end components were manufactured by JJ X-ray A/S (Denmark). 2.3. Double-multilayer monochromator
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
IMAGES
VIDEO
COMMENTS
Beamlines usually end in experimental stations that utilize particle beams or synchrotron light obtained from a synchrotron, or neutrons from a spallation source or research reactor. Beamlines are used in experiments in particle physics, materials science, life science, chemistry, and molecular biology, but can also be used for irradiation tests or to produce isotopes.
Beamline for Schools Beamline for Schools (BL4S) is a physics competition for high school students from all around the world organised at CERN, the European Laboratory for Particle Physics, in Geneva, Switzerland, and DESY, the German Electron Synchrotron, in Hamburg, Germany. Teams of high school students can propose an experiment that they want to perform at a beamline, that is, a part of a ...
1. Examples for experiments that can be done at the T9 beam line. Example 1:Explore the world of antimatter. The particles in the beam at the T9 beam line are a mix of matter and antimatter particles. Some of the properties of antimatter have only been theoretically predicted but never been measured in an experiment.
Beamline scientist Mary Upton, pictured here at beamline 27-ID of the Advanced Photon Source. Upton works with visiting scientists on inelastic X-ray scattering experiments, and helps them design those experiments and collect data. (Image by Mark Lopez / Argonne National Laboratory.)
Beamline scientists connect the world's researchers to the Every successful experiment at the Advanced Photon Source relies on the knowledge and skills of the beamline scientists who enable the research. What makes a good beamline scientist? Today, four of them weigh in.
In order to succeed in Beamline for Schools you can either propose a creative experiment or idea yourself or take one of the examples and work out the details of that experiment.
From 14 to 28 September, the three winning teams of the 2023 edition of the Beamline for Schools competition had the chance to use the test-beam facilities at CERN and DESY to perform their experiments.
The eighth edition of Beamline for Schools ended on 21 September 2021, when the two winning teams left DESY in Hamburg, Germany, with a smile on their faces and many fond memories. The team "EXTRA" from Italy and the team "Teomitzli" from Mexico were selected from among 289 participants from all over the world and won a two-week stay in Hamburg to carry out the experiments that they ...
The Beamline for Schools competition offers high-school students the chance to conduct experiments at a real, functioning physics beamline at CERN. More about the competition.
VITO experiment. The Versatile Ion polarisation Technique Online ( VITO) experiment is a permanent experimental setup located in the ISOLDE facility at CERN, in the form of a beamline. The purpose of the beamline is to perform a wide range of studies using spin-polarised short-lived atomic nuclei. VITO uses circularly-polarised laser light to ...
Researchers in Berkeley Lab's CAMERA have been working with beamline scientists at Brookhaven National Laboratory to develop and test SMART, a mathematical method that enables autonomous experimental decision making without human interaction.
13th July 2012. Most experiments require a trigger because they need to know when to record data. I'll distinguish. Trivial triggers : triggers which select when to record data in a periodic manner or in a way which is otherwise independent of the actual properties of the event being selected. Non-trival triggers : triggers which select ...
At Fermilab, the DONUT accelerator-based neutrino experiment led in 2000 to the discovery of the tau neutrino, the third of the three known types of neutrinos. The NuMI beamline and the Booster Neutrino beamline deliver high intensity neutrino beams to Fermilab experiments such as NOvA, ICARUS, and ANNIE with MicroBooNE having recently ...
Introduction In order to succeed in Beamline for Schools (BL4S) you can either propose an exper-iment yourself or take one of the examples described in this document and work out the details of that experiment. We suggest you to have a look at the proposals of the winning experiments of the previous editions of the competition to understand how detailed you should describe your experiment. One ...
CERN's Beamline for Schools Competition (BL4S) is a science competition open to high school students from all over the world. The prize, fabulous by any standards, is the opportunity to conduct an experiment for 10 days on-site at CERN, in Geneva. Now entering its tenth year, BL4S has enabled CERN to engage almost 22 teams and more than 17,000 students from across the globe to experience ...
Please note that in order to succeed in Beamline for Schools you can either propose a creative experiment or idea yourself or take one of the examples and work out the details of that experiment.
Developing Beamline Capabilities as a Team NSLS-II was designed to include an accelerator complex that provides space for up to 60 beamlines. Currently, NSLS-II operates 29 beamlines that are organized according to the research capabilities they offer and staff expertise. Interdisciplinary teams of experts designed and constructed these 29 cutting-edge beamlines through multiple beamline ...
The ALS Computing group performed experiments and tested their machine learning models at Beamline 8.3.2. Clockwise from back left: Tanny Chavez, Dylan McReynolds, Raja Vyshnavi Sriramoju, Seij De Leon, Dula Parkinson, Wiebke Koepp.
ABSTRACT. In an APS, machine learning (ML) controls automated laboratory equipment, allowing for ML-driven experiment design, execution, and analysis in a closed loop. The ML-driven closed-loop experiment cycle of APS promises to allow researchers to perform the minimum number of experiments necessary to explore the search space and identify ...
Mamba. : a systematic software solution for beamline experiments at HEPS. To cater for the diverse experiment requirements at the High Energy Photon Source (HEPS) with often limited human resources, Bluesky was chosen as the basis for our software framework, Mamba. In our attempt to address Bluesky 's lack of integrated graphical user ...
Experimental Beamlines The 70 MeV Drive and the 15 MeV Witness linacs propagate in opposite directions, and come to a common area designated as the beamline switchyard where each linac can branch out into any of several beamlines where experiments are conducted.
et configurations. In a collider (such as the Large Hadron Collider (LHC)), accelerated particle beams travel at close to the speed of light before they are made to collide. In a fixed-target experiment a beam of accelerated particles collides with a target, which can be a solid, liquid, or gas. The Beamline for Schools (BL4S) experiment is of the
A special part of the beamline, known as the ' interconnect', is used to steer positrons and antiprotons between ALPHA's spectroscopy and gravity experiments. The interconnect uses an arrangement of seven independent magnets to create a curved magnetic field.
QCT is a full-field soft X-ray tomography beamline for imaging frozen cells with nanoscale resolution without the need for chemical fixation. This cryo-soft X-ray tomography beamline will be complementary to current methods, providing even more detail into cellular structure and functions.
The recently inaugurated beamline ID10-BEATS for hard X-ray full-field tomography at the SESAME synchrotron in Jordan is presented. The design, performance and scientific applications of the beamline, which was developed within the European Horizon 2020 project BEAmline for Tomography at SESAME, are illustrated.
Beam time for non-proprietary experiments is granted based on a merit review of proposals. ... The S2 beamline will also have space for auxiliary instruments and may be used as a general-purpose beamline.Detailed information regarding proposal preparation can be found in the Call for Proposals.FRIB supports a community of 1,800 scientists from ...