New Design Packs Two Qubits into One Superconducting Junction

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Published: Monday, October 21 2024 00:01

Quantum computers are potentially revolutionary devices and the basis of a growing industry. However, their technology isn’t standardized yet, and researchers are still studying the physics behind the diverse ways to build these quantum devices. Even the most basic building blocks of a quantum computer—qubits—are still an active research topic.A superconducting circuit studied in Alicia Kollár’s lab. The middle of the three rectangles along the bottom are junctions that hold quantum states that may each be used as a qubit. A proposal to adjust the dimensions of the junctions would allow chips like this to host twice as many qubits.

In an article published September 23, 2024 in the journal Physical Review A, JQI researchers proposed a way to use the physics of superconducting junctions to let each function as more than one qubit. They also outlined a method to use the new qubit design in quantum simulations. While these proposed qubits might not immediately replace their more established peers, they illustrate the rich variety of quantum physics that remains to be explored and harnessed in the field.

Superconducting junctions are part of many diverse qubit designs, including those in the prototype quantum computers of IBM and Google. All the designs feature an island made of a superconductor joined to the rest of a superconducting circuit by a thin layer of insulator that forms the junction between the two sections. To cross the barrier, electrons in the circuit must quantum tunnel through the junction, influencing which quantum states the circuit can naturally hold.

JQI Fellow Mohammad Hafezi and JQI postdoctoral researcher Andrey Grankin, who is the first author of the paper, reviewed the research on junctions in superconducting circuits, and what they found left them wondering if the existing qubit designs were taking advantage of the full breadth of physics that can be realized in superconducting junctions. The design of a junction—the geography of the superconducting island—impacts which states it can host, and current designs have focused on small junctions and the simplest states.

In some qubit designs, the quantum states depend on the geography of the island because of how electrons in a superconductor are free to move around like a fluid. Like water in a small pool, the electrons can slosh back and forth and form waves that are influenced by their surroundings.

Certain electron waves isolated onto a superconducting island can be very stable and long lasting, which makes them useful for storing quantum information in a qubit. The waves that are stable are examples of a more general phenomenon, called standing waves, that occur when a wave isn’t interrupted during the slope of one of its hills or valleys; instead, its oscillations are perfectly completed at the edges (the walls of a pool, the points where a string is being held, etc.). A guitar string’s harmonics are also examples of standing waves. 

But just having a stable standing wave in the superconducting electrons isn’t enough to be useful for quantum calculations. To use a standing wave as a qubit, a quantum computer must be able to distinguish it from all other standing waves and individually target it. Current superconducting qubit designs circumvent this issue by using short junctions that host just a single standing wave; as long as a junction is sufficiently short, the physics governing the superconducting electrons effectively only allows a single standing wave on the superconducting island. Researchers have also studied junctions that meet along very long interfaces and found that they can easily host a vast array of standing waves. Unfortunately, the abundance of standing waves comes with a downside: The more standing waves there are, the more similar the waves become, which makes them difficult to tell apart and inconvenient for quantum computing. 

“Historically short and long junctions were researched quite extensively,” says Grankin, who is the first author of the paper. “But the intermediate junction lengths have not been studied.”

Hafezi—who is also a Minta Martin professor of electrical and computer engineering and physics at the University of Maryland (UMD) and a Senior Investigator at the National Science Foundation Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS)—and Grankin became interested in this intermediate regime. The pair consulted with JQI Fellow Alicia Kollár, another author of the paper who works with superconducting qubits in her research.

“Andrey and Mohammad came to me with a creative new idea for how to make a junction host multiple qubit excitations,” says Kollár, who is also a Chesapeake Assistant Professor of Physics and a Co-Associate Director of Research for RQS. “Our main challenge was coming up with a design that would yield practical device parameters and a device that is actually within reach of current state-of-the-art fabrication techniques.”

Together, the group explored the behaviors of electrons in the intermediate case and if it is practical to produce multiple excitations that can be easily distinguished and separately manipulated. Since the pool of electrons is within a solid superconductor, setting them into motion isn’t as simple as plucking a string. To push and pull on electrons you need an electric field. One way that physicists like to push electrons around is using a special reflective chamber—or resonator—that is full of electric and magnetic fields in the form of light. 

Light in a resonator can form its own standing waves that act on the electrons. Similar to a guitar string vibrating due to the sound waves from another string—or more dramatically the sound of a singer’s voice shaking a glass until it shatters—the right light waves inside a resonator can excite electrons in a superconducting junction into a standing wave, which physicists call a mode of the junction. 

The team analyzed how medium-sized junctions should behave inside a resonator and found promising results. The various modes of a junction each respond more or less strongly to particular frequencies of light, so light can be selected to target a specific mode. The response of a mode to a standing wave of light in a resonator also depends on whether the symmetries of the mode and the light match. If the waves of light in the resonator are symmetrical across the center of a junction, they naturally push electrons into waves with a similar symmetry. For instance, if the light waves crossing the junction form a hill on one side and a valley on the other, they can’t push the electrons into a simple hill reflected across the center of the device, but they might be able to excite a similarly lopsided mode of the junction. 

So, light that creates one mode in the superconducting electrons may be ignored by another mode. In the paper, the team described a method of exploiting these two effects to excite or manipulate only a targeted mode. The researchers proposed a design where two distinct modes are targeted so that a single junction functions as two independent qubits. They also described a method to use a one-dimensional line of junctions to simulate interactions between two-dimensional grids of quantum particles. However, they haven’t yet tackled fabricating the junctions and demonstrating the feasibility of their proposal.

“This project started from a fundamental interest in the electrodynamics of extended junctions,” Grankin says. “Then it turned out to be also useful from the quantum information and simulation perspective.”

Original story by Bailey Bedford: https://jqi.umd.edu/news/new-design-packs-two-qubits-one-superconducting-junction

HAWC Finds High-Energy Gamma-Ray Emissions from Microquasar V4641 Sagittarii

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Published: Wednesday, October 16 2024 00:01

A new study in Nature, “Ultra-high-energy gamma-ray bubble around microquasar V4641 Sgr,"   has  revealed a groundbreaking discovery by researchers from the High Altitude Water Cherenkov (HAWC) observatory:  TeV gamma-ray emissions from V4641 Sagittarii (V4641 Sgr), a binary system composed of a black hole and a main sequence B-type companion star. This discovery provides fresh insights into particle acceleration in large-scale jets emitted from microquasars, which serve as natural laboratories for studying high-speed jets produced by matter falling onto spinning black holes. The findings show that V4641 Sgr's gamma-ray emissions occur at similar distances from the black hole as those observed in another well-known microquasar, SS 433. This makes V4641 Sgr stand out for its super-Eddington accretion and one of the fastest superluminal jets in the Milky Way. With a gamma-ray spectrum that ranks among the hardest of any known TeV sources, the emissions are detected at energies exceeding 200 TeV. Schematic illustration of the V4641 Sgr region.

The study's results indicate that the gamma rays are likely produced by extremely high-energy protons. While studies of SS 433 indicate that the emission from this object is likely electrons, the high energy emission at large distances from V4641 argue against electrons due to their rapid energy loss at high energies. The implications are profound: the environment around the microquasar may play a critical role in determining whether the emission from the large-scale jets comes from electrons or protons, and they could play a significant role as a source of Galactic cosmic rays. These observations open new avenues for understanding particle acceleration in extreme environments and contribute to the broader study of high-energy astrophysics. 

 "At a zenith angle of 46° from HAWC's field of view, the microquasar V4641 Sgr has accelerated particles to the knee of the cosmic-ray spectrum, pushing the boundaries of our understanding of particle acceleration and transport in such extreme environments," said Dr. Dezhi Huang, a Post-Doctoral researcher at the University of Maryland. "HAWC observatory continues to deliver exceptional performance, providing valuable insights into these high-energy processes that were previously beyond our reach. "

“The HAWC survey has discovered for the first time very high energy gamma rays from the extended 100 pc jet of the microquasar V4641 Sgr. This proves that jets launched in accreting systems can accelerate particles up to PeV energies, and therefore that microquasars are potentially significant contributors to the Galactic cosmic ray population at high energies," saidDr. Sabrina Casanova, Professor from Institute of Nuclear Physics of the Polish Academy of Sciences. "Furthermore, although very high energy protons are strongly suspected to exist in the kpc jets of active galactic nuclei (AGN), the associated hundred TeV emission has never been observed from an extended region from an AGN jet due to strong gamma-ray absorption over the long distances to Earth.”Differential spectrum weighted by E2 for the northern and southern sources in a model with two point sources and for the asymmetric extended source in a model with a single asymmetric extended source. The shaded regions indicate the best-fit spectra and 1σ statistical uncertainties when fitting a single-power-law model to the data from 10 to >200 TeV. The markers correspond to the best-fit values and their 1σ statistical uncertainties obtained when fitting a single-power-law model to data in individual energy bins. The chosen energy range for plotting the spectrum is specified in the Methods.

This study was supported by the collaborative efforts of multiple institutions, with major contributions from the University of Maryland. Distinguished University Professor Jordan Goodman, a member of the collaboration's internal editorial board, played a key role in guiding the publication. Dr. Dezhi Huang, one of the corresponding authors, led significant aspects of the analysis, while Dr. Kristi Engel helped refine the paper. Additional support came from UMD HAWC group members Research Scientist Dr. Andrew Smith, Project Engineer Michael Schneider, Dr. Zhen Wang, Dr. Jason Fan and graduate students Sohyoun Yun-Cárcamo.

More on the finding: https://www.nature.com/articles/d41586-024-03191-x

Nobel Prize Celebrates Interplay of Physics and AI

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Published: Friday, October 11 2024 00:01

On October 8, the Nobel Prize in physics was awarded to John Hopfield and Geoffrey E. Hinton for their foundational discoveries and inventions that have enabled artificial neural networks to be used for machine learning—a widely used form of AI. The award highlights how the field of physics is intertwined with neural networks and the field of AI.(Credit: © Johan Jarnestad/The Royal Swedish Academy of Sciences)

An artificial neural network is a collection of nodes that connect in a way inspired by neurons firing in a living brain. The connections allow a network to store and manipulate information. While neural network research is closely tied to the fields of neuroscience and computer science, it is also connected with physics. Ideas and tools from physics were integral in the development of neural networks for machine learning tasks. And once machine learning was refined into a powerful tool, physicists across the world, including at the University of Maryland, have been deploying it in diverse research efforts.

Hopfield invented a neural network, called the Hopfield network, that can work as a memory that stores patterns in data—this can be used for tasks like recognizing patterns in images. Each node in its network can be described as a pixel in an image, and it can be used to find an image in its memory from its training that most closely resembles a new image that it is presented to it. But the process used to compare images can also be described in terms of the physics that govern the quantum property of spin.

The spin of a quantum particle makes it behave like a tiny bar magnet, and when many magnets are near each other, they all work to orient themselves in specific ways (north poles repelled by the other north poles and attracted to south poles). Physicists can characterize a group of spins based on their interactions and the energy associated with the orientation all the spins want to fall into. Similarly, a Hopfield network can be described as characterizing images based on an energy defined by the connections between nodes.

Hinton used the tools of statistical physics to build on Hopfield’s work. He developed an approach to using neural networks called Boltzmann machines. The learning method of these neural networks fit the description of a specific type of spin orientation found in materials, called a spin glass. A Boltzmann machine can be used to identify characteristics of the data it was trained with. These networks can help classify images or create new elements that fit the pattern it has been trained to recognize.

Following the initial work of Hopefield and Hinton a broad variety of neural networks and machine learning applications have arisen. As neural networks have expanded beyond the forms developed by Hopfield and Hinton, they still resemble common physics models, and some physicists have chosen to apply their skills to understanding the large, often messy, models that describe neural networks.

“Artificial neural networks represent a very complicated, many-body problem, and physicists, especially condensed matter physicists, that's what we do,” says JQI Fellow Maissam Barkeshli, a theoretical physicist who has applied his expertise to studying artificial neural networks. “We study complex systems, and then we try to tease out interesting, qualitatively robust behavior. So neural network research is really within the purview of physicists.”

In a paper that Barkeshli and UMD graduate student Dayal Kalra shared at the Conference on Neural Information Processing Systems last year, the pair presented the results of their investigation of the impact of the learning rate—the size of steps that are made each time the network’s parameters are changed during training—on the optimization of a neural network. Their analysis predicted distinct behaviors for the neural network depending on the learning rate used.

“Our work focused on documenting and explaining some intriguing phenomena that we observed as we tuned the parameters of the training algorithm of a neural network,” Barkeshli says. “It is important to understand these phenomena because they deeply affect the ability of neural networks to learn complicated patterns in the data.”

Other similar questions in neural network research remain, including how the information is encoded in a network, and Barkeshli says that many of the open questions are likely to benefit from the perspectives and tools of physicists.

In addition to the field of machine learning benefitting from the tools of physics, it has also provided valuable tools for physicists to use in their research. Similar to the diverse uses of AI to play board games, create quirky images and write emails, the applications of machine learning have taken many forms in physics research.

“The laureates’ work has already been of the greatest benefit,” says Ellen Moons, Chair of the Nobel Committee for Physics. “In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties.”

Neural networks can also be useful for physicists who need to identify relevant data so they can focus their attention effectively. For instance, the scientific background document that was shared as part of the prize announcement cites the use of neural networks in the analysis of data from the IceCube neutrino detector at the South Pole. The project was a collaboration of many researchers, including UMD physics professors Kara Hoffman and Gregory Sullivan and UMD physics assistant professor Brian Clark, that produced the first neutrino image of the Milky Way.

Neutrinos are a group of subatomic particles that are notoriously difficult to detect since they have little mass and no electrical charge, which makes interactions uncommon (only about one out of 10 billion neutrinos is expected to interact as it travels all the way through the earth). The lack of interactions means neutrinos can be used to observe parts of the universe where light and other signals have been blocked or deflected, but it also means researchers have to work hard to observe them. The IceCube detector includes a cubic kilometer of ice that neutrinos can interact with. When an interaction is observed, researchers must determine if it was actually an interaction with a neutrino interaction or another particle. They must also determine the direction the detected neutrino came from and whether it likely originated from a distant source or if it was produced by particle interactions that occurred in the earth’s atmosphere.

UMD postdoctoral researcher Stephen Sclafani worked on the project as a graduate student at Drexel University. He was an author of the paper sharing the results and was a lead in the project’s use of machine learning in their analysis. Neural networks helped Sclafani and his colleagues select the desired neutron interactions observed by the detector from data that had been collected over ten years. The approach increased their efficiency at identifying relevant events and provided them with approximately 30 times the amount of data to use in generating their neutrino map of our galaxy.

“Initially, as many people were, I was surprised by this year's prize selection,” says Sclafani. “But neural networks are currently revolutionizing the way we do physics. The breakthroughs of Hopfield and Hinton are responsible for many other results and are grounded in statistical physics.”

Some physics research applications of neural networks draw more directly on the physics foundation of neural networks. Earlier this year, JQI Fellow Charles Clark and JQI graduate student Ruizhi Pan proposed new tools to expand the use of machine learning in quantum physics research. They investigated a type of neural network called a restricted Boltzmann machine (RBM)—a variation of Boltzmann machines with additional restrictions on the networks. Their research returned to the spin description of the network and investigated how well various numbers of nodes can do at approximating the state that results from the spin interactions of many quantum particles.

"We, and many others, thought that the RBM framework, applied by 2024 Nobel Physics Laureate Geoffrey Hinton to fast learning algorithms about 20 years ago, might offer advantages for solving problems of quantum spin systems," says Clark. "This proved to be the case, and the research on quantum models using the RBM framework is an example of how advances in mathematics can lead to unanticipated developments in the understanding of physics, as was the case for calculus, linear algebra, and the theory of Hilbert spaces."

There are numerous additional ways that neural networks are also being developed into valuable tools for physics research, and this year’s Nobel Prize in physics celebrates that contribution as well as its roots in the field.

For more information about the prize winners and their research that the award recognizes, see the press releases from the Royal Swedish Academy of Sciences.

Original story by Bailey Bedford:  jqi.umd.edu/news/nobel-prize-celebrates-interplay-physics-and-ai

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https://jqi.umd.edu/news/attacking-quantum-models-ai-when-can-truncated-neural-networks-deliver-results

https://jqi.umd.edu/news/neural-networks-take-quantum-entanglement

High Altitude Water Cherenkov Observatory Sheds Light on Origin of Galactic Cosmic Rays

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Published: Wednesday, September 25 2024 00:01

HAWC observes Ultra-High Energy gamma rays confirming Galactic Center as a source of Ultra-High Energy cosmic ray protons in the Milky Way

The High-Altitude Water Cherenkov (HAWC) Observatory, located on the slopes of the Sierra Negra volcano in Mexico, has achieved a groundbreakingHAWC by Jordan Goodman milestone with the first detection of gamma rays exceeding 100 TeV from the Galactic Center. This provides strong evidence for the existence of a PeVatron—a source capable of accelerating particles to energies of up to petaelectronvolts (PeV), which is over one hundred times the energy achieved by particle accelerators on Earth. PeVatrons have long intrigued astrophysicists due to their role in high-energy cosmic particle acceleration. While magnetic fields in space deflect charged particles, making it difficult to pinpoint their origin, gamma rays offer a direct view into these extreme acceleration processes, shedding light on their origins within our Galaxy.

The figure shows the best-fit spectrum of the source detected by HAWC, and the resulting spectrum after subtracting two known point sources that are coincident with ours. This resulting spectrum corresponds to the diffuse emission from the Galactic Center and it shows that it extends without evidence of a cutoff to over 100 TeV.The center of our Galaxy hosts a range of remarkable astrophysical objects, including Sagittarius A*, a supermassive black hole with a mass approximately four million times that of the Sun. It is surrounded by neutron stars, white dwarfs stripping material from nearby stars, and extremely hot, dense gas clouds with temperatures reaching millions of degrees. These environments provide ideal conditions for the interaction of PeV protons, freshly accelerated by the suspected PeVatron, with protons from the surrounding matter. These  interactions produce neutral pions, which quickly decay into gamma rays, contributing to the observed photon spectrum between 6 and 114 TeV. The lack of a spectral cutoff strongly suggests a hadronic origin for these gamma rays. Furthermore, the short escape time of the PeV protons suggests the need for a quasi-continuous injection of particles into the gas to maintain the observed gamma-ray production.

The dense interstellar gas between Earth and the Galactic Center obscures this intriguing region from optical observation. Thus, the findings from the HAWC Gamma-Ray observatory provide valuable insights into the high-energy processes occurring at the core of our Galaxy, shedding light on the origin of Galactic cosmic rays.

The particle astrophysics group at UMD plays an important role in the operations of the HAWC Observatory. This particular study was led by Sohyoun Yun-Cárcamo (Ph.D. candidate), Dezhi Huang (postdoc), and Jason Fan (former UMD Ph.D. student). Other UMD authors of this paper are Jordan Goodman, Andrew Smith, Kristi Engel, Elijah Willox, and Zhen Wang.

Publication: https://iopscience.iop.org/article/10.3847/2041-8213/ad772e

UMD Physicists Advance NASA’s Mission to ‘Touch the Sun’

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Published: Wednesday, September 11 2024 05:24

Those who say there’s “nothing new under the sun” must not know about NASA’s Parker Solar Probe mission. Since its launch in 2018, this spacecraft has been shedding new light on Earth’s sun—and University of Maryland physicists are behind many of its discoveries.

At its core, the Parker Solar Probe is “on a mission to touch the sun,” in NASA’s words. It endures extreme conditions while dipping in and out of the corona—the outermost layer of the sun’s atmosphere—to collect data on magnetic fields, plasma and energetic particles. The corona is at least 100 times hotter than the sun’s surface, but it’s no match for the spacecraft’s incredible speed and carbon composite shield, which can survive 2,500 degrees Fahrenheit. Last year, the spacecraft broke its own record for the fastest object ever made by humans. Parker Solar Probe (courtesy of NASA)

This engineering feat was built to solve solar mysteries that have long confounded scientists: What makes the sun’s corona so much hotter than its surface, and what powers the sun’s supersonic wind? These questions aren’t just of interest to scientists, either. The solar wind, which carries plasma and part of the sun’s magnetic field, can cause geomagnetic storms capable of knocking out power grids on Earth or endangering astronauts in space.

To better understand these mechanisms, the Parker Solar Probe will attempt its deepest dive into the corona on December 24, 2024, with plans to come within 3.9 million miles of the sun’s surface. Researchers hope its findings will help them predict space weather with greater accuracy and frequency in the future.

James Drake, a Distinguished University Professor in UMD’s Department of Physics and Institute for Physical Science and Technology (IPST), is helping to move the needle closer to that goal as a member of the Parker Solar Probe research team.

“This mission is what's called a discovery mission, and with a discovery mission we can never be sure what we're going to find,” Drake said. “But of course, everybody is most excited about the data that will come from the Parker Solar Probe getting very close to the sun because that will reveal new information about the solar wind.” 

Reconnecting the dots

Drake and Marc Swisdak, a research scientist in UMD’s Institute for Research in Electronics & Applied Physics (IREAP), have been involved with this mission since its inception. The researchers were asked to join because of their expertise in magnetic reconnection, a process that occurs when magnetic fields pointing in opposite directions cross-connect, releasing large amounts of magnetic energy.

Before the Parker Solar Probe, it was known that magnetic reconnection could produce solar flares and coronal mass ejections that launch magnetic energy and plasma out into space. However, this mission revealed just how important magnetic reconnection is to so many other solar processes. 

Early Parker Solar Probe data showed that magnetic reconnection was happening frequently near the equatorial plane of the heliosphere, the giant magnetic bubble that surrounds the sun and all of the planets. More specifically, this activity was observed in the heliospheric current sheet, which divides sectors of the magnetic field that point toward and away from the sun. 

“That was a big surprise,” Drake said of their findings. “Every time the spacecraft crossed the heliospheric current sheet, we saw evidence for reconnection and the associated heating and energization of the ambient plasma.”

In 2021, the Parker Solar Probe made another unexpected discovery: the existence of switchbacks in the solar wind, which Drake described as “kinks in the magnetic field.” Characterized by sharp changes in the magnetic field’s direction, these switchbacks loosely trace the shape of the letter S.

“No one predicted the switchbacks—at least not the magnitude and number of them—when Parker launched,” Swisdak said. 

To explain this odd phenomenon, Drake, Swisdak and other collaborators theorized that switchbacks were produced by magnetic reconnection in the corona. While the exact origin of those switchbacks hasn’t been definitively solved, it prompted UMD’s team to take a closer look at magnetic reconnection, especially its role in driving the solar wind.

“The role of reconnection has gone from something that was not necessarily that significant at the beginning to a major component of the entire Parker Solar Probe mission,” Drake said. “Because of our group's expertise on the magnetic reconnection topic, we have played a central role in much of this work.”

Last year, Drake and Swisdak co-authored a study with other members of the Parker science team that explained how the sun’s fast wind—one of two types of solar wind—can surpass 1 million miles per hour. They once again saw that magnetic reconnection was responsible, specifically the kind that occurs between open and closed magnetic fields, known as interchange reconnection.

To test their theories about solar activity, the UMD team also uses computer simulations to try to reproduce Parker observations. 

“I think that one of the things that convinced people that magnetic reconnection was a major driver of the solar wind is that our computer simulations were able to produce the energetic particles that they saw in the Parker Solar Probe data,” Drake said. 

As part of his dissertation, physics Ph.D. student Zhiyu Yin built the simulation model that is used to see how particles might accelerate during magnetic reconnection.

“Magnetic reconnection is very important, and our simulation model can help us connect theory with observations,” Yin said. “I'm really honored to be part of the Parker Solar Probe mission and to contribute to its work, and I believe it could lead to even more discoveries about the physics of the sun, giving us the confidence to take on more projects in exploring the solar system and other astrophysical realms.”

Swisdak explained that simulations also help researchers push past the limitations of space probes.

“Observations are measuring something that is real, but they’re limited. Parker can only be in one place at one time, it has a limited lifetime and it’s also very hard to run reproducible experiments on it,” Swisdak said. “Computations have complementary advantages in that you can set up a simulation based on what Parker is observing, but then you can tweak the parameters to see the bigger picture of what we think is happening.”

‘Things no one has seen’

There are still unsolved mysteries, including the exact mechanisms that produce switchbacks and drive the solar wind, but researchers hope that the Parker Solar Probe will continue to answer these and other important questions. The sun is currently experiencing more intense solar flares and coronal mass ejections than usual, which could yield new and interesting data on the mechanisms that energize particles in these explosive events.

This research also has wider relevance. Studying the solar wind can help scientists understand other winds throughout the universe, including the powerful winds produced by black holes and rapidly rotating stars called pulsars. Winds can even offer clues about the habitability of planets because of their ability to deflect harmful cosmic rays, which are forms of radiation.

“One of the reasons why the solar wind is important is because it protects planetary bodies from these very energetic particles that are bouncing around the galaxy,” Drake said. “If we didn't have that solar wind protecting us, it's not totally clear whether the Earth would have been a habitable environment.”

As the spacecraft prepares for its December descent into the sun, the UMD team is eager to see what the new observations will reveal.

“One of the nice things about being involved with this mission is that it’s a chance to make observations of things that no one has seen before. It lets you go into a new regime of space and say, ‘Alright, we thought things would look this way, and inevitably they don't,’” Swisdak said. “The ability to get close enough to the sun to see where the solar wind starts and where coronal mass ejections begin—and being able to take direct measurements of those phenomena—is really exciting.”