How is the Force like dark matter?

For the first time, the Fermilab Office of Education and Public Outreach hosted a series of a new kind of professional development workshop — one where teachers played the roles not only of educator, but also student and curriculum designer. The events were aimed at providing new knowledge, tools and techniques for educators passionate about spreading STEM fields.

In total, 14 people attended the first workshop on Oct. 10, which was developed by the Biological Sciences Curriculum Study, or BSCS, a nonprofit organization focused on science teaching and learning. Participants included education program leaders from both the Fermilab Education Office and Argonne National Laboratory, along with teachers who serve as instructors for Fermilab teacher workshops.

“I thoroughly enjoyed the workshop,” said Milton Harris, teacher at Clarendon Hills Middle School and program instructor at the Fermilab Beauty & Charm program, an activity-based science program for middle schoolers. “What I liked most was the focus on transformative teaching and learning as it relates to the NGSS.”

NGSS, or Next Generation Science Standards, is a multistate agreement to provide a benchmark for science education in schools.

The first of four sessions focused on developing and using models — one of the eight NGSS science and engineering practices.

During the workshop, representatives embedded participants in the workshop from multiple angles.

“The presenter really made us think about and envision what this would look like from the perspective of a student, a teacher and a professional development provider,” Harris said.

This first session was also designed to provide a platform for the attendees to discuss NGSS practices, as well as strategies for the most effective professional development. Further sessions were delivered each day of the week, until Oct. 13.

“The workshop was a unique opportunity for us to bring together our teacher leaders and our educational program leaders,” said Susan Dahl of the Fermilab Office of Education and co-organizer of the events. “They are experiencing NGSS learning as both a learner and as a teacher and, in doing so, can consider how they as professional development planners and providers can develop experiences for the teachers in our workshops and the students on our field trips.”

The experience for Fermilab’s teacher leaders was funded by the Fermilab Friends for Science Education, a not-for-profit organization that supports science education programs at Fermilab.

Fermilab’s Office of Education provides educational resources and support to a wide array of audiences, from the public to teachers to laboratory staff, in the pursuit of developing the STEM workforce and stimulating science literacy. Working with external institutes also engenders successful professional development.

“By including our education colleagues from Argonne, we can establish potential relationships between our areas of expertise,” Dahl said, “and see possibilities to work together to complement our work.”

Editor’s note: This release was issued by the international Interactions Collaboration, a group of science communicators representing the world’s particle physics laboratories. Fermilab is a member of this collaboration and is sponsoring several Dark Matter Day events. 

The world will soon be celebrating the hunt for the universe’s most elusive matter in a series of Dark Matter Day (www.darkmatterday.com) events planned in over a dozen countries.

The events, planned on and around the formally recognized day on Oct. 31, 2017, will engage the public in discussions about dark matter, which together with dark energy makes up about 95 percent of the mass and energy in our universe. Although we know through its gravitational effects that dark matter greatly dwarfs the visible matter in our universe, we know little about it.

How can I get involved? 

Universities, institutions, science centers and individuals have already announced Dark Matter Day-themed events in Austria, Brazil, Canada, Chile, Colombia, France, Germany, Italy, Mexico, Peru, Spain, Sweden, Switzerland, and in the U.K. and U.S., with more events on the way. There are also several online events planned if you can’t be there in person.

• View the full events list, a country-sorted list, or a list of online or virtual events.
• There still time to organize your own event. Check out our Event Starter Kit.
• Also, you can help promote dark matter day to your friends, colleagues, and social network. Join an online campaign (via Thunderclap) to get the word out about Dark Matter Day, and use #darkmatterday in your social posts.

What is dark matter?

Dark matter explains how galaxies spin at a faster-than-expected rate without coming apart. Scientists know from these and other space observations that there is “missing” mass — something we can’t see — that makes up an estimated 95 percent of the total mass and energy of the universe. So a big part of the universe is largely unknown to us.

Finding out what dark matter is made of is a pressing pursuit in physics. We don’t yet know if it’s composed of undiscovered particles or whether it requires some other change in our understanding of the universe’s laws of physics. A host of innovative experiments are searching for the source of dark matter using different types of tools, such as mile-deep detectors, powerful particle beams, and space-based and ground-based telescopes.

Why is there a day dedicated to dark matter?

Revealing dark matter’s true nature will tell us a lot about the origins, evolution and overall structure in the universe and will reshape our understanding of physics.

Dark Matter Day events are intended to educate the public about the importance of learning all we can about dark matter to develop a fuller picture of the unseen universe. Focusing more brain power and scientific resources on dark matter’s mysteries could lead to new ideas and new discoveries.

Who is behind Dark Matter Day?

This first-ever Dark Matter Day campaign was conceived by the Interactions Collaboration, a group of science communicators representing the world’s particle physics laboratories. The collaboration also runs the www.darkmatterday.com website as a resource for people who want to host or attend local Dark Matter Day events.

Need more help?

Members of the Interactions Collaboration want you to be a part of Dark Matter Day. Please send an email to darkmatterday@interactions.org with any questions, comments or suggestions.

For a press contact in your region visit: http://www.darkmatterday.com/contacts

The Interactions Collaboration (Interactions.org) seeks to support the international science of particle physics and to set visible footprints for peaceful collaboration across all borders. The www.darkmatter.com website was developed and is jointly maintained by the Interactions Collaboration, whose members represent the world’s particle physics laboratories and institutions in Europe, North America, Asia, and Australia, with funding provided by science funding agencies from many nations.

Editor’s note: The following press release was issued by Caltech. Fermilab is part of a continuing collaboration on this work, pursuing quantum technology for new scientific applications and discoveries. Daniel Lidar, one of the co-authors on the paper referenced in the release, will give a Colloquium talk at Fermilab on Dec. 6 as part of the Near-Term Applications of Quantum Computing conference, which Fermilab will host Dec. 6 and 7. Read more about this application at the INQNET website. 

Researchers from Caltech and the University of Southern California (USC) report the first application of quantum computing to a physics problem. By employing quantum-compatible machine learning techniques, they developed a method of extracting a rare Higgs boson signal from copious noise data. Higgs is the particle that was predicted to imbue elementary particles with mass and was discovered at the Large Hadron Collider in 2012. The new quantum machine learning method is found to perform well even with small data sets, unlike the standard counterparts.

Despite the central role of physics in quantum computing, until now, no problem of interest for physics researchers has been resolved by quantum computing techniques. In this new work, the researchers successfully extracted meaningful information about Higgs particles by programming a quantum annealer — a type of quantum computer capable of running only optimization tasks — to sort through particle measurement data littered with errors. Caltech’s Maria Spiropulu, the Shang-Yi Ch’en professor of physics, conceived the project and collaborated with Daniel Lidar, pioneer of the quantum machine learning methodology and Viterbi professor of engineering at USC who is also a distinguished Moore scholar in Caltech’s Division of Physics, Mathematics and Astronomy.

The quantum program seeks patterns within a data set to tell meaningful data from junk. It is expected to be useful for problems beyond high-energy physics. The details of the program as well as comparisons to existing techniques are detailed in a paper published on Oct. 19 in the journal Nature.

A popular computing technique for classifying data is the neural network method, known for its efficiency in extracting obscure patterns within a data set. The patterns identified by neural networks are difficult to interpret, as the classification process does not reveal how they were discovered. Techniques that lead to better interpretability are often more error-prone and less efficient.

“Some people in high-energy physics are getting ahead of themselves about neural nets, but neural nets aren’t easily interpretable to a physicist,” said USC’s physics graduate student Joshua Job, co-author of the paper and guest student at Caltech. The new quantum program is “a simple machine learning model that achieves a result comparable to more complicated models without losing robustness or interpretability,” Job said.

With prior techniques, the accuracy of classification depends strongly on the size and quality of a training set, which is a manually sorted portion of the data set. This is problematic for high-energy physics research, which revolves around rare events buried in large amount of noise data.

“The Large Hadron Collider generates a huge number of events, and the particle physicists have to look at small packets of data to figure out which are interesting,” Job said.

The new quantum program “is simpler, takes very little training data, and could even be faster. We obtained that by including the excited states,” Spiropulu said.

Excited states of a quantum system have excess energy that contributes to errors in the output.

“Surprisingly, it was actually advantageous to use the excited states, the suboptimal solutions,” Lidar said. “Why exactly that’s the case, we can only speculate. But one reason might be that the real problem we have to solve is not precisely representable on the quantum annealer. Because of that, suboptimal solutions might be closer to the truth.”

Modeling the problem in a way that a quantum annealer can understand proved to be a substantial challenge that was successfully tackled by Spiropulu’s former graduate student at Caltech, Alex Mott, who is now at DeepMind.

“Programming quantum computers is fundamentally different from programming classical computers. It’s like coding bits directly. The entire problem has to be encoded at once, and then it runs just once as programmed,” Mott said.

Despite the improvements, the researchers do not assert that quantum annealers are superior. The ones currently available are simply “not big enough to even encode physics problems difficult enough to demonstrate any advantage,” Spiropulu said.

“It’s because we’re comparing a thousand qubits — quantum bits of information — to a billion transistors,” said Jean-Roch Vlimant, a postdoctoral scholar in high-energy physics at Caltech. “The complexity of simulated annealing will explode at some point, and we hope that quantum annealing will also offer speedup.”

The researchers are actively seeking further applications of the new quantum-annealing classification technique.

“We were able to demonstrate a very similar result in a completely different application domain by applying the same methodology to a problem in computational biology,” Lidar said.

“There is another project on particle-tracking improvements using such methods, and we’re looking for new ways to examine charged particles,” Vlimant said.

“The result of this work is a physics-based approach to machine learning that could benefit a broad spectrum of science and other applications,” Spiropulu said. “There is a lot of exciting work and discoveries to be made in this emergent cross-disciplinary arena of science and technology.”

This project was supported by the United States Department of Energy, Office of High Energy Physics, Research Technology, Computational HEP, and Fermi National Accelerator Laboratory as well as the National Science Foundation. The work was also supported by the AT&T Foundry Innovation Centers through INQNET (INtelligent Quantum NEtworks and Technologies), a program for accelerating quantum technologies.