This March, scientists from around the world gathered in LaThuile, Italy, for the 53rd annual Recontres de Moriond conference, one of the longest running and most prestigious conferences in particle physics. This conference is broken into two distinct weeks, with the first week usually covering electroweak physics and the second covering processes involving quantum chromodynamics. Fermilab and the LHC Physics Center were well represented at the conference.
Data science is one of the world’s fastest growing industries, and as a consequence, a large ecosystem of software tools to enable data mining at ever increasing scales has emerged. Data processing campaigns have distilled the more than 100 petabytes of raw data produced by the CMS experiment to around 10 terabytes. Even this reduced data is still unwieldy for HEP researchers to analyze. Fermilab researchers is currently leading an effort using novel approaches to complete two full CMS analyses.
With the warmth of holiday cheer in the air, some physicists decided to hit the pub after a conference in December 2014 and do what many physicists tend to do after work: keep talking about physics. That evening’s topic of conversation: dark energy particles. The chat would lead to a new line of investigation at the Large Hadron Collider at CERN. Every second, the universe grows a little bigger. Scientists are using the LHC to try to find out why.
Fermilab has enormous, decades-long experience in building silicon detectors. Thanks to an exceptional, cooperative team with levels of experience and capabilities that lead the world, we were able to quickly put together a design for a tracking system that could be used for muon tomography — using muons to see inside solid objects, similar to how we use X-rays. The system won an R&D 100 Award.
From Jan. 14-18, more than 100 graduate students, postdocs and faculty from around the world came to Fermilab to participate in the school. Students are coached by CMS experts in hands-on sessions covering topics such as particle identification, statistics, machine learning and triggering. They also learn how to write high-quality scientific publications.
Science fiction sometimes borrows from science fact. In the movie “Spider-Man: Into the Spider-Verse,” the writers blended multiverses and alternate realities with the real-world Large Hadron Collider and the Compact Muon Solenoid. In this 6-minute video, Fermilab’s Don Lincoln gives you the low-down on what is real and what is made up.
Machine learning is revolutionizing data analysis. Recent leaps in driverless car navigation and the voice recognition features of personal assistants are possible because of this form of artificial intelligence. As data sets in the Information Age continue to grow, companies are building tools that make machine learning faster and more efficient. Fermilab is taking cues from industry to improve their own “big data” processing challenges.
The Distinguished Researchers program has been a defining feature of the LPC at Fermilab for the last eight years. The 21 CMS physicists selected as LPC Distinguished Researchers, 18 juniors and three seniors, are accomplished individuals at different stages of their careers. This program provides resources to help strengthen and expand their research programs. This year’s Distinguished Researchers were selected by the LPC Management Board in a competitive process.
The success of a scientific experiment can be measured in a few ways, but perhaps the best one is number of scientific publications. Even there, there are different ways of counting them, but a good method is the number of publications submitted per year. And in 2018, CMS had a banner year in terms of scientific output. The CMS collaboration broke a record, with 141 scientific papers submitted to peer reviewed journals. That’s nearly three each week. The previous record in high-energy physics was also held by CMS. In 2017, the CMS experiment submitted 132 papers.