CERN to supercharge world’s most powerful atom smasher to hunt new physics

Team to construct vital detector parts and analysis tools for High-Luminosity-LHC upgrade.

High energy particles collide at CERN’s Large Hadron Collider (LHC) to create rarer, more massive particles like the Higgs boson, which was found there in 2012. Nevertheless, a great deal remains to be discovered about this particle, whose characteristics set it apart from all other fundamental particles.

To fulfill more advanced tests, the world’s largest and most potent accelerator is set to get a significant upgrade. The US Department of Energy’s SLAC National Accelerator Laboratory is poised to play a crucial role.

According to SLCA, its experimentalists and theorists will work together to build essential detector parts and data analysis tools for a High-Luminosity-LHC (HL-LHC) upgrade. The advancements are slated to help researchers probe the Higgs boson and explore physics beyond the Standard Model.

The LHC works by lining miles-long and pipe-like “tracks” with superconducting magnets, which can bend, direct, and accelerate a beam of high-energy particles to nearly the speed of light.

Crucial role in HL-LHC detector upgrade

The LHC is being upgraded to the HL-LHC to measure self-coupling for the first time and investigate the properties of the Higgs boson. The luminosity of the LHC, which represents the quantity of collisions over a period of time, will rise as a result of the HL-LHC. More collisions increase the likelihood that uncommon particles will emerge and be detected by the accelerator’s detectors in addition to the number of Higgs bosons.

SLAC must finalize the internal assembly of a crucial detector component before the HL-LHC commences data collection in 2029.

The rapid succession of billions of collisions anticipated at the HL-LHC presents a challenge for the ATLAS detector in effectively detecting and distinguishing particles generated in these collisions. The detector faces difficulty identifying significant events amidst the dense accumulation of collisions.

According to researchers, upgrading the two deepest layers of ATLAS, which are closest to the collisions, is the responsibility of SLAC, which will be done in collaboration with thirteen other national laboratories and institutions.

The Inner Tracker (ITk), whose tiny silicon sensors, or pixels, will trace the pathways of post-collision particles, will replace the heart of ATLAS.

“ITk is absolutely crucial to the upgrade and fits exactly within that priority of P5. Whereas most systems being delivered to CERN will be assembled there, SLAC is delivering the whole, fully assembled pixel inner system detector,” said Philippe Grenier, level 2 manager of the ITk upgrade and lead scientist at SLAC, in a statement.

Furthermore, the High Granularity Timing Detector (HGTD), a new subdetector that will provide exact timing data to ITk in order to help prevent pileup situations, is also been proposed by the ATLAS group at SLAC.

Enhancing data analysis and trigger systems

The team at SLAC will later assist in analyzing ATLAS data once the HL-LHC is operational. Researchers are now tasked with expanding and improving the use of AI to evaluate data from the HL-LHC more effectively. Algorithms for machine learning are frequently employed to extract patterns from detector data.

To reconstruct events more accurately, the researchers must develop advanced machine learning algorithms that can reduce pileup interactions even after upgrading ATLAS.

“The work on improved HL-LHC reconstruction algorithms will be key in enabling the full physics potential of the HL-LHC, and is synergistic with the detector work,” said Schwartzman.

Additionally, the ATLAS group is working to integrate AI into additional data collection phases, such as the trigger system. There will be five to seven billion collisions produced by the HL-LHC each second.

According to researchers, ATLAS’s trigger system rapidly but roughly reconstructs the particles created during these occurrences to select which events to retain and discard.

The ATLAS group is collaborating with the Technology Innovation Directorate at SLAC to figure out how to integrate ultrafast AI into the trigger hardware.

“One of the things we’re excited about is putting AI right on these electronics to run at the microsecond or even nanosecond level to help with those pattern recognition algorithms, said Michael Kagan, a lead staff scientist at SLAC working on these pattern recognition algorithms.

The team is also pioneering rapid machine learning tools for ATLAS’s trigger, diverging from traditional event selection. The new algorithm flags anomalies within data, potentially unveiling physics beyond the Standard Model, even events dismissed by the trigger system.

According to researchers, the approach holds promise beyond HL-LHC, potentially aiding applications like self-driving cars and managing data influx at facilities like SLAC’s LCLS-II. Fast ML’s adaptability hints at broader scientific and technological implications beyond high-energy physics research.

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