The Standard Model of particle physics is a very well-tested theory that describes the fundamental particles and their interactions. However, it does have several key limitations. For example, it doesn’t account for dark matter or why neutrinos have masses.

One of the main aims of experimental particle physics at the moment is therefore to search for signs of new physical phenomena beyond the Standard Model.

Finding something new like this would point us towards a better theoretical model of particle physics: one that can explain things that the Standard Model isn’t able to.

These searches often involve looking for rare or unexpected signals in high-energy particle collisions such as those at CERN’s Large Hadron Collider (LHC).

In a new paper published by the CMS collaboration, a new analysis method was used to search for new particles produced by proton-proton collisions at the at the LHC.

These particles would decay into two jets, but with unusual internal structure not typical of known particles like quarks or gluons.

The researchers used advanced machine learning techniques to identify jets with different substructures, applying various anomaly detection methods to maximise sensitivity to unknown signals.

Unlike traditional strategies, anomaly detection methods allow the AI models to identify anomalous patterns in the data without being provided specific simulated examples, giving them increased sensitivity to a wider range of potential new particles.

This time, they didn’t find any significant deviations from expected background values. Although no new particles were found, the results enabled the team to put several new theoretical models to the test for the first time.  They were also able to set upper bounds on the production rates of several hypothetical particles.

Most importantly, the study demonstrates that machine learning can significantly enhance the sensitivity of searches for new physics, offering a powerful tool for future discoveries at the LHC.

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First proposed in 1964, the Higgs boson plays a key role in explaining why many elementary particles of the Standard Model have a rest mass. Many decades later the Higgs boson was observed in 2012 by the ATLAS and CMS collaborations at the Large Hadron Collider (LHC), confirming the decades old prediction.  

This discovery made headline news at the time and, since then, the two collaborations have been performing a series of measurements to establish the fundamental nature of the Higgs boson field and of the quantum vacuum. Researchers certainly haven’t stopped working on the Higgs though. In subsequent years, a series of measurements have been performed to establish the fundamental nature of the new particle. 

One key measurement comes from studying a process known as off-shell Higgs boson production. This is the creation of Higgs bosons with a mass significantly higher than their typical on-shell mass of 125 GeV.  This phenomenon occurs due to quantum mechanics, which allows particles to temporarily fluctuate in mass.

This kind of production is harder to detect but can reveal deeper insights into the Higgs boson’s properties, especially its total width, which relates to how long it exists before decaying. This in turn, allows us to test key predictions made by the Standard Model of particle physics.

Previous observations of this process had been severely limited in their sensitivity. In order to improve on this, the ATLAS collaboration had to introduce a completely new way of interpreting their data (read here for more details).

They were able to provide evidence for off-shell Higgs boson production with a significance of 2.5𝜎 (corresponding to a 99.38% likelihood), using events with four electrons or muons, compared to a significance of 0.8𝜎 using traditional methods in the same channel.

The results mark an important step forward in understanding the Higgs boson as well as other high-energy particle physics phenomena.

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