Geneva – Physicists at the ATLAS experiment at the Large Hadron Collider (LHC) have achieved a significant milestone in the hunt for supersymmetry (SUSY), surpassing previous limitations set by the Large Electron–Positron (LEP) collider experiments over two decades ago. The new results focus on the search for compressed higgsinos, hypothetical particles that could explain the mass of the Higgs boson and potentially account for the universe’s dark matter.
Supersymmetry proposes that every known particle has a heavier “superpartner.” Higgsinos are the superpartners of the Higgs boson and finding evidence of their existence is a key goal in particle physics. However, these particles are not expected to appear in isolation. instead, they mix to form neutralinos and charginos, making their detection incredibly challenging. The ATLAS collaboration’s latest findings represent a crucial step forward in exploring a particularly difficult region of the parameter space – the “compressed mass spectrum” – where the mass difference between these particles is small.
The search for these elusive particles has been hampered by the difficulty in reconstructing and identifying them when their masses are close together. To overcome these hurdles, the ATLAS team employed advanced machine-learning techniques to analyze data from the LHC’s Run-2 dataset, focusing on the pair production of the lightest higgsino-like states: a chargino (χ̃±1) and two neutralinos (χ̃01 and χ̃02). The success of this analysis hinges on precisely determining the mass splitting between these states, as it directly impacts how they would appear in the experiment.
New Techniques Reveal Hidden Signals
The ATLAS collaboration utilized two distinct search strategies, each optimized for different mass-splitting regimes. The first, a “displaced track” search, targeted mass splittings between 0.3 and 1 GeV. In these scenarios, the chargino decays into a neutralino and a low-momentum charged pion, creating a track that appears slightly offset from the original collision point. To identify these subtle signals, researchers developed dedicated neural networks, one analyzing overall event characteristics and the other focusing specifically on the displaced track’s properties.
The second search, dubbed “one-lepton-one-track” (1L1T), focused on larger mass splittings, between 1 and 3 GeV. Here, the heavier neutralino decays into a lighter neutralino and two low-momentum leptons, with one lepton potentially evading standard detection algorithms. To overcome this, the team created new neural-network-based algorithms capable of identifying lepton-like tracks with very low momenta – as low as 0.5 GeV for electrons and 1 GeV for muons. A parameterized neural network was then used to enhance signal sensitivity by focusing on kinematic features dependent on the mass splitting.
Extending the Boundaries of Higgsino Searches
The analysis, consistent with predictions from the Standard Model, has established new limits on higgsino masses at the 95% confidence level. The 1L1T search excluded scenarios with a mass difference between the chargino and the lightest neutralino between approximately 0.8 and 2 GeV, extending previous LEP limits to a chargino mass of 132 GeV for a mass splitting of 1.8 GeV. The displaced track search extended previous ATLAS exclusion limits by about 30 GeV, reaching chargino masses up to 199 GeV for a mass splitting of 0.6 GeV. Crucially, both searches excluded chargino masses below 126 GeV within the targeted mass splitting ranges, effectively surpassing all previous LEP results. The ATLAS collaboration has now established constraints across the entire range of higgsino mass splittings.
This progress is a significant step in the broader search for supersymmetry, a theory that, if proven, could resolve several outstanding mysteries in particle physics, including the nature of dark matter. The upcoming Run-3 dataset from the LHC, combined with continued advancements in analysis techniques, promises to further refine these searches and potentially unlock new discoveries beyond the Standard Model.
What comes next for the ATLAS collaboration is a deeper dive into the Run-3 data, which will provide a larger sample size and increased collision energy. These improvements will allow physicists to probe even smaller mass splittings and potentially uncover evidence of supersymmetry. The continued development of sophisticated machine-learning algorithms will also be crucial in teasing out subtle signals from the background noise.
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