Die Forschungsaktivitäten konzentrieren sich auf die Hochenergiephysik, insbesondere die Suche nach Higgs-Bosonen und Higgsinos am Large Hadron Collider. Parallel dazu werden experimentelle Arbeiten zur Coulomb-Kristallisation hochgeladener Ionen in lasergekühlten Matrizen durchgeführt. Die Methodik umfasst theoretische Modellierung, Computersimulationen und spektroskopische Analyseverfahren.
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Prof. Dr. Cigdem Issever
HU-FIS-Profil ↗Förderer: Horizon 2020: ERC Advanced Grant Zeitraum: 06/2019 - 05/2025 Projektleitung: Prof. Dr. Cigdem Issever
Journal of High Energy Physics · DOI
A bstract This paper presents two searches for the electroweak production of higgsinos with compressed mass spectra using 140 fb − 1 of $$ \sqrt{s}=13 $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> <mml:mo>=</mml:mo> <mml:mn>13</mml:mn> </mml:math> TeV proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider. Events are required to feature an energetic jet, large missing transverse momentum, and at least one low-momentum charged particle that serves as a candidate higgsino decay product. In the first search, targeting higgsino mass splittings in the range of 0.3–1 GeV, the higgsinos are expected to predominantly decay into pions that are identified as low-momentum charged particles with large transverse impact parameters due to the long higgsino lifetime ( cτ ≈ 𝒪(0 . 1–10 mm)), and neural networks are used to discriminate between signal and background processes. The second search targets larger mass splittings in the range of 1–3 GeV, where the higgsinos are expected to decay promptly into low-momentum leptons, one of which is identified by dedicated low-momentum electron or muon taggers based on neural networks utilising tracking and calorimeter information. No significant excess above the Standard Model prediction is observed in either search and the results are interpreted within simplified models, to set lower limits on the masses of the higgsino-like charginos and neutralinos. Together, these searches exclude chargino masses below 126 GeV at 95% confidence level for mass splittings between the chargino and lightest neutralino in the range of 0.3–2 GeV. This represents the first ATLAS constraints in a portion of this parameter space and surpasses the limits previously set by other experiments.
Physical review. A/Physical review, A · DOI
We report the sympathetic cooling and Coulomb crystallization of xenon highly charged ions (HCIs) with laser-cooled <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:msup> <a:mrow> <a:mi>Ca</a:mi> </a:mrow> <a:mo>+</a:mo> </a:msup> </a:math> ions. The HCIs are produced in a compact electron beam ion trap, then charge selected, decelerated, and finally injected into a cryogenic linear Paul trap. There, they are captured into <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"> <b:mmultiscripts> <b:mi>Ca</b:mi> <b:none/> <b:mo>+</b:mo> <b:mprescripts/> <b:none/> <b:mn>40</b:mn> </b:mmultiscripts> </b:math> Coulomb crystals, and cocrystallized within them, causing dark voids in their fluorescence images. Fine control over the number of trapped ions and HCIs allows us to realize mixed-species crystals with arbitrary ordering patterns. By investigating <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"> <c:mrow> <c:msup> <c:mrow> <c:mi>Xe</c:mi> </c:mrow> <c:mrow> <c:mi>q</c:mi> <c:mo>+</c:mo> </c:mrow> </c:msup> <c:mo>−</c:mo> <c:msup> <c:mrow> <c:mi>Ca</c:mi> </c:mrow> <c:mo>+</c:mo> </c:msup> </c:mrow> </c:math> strings, we confirm the HCI charge states, measure their lifetime, and characterize the mixed-species motional modes. Our system effectively combines the established quantum control toolbox for <d:math xmlns:d="http://www.w3.org/1998/Math/MathML"> <d:msup> <d:mrow> <d:mi>Ca</d:mi> </d:mrow> <d:mo>+</d:mo> </d:msup> </d:math> with the rich set of atomic properties of Xe highly charged ions, providing a resourceful platform for optical frequency metrology, searches for signatures of new physics, and quantum information science.