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DTSTART:20250101T000000
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DTSTART;TZID=UTC:20251002T160000
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CREATED:20250919T213846Z
LAST-MODIFIED:20250919T213846Z
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SUMMARY:Speaker: Xinran Li (LBNL) - Title: Athermal phonon detectors for light dark matter search\, go small and go large
DESCRIPTION:Research Progress Meeting \nDate: October 2\, 2025 \nTime: 4:00- 5:00 pm \nLocation: Sessler Conference Room- 50A-5132 [In-Person and HYBRID]  \nSpeaker: Xinran Li (LBNL) \nTitle: Athermal phonon detectors for light dark matter search\, go small and go large \nAbstract: Light dark matter below 100MeV could only deposit sub-eV energies in detectors\, which is invisible to traditional solid state ionizing detectors. Athermal phonon detectors that use superconducting sensors to read out the phonon signals from the target crystals have the potential to achieve sub-eV energy threshold.\nAs a member of the TESSERACT collaboration\, I work on the development of transition edge sensors (TESs). Our recent DM search achieved 1.5eV threshold and extended the constraint on DM mass down to 44MeV with a 0.23g silicon detector. Our latest understanding of the stress-induced low energy excess events suggests that an even smaller target could help us achieve sub-eV threshold.\nOn the other hand\, to go large\, envisioning the next generation 10~100g scale large light dark matter detector\, kinetic inductance devices (KIDs) have the advantages of being easily scalable through multiplexing. I lead the effort to develop Hafnium (Hf) low critical temperature (Tc) KIDs for phonon sensing. The internal energy resolution of Hf KID is compatible to TESs\, while improving phonon collection efficiency is challenging. Several paths forward are defined and being actively pursued\, including quasiparticle trapping\, phonon blocking\, and parametric amplification. \nhttps://lbnl.zoom.us/j/98854322464?pwd=K2tKUm1VZjRlV1J5RHE3cXdHQzRxdz09 \nMeeting ID: 988 5432 2464\n\nPasscode: 142239
URL:https://rpm.physics.lbl.gov/event/speaker-xinran-li-lbnl-title-athermal-phonon-detectors-for-light-dark-matter-search-go-small-and-go-large/
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