A Transition Edge Sensor Operated in Coincidence with a High Sensitivity Athermal Phonon Sensor for Photon Coupled Rare Event Searches
arXiv:2408.11158 · doi:10.1063/5.0234265
Abstract
Experimental searches for axions or dark photons that couple to the standard model photon require photosensors with low noise, broadband sensitivity, and near zero backgrounds. Here, we introduce an experimental architecture, in which a small photon sensor, in our case a Transition Edge Sensor (TES) with a photon energy resolution meV, is colocated on the same substrate as a large high sensitivity athermal phonon sensor (APS) with a phonon energy resolution meV. We show that single 3.061 eV photons absorbed in the photon-sensing TES deposit 35\% of their energy in the electronic system of the TES, while 26\% of the photon energy leaks out of the photon-sensing TES during the downconversion process and becomes absorbed by the APS. Backgrounds, which we associate with the broadly observed ``low energy excess'' (LEE), are observed to be largely coupled to either the TES (``singles'' LEE), or phonon system, (``shared'' LEE). At high energies, these backgrounds can be efficiently discriminated from TES photon absorption events, while at low energies, their misidentification as photon events is well modeled. With significant sensitivity improvements to both the TES and APS, this coincidence technique could be used to suppress backgrounds in bosonic dark matter searches down to energies near the superconducting bandgap of the sensor.
5 pages, 5 figures
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Cited by in corpus (5)
- First Limits on Light Dark Matter Interactions in a Low Threshold Two Channel Athermal Phonon Detector from the TESSERACT Collaboration
- Low Energy Backgrounds and Excess Noise in a Two-Channel Low-Threshold Calorimeter
- Low-Energy Backgrounds in Solid-State Phonon and Charge Detectors
- Spontaneous generation of athermal phonon bursts within bulk silicon causing excess noise, low energy background events and quasiparticle poisoning in superconducting sensors
- Binary Classification of Light and Dark Time Traces of a Transition Edge Sensor Using Convolutional Neural Networks