First measurements of remoTES cryogenic calorimeters: easy-to-fabricate particle detectors for a wide choice of target materials

Abstract

Low-temperature calorimeters based on a readout via Transition Edge Sensors (TESs) and operated below $100$ mK are well suited for rare event searches with outstanding resolution and low thresholds. We present first experimental results from two detector prototypes using a novel design of the thermometer coupling denoted remoTES, which further extends the applicability of the TES technology by including a wider class of potential absorber materials. In particular, this design facilitates the use of materials whose physical and chemical properties, as e.g. hygroscopicity, low hardness and low melting point, prevent the direct fabrication of the TES onto their surface. This is especially relevant in the context of the COSINUS experiment (Cryogenic Observatory for SIgnals seen in Next-Generation Underground Searches), where sodium iodide (NaI) is used as absorber material. With two remoTES prototype detectors operated in an above-ground R&D facility, we achieve energy resolutions of $\sigma =87.8$ eV for a $2.33$ g silicon absorber and $\sigma =193.5$ eV for a $2.27$ g $\alpha$-TeO${}_2$ absorber, respectively. RemoTES calorimeters offer - besides the wider choice of absorber materials - a simpler production process combined with a higher reproducibility for large detector arrays and an enhanced radiopurity standard.

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Cited by in corpus (8)

• Results from a Prototype TES Detector for the Ricochet Experiment
• Deep-underground dark matter search with a COSINUS detector prototype
• Array of Cryogenic Calorimeters to Evaluate the Spectral Shape of forbidden $\beta$-decays: the ACCESS project
• Particle discrimination in a NaI crystal using the COSINUS remote TES design
• First demonstration of a TES based cryogenic Li${}_2$MoO${}_4$detector for neutrinoless double beta decay search
• Dark Matter-Electron Scattering Search Using Cryogenic Light Detectors
• Water Cherenkov muon veto for the COSINUS experiment: design and simulation optimization
• Optimal operation of cryogenic calorimeters through deep reinforcement learning