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Jose Alejandro Rubiera Gimeno

Publications and source records attributed to Jose Alejandro Rubiera Gimeno.

2 recordsLinked to original sources

Characterization of a Two-Channel Optical and Near-infrared Transition Edge Sensor System for Rare-Event Searches

Transition edge sensors (TESs) are superconducting energy-resolving microcalorimeters that have demonstrated low background rates as well as quantum efficiencies close to unity for photons at optical and near-infrared wavelengths. This makes these detectors well suited for rare-event searches. We report on the comprehensive characterization of a two-channel detector module consisting of two tungsten TESs optimized for the detection of photons with a wavelength of 1064nm. The devices achieve a system detection efficiency of $(86\pm1)$%, an energy resolution better than 7%, and a background dark-count rate of photon-like events below 6mHz when coupled to an optical fiber. Using an unbinned likelihood framework, we find the dark count rate to be compatible with blackbody radiation from the room-temperature laboratory environment. Thanks to the energy resolution of the TESs, we show that it is possible to detect monochromatic signals at 1064nm with photon rates $\geqslant 2.7_{-0.6}^{+0.8} \times10^{-5}$Hz, which corresponds to a power of $\geqslant(5.0_{-1.1}^{+1.4})\times10^{-24}$W, within 20 days of measurement time at the 5$σ$ confidence level. This makes our detectors well suited for searches for hypothetical axions and axion-like particles with experiments such as the Any Light Particle Search II (ALPS II) or axion interferometers. The developed methodologies are not only applicable to axion searches, but are also relevant for rare-event searches with TESs in general.

physics.ins-det↗

A first application of machine and deep learning for background rejection in the ALPS II TES detector

Axions and axion-like particles are hypothetical particles predicted in extensions of the standard model and are promising cold dark matter candidates. The Any Light Particle Search (ALPS II) experiment is a light-shining-through-the-wall experiment that aims to produce these particles from a strong light source and magnetic field and subsequently detect them through a reconversion into photons. With an expected rate $\sim$ 1 photon per day, a sensitive detection scheme needs to be employed and characterized. One foreseen detector is based on a transition edge sensor (TES). Here, we investigate machine and deep learning algorithms for the rejection of background events recorded with the TES. We also present a first application of convolutional neural networks to classify time series data measured with the TES.

hep-ex↗