Search

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 ≈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, the machine and deep learning algorithms for the rejection of background events recorded with the TES are investigated. A first application of convolutional neural networks to classify time series data measured with the TES is also presented. [ABSTRACT FROM AUTHOR]

Transition Edge Sensors (TESs) have found widespread application in fundamental studies due to their low background rate, high efficiency, and excellent energy resolution. This makes them suitable candidates for ALPS II, which investigates the existence of new particles (axions and axion-like-particles) which couple very weakly to photons. ALPS II anticipates an extremely low signal rate < 10 - 5 cps (amounting to ∼ 1–2 photons a day). The detection of these low energy (∼ 1 eV, 1064 nm) photons with a high energy resolution is necessary for ALPS II. We show that with our TES setup, we can analyze the TES pulses with different methods such as pulse fitting and Principal Component Analysis (PCA). These achieve (using the standard deviation) an energy resolution ( Δ E / E ) down to ∼ 8 % . The pulse analysis, with a chosen fitting approach, assists also in achieving a very low dark count rate O 10 - 6 cps for 1064 nm photon signal searches in the TES. [ABSTRACT FROM AUTHOR]