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The Any Light Particle Search II (ALPS II) is a light shining through a wall experiment probing the existence of axions and axion-like particles using a 1064 nm laser source. While ALPS II is already taking data using a heterodyne based detection scheme, cryogenic transition edge sensor (TES) based single-photon detectors are planned to expand the detection system for cross-checking the potential signals, for which a sensitivity on the order of 10 -24  W is required. In order to reach this goal, we have investigated the use of convolutional neural networks (CNN) as binary classifiers to distinguish the experimentally measured 1064 nm photon triggered (light) pulses from background (dark) pulses. Despite rigorous hyperparameter optimization, the CNN based binary classifier did not outperform our previously optimized cut-based analysis in terms of detection significance. Our findings suggest that training confusion, introduced by near-1064 nm black-body photon triggers in the extrinsics background, is a significant factor limiting the CNNs performance for the associated dataset. The fiber coupled black-body radiation was identified as the limiting background source as concluded in our previous works. Given our results, we recommend that future studies explore regression-based CNNs, placing greater emphasis on the use of standardized and carefully structured training data rather than on extensive hyperparameter optimization. While the presented results and associated conclusions are obtained for a TES designed to be used in the ALPS II experiment, they should hold equivalently well for any device whose output signal can be considered as a univariate time trace.

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.

The Any Light Particle Search II (ALPS II) experiment at DESY, Hamburg, is a Light-Shining-through-a-Wall (LSW) experiment aiming to probe the existence of axions and axion-like particles (ALPs), which are candidates for dark matter. Data collection in ALPS II is underway utilizing a heterodyne-based detection scheme. A complementary run for confirmation or as an alternative method is planned using single photon detection, requiring a sensor capable of measuring low-energy photons (\\(1064\\,nm\\), \\(1.165\\,eV\\)) with high efficiency (higher than \\(50\\,\\%\\)) and a low background rate (below \\(7.710^-6\\,cps\\)). To meet these requirements, we are investigating a tungsten Transition Edge Sensor (TES) provided by NIST, which operates in its superconducting transition region at millikelvin temperatures. This sensor exploits the drastic change in resistance caused by the absorption of a single photon. We find that the background observed in the setup with a fiber-coupled TES is consistent with Black Body Radiation (BBR) as the primary background contributor. A framework was developed to simulate BBR propagation to the TES under realistic conditions. The framework not only allows the exploration of background reduction strategies, such as improving the TES energy resolution, but also reproduces, within uncertainties, the spectral distribution of the observed background. These simulations have been validated with experimental data, in agreement with the modeled background distribution, and show that the improved energy resolution reduces the background rate in the \\(1064\\,nm\\) signal region by one order of magnitude, to approximately \\(10^-4\\,cps\\). However, this rate must be reduced further to meet the ALPS II requirements.

Transition Edge Sensors (TES) are superconducting microcalorimeters that can be used for single-photon detection with extremely low backgrounds. When they are within their superconducting transition region, small temperature fluctuations - like the energy deposited by single photons - lead to large resistance variations. These variations can be measured using Superconducting Quantum Interference Devices (SQUIDs). This technology is planned to be used as a single-photon detector for later runs of the ALPS II experiment, a light-shining-through-walls experiment at DESY Hamburg, searching for Axion-Like Particles (ALPs), which are possible Dark Matter (DM) candidates. Due to the very low dark count rates in our setup, our TES system might be viable for direct DM searches at sub-MeV masses through electron-scattering of DM in the superconducting chip, as well. Simulations concerning background rejection and calibration methods demonstrate the needed sub-eV sensitivity already.

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 \\((861)\\)%, 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 \\( 2.7_-0.6^+0.8 10^-5\\)Hz, which corresponds to a power of \\((5.0_-1.1^+1.4)10^-24\\)W, within 20 days of measurement time at the 5\\(\\) 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.

Light-shining-through-walls experiments are the search experiments for weakly interacting slim particles (WISPs) with the smallest model dependence. They have the advantage that not only the detection, but also the production of the WISPs takes place in the laboratory and can thus be controlled. Using lasers is the preferred option for most of the mass region and has led to the world's most stringent laboratory limits (ALPS I) there. At CERN, OSQAR promises to surpass these and at DESY ALPS II is currently set up, which is planning to probe the axion-like particle to photon coupling down to \\(|g_a| 210^-11\\) GeV\\(^-1\\), which is in a region favored by many astrophysical hints.

The Any Light Particle Search II (ALPS II) is a light shining through a wall experiment probing the existence of axions and axion-like particles using a 1064 nm laser source. While ALPS II is already taking data using a heterodyne based detection scheme, cryogenic transition edge sensor (TES) based single-photon detectors are planned to expand the detection system for cross-checking the potential signals, for which a sensitivity on the order of \\(10^-24\\) W is required. In order to reach this goal, we have investigated the use of convolutional neural networks (CNN) as binary classifiers to distinguish the experimentally measured 1064 nm photon triggered (light) pulses from background (dark) pulses. Despite extensive hyperparameter optimization, the CNN based binary classifier did not outperform our previously optimized cut-based analysis in terms of detection significance. This suggests that the used approach is not generally suitable for background suppression and improving the energy resolution of the TES. We partly attribute this to the training confusion induced by near-1064 nm black-body photon triggers in the background, which we identified as the limiting background source as concluded in our previous works. However, we argue that the problem ultimately lies in the binary classification based approach and believe that regression models would be better suitable for addressing the energy resolution. Unsupervised machine learning models, in particular neural network based autoencoders, should also be considered potential candidates for the suppression of noise in time traces. While the presented results and associated conclusions are obtained for TES designed to be used in the ALPS II experiment, they should hold equivalently well for any device whose output signal can be considered as a univariate time trace.

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, 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.

We propose the use of transition-edge sensor (TES) single-photon detectors as a simultaneous target and sensor for direct dark matter searches, and report results from the first search of this kind. We perform a 489 h science run with a TES device optimized for the detection of 1064 nm photons, with a mass of ~0.2 ng and an energy threshold of ~0.3 eV, and set new limits on dark matter interactions with both electrons and nucleons for dark matter with mass below the MeV scale. With their excellent energy resolution, TESs enable search strategies that are complementary to recent results from superconducting nanowire single-photon detectors and kinetic inductance detectors. We show that next-generation TES arrays hold promise to probe new regions of light dark matter parameter space.

We propose the use of transition-edge sensor (TES) single-photon detectors as a simultaneous target and sensor for direct dark matter searches, and report results from the first search of this kind. We perform a 489 h science run with a TES device optimized for the detection of 1064 nm photons, with a mass of ~0.2 ng and an energy threshold of ~0.3 eV, and set new limits on dark matter interactions with both electrons and nucleons for dark matter with mass below the MeV scale. With their excellent energy resolution, TESs enable search strategies that are complementary to recent results from superconducting nanowire single-photon detectors and kinetic inductance detectors. We show that next-generation TES arrays hold promise to probe new regions of light dark matter parameter space.