Related papers: Optimal operation of cryogenic calorimeters throug…
Phonon-mediated cryogenic calorimeters find application in rare event searches due to their intrinsically low energy threshold. Achieving the best sensitivity for this kind of detectors is crucial for signal identification, leading to…
The CRESST experiment aims at the direct detection of sub-GeV dark matter particles via elastic scattering off nuclei in different target crystals at cryogenic temperatures. The advancement in W-TES sensors allowed the CRESST detectors to…
The CRESST (Cryogenic Rare Event Search with Superconducting Thermometers) and the EURECA (European Underground Rare Event Calorimeter Array) experiments are direct dark matter search experiments where cryogenic detectors are used to detect…
Common approaches to control a data-center cooling system rely on approximated system/environment models that are built upon the knowledge of mechanical cooling and electrical and thermal management. These models are difficult to design and…
The CRESST experiment (Cryogenic Rare Event Search with Superconducting Thermometers) searches for dark matter via the phonon and light signals of elastic scattering processes in scintillating crystals. The discrimination between a possible…
Cryogenic characterization of transition-edge sensor (TES) bolometers is a time- and labor-intensive process. As new experiments deploy larger and larger arrays of TES bolometers, the testing process will become more of a bottleneck. Thus…
Cryogenic detectors are at the forefront of rare-event search experiments, including direct detection of dark matter, coherent elastic neutrino-nucleus scattering, neutrinoless double-beta decay, and searches for fractionally charged…
Cryogenic scintillating calorimeters are ultrasensitive particle detectors for rare event searches, particularly for the search for dark matter and the measurement of neutrino properties. These detectors are made from scintillating target…
Machine-learning techniques are emerging as a valuable tool in experimental physics, and among them, reinforcement learning offers the potential to control high-dimensional, multistage processes in the presence of fluctuating environments.…
Astronomical observations of cosmic sources in the far-infrared and X-ray bands require extreme sensitivity. The most sensitive detectors are cryogenic bolometers and calorimeters operating typically at about 100 mK. The last stage of…
The accurate and precise extraction of information from a modern particle physics detector, such as an electromagnetic calorimeter, may be complicated and challenging. In order to overcome the difficulties we propose processing the detector…
Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision…
The Cryogenic Rare Event Search with Superconducting Thermometers Phase II (CRESST-II) at the L.N.G.S in Italy is searching for Dark Matter using low-temperature calorimeters. These detectors allow to discriminate different particles by…
A vacuum compatible cryogenic accelerometer is presented which will reach $<0.5$ p$g$ Hz$^{-1/2}$ sensitivity from 1 mHz to 10 Hz with a maximum sensitivity of 10 f$g$ Hz$^{-1/2}$ around 10 Hz. This figure can be translated to a…
The CRESST experiment employs cryogenic calorimeters for the sensitive measurement of nuclear recoils induced by dark matter particles. The recorded signals need to undergo a careful cleaning process to avoid wrongly reconstructed recoil…
We present the current status of CRESST(Cryogenic Rare Event Search using Superconducting Thermometers) project and new results concerning the development of new detectors based on the simultaneous measurement of phonons and scintillation…
The CRESST experiment is a direct dark matter search which aims to measure interactions of potential dark matter particles in an earth-bound detector. With the current stage, CRESST-III, we focus on a low energy threshold for increased…
Optimal filtering is the crucial technique for the data analysis of transition-edge-sensor (TES) calorimeters to achieve their state-of-the-art energy resolutions. Filtering out the `bad' data from the dataset is important because it…
Cryogenic macro-calorimeters instrumented with NTD thermistors have been developed for several decades. The choice of the optimal bias current is crucial for a proper operation of these detectors, both in terms of energy resolution and…
Cold atom traps are at the heart of many quantum applications in science and technology. The preparation and control of atomic clouds involves complex optimization processes, that could be supported and accelerated by machine learning. In…