6 papers
Six Dimensions of Benchmarking Time-Series Databases
Jalal Mostafa, Sandro Melissano, Nicholas Tan Jerome +2
Time-series databases (TSDBs) employ diverse storage architectures optimized for specific workload characteristics, leading to distinct performance profiles and bottlenecks that ar…
Optimizing Sensor Placement for Hydrogen Leak Detection in Enclosed Infrastructure: A Comparative Study Using CFD-informed Genetic Algorithm and DeepSets Neural Surrogate
Fangnian Wang, Nicholas Tan Jerome, Thomas Jordan +1
Hydrogen infrastructure in enclosed environments, such as parking facilities for fuel cell vehicles, presents significant safety challenges due to hydrogen's low ignition energy an…
When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters
Nicholas Tan Jerome, Frank Simon
Deploying a time series foundation model requires GPU infrastructure, engineering overhead, and carries no guarantee of improvement over XGBoost. We provide the first systematic br…
Forecasting Source Stability in Scientific Experiments using Temporal Learning Models: A Case Study from Tritium Monitoring
Nicholas Tan Jerome, Nadia Aouadi, Christoph Koehler +2
The Karlsruhe Tritium Neutrino Experiment (KATRIN) aims to measure the absolute neutrino mass with unprecedented sensitivity, requiring precise monitoring of the windowless gaseous…
The Remote Analog to Digital Conversion DAQ System for the TRISTAN Detector Upgrade
Andrew S. Gavin, Matthias Balzer, Suren Chilingaryan +10
The TRISTAN detector is an upgrade to the KATRIN experiment to enable a differential measurement of the tritium -decay spectrum to search for sterile neutrinos with keV masses.…
Sterile-neutrino search based on 259 days of KATRIN data
Himal Acharya, Max Aker, Dominic Batzler +132
Neutrinos are the most abundant fundamental matter particles in the Universe and play a crucial role in particle physics and cosmology. Neutrino oscillation, discovered about 25 ye…