arXiv subjects

M. Hübner

Publications and source records attributed to M. Hübner.

2 recordsLinked to original sources

arXiv2026

Economical and ecological impact of sector coupling applied to computing clusters

The rising share of abundant renewable energy inevitably increases volatility in the electricity production. The concept of sector coupling means that the volatility of electricity production to a large degree can be absorbed by dispatching electricity consumption whenever excess renewable energy is available. A system that is dynamically operated based on this principle can lower its total environmental impact. In addition, operational costs might be reducible as electricity prizes strongly depend on the residual load of the energy system. High-performance computing clusters in the field of science represent an ideal testing ground for such dynamic operation. Short-term delays in computing results due to electricity production being associated with high costs or carbon emissions are often negligible, provided that an overall computing target remains constant over long time periods. This study simulates the simplified operation of computing clusters using publicly available data on electricity production in Germany. The optimal utilisation along with associated carbon emission and cost reductions are determined separately. Hardware acquisition costs and embedded emissions are taken into account. The stability of a fixed computing target given the determined utilisation optima is evaluated in two validation periods. Additional simulations with modified parameters are carried out to estimate potential conditions under which dynamic operation of a computing cluster would continue to enable savings in the future.

cs.DC↗

arXiv2022

Searching for quasi-periodic oscillations in astrophysical transients using Gaussian processes

Analyses of quasi-periodic oscillations (QPOs) are important to understanding the dynamic behaviour in many astrophysical objects during transient events like gamma-ray bursts, solar flares, magnetar flares and fast radio bursts. Astrophysicists often search for QPOs with frequency-domain methods such as (Lomb-Scargle) periodograms, which generally assume power-law models plus some excess around the QPO frequency. Time-series data can alternatively be investigated directly in the time domain using Gaussian Process (GP) regression. While GP regression is computationally expensive in the general case, the properties of astrophysical data and models allow fast likelihood strategies. Heteroscedasticity and non-stationarity in data have been shown to cause bias in periodogram-based analyses. Gaussian processes can take account of these properties. Using GPs, we model QPOs as a stochastic process on top of a deterministic flare shape. Using Bayesian inference, we demonstrate how to infer GP hyperparameters and assign them physical meaning, such as the QPO frequency. We also perform model selection between QPOs and alternative models such as red noise and show that this can be used to reliably find QPOs. This method is easily applicable to a variety of different astrophysical data sets. We demonstrate the use of this method on a range of short transients: a gamma-ray burst, a magnetar flare, a magnetar giant flare, and simulated solar flare data.

astro-ph.IM↗