10 papers
Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures
Waleed Esmail, Stuart Russell, Jana Klinge +2
Forecasting seismic waveforms beyond observed data remains challenging due to the nonlinear, dispersive, and multi-scale nature of seismic wave propagation. In this work, we introd…
WavePID: Low-energy flavor identification using single-PMT time series in IceCube
R. Abbasi, The IceCube Collaboration, M. Ackermann +438
The IceCube Neutrino Observatory, a cubic-kilometer detector at the South Pole, identifies neutrino flavor through event morphology. Sparse photon detection makes this classificati…
When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms
Waleed Esmail, Stuart Russell, Jana Klinge +2
Long-horizon autoregressive forecasting of oscillatory physical signals, such as seismograms, gravitational-wave strain, and similar wavefields is limited by error accumulation: as…
Constraints on the Population of Common Sources of Gravitational Waves and High-Energy Neutrinos with IceCube During the Third Observing Run of the LIGO and Virgo Detectors
DoÄa Veske, Zsuzsa Márka, Albert Zhang
The discovery of joint sources of high-energy neutrinos and gravitational waves has been a primary target for the LIGO, Virgo, KAGRA, and IceCube observatories. The joint detection…
Towards the Composition of sub-PeV Cosmic Rays at IceCube
Julian Saffer
With the implementation of a low-energy trigger, the surface array of the IceCube Neutrino Observatory is able to record cosmic-ray induced air showers with a primary energy of a f…
Forecasting Seismic Waveforms: A Deep Learning Approach for Einstein Telescope
Waleed Esmail, Alexander Kappes, Stuart Russell +1
We introduce \textit{SeismoGPT}, a transformer-based model for forecasting three-component seismic waveforms in the context of future gravitational wave detectors like the Einstein…