Sparse regression on a library of candidate features has developed as the prime method to discover the PDE underlying a spatio-temporal dataset. As these features consist of higher order derivatives, model discovery is typically limited to low-noise and dense datasets due to the erros inherent to numerical differentiation. Neural network-based approaches circumvent this limit, but to date have ignored advances in sparse regression algorithms. In this paper we present a modular framework that combines deep-learning based approaches with an arbitrary sparse regression technique. We demonstrate with several examples that this combination facilitates and enhances model discovery tasks. We release our framework as a package at https://github.com/PhIMaL/DeePyMoD Preprint. Under review.
KM3NeT, a neutrino telescope currently under construction in the Mediterranean Sea, consists of a network of large-volume Cherenkov detectors. Its two different sites, ORCA and ARCA, are optimised for few GeV and TeV-PeV neutrino energies, respectively. This allows for studying a wide range of physics topics spanning from the determination of the neutrino mass hierarchy to the detection of neutrinos from astrophysical sources. Deep learning techniques provide promising methods to analyse the signatures induced by charged particles traversing the detector. This document will cover a deep learning based approach using graph convolutional networks to classify and reconstruct events in both the ORCA and ARCA detector. Performance studies on simulations as well as applications to real data will be presented, together with comparisons to classical approaches.
We use Bayesian inference together with the MOPED compression algorithm to help determine which species should be prioritised for future detections in order to better constrain the values of binding energies in the ISM.
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