Graph Neural Networks for low-energy event classification & reconstruction in IceCube

IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV–100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%–20% compared to current maximum likelihood techniques in the energy range of 1 GeV–30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events.Peer Reviewe

Distributing the Benefit of the Doubt: Scientists, Regulators, and Drug Safety

This article examines how scientists and regulators distribute the benefit of the doubt about drug safety under conditions of scientific uncertainty. The focus of the empirical research is the regulatory controversy over the hepatorenal toxicity of benoxaprofen in the United Kingdom and the United States. By scrutinizing the technical coherence of the arguments put forward by industrial and government scientists, it is concluded that these scientists are willing to award the commercial interests of the pharmaceutical industry an enormous benefit of the sccentific doubt, which is not consistent with the best interests of patients. Interpretative flexibility, the burden of proof falling on regulators and their trust in, and dependence on, industrial scientists facilitates that distribution of the benefit of the scientific doubt. However, regulatory authorities' need for viability and the rationality common to opposing scientific views suggest that it is possible, in principle, to alter this dominant trend. To achieve adequate patient protection, drug regulation in the United Kingdom and the United States requires extensive reform. Some preliminary policy changes are sketched