Identification of time-correlated neutrino clusters in populations of astrophysical transient sources

The detection of astrophysical neutrinos from transient sources can help to understand the origin of the neutrino diffuse flux and to constrain the underlying production mechanisms. In particular, proton-neutron collisions may produce GeV neutrinos. However, at these energies, neutrino data from large water Cherenkov telescopes, like KM3NeT and IceCube, are dominated by the well-known atmospheric neutrino flux. It is then necessary to identify a sub-dominant component due to an astrophysical emission based on time correlation across messengers. The contribution covers several methods to search for such a signal in short time windows centered on observed transient sources, including a novel approach based on the distribution of time differences. Their performance is compared in the context of subpopulations of astrophysical sources that may show prompt or delayed neutrino emissions. The outlook for the usage of such techniques in actual analyses is also presented.Comment: Presented at the 38th International Cosmic Ray Conference (ICRC2023). 8 pages, 4 figure

The classification and categorisation of Gamma-Ray Bursts with machine learning techniques for neutrino detection

While Gamma-Ray Burst (GRBs) are clear and distinct observed events, every individual GRB is unique. In fact, GRBs are known for their variable behaviour, and BATSE was already able to discover two categories of GRB from the T90 distribution; the short and long GRBs. These two categories match up with the expected two types of GRB progenitors. Nowadays, more features have been found to be able to further distinguish them, such as the hardness ratio or the presence of supernovae. However, that does not mean that it is by any means simple to categorise individual GRBs. Furthermore, more GRB categories have been theorised as well, such as low-luminosity or X-ray-rich GRBs. These different types of GRBs also could indicate a different neutrino spectrum, with different types of GRBs more likely to emit higher amounts of neutrinos. We present an ongoing effort to use machine learning to categorise and classify GRBs, searching for subpopulations that could yield a larger neutrino flux. We specifically use unsupervised learning, as it allows hidden patterns and correlations to come to light. With the help of features such as the T90, hardness, fluence, SNR, spectral index and even the full light curve and spectra, different structures and categories of Gamma-Ray bursts can be found.Comment: Presented at the 38th International Cosmic Ray Conference (ICRC2023

A better communicator is always a better scientist, or the reason why every PhD student should engage in science outreach

The ability to communicate with all audiences is a skill that is rapidly becoming a must-have for any future scientist. As more physicists engage in communicating science to non-expert audiences, research shows that this experience helps them to get a better understanding of their own research and the impact on society, improves the perception of science by lay audiences and can also become an area of personal growth as a citizen. A recent deployment of a PhD student to the Amundsen Scott South Pole Station, as part of the IceCube Collaboration, provided a ready opportunity to spark interest. We present results of the efforts made by the Universit\'e libre de Bruxelles (ULB), the Vrije Universiteit Brussel (VUB) and the Interuniversity Institute for High Energies, IIHE (ULB-VUB), to introduce Belgian students and citizens to science and the life of a scientist. The essential parts of this program will be identified to show why the contributions of a PhD student to the organization of these activities are beneficial to the development of new skills as a scientist, but also to broaden the audiences and the impact of the local university and/or the specific research outreach program.Comment: Submitted to the 35th International Cosmic Ray Conference (ICRC 2017, Busan, South Korea

Constraints and prospects on gravitational wave and neutrino emission using GW150914

The recent LIGO observation of gravitational waves from a binary black hole merger triggered several follow-up searches from both electromagnetic wave as well as neutrino observatories. Since in general, it is expected that all matter has been removed from the binary black hole environment long before the merger, no neutrino emission is expected from such mergers. Still, it remains interesting to test this hypothesis. The ratio of the energy emitted in neutrinos with respect to gravitational waves represents a useful parameter to constrain the environment of such astrophysical events. In addition to putting constraints by use of the non-detection of counterpart neutrinos, it is also possible to consider the diffuse neutrino flux measured by the IceCube observatory as the maximum contribution from an extrapolated full class of BBHs. Both methods currently lead to similar bounds on the fraction of energy that can be emitted in neutrinos. Nevertheless, combining both methods should allow to strongly constrain the source population in case of a future neutrino counterpart detection. The proposed approach can and will be applied to potential upcoming LIGO events, including binary neutron stars and black hole-neutron star mergers, for which a neutrino counterpart is expected.Comment: 8 pages, 2 figures. In Proceedings of the 35th International Cosmic Ray Conference (ICRC2017), Busan, Kore

Exploiting synergies between neutrino telescopes for the next galactic core-collapse supernova

Observing and characterizing the next galactic core-collapse supernova will be a critical step for neutrino experiments. Extracting information about the supernova progenitors and neutrino properties within minutes after an observation will in particular be crucial in order to optimize analysis strategies at other observatories. Moreover, certain classes of progenitors, with strong magnetic fields, could give rise to gamma-ray bursts but have been underinvestigated to date. In this contribution we propose a strategy to combine results from next-generation neutrino experiments, focusing notably on the determination of the progenitor mass and the neutrino mass ordering. Additionally, we investigate the impact of strong magnetic fields on neutrino observations, and demonstrate the detectability of the associated effects in upcoming experiments

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches