Double cascade reconstruction in KM3NeT/ARCA

The detection of astrophysical $\nu_\tau$ is an important verification of the observed flux of high-energy neutrinos. A flavour ratio of approximately $\nu_{e} : \nu_\mu : \nu_\tau \approx 1 : 1 : 1$ is predicted for astrophysical neutrinos measured at Earth due to neutrino oscillations. On top of this, the $\nu_\tau$ offers a unique channel for neutrino astronomy due to absence of an atmospheric $\nu_\tau$ background contribution. When a $\nu_\tau$ interacts it produces a particle cascade and often a $\tau$ lepton which in turn decays mainly into another cascade. This results in a double cascade signature. An excellent angular resolution can be achieved when both cascade vertices are reconstructed. The KM3NeT/ARCA detector, which is under construction in the Mediterranean sea, will be able to detect this signature due to its timing and spatial resolution for cascades. We will discuss the dedicated reconstruction algorithm and performance for reconstructing double cascades using KM3NeT. The angular deviation reaches sub-degree level for tau lengths larger than 25 meters.Comment: 6 pages, 2 figures, VLVnT 2021 contributio

Architecture and performance of the KM3NeT front-end firmware

The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine

Incidence of HCV Reinfection among HIV-Positive MSM and Its Association with Sexual Risk Behavior: A Longitudinal Analysis

Background: Human immunodeficiency virus (HIV)-positive men who have sex with men (MSM) are at high risk of hepatitis C virus (HCV) reinfection following clearance of HCV, but risk factors specifically for reinfection have never been comprehensively assessed. Methods: Using data from a prospective observational cohort study among HIV-positive MSM with an acute HCV infection (MOSAIC), the incidence of HCV reinfection following spontaneous clearance or successful treatment was assessed. A univariable Bayesian exponential survival model was used to identify risk factors associated with HCV reinfection. Results: In total, 122 HIV-positive MSM who had a spontaneously cleared or successfully treated HCV infection between 2003 and 2017 were included. During a median follow-up of 1.4 years (interquartile range [IQR] 0.5-3.8), 34 HCV reinfections were observed in 28 patients. The incidence of HCV reinfection was 11.5/100 person-years and among those with reinfection, median time to reinfection was 1.3 years (IQR 0.6-2.7). HCV reinfection was associated with receptive condomless anal intercourse, sharing of sex toys, group sex, anal rinsing before sex, ≥10 casual sex partners in the last 6 months, nadir CD4 cell count <200 cells/mm3, and recent CD4 cell count <500 cells/mm3. Conclusions: Incidence of HCV reinfection was high and strongly associated with sexual risk behavior, highlighting the need for interventions to reduce risk behavior and prevent HCV reinfections among HIV-positive MSM

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

Architecture and performance of the KM3NeT front-end firmware

The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine