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

Investigation of maximum velocity induced by body-force fields for simpler modeling of plasma actuators

The relation between the parameters of the body-force field generated by a plasma actuator and the maximum induced velocity in quiescent air is investigated by expressing the body-force distribution as the Gaussian function of the spatial coordinates. The aim of this study is to identify the dominant parameters for modeling of the body-force distribution. For that purpose, the parametric study using numerical simulations and dimensional analysis are conducted to derive the nondimensional key parameters. It is found that the nondimensional maximum induced velocity is determined by the Reynolds number calculated by three parameters: the total induced momentum per unit time, the height of the center of gravity of the body-force distribution, and the standard deviation from the center of gravity. In addition, the relation for the Gaussian body-force distribution turns out to be applicable to a conventional model, i.e, the Suzen model, even though the shapes of the distribution differ. Thus, we conclude that the three body-force parameters above are the key parameters for the maximum velocity induced by a plasma actuator

Dominant parameters for maximum velocity induced by body-force models for plasma actuators

This study investigates the relationship between body-force fields and maximum velocity induced in quiescent air for development of a simple body-force model of a plasma actuator. Numerical simulations are conducted with the body force near a wall. The spatial distribution and temporal variation of the body force are a Gaussian distribution and steady actuation, respectively. The dimensional analysis is performed to derive a reference velocity and Reynolds number based on the body-force distribution. It is found that the derived Reynolds number correlates well with the nondimensional maximum velocity induced in quiescent conditions when the center of the Gaussian distribution is fixed at the wall. Additionally, two flow regimes are identified in terms of the Reynolds number. Considering the variation of the center of gravity of force fields, another Reynolds number is defined by introducing a new reference length. The nondimensional maximum velocity is found to be scaled with the latter Reynolds number, i.e., the maximum induced velocity in quiescent conditions is determined from three key parameters of the force field: the total induced momentum per unit time, the height of the center of gravity, and the standard deviation from it. This scaling turns out to be applicable to existing body-force models of the plasma actuator, despite the force distributions different from the Gaussian distribution. Comparisons of velocity profiles with experimental data validate the results and show that the flow induced by a plasma actuator can be simulated with simple force distributions by adjustment of the key body-force parameters

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