Recent Achievements of the ERNA Collaboration

For more than two decades, the ERNA collaboration has investigated nuclear processes of astrophysical interest through the direct measurement of cross sections or the identification of the nucleosynthesis effects. Measurements of cross-section, reported in this publication, of radiative capture reactions have been mainly conducted using the ERNA Recoil Mass Separator, and more recently with an array of charged particle detector telescopes designed for nuclear astrophysics measurements. Some results achieved with ERNA will be reviewed, with a focus on the results most relevant for nucleosynthesis in AGB and advanced burning phases

A New Low-Energy Proton Irradiation Facility to Unveil the Mechanistic Basis of the Proton-Boron Capture Therapy Approach

Protontherapy (PT) is a fast-growing cancer therapy modality thanks to much-improved normal tissue sparing granted by the charged particles' inverted dose-depth profile. Protons, however, exhibit a low biological effectiveness at clinically relevant energies. To enhance PT efficacy and counteract cancer radioresistance, Proton–Boron Capture Therapy (PBCT) was recently proposed. PBCT exploits the highly DNA-damaging α-particles generated by the p + 11B→3α (pB) nuclear reaction, whose cross-section peaks for proton energies of 675 keV. Although a significant enhancement of proton biological effectiveness by PBCT has been demonstrated for high-energy proton beams, validation of the PBCT rationale using monochromatic proton beams having energy close to the reaction cross-section maximum is still lacking. To this end, we implemented a novel setup for radiobiology experiments at a 3-MV tandem accelerator; using a scattering chamber equipped with an Au foil scatterer for beam diffusion on the biological sample, uniformity in energy and fluence with uncertainties of 2% and 5%, respectively, was achieved. Human cancer cells were irradiated at this beamline for the first time with 685-keV protons. The measured enhancement in cancer cell killing due to the 11B carrier BSH was the highest among those thus far observed, thereby corroborating the mechanistic bases of PBCT

The Status and Future of Direct Nuclear Reaction Measurements for Stellar Burning

The study of stellar burning began just over 100 years ago. Nonetheless, we do not yet have a detailed picture of the nucleosynthesis within stars and how nucleosynthesis impacts stellar structure and the remnants of stellar evolution. Achieving this understanding will require precise direct measurements of the nuclear reactions involved. This report summarizes the status of direct measurements for stellar burning, focusing on developments of the last couple of decades, and offering a prospectus of near-future developments.Comment: Accepted to Journal of Physics G as a Major Report. Corresponding author: Zach Meisel ([email protected]

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

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