Abstract: This paper presents the electron and photon energy calibration obtained with the ATLAS detector using 140 fb-1 of LHC proton -proton collision data recorded at -Js = 13 TeV between 2015 and 2018. Methods for the measurement of electron and photon energies are outlined, along with the current knowledge of the passive material in front of the ATLAS electromagnetic calorimeter. The energy calibration steps are discussed in detail, with emphasis on the improvements introduced in this paper. The absolute energy scale is set using a large sample of Z -boson decays into electron -positron pairs, and its residual dependence on the electron energy is used for the first time to further constrain systematic uncertainties. The achieved calibration uncertainties are typically 0.05% for electrons from resonant Z -boson decays, 0.4% at ET – 10 GeV, and 0.3% at ET – 1 TeV; for photons at ET <^>' 60 GeV, they are 0.2% on average. This is more than twice as precise as the previous calibration. The new energy calibration is validated using .11tfr -, ee and radiative Z -boson decays.

Abstract: We report new measurements of the drift velocity and longitudinal diffusion coefficients of electrons in pure xenon gas and in xenon-helium gas mixtures at 1-9 bar and electric field strengths of 50-300 V/cm. In pure xenon we find excellent agreement with world data at all E/P, for both drift velocity and diffusion coefficients. However, a larger value of the longitudinal diffusion coefficient than theoretical predictions is found at low E/P in pure xenon, below the range of reduced fields usually probed by TPC experiments. A similar effect is observed in xenon-helium gas mixtures at somewhat larger E/P. Drift velocities in xenon-helium mixtures are found to be theoretically well predicted. Although longitudinal diffusion in xenon-helium mixtures is found to be larger than anticipated, extrapolation based on the measured longitudinal diffusion coefficients suggest that the use of helium additives to reduce transverse diffusion in xenon gas remains a promising prospect.

Abstract: The KM3NeT Collaboration is building and operating two deep sea neutrino telescopes at the bottom of the Mediterranean Sea. The telescopes consist of latices of photomultiplier tubes housed in pressure-resistant glass spheres, called digital optical modules and arranged in vertical detection units. The two main scientific goals are the determination of the neutrino mass ordering and the discovery and observation of high-energy neutrino sources in the Universe. Neutrinos are detected via the Cherenkov light, which is induced by charged particles originated in neutrino interactions. The photomultiplier tubes convert the Cherenkov light into electrical signals that are acquired and timestamped by the acquisition electronics. Each optical module houses the acquisition electronics for collecting and timestamping the photomultiplier signals with one nanosecond accuracy. Once finished, the two telescopes will have installed more than six thousand optical acquisition nodes, completing one of the more complex networks in the world in terms of operation and synchronization. The embedded software running in the acquisition nodes has been designed to provide a framework that will operate with different hardware versions and functionalities. The hardware will not be accessible once in operation, which complicates the embedded software architecture. The embedded software provides a set of tools to facilitate remote manageability of the deployed hardware, including safe reconfiguration of the firmware. This paper presents the architecture and the techniques, methods and implementation of the embedded software running in the acquisition nodes of the KM3NeT neutrino telescopes. Program summary Program title: Embedded software for the KM3NeT CLB CPC Library link to program files: https://doi.org/10.17632/s847hpsns4.1 Licensing provisions: GNU General Public License 3 Programming language: C Nature of problem: The challenge for the embedded software in the KM3NeT neutrino telescope lies in orchestrating the Digital Optical Modules (DOMs) to achieve the synchronized data acquisition of the incoming optical signals. The DOMs are the crucial component responsible for capturing neutrino interactions deep underwater. The embedded software must configure and precisely time the operation of each DOM. Any deviation or timing mismatch could compromise data integrity, undermining the scientific value of the experiment. Therefore, the embedded software plays a critical role in coordinating, synchronizing, and operating these modules, ensuring they work in unison to capture and process neutrino signals accurately, ultimately advancing our understanding of fundamental particles in the Universe. Solution method: The embedded software on the DOMs provides a solution based on a C-based bare-metal application, operating without a real-time embedded OS. It is loaded into the RAM during FPGA configuration, consuming less than 256 kB of RAM. The software architecture comprises two layers: system software and application. The former offers OS-like features, including a multitasking scheduler, firmware updates, peripheral drivers, a UDP-based network stack, and error handling utilities. The application layer contains a state machine ensuring consistent program states. It is navigated via slow control events, including external inputs and autonomous responses. Subsystems within the application code control specific acquisition electronics components via the associated driver abstractions. Additional comments including restrictions and unusual features: Due to the operation conditions of the neutrino telescope, where access is restricted, the embedded software implements a fail-safe procedure to reconfigure the firmware where the embedded software runs.

Abstract: Conventional PET systems can be augmented with additional detectors placed in close proximity of the region of interest. We developed a high resolution PET insert module to evaluate the added benefit of such a combination. The insert module consists of two back-to-back 1 mm thick silicon sensors, each segmented into 1040 1 mm(2) pads arranged in a 40 by 26 array. A set of 16 VATAGP7.1 ASICs and a custom assembled data acquisition board were used to read out the signal from the insert module. Data were acquired in slice (20) geometry with a Jaszczak phantom (rod diameters of 12-4.8 mm) Filled with F-18-FDG and the images were reconstructed with ML-EM method. Both data with full and limited angular coverage from the insert module were considered and three types of coincidence events were combined. The ratio of high-resolution data that substantially improves quality of the reconstructed image for the region near the surface of the insert module was estimated to be about 4%. Results from our previous studies suggest that such ratio could be achieved at a moderate technological expense by using an equivalent of two insert modules (an effective sensor thickness of 4 mm).

Abstract: 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.