The Advanced GAmma Tracking Array (AGATA) is a European project to develop and operate the next generation γ-ray spectrometer. AGATA is based on the technique of γ-ray energy tracking in electrically segmented high-purity germanium crystals. This technique requires the accurate determination of the energy, time and position of every interaction as a γ ray deposits its energy within the detector volume. Reconstruction of the full interaction path results in a detector with very high efficiency and excellent spectral response. The realisation of γ-ray tracking and AGATA is a result of many technical advances. These include the development of encapsulated highly segmented germanium detectors assembled in a triple cluster detector cryostat, an electronics system with fast digital sampling and a data acquisition system to process the data at a high rate. The full characterisation of the crystals was measured and compared with detector-response simulations. This enabled pulse-shape analysis algorithms, to extract energy, time and position, to be employed. In addition, tracking algorithms for event reconstruction were developed. The first phase of AGATA is now complete and operational in its first physics campaign. In the future AGATA will be moved between laboratories in Europe and operated in a series of campaigns to take advantage of the different beams and facilities available to maximise its science output. The paper reviews all the achievements made in the AGATA project including all the necessary infrastructure to operate and support the spectrometer

High-energy particle accelerators have been crucial in providing a deeper understanding of fundamental particles and the forces that govern their interactions. To increase the energy of the particles or to reduce the size of the accelerator, new acceleration schemes need to be developed. Plasma wakefield acceleration, in which the electrons in a plasma are excited, leading to strong electric fields (so called 'wakefields'), is one such promising acceleration technique. Experiments have shown that an intense laser pulse or electron bunch traversing a plasma can drive electric fields of tens of gigavolts per metre and above-well beyond those achieved in conventional radio-frequency accelerators (about 0.1 gigavolt per metre). However, the low stored energy of laser pulses and electron bunches means that multiple acceleration stages are needed to reach very high particle energies. The use of proton bunches is compelling because they have the potential to drive wakefields and to accelerate electrons to high energy in a single acceleration stage. Long, thin proton bunches can be used because they undergo a process called self-modulation, a particle-plasma interaction that splits the bunch longitudinally into a series of high-density microbunches, which then act resonantly to create large wakefields. The Advanced Wakefield (AWAKE) experiment at CERN uses high-intensity proton bunches-in which each proton has an energy of 400 gigaelectronvolts, resulting in a total bunch energy of 19 kilojoules-to drive a wakefield in a ten-metre-long plasma. Electron bunches are then injected into this wakefield. Here we present measurements of electrons accelerated up to two gigaelectronvolts at the AWAKE experiment, in a demonstration of proton-driven plasma wakefield acceleration. Measurements were conducted under various plasma conditions and the acceleration was found to be consistent and reliable. The potential for this scheme to produce very high-energy electron bunches in a single accelerating stage means that our results are an important step towards the development of future high-energy particle accelerators.

We give direct experimental evidence for the observation of the full transverse self-modulation of a long, relativistic proton bunch propagating through a dense plasma. The bunch exits the plasma with a periodic density modulation resulting from radial wakefield effects. We show that the modulation is seeded by a relativistic ionization front created using an intense laser pulse copropagating with the proton

We observe for the first time an effect on the driver caused by the motion of ions in a plasma wakefield accelerator. The effect manifests itself as a beam tail, which only occurs when sufficient motion of ions suppresses wakefields. By changing the plasma ions (helium, argon, xenon) in the experiment, we show that the effect depends inversely on the ion mass, as predicted from theory and simulations. Wakefields are driven resonantly by multiple bunches, and simulation results indicate that the ponderomotive force causes the motion of ions. In this case, the effect is also expected to depend on the amplitude of the wakefields, as also observed by varying the bunch charge.

During the last years, Deep Neural Networks have reached the highest performances in image classification. Nevertheless, such a success is mostly based on supervised and off-line learning: they require thus huge labeled datasets for learning, and once it is done, they cannot adapt to any change in the data from the environment. In the context of brain-inspired computing, we apply Kohonen-based Self-Organizing Maps for unsupervised learning without labels, and we explore original extensions such as the Dynamic SOM that enables continuous learning and the Pruning Cellular SOM that includes synaptic pruning in neuromorphic circuits. After presenting the three models and the experimental setup for MNIST classification, we compare different methods for automatic labeling based on very few labeled data (1% of the training dataset), and then we compare the performances of the three Kohonen-based Self-Organizing Maps with STDP-based Spiking Neural Networks in terms of accuracy, dynamicity and scalability.Index Terms-brain-inspired computing, self-organizing maps, unsupervised learning, embedded image classification.