Abstract: A telescope for a beam test have been developed as a result of a collaboration among the University of Liverpool, Centro Nacional de Microelectronica (CNM) of Barcelona and Instituto de Fisica Corpuscular (IFIC) of Valencia. This system is intended to carry out both analogue charge collection and spatial resolution measurements with different types of microstrip or pixel silicon detectors in a beam test environment. The telescope has four XY measurement as well as trigger planes (XYT board) and it can accommodate up to twelve devices under test (DUT board). The DUT board uses two Beetle ASICs for the readout of chilled silicon detectors. The board could operate in a self-triggering mode. The board features a temperature sensor and it can be mounted on a rotary stage. A peltier element is used for cooling the DUT. Each XYT board measures the track space points using two silicon strip detectors connected to two Beetle ASICs. It can also trigger on the particle tracks in the beam test. The board includes a CPLD which allows for the synchronization of the trigger signal to a common clock frequency, delaying and implementing coincidence with other XYT boards. An Alibava mother board is used to read out and to control each XYT/DUT board from a common trigger signal and a common clock signal. The Alibava board has a TDC on board to have a time stamp of each trigger. The data collected by each Alibava board is sent to a master card by means of a local data/address bus following a custom digital protocol. The master board distributes the trigger, clock and reset signals. It also merges the data streams from up to sixteen Alibava boards. The board has also a test channel for testing in a standard mode a XYT or DUT board. This board is implemented with a Xilinx development board and a custom patch board. The master board is connected with the DAQ software via 100M Ethernet. Track based alignment software has also been developed for the data obtained with the DAQ software.

Abstract: The simulation and analysis of High Energy Physics experiments require a realistic simulation of the detector material and its distribution. The challenge is to describe all active and passive parts of large scale detectors like ATLAS in terms of their size, position and material composition. The common method for estimating the radiation length by weighing individual components, adding up their contributions and averaging the resulting material distribution over extended structures provides a good general estimate, but can deviate significantly from the material actually present. A method has been developed to assess its material distribution with high spatial resolution using the reconstructed scattering angles and hit positions of high energy electron tracks traversing an object under investigation. The study presented here shows measurements for an extended structure with a highly inhomogeneous material distribution. The structure under investigation is an End-of-Substructure-card prototype designed for the ATLAS Inner Tracker strip tracker – a PCB populated with components of a large range of material budgets and sizes. The measurements presented here summarise requirements for data samples and reconstructed electron tracks for reliable image reconstruction of large scale, inhomogeneous samples, choices of pixel sizes compared to the size of features under investigation as well as a bremsstrahlung correction for high material densities and thicknesses.

Abstract: The semiconductor tracker (SCT) is one of the tracking systems for charged particles in the ATLAS detector. It consists of 4088 silicon strip sensor modules. During Run 2 (2015-2018) the Large Hadron Collider delivered an integrated luminosity of 156 fb(-1) to the ATLAS experiment at a centre-of-mass proton-proton collision energy of 13 TeV. The instantaneous luminosity and pile-up conditions were far in excess of those assumed in the original design of the SCT detector. Due to improvements to the data acquisition system, the SCT operated stably throughout Run 2. It was available for 99.9% of the integrated luminosity and achieved a data-quality efficiency of 99.85%. Detailed studies have been made of the leakage current in SCT modules and the evolution of the full depletion voltage, which are used to study the impact of radiation damage to the modules. '

Abstract: Liquid scintillators are a common choice for neutrino physics experiments, but their capabilities to perform background rejection by scintillation pulse shape discrimination is generally limited in large detectors. This paper describes a novel approach for a pulse shape based event classification developed in the context of the Double Chooz reactor antineutrino experiment. Unlike previous implementations, this method uses the Fourier power spectra of the scintillation pulse shapes to obtain event-wise information. A classification variable built from spectral information was able to achieve an unprecedented performance, despite the lack of optimization at the detector design level. Several examples of event classification are provided, ranging from differentiation between the detector volumes and an efficient rejection of instrumental light noise, to some sensitivity to the particle type, such as stopping muons, ortho-positronium formation, alpha particles as well as electrons and positrons. In combination with other techniques the method is expected to allow for a versatile and more efficient background rejection in the future, especially if detector optimization is taken into account at the design level.

Abstract: Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge.