The prospect of growth of a railway system impacts both the network size and its occupation. Due to the overloaded infrastructure, it is necessary to increase reliability by adopting fast maintenance services to reach economic and security conditions. In this context, one major problem is the excessive friction caused by the wheels. This contingency may cause ruptures with severe consequences. While eddy’s current approaches are adequate to detect superficial damages in metal structures, there are still open challenges concerning automatic identification of rail defects. Herein, we propose an embedded system for online detection and location of rails defects based on eddy current. Moreover, we propose a new method to interpret eddy current signals by analyzing their wavelet transforms through a convolutional neural network. With this approach, the embedded system locates and classifies different types of anomalies, enabling an optimization of the railway maintenance plan. Field tests were performed, in which the rail anomalies were grouped in three classes: squids, weld and joints. The results showed a classification efficiency of ~98%, surpassing the most commonly used methods found in the literature.

The search for a novel technology able to detect and reconstruct nuclear and electron recoil events with the energy of a few $\mathrm{keV}$ has become more and more important now that large regions of high-mass dark matter (DM) candidates have been excluded. Moreover, a detector sensitive to incoming particle direction will be crucial in the case of DM discovery to open the possibility of studying its properties. Gaseous time projection chambers (TPC) with optical readout are very promising detectors combining the detailed event information provided by the TPC technique with the high sensitivity and granularity of latest-generation scientific light sensors. The CYGNO experiment (a CYGNus module with Optical readout) aims to exploit the optical readout approach of multiple-GEM structures in large volume TPCs for the study of rare events as interactions of low-mass DM or solar neutrinos. The combined use of high-granularity sCMOS cameras and fast light sensors allows the reconstruction of the 3D direction of the tracks, offering good energy resolution and very high sensitivity in the few keV energy range, together with a very good particle identification useful for distinguishing nuclear recoils from electronic recoils. This experiment is part of the CYGNUS proto-collaboration, which aims at constructing a network of underground observatories for directional DM search. A one cubic meter demonstrator is expected to be built in 2022/23 aiming at a larger scale apparatus (30 m${}^3$–100 m${}^3$) at a later stage.

The Neutrinos Angra (ν-Angra) Experiment aims to construct an antineutrinos detection device capable of monitoring the Angra dos Reis nuclear reactor activity. Nuclear reactors are intense sources of antineutrinos, and the thermal power released in the fission process is directly related to the flow rate of these particles. The antineutrinos energy spectrum also provides valuable information on the nuclear source isotopic composition. The proposed detector will be equipped with photomultipliers tubes (PMT) which will be readout by a custom Amplifier-Shaper-Discriminator circuit designed to condition its output signals to the acquisition modules to be digitized and processed by an FPGA. The readout circuit should be sensitive to single photoelectron signals, process fast signals, with a full-width-half-amplitude of about 5 ns, have a narrow enough output pulse width to detect both particles coming out from the inverse beta decay (νe + p → n + e +), and its output amplitude should be linear to the number of photoelectrons generated inside the PMT, used for energy estimation. In this work, some of the main PMT characteristics are measured and a new readout circuit is proposed, described and characterized.

Railway track circuit failures can cause significant train delays and economic losses. A crucial point of the railway operation system is the corrective maintenance process. During this operation, the railway lines have the circulation of trains interrupted in the respective sector, where traffic restoration occurs only after completing the maintenance process. Depending on the cause and length of the track circuit, identifying and solving the problem may take a long time. A tool that assists in track circuit fault detection during an inspection adds agility and efficiency in its restoration and cost reduction. This paper presents a new method, based on frequency domain reflectometry, to diagnose and locate false occupancy failures of track circuits. Initially, simulations are performed considering simplified track circuit approximations to demonstrate the operation of the proposed method, where the fault position is estimated by identifying the null points and through non-linear regression on signal amplitude response. A field test is then carried out in a track circuit approximately 1500 m long to validate the proposed method. The results show that the proposed method can identify and estimate the fault location due to a short circuit between rails with high accuracy.

The use of gaseous Time Projection Chambers enables the detection and the detailed study of rare events due to particles interactions with the atoms of the gas with energy releases as low as a few keV. Due to this capability, these instruments are being developed for applications in the field of astroparticle physics, such as the study of dark matter and neutrinos. To acquire events occurring in the sensitive volume with a high granularity, the Cygno collaboration is developing a solution where the light generated during the avalanche processes occurring in a multiplication stage based on Gas Electron Multiplier (GEM) is read out by optical sensors with very high sensitivity and spatial resolution. To achieve a high light output, gas gain values of the order of $$10^5\text {-}10^6$$ 10 5 - 10 6 are needed. In this working condition, a dependence of the detector response on the spatial density of the charge collected in the GEM holes has been observed, indicating a gain-reduction effect likely caused by space-charge buildup within the multiplication channels. This paper presents data collected with a prototype featuring a sensitive volume of about two liters, together with a model developed by the collaboration to describe and predict the gain dependence on charge density. A comparison with experimental data shows that the model reproduces, with a percent-level precision, the gain behaviour over nearly one order of magnitude.