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Infrared interferometers are optical devices that can measure optical path-length differences by measuring changes in the refractive index. Several arrangements can be deployed, from single channel devices to multichannel double color heterodyne interferometers. These type of devices are typically used to recover the spatial electron density profile of fusion plasmas. This process involves measuring precisely the phase differences of the intermediate frequencies. A source of error that affects the measurements is the crosstalk that appears due to the coupling of the signals into the different channels of the interferometers. The inter-channel coupling of the signals is extremely difficult to eliminate specially when the probing frequencies are close to each other. In this paper it is shown that Independent Component Analysis effectively eliminates inter-crosstalk coupling in such devices. Furthermore, it is shown how the Signal-to-Noise ratio is dramatically increased when this technique is used., Comment: 6 pages. Preprint submitted to IEEE

To address the possible lack or total absence of pulses from particle detectors during the development of its associate electronics, we propose a model that can generate them without losing the features of the real ones. This model is based on artificial neural networks, namely Generative Adversarial Networks (GAN). We describe the proposed network architecture, its training methodology and the approach to train the GAN with real pulses from a scintillator receiving radiation from sources of ${}^{137}$Cs and ${}^{22}$Na. The Generator was installed in a Xilinx's System-On-Chip (SoC). We show how the network is capable of generating pulses with the same shape as the real ones that even match the data distributions in the original pulse-height histogram data.

Pulse shaping is a common technique for optimizing signal-to-noise ratio (SNR) in particle detectors. Although analog or digital linear shapers are typically used for this purpose, there are nonlinear approaches, such as neural networks (NN), which have demonstrated their potential to outperform linear ones. Their nonlinear nature makes it possible to optimize the SNR of incoming pulses and extract diverse information, such as particle type and energy, with extremely short shaping time to avoid crowding. This paper shows three different NNs for shaping pulses: (a) convolutional NN (CNN); (b) recurrent NN (RNN); (c) self-attenuating NN. These NNs shape the pulses and return them unfolded avoiding stacking, and even estimate the height of the pulses when there has been saturation in the preamplifier. In this work we show the architectures of the NNs and their results using CR--RC pulses with Brownian and white noise, but they could be extrapolated to any shape and type of noise. The results obtained show that when the noise level is low and the frequency is low, all the topologies presented are a valid solution, but with white noise and high pulse arrival frequency, CNN is a better solution than the others. In the case of Brownian noise, the three topologies presented give similar results.