Context. Point sources are one of the main contaminants to the recovery of the cosmic microwave background signal at small scales, and their careful detection will be important for the next generation of cosmic microwave background experiments like LiteBird. Aims. We want to develop a method based on fully convolutional networks to detect sources in realistic simulations, and to compare its performance against one of the most used point source detection method in this context, the Mexican hat wavelet 2 (MHW2). The frequencies for our analysis are the 143, 217, and 353 GHz Planck channels. Methods. We produce realistic simulations of point sources at each frequency taking into account potential contaminating signals as the cosmic microwave background, the cosmic infrared background, the Galactic thermal emission, the thermal Sunyaev-Zel'dovich effect, and the instrumental and point source shot noises. We first produce a set of training simulations at 217 GHz to train the neural network that we named PoSeIDoN. Then we apply both PoSeIDoN and the MHW2 to recover the point sources in the validating simulations at all the frequencies, comparing the results by estimating the reliability, completeness, and flux density estimation accuracy. Moreover, the receiver operating characteristic (ROC) curves are computed in order to asses the methods'performance. Results. In the extra-galactic region with a 30 • galactic cut, the neural network successfully recovers point sources at 90% completeness corresponding to 253, 126, and 250 mJy for 143, 217, and 353 GHz respectively. In the same validation simulations the wavelet with a 3σ flux density detection limit recovers point sources up to 181, 102, and 153 mJy at 90% completeness. To reduce the number of spurious sources, we also apply a safer 4σ flux density detection limit, the same as in the Planck catalogues, increasing the 90% completeness levels: 235, 137, and 192 mJy. In all cases PoSeIDoN produces a much lower number of spurious sources with respect to MHW2. As expected, the results on spurious sources for both techniques worsen when reducing the galactic cut to 10 •. Conclusions. Our results suggest that using neural networks is a very promising approach for detecting point sources using data from cosmic microwave background experiments, providing overall better results in dealing with spurious sources with respect to the more usual filtering approaches. Moreover, PoSeIDoN gives competitive results even at the 217 GHz nearby channels where the network was not trained.

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We analyzed the photometry of 20038 cool stars from campaigns 12, 13, 14 and 15 of the K2 mission in order to detect, characterize and validate new planetary candidates transiting low-mass stars. We present a catalogue of 25 new periodic transit-like signals in 22 stars, of which we computed the parameters of the stellar host for 19 stars and the planetary parameters for 21 signals. We acquired speckle and AO images, and also inspected archival Pan-STARRS1 images and Gaia DR2 to discard the presence of close stellar companions and to check possible transit dilutions due to nearby stars. False positive probability (FPP) was computed for 22 signals, obtaining FPP < $1\%$ for 17. We consider 12 of them as statistically validated planets. One signal is a false positive and the remaining 12 signals are considered as planet candidates. 20 signals have orbital period Porb < 10 d, 2 have 10 d < Porb < 20 d and 3 have Porb > 20 d. Regarding radii, 11 candidates and validated planets have computed radius R <2R⊕, 9 have 2R⊕ < R <4R⊕, and 1 has R >4R⊕. 2 validated planets and 2 candidates are located in moderately bright stars ($\rm m_{kep}<13$) and 2 validated planets and 3 candidates have derived orbital radius within the habitable zone according to optimistic models. Of special interest is the validated warm super-Earth K2-323 b (EPIC 248616368 b) with T$_{\rm eq} = 318^{+24}_{-43} \, K$, Sp = 1.7 ± 0.2 S⊕, Rp = 2.1 ± 0.1 R⊕, located in a m$\rm _{kep}$ = 14.13 star.

Depth profiling with PP-TOFMS of REEs (as substitutes of radioactive elements) in studies of stainless steel corrosion and contaminant uptake.