Improved detection of farside solar active regions using deep learning

The analysis of waves in the visible side of the Sun allows the detection of active regions in the farside through local helioseismology techniques. The knowledge of the magnetism in the whole Sun, including the non-visible hemisphere, is fundamental for several space weather forecasting applications. Seismic identification of farside active regions is challenged by the reduced signal-to-noise, and only large and strong active regions can be reliable detected. Here we develop a new methodology to improve the identification of active region signatures in farside seismic maps. We have constructed a deep neural network that associates the farside seismic maps obtained from helioseismic holography with the probability of presence of active regions in the farside. The network has been trained with pairs of helioseismic phase shift maps and Helioseismic and Magnetic Imager magnetograms acquired half a solar rotation later, which were used as a proxy for the presence of active regions in the farside. The method has been validated using a set of artificial data, and it has also been applied to actual solar observations during the period of minimum activity of the solar cycle 24. Our approach shows a higher sensitivity to the presence of farside active regions than standard methods applied up to date. The neural network can significantly increase the number of detected farside active regions, and will potentially improve the application of farside seismology to space weather forecasting.Comment: Accepted for publication in A&

Study of the mitigation of tram-induced vibrations on different track typologies

Nowadays there is a growing development of urban tram and underground networks with the aim of improving resident’s mobility and reducing the environmental impact. Among the issues related to this fact one finds the vibration generated by the vehicles and transmitted through the track and the ground. This may cause an important impact for both residents and structures. In order to study this phenomenon, a comprehensive campaign of measurements has been carried out in certain sections of the tram network in Alicante (Spain). In addition, an analytical model has been developed and calibrated with part of the data obtained. Using both experimental measures and the computer model vibration within the track is analyzed. Special attention is paid to the alleviation capability of the different materials and typologies present in the track. From this study, a strong relation between the Young Modulus and the frequency range alleviated by each material is obtained, and more rigid typologies are shown to be more efficient for low frequency vibrations which are the ones potentially disturbing for humans

PCA detection and denoising of Zeeman signatures in stellar polarised spectra

Our main objective is to develop a denoising strategy to increase the signal to noise ratio of individual spectral lines of stellar spectropolarimetric observations. We use a multivariate statistics technique called Principal Component Analysis. The cross-product matrix of the observations is diagonalized to obtain the eigenvectors in which the original observations can be developed. This basis is such that the first eigenvectors contain the greatest variance. Assuming that the noise is uncorrelated a denoising is possible by reconstructing the data with a truncated basis. We propose a method to identify the number of eigenvectors for an efficient noise filtering. Numerical simulations are used to demonstrate that an important increase of the signal to noise ratio per spectral line is possible using PCA denoising techniques. It can be also applied for detection of magnetic fields in stellar atmospheres. We analyze the relation between PCA and commonly used well-known techniques like line addition and least-squares deconvolution. Moreover, PCA is very robust and easy to compute.Comment: accepted to be published in A&

Signatures of the impact of flare ejected plasma on the photosphere of a sunspot light-bridge

We investigate the properties of a sunspot light-bridge, focusing on the changes produced by the impact of a plasma blob ejected from a C-class flare. We observed a sunspot in active region NOAA 12544 using spectropolarimetric raster maps of the four Fe I lines around 15655 \AA\ with the GREGOR Infrared Spectrograph (GRIS), narrow-band intensity images sampling the Fe I 6173 \AA\ line with the GREGOR Fabry-P\'erot Interferometer (GFPI), and intensity broad band images in G-band and Ca II H band with the High-resolution Fast Imager (HiFI). All these instruments are located at the GREGOR telescope at the Observatorio del Teide, Tenerife, Spain. The data cover the time before, during, and after the flare event. The analysis is complemented with Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI) data from the Solar Dynamics Observatory (SDO). The physical parameters of the atmosphere at differents heights were inferred using spectral-line inversion techniques. We identify photospheric and chromospheric brightenings, heating events, and changes in the Stokes profiles associated to the flare eruption and the subsequent arrival of the plasma blob to the light bridge, after traveling along an active region loop. The measurements suggest that these phenomena are the result of reconnection events driven by the interaction of the plasma blob with the magnetic field topology of the light bridge.Comment: Accepted for publication in A&

FarNet-II: An improved solar far-side active region detection method

Context. Activity on the far side of the Sun is routinely studied through the analysis of the seismic oscillations detected on the near side using helioseismic techniques such as phase shift sensitive holography. Recently, the neural network FarNet was developed to improve these detections. Aims. We aim to create a new machine learning tool, FarNet II, which further increases the scope of FarNet, and to evaluate its performance in comparison to FarNet and the standard helioseismic method for detecting far side activity. Methods. We developed FarNet II, a neural network that retains some of the general characteristics of FarNet but improves the detections in general, as well as the temporal coherence among successive predictions. The main novelties are the implementation of attention and convolutional long short term memory (ConvLSTM) modules. A cross validation approach, training the network 37 times with a different validation set for each run, was employed to leverage the limited amount of data available. We evaluate the performance of FarNet II using three years of extreme ultraviolet observations of the far side of the Sun acquired with the Solar Terrestrial Relations Observatory (STEREO) as a proxy of activity. The results from FarNet II were compared with those obtained from FarNet and the standard helioseismic method using the Dice coefficient as a metric. Results. FarNet II achieves a Dice coefficient that improves that of FarNet by over 0.2 points for every output position on the sequences from the evaluation dates. Its improvement over FarNet is higher than that of FarNet over the standard method. Conclusions. The new network is a very promising tool for improving the detection of activity on the far side of the Sun given by pure helioseismic techniques. Space weather forecasts can potentially benefit from the higher sensitivity provided by this novel method.Comment: Accepted for publication in Astronomy and Astrophysics. Abridged abstrac

The discrepancy in G-band contrast: Where is the quiet Sun?

We compare the rms contrast in observed speckle reconstructed G-band images with synthetic filtergrams computed from two magneto-hydrodynamic simulation snapshots. The observations consist of 103 bursts of 80 frames each taken at the Dunn Solar Telescope (DST), sampled at twice the diffraction limit of the telescope. The speckle reconstructions account for the performance of the Adaptive Optics (AO) system at the DST to supply reliable photometry. We find a considerable discrepancy in the observed rms contrast of 14.1% for the best reconstructed images, and the synthetic rms contrast of 21.5% in a simulation snapshot thought to be representative of the quiet Sun. The areas of features in the synthetic filtergrams that have positive or negative contrast beyond the minimum and maximum values in the reconstructed images have spatial scales that should be resolved. This leads us to conclude that there are fundamental differences in the rms G-band contrast between observed and computed filtergrams. On the basis of the substantially reduced granular contrast of 16.3% in the synthetic plage filtergram we speculate that the quiet-Sun may contain more weak magnetic field than previously thought.Comment: 16 pages, 8 figure

Railway traffic induced vibrations: comparison of analytical and finite element models

The recent increase in the use of the railway and the establishment of more restrictive policies of harmful environmental effects of railway transport highlights the need to investigate ground vibrations related to trains. Therefore models to evaluate how this phenomenon affects have been performed. This article aims to expose both analytical and 3D-FE models and to compare theoretical formulation and results. Models have been calibrated and validated with real data. Furthermore, a simulation of the acceleration level of different railway infrastructure elements has been achieved

Managing emergency situations in the smart city: The smart signal

In a city there are numerous items, many of them unnoticed but essential; this is the case of the signals. Signals are considered objects with reduced technological interest, but in this paper we prove that making them smart and integrating in the IoT (Internet of Things) could be a relevant contribution to the Smart City. This paper presents the concept of Smart Signal, as a device conscious of its context, with communication skills, able to offer the best message to the user, and as a ubiquitous element that contributes with information to the city. We present the design considerations and a real implementation and validation of the system in one of the most challenging environments that may exist in a city: a tunnel. The main advantages of the Smart Signal are the improvement of the actual functionality of the signal providing new interaction capabilities with users and a new sensory mechanism of the Smart City

Probing the Solar Atmosphere Using Oscillations of Infrared CO Spectral Lines

Oscillations were observed across the whole solar disk using the Doppler shift and line depth of spectral lines from the CO molecule near 4666~nm with the National Solar Observatory's McMath/Pierce solar telescope. Power, coherence, and phase spectra were examined, and diagnostic diagrams reveal power ridges at the solar global mode frequencies to show that these oscillations are solar p-modes. The phase was used to determine the height of formation of the CO lines by comparison with the IR continuum intensity phase shifts as measured in Kopp et al., 1992; we find the CO line formation height varies from 425 < z < 560 km as we move from disk center towards the solar limb 1.0 > mu > 0.5. The velocity power spectra show that while the sum of the background and p-mode power increases with height in the solar atmosphere as seen in previous work, the power in the p-modes only (background subtracted) decreases with height, consistent with evanescent waves. The CO line depth weakens in regions of stronger magnetic fields, as does the p-mode oscillation power. Across most of the solar surface the phase shift is larger than the expected value of 90 degrees for an adiabatic atmosphere. We fit the phase spectra at different disk positions with a simple atmospheric model to determine that the acoustic cutoff frequency is about 4.5 mHz with only small variations, but that the thermal relaxation frequency drops significantly from 2.7 to 0 mHz at these heights in the solar atmosphere