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Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. ; The magnetic field plays an essential role in the initiation and evolution of different solar phenomena in the corona. The structure and evolution of the 3D coronal magnetic field are still not very well known. A way to ascertain the 3D structure of the coronal magnetic field is by performing magnetic field extrapolations from the photosphere to the corona. In previous work, it was shown that by prescribing the 3D-reconstructed loops' geometry, the magnetic field extrapolation produces a solution with a better agreement between the modeled field and the reconstructed loops. This also improves the quality of the field extrapolation. Stereoscopy, which uses at least two view directions, is the traditional method for performing 3D coronal loop reconstruction. When only one vantage point of the coronal loops is available, other 3D reconstruction methods must be applied. Within this work, we present a method for the 3D loop reconstruction based on machine learning. Our purpose for developing this method is to use as many observed coronal loops in space and time for the modeling of the coronal magnetic field. Our results show that we can build machine-learning models that can retrieve 3D loops based only on their projection information. Ultimately, the neural network model will be able to use only 2D information of the coronal loops, identified, traced, and extracted from the extreme-ultraviolet images, for the calculation of their 3D geometry. © 2021. The Author(s). Published by the American Astronomical Society. ; Data are courtesy of NASA/SDO and the HMI science teams. I.C. acknowledges DFG-grant WI 3211/5-1. The HMI data are provided courtesy of NASA/SDO and the HMI science team. R.G. acknowledges financial support by the Portuguese Government through the Foundation for Science and Technology-FCT FEDER-European Regional Development Fund through COMPETE 2020-Operational Programme Competitiveness and Internationalization. ; With funding from the Spanish government through the Severo Ochoa Centre of Excellence accreditation SEV-2017-0709. ; Peer reviewed

Future ground-based telescopes will expand our capabilities for simultaneous multi-line polarimetric observations in a wide range of wavelengths, from the near-ultraviolet to the near-infrared. This creates a strong demand to compare candidate spectral lines to establish a guideline of the lines that are most appropriate for each observation target. We focused in this first work on Zeeman-sensitive photospheric lines in the visible and infrared. We first examined their polarisation signals and response functions using a 1D semi-empirical atmosphere. Then we studied the spatial distribution of the line core intensity and linear and circular polarisation signals using a realistic 3D numerical simulation. We ran inversions of synthetic profiles, and we compared the heights at which we obtain a high correlation between the input and the inferred atmosphere. We also used this opportunity to revisit the atomic information we have on these lines and computed the broadening cross-sections due to collisions with neutral hydrogen atoms for all the studied spectral lines. The results reveal that four spectral lines stand out from the rest for quiet-Sun and network conditions: Fe» I 5250.2, 6302, 8468, and 15 648 A. The first three form higher in the atmosphere, and the last line is mainly sensitive to the atmospheric parameters at the bottom of the photosphere. However, as they reach different heights, we strongly recommend using at least one of the first three candidates together with the Fe» I 15 648 A line to optimise our capabilities for inferring the thermal and magnetic properties of the lower atmosphere. © ESO 2021. ; C. Quintero Noda was supported by the EST Project Office, funded by the Canary Islands Government (file SD 17/01) under a direct grant awarded to the IAC on ground of public interest, and this activity has also received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739500. This work was supported by the Research Council of Norway through its Centres of Excellence scheme, project number 262622, and through grants of computing time from the Programme for Supercomputing. P. S. Barklem received financial support from the Swedish Research Council. The Centre for Earth and Space Research of University of Coimbra is funded by National Funds through FCT – Foundation for Science and Technology (project: UID/MULTI/00611/2019) and FEDER – European Regional Development Fund through COMPETE 2020 – Operational Programme Competitiveness and Internationalization (project: POCI-01-0145-FEDER-006922). This work has been funded by the Spanish Ministry of Science and Innovation through project ESP-2016-77548-C5-1-R, including a percentage from FEDER funds, and through the Centro de Excelencia Severo Ochoa grant SEV-2017-0709 awarded to the Instituto de Astrofísica de Andalucía in the period 2018-2022. D. Orozco Suárez also acknowledges financial support through the Ramón y Cajal fellowships. This work was supported by the ISAS/JAXA Small Mission-of-Opportunity program for novel solar observations and the JSPS KAKENHI Grant Number JP18H05234. ; Peer reviewed