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LOCNES: Low Cost NIR Extended Solar Telescope

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arxiv 1808.03185 v1 pith:YFCCPAIA submitted 2018-08-09 astro-ph.IM

classification astro-ph.IM
keywords solarstellarallowcostlocnesnoiseobservedstar
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abstract

The search for telluric extrasolar planets with the Radial Velocity (RV) technique is intrinsically limited by the stellar jitter due to the activity of the star, because stellar surface inhomogeneities, including spots, plages and convective granules, induce perturbations hiding or even mimicking the planetary signal. This kind of noise is poorly understood in all the stars, but the Sun, due to their unresolved surfaces. For these reasons, the effects of the surface inhomogeneities on the measurement of the RV are very difficult to characterize. On the other hand, a better knowledge of these phenomena can allow us a step forward in our understanding of solar and stellar RV noise sources. This will allow to develop more tools for an optimal activity correction leading to more precise stellar RVs. Due to the high spatial resolution with which the Sun is observed, this noise is well known for it. Despite this, a link is lacking between the single observed photospheric phenomena and the behavior of the Sun observed as a star. LOCNES (Low Cost NIR Extended Solar Telescope) will allow to gather time series of RVs in order to disentangle the different contributions to the stellar (i.e., solar) RV jitter. Since July 2015, a Low Cost Solar Telescope (LCST) has been installed outside the TNG dome to feed solar light to the HARPS-N spectrograph (0.38-0.69 $\mu$m; R=115000). The refurbishment of the Near Infrared (NIR) High Resolution Spectrograph GIANO (now GIANO-B) and the new observing mode GIARPS at TNG (simultaneous observations in visible with HARPS-N and in NIR with GIANO-B) is a unique opportunity to extend the wavelength range up to 2.4 $\mu$m for measuring the RV time series of the Sun as a star. This paper outlines the LOCNES project and its scientific drivers.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Modeling Doppler Shifts in Radial-Velocity Data with Deep Learning toward Earth-mass Exoplanet Detection

    astro-ph.IM 2026-06 unverdicted novelty 6.0 of 10

    Deep neural networks using temperature-based spectral representations recover planetary Doppler signals with amplitudes of at least 25 cm/s from HARPS-N solar spectra under cross-validation.

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