Many novel methods have been proposed to mitigate stellar activity for
exoplanet detection as the presence of stellar activity in radial velocity (RV)
measurements is the current major limitation. Unlike traditional methods that
model stellar activity in the RV domain, more methods are moving in the
direction of disentangling stellar activity at the spectral level. The goal of
this paper is to present a novel convolutional neural network-based algorithm
that efficiently models stellar activity signals at the spectral level,
enhancing the detection of Earth-like planets. We trained a convolutional
neural network to build the correlation between the change in the spectral line
profile and the corresponding RV, full width at half maximum (FWHM) and
bisector span (BIS) values derived from the classical cross-correlation
function. This algorithm has been tested on three intensively observed stars:
Alpha Centauri B (HD128621), Tau ceti (HD10700), and the Sun. By injecting
simulated planetary signals at the spectral level, we demonstrate that our
machine learning algorithm can achieve, for HD128621 and HD10700, a detection
threshold of 0.5 m/s in semi-amplitude for planets with periods ranging from 10
to 300 days. This threshold would correspond to the detection of a
$\sim$4$\mathrm{M}_{\oplus}$ in the habitable zone of those stars. On the
HARPS-N solar dataset, our algorithm is even more efficient at mitigating
stellar activity signals and can reach a threshold of 0.2 m/s, which would
correspond to a 2.2$\mathrm{M}_{\oplus}$ planet on the orbit of the Earth. To
the best of our knowledge, it is the first time that such low detection
thresholds are reported for the Sun, but also for other stars, and therefore
this highlights the efficiency of our convolutional neural network-based
algorithm at mitigating stellar activity in RV measurements.

通过使用卷积神经网络在光谱水平有效模拟恒星活动信号，提高了地球类行星的检测准确性，并且实现了更低的检测阈值。