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Detecting exoplanets and measuring their masses is a priority for astrophysics. Stellar astrometry is capable of detecting and unequivocally measure exoplanet masses, solving the system inclination ambiguity that constrains the accuracy of radial velocity techniques. The astrometric signal of an Earth-like planet around nearby stars is around ∼ 1 micro-arcsecond (μas), while current instrumentation (Hubble, GAIA) reaches 25 μas at best. After photon noise, the main limiting factor to measure such astrometric signals is related to the optical distortions that arise from small deformations of the optical system. A novel technique, called diffractive pupil, allows to obtain distortion-calibrated astrometry vectors from the image. Currently, analytical methods are being used to extract the distortion map from the diffractive features. However, different sources of noise limit the accuracy of the algorithm. In this paper, we test the ability of machine learning to detect telescope pointing errors and astrometry signals injected in simulated images, as a first step towards distortion calibration correction using machine learning. Our image simulator generates a star field modified by telescope pointing errors, optical distortion, and common noise sources such as photon noise, flat field, read out, and dark current. We evaluate the performance of legacy analytical algorithms for astrometry and compare it with the results of machine learning runs. We find that this type of algorithms show better results than the analytical approach by two orders of magnitude when detecting astrometric signals in images with telescope pointing error perturbations, achieving a Mean Absolute Error (MAE) of ∼ 1.7 × 10−6 px for the predicted target star translations in comparison with the ∼ 5 × 10−4 px MAE obtained by the traditional approach.
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