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Regression methods based in Machine Learning Algorithms (MLA) have become an important tool for data analysis in many different disciplines. In this work, we use MLA in an astrophysical context; our goal is to measure the mean longitudinal magnetic field in stars (H_ eff) from polarized spectra of high resolution, through the inversion of the so-called multi-line profiles. Using synthetic data, we tested the performance of our technique considering different noise levels: In an ideal scenario of noise-free multi-line profiles, the inversion results are excellent; however, the accuracy of the
In this work we perform an observational data analysis on Einsteinian cubic gravity and $f(P)$ gravity with the objective of constraining the parameter space of the theories. We use the 30 point $z-H(z)$ cosmic chronometer data as the observational t
We present data collected using the camera PISCES coupled with the Firt Light Adaptive Optics (FLAO) mounted at the Large Binocular Telescope (LBT). The images were collected using two natural guide stars with an apparent magnitude of R<13 mag. Durin
Are the kG-strength magnetic fields observed in young stars a fossil field left over from their formation or are they generated by a dynamo? We use radiation non-ideal magnetohydrodynamics simulations of the gravitational collapse of a rotating, magn
This review discusses the problem of reconstruction of surface magnetic field topologies of early-type stars with a focus on mapping methods utilising information content of high-resolution spectropolarimetric observations. Basic principles of the Ze
Purpose: We sought to evaluate the feasibility of using machine learning algorithms for multipoint plastic scintillator detector calibration in high-dose-rate brachytherapy. Methods: The dosimetry system consisted of an optimized 1-mm-core mPSD and a