Multifrequency polarimetry of High-Synchrotron Peaked blazars probes the shape of their jets

Multifrequency polarimetry is emerging as a powerful probe of blazar jets, especially due to the advent of the Imaging X-ray Polarimetry Explorer (IXPE) space observatory. We study the polarization of High-Synchrotron Peaked (HSP) blazars, where both optical and X-ray emission can be attributed to synchrotron radiation from a population of non-thermal electrons. We adopt an axisymmetric stationary force-free jet model, where the electromagnetic fields are determined by the jet shape. In particular, the jet geometry is defined by the pressure profile of the external medium confining the jet. When jets are confined by a windy-like medium, they acquire a quasi-parabolic shape. In this case, the X-ray polarization degree is Pi_X ∼ 15−40%, and the optical polarization degree is Pi_O ∼ 5−20%. The polarization degree is strongly chromatic, as Pi_X/Pi_O ∼ 2−5. The chromaticity is due to the softening of the electron distribution at high energies, and is much stronger than for a uniform magnetic field. The Electric Vector Position Angle (EVPA) is aligned with the projection of the jet axis on the plane of the sky. These results compare very well with multifrequency polarimetric observations of HSP blazars. Instead, when the jet is nearly cylindrical, the polarization degree is large and weakly chromatic (we find Pi_X ∼ 70% and Pi_O ∼ 60%), close to the expected values for a uniform magnetic field. The EVPA is perpendicular to the projection of the jet axis on the plane of the sky. We also provide analytical approximated formulae, valid for small viewing angles, for both the polarization degree and the EVPA as a function of the spectral-index. The polarization degree is highly chromatic unlike the EVPA. The polarization degree and the EVPA may be less sensitive to the specific particle acceleration process (e.g., magnetic reconnection or shocks) than previously thought. The Euclid mission: current status, results from early observations, and future prospects / 356 Mask R-CNN for the Automated Detection of Bright Gravitational Arcs in Euclid Galaxy Clusters Departamento de Física Moderna Santander 2 Universita’ degli Studi di Milano 3 INAF Napoli 4 Universita’ degli Studi di Ferrara 5 INAF Padova 6 Università di Napoli Federico II 7 INAF Bologna 8 Universität Heidelberg In the era of big data, developing robust methods for the autonomous extraction of information from vast multi-dimensional datasets is pivotal. This work focuses on utilizing a region-based convolutional neural network (Mask R-CNN) to automatically detect bright arcs produced by strong gravitational lensing in galaxy clusters, specifically for the upcoming Euclid survey. These kinds of events offer a powerful tool for probing the mass distribution of galaxy clusters and the large-scale structure of the universe. To replicate the observational conditions expected from Euclid, we used downgraded Hubble Space Telescope images of real galaxy clusters. We trained the network by simulating strong lensing events on these clusters, leveraging high-precision lensing models of 10 massive galaxy clusters within a redshift range of 0.2 < z < 0.6. A training set of 4000 2’x2’images was generated by injecting Sersic sources near the caustic lines of each galaxy cluster. Our model achieves high completeness in the simulated test images, successfully recovering all the brightest arcs. The Mask R-CNN’s training procedure involves direct processing of full-field images, circumventing the need for pre-extracted cutouts. The successful application of this method demonstrates the feasibility of using advanced neural network architectures to enhance the detection of astrophysical phenomena. Our approach is well-suited to handle the massive amounts of data expected from next-generation telescopes, showcasing the potential of deep learning to significantly advance the field of astrophysics. Current status of the H_0 and growth tensions: theoretical models and model-independent