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تسجيل مستخدم جديدPredictions of the pseudo-complex theory of Gravity for EHT observations: I. Observational tests
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A modified theory of gravity, avoiding singularities in the standard theory of gravitation, has been developed by Hess & Greiner, known as the pseudo-complex theory of gravitation. The pc-GR theory shows remarkable observational differences with respect to standard GR. The intensity profiles are significantly different between both theories, which is a rare phenomenon in astrophysics. This will allow robust tests of both theories using Event Horizon Telescope (EHT) observations of the Galactic Center. We also predict the time evolution of orbiting matter. In this paper we summarize the observational tests we have developed to date. The theory is described in the second paper of this series (Hess et al. 2019, referred to as paper II hereafter).
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We present a resume on the modified theory of gravity, called pseudo-complex General Relativity (pc-GR). It is the second in a series of papers, where the first one (Boller et al. 2019, referred to as paper I) discussed the observational consequences
of pc-GR. In this paper, we concentrate on the underlying theory. PC-GR involves an algebraic extension of the standard theory of GR and it depends on two phenomenological parameters. An element included in pc-GR that is not present in standard GR is the energy-momentum tensor corresponding to an anisotropic ideal fluid, which we call dark energy. The two parameters are related to the coupling of mass to the dark energy and its fall-off as a function of r. The consequences and predictions of this theory will be discussed in the context of the observational results of the Even Horizon Telescope, expected soon. Our main result is that due to the accumulation of dark energy near a large mass, the modified theory predicts a dark ring followed by a bright ring in the emission profile of the accretion disc. We also discuss the light ring in the equatorial plane.
Granato et al(2004) have elaborated a physically grounded model exploiting the mutual feedback between star-forming spheroidal galaxies and the active nuclei growing in their cores to overcome, within the hierarchical clustering scenario for galaxy f
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Polar ring galaxies are ideal objects with which to study the three-dimensional shapes of galactic gravitational potentials since two rotation curves can be measured in two perpendicular planes. Observational studies have uncovered systematically lar
ger rotation velocities in the extended polar rings than in the associated host galaxies. In the dark matter context, this can only be explained through dark halos that are systematically flattened along the polar rings. Here, we point out that these objects can also be used as very effective tests of gravity theories, such as those based on Milgromian dynamics (MOND). We run a set of polar ring models using both Milgromian and Newtonian dynamics to predict the expected shapes of the rotation curves in both planes, varying the total mass of the system, the mass of the ring with respect to the host, as well as the size of the hole at the center of the ring. We find that Milgromian dynamics not only naturally leads to rotation velocities being typically higher in the extended polar rings than in the hosts, as would be the case in Newtonian dynamics without dark matter, but that it also gets the shape and amplitude of velocities correct. Milgromian dynamics thus adequately explains this particular property of polar ring galaxies.
Machine learning is becoming a popular tool to quantify galaxy morphologies and identify mergers. However, this technique relies on using an appropriate set of training data to be successful. By combining hydrodynamical simulations, synthetic observa
tions and convolutional neural networks (CNNs), we quantitatively assess how realistic simulated galaxy images must be in order to reliably classify mergers. Specifically, we compare the performance of CNNs trained with two types of galaxy images, stellar maps and dust-inclusive radiatively transferred images, each with three levels of observational realism: (1) no observational effects (idealized images), (2) realistic sky and point spread function (semi-realistic images), (3) insertion into a real sky image (fully realistic images). We find that networks trained on either idealized or semi-real images have poor performance when applied to survey-realistic images. In contrast, networks trained on fully realistic images achieve 87.1% classification performance. Importantly, the level of realism in the training images is much more important than whether the images included radiative transfer, or simply used the stellar maps (87.1% compared to 79.6% accuracy, respectively). Therefore, one can avoid the large computational and storage cost of running radiative transfer with a relatively modest compromise in classification performance. Making photometry-based networks insensitive to colour incurs a very mild penalty to performance with survey-realistic data (86.0% with r-only compared to 87.1% with gri). This result demonstrates that while colour can be exploited by colour-sensitive networks, it is not necessary to achieve high accuracy and so can be avoided if desired. We provide the public release of our statistical observational realism suite, RealSim, as a companion to this paper.
An active stage of relativistic astrophysics started in 1963 since in this year, quasars were discovered, Kerr solution has been found and the first Texas Symposium on Relativistic Astrophysics was organized in Dallas. Five years later, in 1967--1968
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