ترغب بنشر مسار تعليمي؟ اضغط هنا

Does magnetic pressure affect the ICM dynamics?

84   0   0.0 ( 0 )
 نشر من قبل Denise Rocha Goncalves
 تاريخ النشر 1999
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

A possible discrepancy found in the determination of mass from gravitational lensing data, and from X-rays observations, has been largely discussed in the latest years (for instance, Miralda-Escude & Babul (1995)). Another important discrepancy related to these data is that the dark matter is more centrally condensed than the X-ray-emitting gas, and also with respect to the galaxy distribution (Eyles et al. 1991). Could these discrepancies be consequence of the standard description of the ICM, in which it is assumed hydrostatic equilibrium maintained by thermal pressure? We follow the evolution of the ICM, considering a term of magnetic pressure, aiming at answering the question whether or not these discrepancies can be explained via non-thermal terms of pressure. Our results suggest that the magnetic pressure could only affect the dynamics of the ICM on scales as small as < 1kpc. Our models are constrained by the observations of large and small scale fields and we are successful at reproducing available data, for both Faraday rotation limits and inverse Compton limits for the magnetic fields. In our calculations the radius (from the cluster center) in which magnetic pressure reaches equipartition is smaller than radii derived in previous works, as a consequence of the more realistic treatment of the magnetic field geometry and the consideration of a sink term in the cooling flow.



قيم البحث

اقرأ أيضاً

56 - D.R. Goncalves 1998
Could the discrepancies found in the determination of mass in clusters of galaxies, from gravitational lensing data and from X-rays observations, be consequence of the standard description of the ICM, in which it is assumed hydrostatic equilibrium ma intained by thermal pressure? In analogy to the interstellar medium of the Galaxy, it is expected a non-thermal term of pressure, which contains contributions of magnetic fields. We follow the evolution of the ICM, considering a term of magnetic pressure, aiming at answering the question whether or not these discrepancies can be explained via non-thermal terms of pressure. Our results suggest that the magnetic pressure could only affect the dynamics of the ICM on scales as small as $la 1 {rm kpc}$. These results are compared to the observations of large and small scale magnetic fields and we are successful at reproducing the available data.
Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. However, the MAML objective is significantly more difficult to optimize comp ared to standard Empirical Risk Minimization (ERM), and little is understood about how much MAML improves over ERM in terms of the fast adaptability of their solutions in various scenarios. We analytically address this issue in a linear regression setting consisting of a mixture of easy and hard tasks, where hardness is related to the condition number of the tasks loss function. Specifically, we prove that in order for MAML to achieve substantial gain over ERM, (i) there must be some discrepancy in hardness among the tasks, and (ii) the optimal solutions of the hard tasks must be closely packed with the center far from the center of the easy tasks optimal solutions. We also give numerical and analytical results suggesting that these insights also apply to two-layer neural networks. Finally, we provide few-shot image classification experiments that support our insights for when MAML should be used and emphasize the importance of training MAML on hard tasks in practice.
Among the many aspects that characterize the COVID-19 pandemic, two seem particularly challenging to understand: (i) the great geographical differences in the degree of virus contagiousness and lethality which were found in the different phases of th e epidemic progression, and (ii) the potential role of the infected peoples blood type in both the virus infectivity and the progression of the disease. A recent hypothesis could shed some light on both aspects. Specifically, it has been proposed that in the subject-to-subject transfer SARS-CoV-2 conserves on its capsid the erythrocytes antigens of the source subject. Thus these conserved antigens can potentially cause an immune reaction in a receiving subject that has previously acquired specific antibodies for the source subject antigens. This hypothesis implies a blood type-dependent infection rate. The strong geographical dependence of the blood type distribution could be, therefore, one of the factors at the origin of the observed heterogeneity in the epidemics spread. Here, we present an epidemiological deterministic model where the infection rules based on blood types are taken into account and compare our model outcomes with the exiting worldwide infection progression data. We found an overall good agreement, which strengthens the hypothesis that blood types do play a role in the COVID-19 infection.
291 - Da Yu , Huishuai Zhang , Wei Chen 2020
It is observed in the literature that data augmentation can significantly mitigate membership inference (MI) attack. However, in this work, we challenge this observation by proposing new MI attacks to utilize the information of augmented data. MI att ack is widely used to measure the models information leakage of the training set. We establish the optimal membership inference when the model is trained with augmented data, which inspires us to formulate the MI attack as a set classification problem, i.e., classifying a set of augmented instances instead of a single data point, and design input permutation invariant features. Empirically, we demonstrate that the proposed approach universally outperforms original methods when the model is trained with data augmentation. Even further, we show that the proposed approach can achieve higher MI attack success rates on models trained with some data augmentation than the existing methods on models trained without data augmentation. Notably, we achieve a 70.1% MI attack success rate on CIFAR10 against a wide residual network while the previous best approach only attains 61.9%. This suggests the privacy risk of models trained with data augmentation could be largely underestimated.
166 - Ignacio Ferreras 2010
Differences in the stellar populations of galaxies can be used to quantify the effect of environment on the star formation history. We target a sample of early-type galaxies from the Sloan Digital Sky Survey in two different environmental regimes: cl ose pairs and a general sample where environment is measured by the mass of their host dark matter halo. We apply a blind source separation technique based on principal component analysis, from which we define two parameters that correlate, respectively, with the average stellar age (eta) and with the presence of recent star formation (zeta) from the spectral energy distribution of the galaxy. We find that environment leaves a second order imprint on the spectra, whereas local properties - such as internal velocity dispersion - obey a much stronger correlation with the stellar age distribution.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا