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Register a new userHow do atomic code uncertainties affect abundance measurements in the intracluster medium?
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Fran\\c{c}ois Mernier
Publication date
2019
fields
Physics
and research's language is
English
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Accurate chemical abundance measurements of X-ray emitting atmospheres pervading massive galaxies, galaxy groups, and clusters provide essential information on the star formation and chemical enrichment histories of these large scale structures. Although the collisionally ionised nature of the intracluster medium (ICM) makes these abundance measurements relatively easy to derive, underlying spectral models can rely on different atomic codes, which brings additional uncertainties on the inferred abundances. Here, we provide a simple, yet comprehensive comparison between the codes SPEXACT v3.0.5 (cie model) and AtomDB v3.0.9 (vapec model) in the case of moderate, CCD-like resolution spectroscopy. We show that, in cool plasmas ($kT lesssim 2$ keV), systematic differences up to $sim$20% for the Fe abundance and $sim$45% for the O/Fe, Mg/Fe, Si/Fe, and S/Fe ratios may still occur. Importantly, these discrepancies are also found to be instrument-dependent, at least for the absolute Fe abundance. Future improvements in these two codes will be necessary to better address questions on the ICM enrichment.
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Wave dark matter ($psi$DM) predicts a compact soliton core and a granular halo in every galaxy. This work presents the first simulation study of an elliptical galaxy by including both stars and $psi$DM, focusing on the systematic changes of the central soliton and halo granules. With the addition of stars in the inner halo, we find the soliton core consistently becomes more prominent by absorbing mass from the host halo than that without stars, and the halo granules become non-isothermal, hotter in the inner halo and cooler in the outer halo, as opposed to the isothermal halo in pure $psi$DM cosmological simulations. Moreover, the composite (star+$psi$DM) mass density is found to follow a $r^{-2}$ isothermal profile near the half-light radius in most cases. Most striking is the velocity dispersion of halo stars that increases rapidly toward the galactic center by a factor of at least 2 inside the half-light radius caused by the deepened soliton gravitational potential, a result that compares favorably with observations of elliptical galaxies and bulges in spiral galaxies. However in some rare situations we find a phase segregation turning a compact distribution of stars into two distinct populations with high and very low velocity dispersions; while the high-velocity component mostly resides in the halo, the very low-velocity component is bound to the interior of the soliton core, resembling stars in faint dwarf spheroidal galaxies.
We show that uncertainties in the values of the surface heavy element abundances of the Sun are the largest source of the theoretical uncertainty in calculating the p-p, pep, 8B, 13N, 15O, and 17F solar neutrino fluxes. We evaluate for the first time the sensitivity (partial derivative) of each solar neutrino flux with respect to the surface abundance of each element. We then calculate the uncertainties in each neutrino flux using `conservative (preferred) and `optimistic estimates for the uncertainties in the element abundances. The total conservative (optimistic) composition uncertainty in the predicted 8B neutrino flux is 11.6% (5.0%) when sensitivities to individual element abundances are used. The traditional method that lumps all abundances into a single quantity (total heavy element to hydrogen ratio, Z/X) yields a larger uncertainty, 20%. The uncertainties in the carbon, oxygen, neon, silicon, sulphur, and iron abundances all make significant contributions to the uncertainties in calculating solar neutrino fluxes; the uncertainties of different elements are most important for different neutrino fluxes. The uncertainty in the iron abundance is the largest source of the estimated composition uncertainties of the important 7Be and 8B solar neutrinos. Carbon is the largest contributor to the uncertainty in the calculation of the p-p, 13N, and 15O neutrino fluxes. However, for all neutrino fluxes, several elements contribute comparable amounts to the total composition uncertainty.
Faraday rotation and synchrotron emission from extragalactic radio sources give evidence for the presence of magnetic fields extending over ~Mpc scales. However, the origin of these fields remains elusive. With new high-resolution grid simulations we studied the growth of magnetic fields in a massive galaxy cluster that in several aspects is similar to the Coma cluster. We investigated models in which magnetic fields originate from primordial seed fields with comoving strengths of 0.1 nG at redshift z=30. The simulations show evidence of significant magnetic field amplification. At the best spatial resolution (3.95 kpc), we are able to resolve the scale where magnetic tension balances the bending of magnetic lines by turbulence. This allows us to observe the final growth stage of the small-scale dynamo. To our knowledge this is the first time that this is seen in cosmological simulations of the intracluster medium. Our mock observations of Faraday Rotation provide a good match to observations of the Coma cluster. However, the distribution of magnetic fields shows strong departures from a simple Maxwellian distribution, suggesting that the three-dimensional structure of magnetic fields in real clusters may be significantly different than what is usually assumed when inferring magnetic field values from rotation measure observations.
Learning rate, batch size and momentum are three important hyperparameters in the SGD algorithm. It is known from the work of Jastrzebski et al. arXiv:1711.04623 that large batch size training of neural networks yields models which do not generalize well. Yao et al. arXiv:1802.08241 observe that large batch training yields models that have poor adversarial robustness. In the same paper, the authors train models with different batch sizes and compute the eigenvalues of the Hessian of loss function. They observe that as the batch size increases, the dominant eigenvalues of the Hessian become larger. They also show that both adversarial training and small-batch training leads to a drop in the dominant eigenvalues of the Hessian or lowering its spectrum. They combine adversarial training and second order information to come up with a new large-batch training algorithm and obtain robust models with good generalization. In this paper, we empirically observe the effect of the SGD hyperparameters on the accuracy and adversarial robustness of networks trained with unperturbed samples. Jastrzebski et al. considered training models with a fixed learning rate to batch size ratio. They observed that higher the ratio, better is the generalization. We observe that networks trained with constant learning rate to batch size ratio, as proposed in Jastrzebski et al., yield models which generalize well and also have almost constant adversarial robustness, independent of the batch size. We observe that momentum is more effective with varying batch sizes and a fixed learning rate than with constant learning rate to batch size ratio based SGD training.
Buoyant bubbles of relativistic plasma in cluster cores plausibly play a key role in conveying the energy from a supermassive black hole to the intracluster medium (ICM) - the process known as radio-mode AGN feedback. Energy conservation guarantees that a bubble loses most of its energy to the ICM after crossing several pressure scale heights. However, actual processes responsible for transferring the energy to the ICM are still being debated. One attractive possibility is the excitation of internal waves, which are trapped in the clusters core and eventually dissipate. Here we show that a sufficient condition for efficient excitation of these waves in stratified cluster atmospheres is flattening of the bubbles in the radial direction. In our numerical simulations, we model the bubbles phenomenologically as rigid bodies buoyantly rising in the stratified cluster atmosphere. We find that the terminal velocities of the flattened bubbles are small enough so that the Froude number ${rm Fr}lesssim 1$. The effects of stratification make the dominant contribution to the total drag force balancing the buoyancy force. In particular, clear signs of internal waves are seen in the simulations. These waves propagate horizontally and downwards from the rising bubble, spreading their energy over large volumes of the ICM. If our findings are scaled to the conditions of the Perseus cluster, the expected terminal velocity is $sim100-200{,rm km,s^{-1}}$ near the cluster cores, which is in broad agreement with direct measurements by the Hitomi satellite.
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