Tuesdays 10:30 - 11:30 | Fridays 11:30 - 12:30
Showing votes from 2021-07-13 11:30 to 2021-07-16 12:30 | Next meeting is Friday Nov 15th, 11:30 am.
Supernovae Ia (SN) are among the brightest objects we can observe and can provide a unique window on the large scale structure of the Universe at redshifts where other observations are not available. The photons emitted by SNe are in fact affected by the density field between the source and the observer, and from the observed luminosity distance it is possible to solve the inversion problem (IP), i.e. to reconstruct the density field which produced those effects. So far the IP was only solved assuming some restrictions about the geometry of the problem, such as spherical symmetry for example, and the approach was based on solving complicated systems of differential equations which required smooth function as inputs, while observational data is not smooth, due to its discrete nature. In order to overcome these limitations we develop for the first time an inversion method which is not assuming any symmetry, and can be applied directly to observational data, without the need of any data smoothing procedure. The method is based on the use of convolutional neural networks (CNN) trained on simulated data, and it shows quite accurate results. The training data set is obtained by first generating random density and velocity profiles, and then computing their effects on the luminosity distance. The CNN is then trained to reconstruct the density field from the luminosity distance. The CNN is a modified version of U-Net to account for the tridimensionality of the data, and can reconstruct the density and velocity fields with a good level of accuracy. The use of neural networks to analyze observational data from future SNe catalogues will allow to reconstruct the large scale structure of the Universe to an unprecedented level of accuracy, at a redshift at which few other observations are available.
We carry out a test of the radial acceleration relation (RAR) for a sample of 10 dynamically relaxed and cool-core galaxy clusters imaged by the Chandra X-ray telescope, which was studied in Giles et al. For this sample, we observe that the best-fit RAR shows a very tight residual scatter equal to 0.09 dex. We obtain an acceleration scale of $1.59 \times 10^{-9} m/s^2$, which is about an order of magnitude higher than that obtained for galaxies. Furthermore, the best-fit RAR parameters differ from those estimated from some of the previously analyzed cluster samples, which indicates that the acceleration scale found from the RAR could be of an emergent nature, instead of a fundamental universal scale.
The information loss paradox is usually stated as an incompatibility between general relativity and quantum mechanics. However, the assumptions leading to the problem are often overlooked and, in fact, a careful inspection of the main hypothesises suggests a radical reformulation of the problem. Indeed, we present a thought experiment involving a black hole that emits radiation and, independently of the nature of the radiation, we show the existence of an incompatibility between (i) the validity of the laws of general relativity to describe infalling matter far from the Planckian regime, and (ii) the so-called central dogma which states that as seen from an outside observer a black hole behaves like a quantum system whose number of degrees of freedom is proportional to the horizon area. We critically revise the standard arguments in support of the central dogma, and argue that they cannot hold true unless some new physics is invoked even before reaching Planck scales. This suggests that the information loss problem, in its current formulation, is not necessarily related to any loss of information or lack of unitarity. Therefore, in principle, semiclassical general relativity and quantum mechanics can be perfectly compatible before reaching the final stage of the black hole evaporation where, instead, a consistent theory of quantum gravity is needed to make any prediction.