Ruprecht Karls Universität Heidelberg

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joint [2018/06/08 11:10]
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joint [2018/07/03 10:40] (current)
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 == 11th July 2018: Selected topics in theory and observations of Dark Energy (Tunch) == == 11th July 2018: Selected topics in theory and observations of Dark Energy (Tunch) ==
  
-Akshay Rana-  TBA+Akshay Rana (University of Delhi, India)"Novel ways to constrain Graviton mass and spatial curvature"​ 
 + 
 +In cosmology, it always remains a topic of great interest to constrain cosmological parameters 
 +by using different approaches and observational probes to confirm their consistency. In the same 
 +direction, I will present two novel ways that can be used to limit the mass of the Graviton and the spatial 
 +curvature of the space. My talk will be focused on two different aspects of present precision cosmology,​ 
 + 
 +1) Firstly, I will discuss about the graviton, which is a massless particle in GR while considered to be 
 +massive in some alternative models of GR like; Massive gravity theories. I will present a novel 
 +approach that can be used to put stringent constraints on its mass using galaxy clusters. 
 + 
 +2) Similarly, the estimation of spatial curvature of the Universe is also one of the most fundamental 
 +issue of modern cosmology. I will discuss a model-independent approach to test the curvature of 
 +space by using statistical features of strong gravitational lensing.
  
  
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-== 28th June 2018: Selected topics in theory and observations of Dark Energy (Tunch) ==+== 27th June 2018: Selected topics in theory and observations of Dark Energy (Tunch) ==
  
-Davi C. Rodrigues-- TBA+Davi C. Rodrigues(Federal University of Espirito Santo, Brazil)-"​Absence of a fundamental acceleration scale in galaxies"​
  
 +The Radial Acceleration Relation confirms that a nontrivial acceleration scale a0 can be found from the internal dynamics of several galaxies. The existence of such a scale is not obvious as far as the standard cosmological model is concerned, and it has been interpreted as a possible sign of modified gravity. We consider 193 high-quality disk galaxies and, using Bayesian inference, we show that the probability of existence of a fundamental acceleration is essentially zero: the null hypothesis is rejected at more than 10σ. We conclude that a0 is of emergent nature. In particular, the MOND theory, a well-known alternative to dark matter that is based on the existence of a fundamental acceleration scale, or any other theory that behaves like it at galactic scales, is ruled out as a fundamental theory for galaxies at more than 10σ.
  
  
-== 21st June 2018: Journal Club == 
  
-Collective Journal Club+ 
 +== 21st June 2018:  Selected topics in theory and observations of Dark Energy (Tunch) ==  
 + 
 +Elena Sellentin (Geneva University)- "​Objective Bayesian analysis of neutrino masses and hierarchy"​ 
 + 
 +Given the precision of current neutrino data, priors still impact noticeably the constraints on neutrino masses and their hierarchy. To avoid our understanding of neutrinos being driven by prior assumptions,​ we construct a prior that is mathematically minimally informative. Using the constructed uninformative prior, we find that the normal hierarchy is favoured but with inconclusive posterior odds of 5.1:1. Better data is hence needed before the neutrino masses and their hierarchy can be well constrained. We find that the next decade of cosmological data should provide conclusive evidence if the normal hierarchy with negligible minimum mass is correct, and if the uncertainty in the sum of neutrino masses drops below 0.025 eV. On the other hand, if neutrinos obey the inverted hierarchy, achieving strong evidence will be difficult with the same uncertainties. Our uninformative prior was constructed from principles of the Objective Bayesian approach. The prior is called a reference prior and is minimally informative in the specific sense that the information gain after collection of data is maximized. The prior is computed for the combination of neutrino oscillation data and cosmological data and still applies if the data improve. 
  
  

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