Day 1 :
Independent researcher, Edinburgh.
Swagatam Sen is a mathematician and statistician with Masters’ degree in Statistics from Indian Statistical Institute which was awarded to him in 2005. Swagatam has had 15 years of experience in Data Science and related quantitative disciplines and is currently running Data Science unit for HSBC in Edinburgh. Aside from his regular profession, he is also an independent researcher in physics working on a number of areas in foundations of physics.
An alternative viewpoint has been achieved to explain observed anomalies in Galaxy rotation curves without requiring any dark matter existence. The explanation is rooted in a characterisation of spacetime as a Kahler manifold on complex 3 dimensions. Using this fundamental extension in our understanding of reality, It has been derived how the appropriate geodesics on that complex spacetime structure, along with field equations of General Relativity, would behave. Using these generic results, it has then been shown that with appropriate choice of metric one can allow for centrally concentrated density distributions that can generate flatter rotation curves. The concept then has been applied to rotation curves of 4 different galaxies to obtain required density distributions, which shows clear absence of any exterior dark matter halo. Instead all 4 galaxies exhibit a massively concentrated core with a fast diminishing negative energy field around it.
Sapcorda Services GmbH, Berlin, Germany
Marios Smyrnaios studied geomatics in Athens and geodesy in Berlin. He holds a PhD in the field of satellite-based navigation from the university of Hannover. He is currently working at Sapcorda Services GmbH as a GNSS systems engineer in the framework of the development of a high precision GNSS correction service. His research interests include satellite navigation, positioning with pseudolites, GNSS signal processing, multipath and high precision GNSS correction solutions.
In the last decades many advances have been made in modeling the different error sources that are biasing GNSS signals and reduce the positioning accuracy. One of the last remaining non-modeled error source in GNSS, in terms of a standard correction model, is multipath. Multipath related biases occur when apart from the direct signal, indirect signal components reach also the receiving antenna. The major contribution of this work is the formulation of closed-form expressions for the characterization of multipath effects present in the GNSS data. A dedicated algorithm is developed which evaluates the before mentioned expressions and is further used in a simulation analysis. Key parameters of the process are simulated and their impact on the resulting error magnitude is characterized. For the validation of the theoretical developments as well as of the developed algorithm a controlled experiment is performed and results will be presented together with a comparison between real and simulated data. Thus, it will be demonstrated that multipath signatures present in the data can be replicated for complete satellite arcs with this new approach. The concept can be used for quantifying and characterize multipath effects either for positioning or for GNSS remote sensing applications.