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Day | Week | Month | Year | Upcoming
Abstract: We investigate the behaviour of droplets and soft elastic objects propelled with a catapult. Experiments show that the ejection velocity depends on both the projectile deformation and the catapult acceleration dynamics. With a subtle matching given by a peculiar value of the projectile/catapult frequency ratio, a 250% kinetic energy gain is obtained as compared to the propulsion of a rigid projectile with the same engine. This superpropulsion has strong potentialities: actuation of droplets, sorting of objects according to their elastic properties, and energy saving for propulsion engines.
Event Time
1000-1100hrs
Venue
C36 Physics
Speaker
Xiao Collins
Abstract: Stripped-envelope supernovae (SE-SNe) are a subset of core-collapse supernovae where the progenitor star has experienced severe mass loss during its evolution. The resulting pre-explosion star contains little or no hydrogen or helium at the moment of core-collapse, and this is visible in its photometric and spectroscopic evolution. They are an important component in the evolution of their local galactic environment and are the primary source of neutron star/black hole binaries in the Universe. However, despite being first identified as a distinct category in the 1980s it is only now that we are beginning to be build samples of sufficient size to investigate the populations properties. In this talk I will present the results of analysis on the largest sample of SE-SNe to dates, which indicates that these SNe have considerable diversity across mass, kinetic energy, specific kinetic energy, luminosity, temporal characteristics, and host environment. These results will then be linked back to some of the key questions in the field; what kind of progenitors give rise to these events and what kind of evolutionary pathways are available? How is mass lost and is there an indication as to the time-scales involved? How do SE-SNe link with other CC-SNe, superluminous-SNe, gamma-ray bursts, and strong sources of gravitational waves?
Event Time
1400 - 1500hrs
Venue
C36 Physics
Speaker
Simon Prentice (Liverpool John Moores University)
Since 2014 CERN, organises Beamline for Schools (BL4S), a worldwide annual competition for high-school students. Teams of students apply with a proposal for a physics experiment at the CERN PS 10GeV/c secondary beam line, and two groups are selected and invited to CERN to perform their experiments using state of the art detectors and data taking systems. Being part of all stages of an experiment, from proposal and design, over data taking to data analysis and publication of results, the teams get a broad picture of being an experimental physicist. During all stages, the students are supported by a small on-site team that implements the proposed experiments and supervises data taking and analysis. I will discuss the concept and experiences gained during four editions of BL4S including technological and communication aspects as well as the impact of the program on participating teams.
Event Time
13:45 - 14:45
Venue
Physics C36
Speaker
Branislav Ristic, Lancaster University/CERN
Abstract
Event Time
3pm-4pm
Venue
C36 Physics
Speaker
Dr. Petri Heikkinen, Royal Holloway, University of London, Egham, Surrey.
The field of exoplanetary spectroscopy is as fast moving as it is new. Analysing currently available observations of exoplanetary atmospheres often invoke large and correlated parameter spaces that can be difficult to map or constrain. This is true for both: the data analysis of observations as well as the theoretical modelling of their atmospheres. Modelling both sets of correlations in data and modelling is key to understanding the nature of exoplanet atmospheres. In this seminar I will discuss how these improvements in machine learning can be applied to exoplanetary spectroscopy to solve some of said correlations in the parameter space. By designing deep neural networks, we can significantly speed up data analysis and interpretation and allow our current models to ‘learn from experience'. Such AI driven systems will help to resolve model correlations, and allow us to build fully autonomous models. Finally, I will present a new deep neural network architecture, specifically designed to learn and classify data from the Cassini-VIMS instrument. This neural network takes both spectral and spatial distributions of surface or cloud compositions and can be shown to significantly out-perform traditional labelling techniques.
Event Time
1400-1500
Venue
C36 Physics
Speaker
Ingo Waldmann (UCL)
The ATLAS Strip Detector Upgrade
Event Time
13:45 - 14:45
Venue
Physics C36
Speaker
Craig Sawyer, Rutherford Appleton Laboratory
After the discovery of the Higgs boson, there have been a lot of ideas on how to best present measurements of the properties of the Higgs boson to learn the most about the Standard Model and physics beyond the SM. I will give an overview of the current ideas and their implementation in the ATLAS experiment as well as show first results. Then I will focus on the efforts in the H -> WW* decay channel and discuss where the input from H -> WW* is most needed for our knowledge of the Higgs boson. I will explain the challenges of performing these measurements and show first results in Run 2. I will conclude by giving and outlook on the measurement of differential cross sections as well as on constraining new physics via global fits of effective field theories.
Event Time
13:45 - 14:45
Venue
Physics C36
Speaker
Kathrin Becker, University of Oxford
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Day | Week | Month | Year | Upcoming