If not explicitly stated otherwise, seminars take place on Wednesdays, at 16:00 in the lecture room A 945 (Troja, building A, 9th floor).

Adam Smetana (IEAP CTU in Prague)

14.10.2020 at 16:00

Background for gravitational wave signal at LISA from refractive index
of solar wind plasma

On-line Seminar available via this link (Vidyo). Slides will be available before the seminar at Indico.

A strong indication is presented that the space-based gravitational antennas, in particular the LISA concept, are going to be sensitive to a strong background signal interfering with the prospected signal of gravitational waves. The false signal is due to variations in the electron number density of the solar wind, causing variations in the refractive index of plasma flowing through interplanetary space. As countermeasures, two solutions are proposed. The first solution is to deploy enough solar wind detectors to the LISA mission to allow for reliable knowledge of the solar wind background. The second solution is to equip the LISA interferometer with a second laser beam with a distinct wavelength to allow cancelling of the background solar wind signal from the interferometric data.

Jiří Novotný (ÚČJF)

4.3.2020 at 16:00

Symmetries, soft theorems, and reconstruction of amplitudes in effective field theories

Carbon nanotubes – nanoreactor or Space elevator? Dr. Gunther Kletetschka (GI ASCR, FacSci CU, UAF)

at 16:00

Friday’s IPNP Coffee Club, room 945, January 17th, 2020, at 13:30

Graphene is the material of future, and carbon nanotubes have a graphene structure that is very strong and lightweight, allowing a space elevator construction. Their production enables incorporation of nano grains of iron, but also of uranium. Thanks to graphene neutron reflective properties, it is possible to design a space-powered nanoreactor.

doc. Jan Vybíral (CTU): Machine learning in solid state physics

at 16:00

January 10th, 2020, IPNP Coffee Club, room 920, at 13:30

At present, it is possible to determine the properties of a given material, without a need for synthesis and experimental measurements, by means of calculations, but these are time-consuming and cannot be used for larger quantities (about 103 or more) of potential new materials. The aim of the lecture will be to show how machine learning can speed up the discovery of new materials.