Spring Workshop on Physics of Data

Session

Astrophysics and Cosmology

Apr 8, 2022, 9:30 AM

Istituto Veneto di Scienze Lettere ed Arti, Venezia

Conveners

Astrophysics and Cosmology

• Michela Mapelli

19. Predicting solar activiy

Carlo Albert (Eawag Institute)

4/8/22, 9:30 AM

20. Data Challenges in “Gravitational-Wave Paleontology”

Floor Broekgaarden

4/8/22, 10:15 AM

The rapidly increasing population of detected gravitational wave sources carries valuable information about the properties of black holes and neutron stars, such as their rates,
masses and spins, that we aim to use to probe their progenitors and answer two of the big open questions in Astronomy today: “How do these sources form?” and “What can we learn from their gravitational waves about the...

23. A new tool on the workbench: studying GW progenitors with SEVN

Giuliano Iorio (University of Padua)

4/8/22, 11:30 AM

In 2015, the LIGO/VIRGO interferometers detected the first gravitational wave (GW) signal coming from the merger of two black holes. Since then, about 90 merging binary compact
objects (BCOs), namely binary neutron stars and black holes, have been detected through GW signals. This wealth of new data provides us with crucial insight on the populations
of BCOs. For this reason, numerical tools...

21. Pulsars spin-up: a semi-analytical approach

Cecilia Sgalletta (SISSA)

4/8/22, 12:15 PM

Pulsars are powerful probes of our universe: thanks to their extraordinary long-term rotational stability and their fast rotation, they allow extremely precise timing measurements. However, the physics behind their spins and magnetic fields evolution is still poorly understood. A particular interest resides in the process of spin-up: neutron stars in binary systems
can be spun-up by accreting...

22. Gravitational Waves and Machine Learning

Jacopo Tissino (GSSI)

4/8/22, 12:30 PM

The LIGO-Virgo collaboration has detected dozens of gravitational wave signals so far, and will do so at an increasing rate in the following years with detector
upgrades. These signals are extremely faint and arrive to us buried in noise; measuring and analyzing them is a hard computational challenge.
I will discuss how machine learning can help in this task, mostly focusing on the...