Data Driven Approach for Extracting Tidal Information from Neutron Star Binary Mergers Observed with the Einstein...

Area of Science:

• Astrophysics
• Gravitational-wave astronomy
• Cosmology

Background:

• Gravitational-wave detection has opened new avenues for observing the Universe, particularly compact binary mergers.
• Accurate gravitational-wave models are critical for interpreting observational data and inferring system properties.
• Next-generation detectors like the Einstein Telescope will enhance the precision of binary neutron star merger observations.

Purpose of the Study:

• To propose a novel, data-informed approach for constructing phenomenological gravitational-wave models.
• To demonstrate the efficacy of this method using mock data from the Einstein Telescope.
• To enhance the accuracy of parameter estimation for compact binary mergers.

Main Methods:

• Development of phenomenological waveform models incorporating observational data.
• Calibration of models using mock data simulating one year of Einstein Telescope operations.
• Comparison with classical methods relying on analytical computations and numerical relativity.

Main Results:

• The proposed data-informed method shows improved results as more events are included in calibration.
• Mock data analysis indicates the potential for more sophisticated gravitational-wave models.
• The approach offers a complementary alternative to traditional waveform model development.

Conclusions:

• The novel data-informed approach is a promising method for developing advanced gravitational-wave models.
• Improved waveform models are essential for maximizing scientific return from next-generation gravitational-wave detectors.
• This technique will lead to more accurate parameter estimation of compact binary merger events.