Machine learning has emerged as a practical approach to address many computing challenges. These novel algorithms, which might not be identified using conventional methods, hold potential for solving tough scientific questions. At Syracuse University, our focus is on how machine learning can assist gravitational-wave astronomy.
Instruments like LIGO or Virgo need effective algorithms for their optimal management, control, and to achieve their scientific potential—this is where machine learning comes into play. These devices encounter noise from various sources, and machine learning has the potential to identify or remove sources of noise from the data. When detecting gravitational waves, traditional optimal methods are in some cases infeasible due to the size of the parameter space or the ability to distinguish signals from noise. Once detected, ML algorithms may also aid in characterizing gravitational-wave sources, providing a clearer understanding of these massive celestial events.
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