Project:	Finding Massive Black Hole Binaries in Preparation for Space-Based Gravitational Wave Detections
Disciplines:	Astrophysics, Astronomy, Computer Science
Mentor:	David Cook, Associate Scientist, (PMA), dcook@ipac.caltech.edu, Phone: 6263951814
Mentor URL:	https://web.ipac.caltech.edu/staff/dcook/  (opens in new window)
Background:	Tremendous astrophysical discoveries have arisen from the direct detection of gravitational waves (GWs) from stellar remnants. The next era of GW astronomy will emerge with the launch of the Laser Interferometer Space Antenna (LISA) in the 2030s, which will identify GW signals from the mergers of massive black holes that reside in the centers of galaxies. These systems should be detectable during the merger phases via electromagnetic emission arising from physical processes modulated by their binary motion. Thus, many studies have tried to find binary black hole candidates via periodic signatures in time-domain surveys. However, despite employing multiple strategies, finding robust binary black hole candidates that will be detectable by LISA has proven difficult due the inherent quasi-periodic fluctuations commonly observed in galaxy centers with a single massive black hole. Our group aims to improve upon these strategies by: creating light curves (brightness over time) for over a billion sources from 3 time-domain surveys (ZTF and others) and applying various machine learning and AI methodologies to better identify real periodic signatures.
Description:	The SURF would work with our group to refine machine learning or AI methodologies to search for periodic signatures in light curve data. This will include: training models based on theoretical periodic signatures, applying these methods to simulated and modified observations, and developing metrics to assess success rates of different methods. Caltech students will be prioritized; however, applicants from other universities are welcome to apply. Interested students should email dcooik[at]ipac[dot]caltech[dot]edu and mjg[at]caltech[dot]edu by Jan 23. Prospective candidates will likely be interviewed in the beginning of February in preparation for the SURF application deadline (Feb 22).
References:	NASA’s LISA website: https://lisa.nasa.gov/ ZTF Time-domain Survey and archive: https://www.ztf.caltech.edu/ https://irsa.ipac.caltech.edu/Missions/ztf.html Examples of Period Finding work: https://scixplorer.org/abs/2015MNRAS.453.1562G/abstract https://scixplorer.org/abs/2025arXiv250910601E/abstract
Student Requirements:	Experience with Python programming and machine learning either in a class or independent projects is preferred. Interest in physics or astrophysics is a benefit.
Programs:	This AO can be done under the following programs:   Program    Available To        SURF    both Caltech and non-Caltech students  Click on a program name for program info and application requirements.