Jack Lloyd-Walters
MPhys MInstP FRAS

Transit Timing Variation (TTV) provides a powerful tool to probe the dynamical configuration of exoplanetary systems from historical transit data (Holman & Murray 2005; Agol et al. 2004). TTV analysis has allowed both verification of planetary parameters (Wang et al. 2017) and the discovery of new planetary bodies (Ballard et al. 2011) from transit observation alone. As part of this work, additional transit light curves have been collected with the 24" Ritchey-Chrétien telescope at Clanfield observatory and combined with the ExoClock database (Kokori et al. 2021, 2022), Exoplanet transit database (Poddaný et al. 2010), and TESS light curves (Ricker 2014) to create a set of historical TTV data for analysis. A set of extensible TTV models have been developed to analytically approximate the chaotic n-body nature of real planetary systems. A computational pipeline to automate model fitting using various parameter optimisation (Storn & Price 1995; Xiang et al. 1997) and model comparison (Akaike 1992, 1974; Hurvich & Tsai 1993; Schwarz 1978) techniques has been developed in-situ, allowing verification of model validity and analysis of TTV candidates using a combination of simulation and historical TTV data. The models developed were found to accurately describe TTV, and could determine the initial system parameters of simulated TTV systems to reasonable accuracy. Future work will allow extensions to these models, providing a more powerful suite of analytical tools for exoplanetary science, and the application of these methods to real exoplanetary systems with the possibility of new planetary discoveries.

Glitches are a frequent occurrence with LIGO data, on the order of 10 an hour, and represent unwanted noise when searching for gravitational wave signals. Due to their similarity to IMBH merger events, they represent an obstacle to any search that deals with higher mass black holes. With a viable model of these glitch events, a full search could more easily distinguish IMBH mergers from short duration glitches. The glitch model created for this report was found to be accurate when searching against LIGO data using traditional matched filtering, and showed high similarity to events identified using the omicron scan, despite the difference in methods for detection. This glitch model was shown to not hinder a search for IMBH’s, as merger templates for GW190521 still responded more strongly than the glitch model, showing it’s safety in respect to true mergers. As such, this could pose a viable model for an extended search across a much larger swathe of LIGO data, with higher mass resolutions providing a logical improvement.