Searching Beyond the Standard Model: Bridging the Gap Between Theory and Data

The current standard model (SM) of particle physics is known to be an incomplete model of the Universe. Experiments have given strong evidence for the existence of dark matter. Other alterations to the SM could include the presence of extra forces and/or non-standard cosmologies. In our current era, we have many experimental methods that can probe the boundaries of the SM using particle colliders, astrophysical observatories, or gravitational wave interferometers. In the latter case, we explore the possibility of detecting gravitational waves generated by first-order phase transitions in multiple hidden sectors. These hidden sectors are completely decoupled from the SM, hosting exotic particles with unique forces. Each thermally decoupled sector evolves and undergoes phase transitions at completely different times. We take N-naturalness as a sample model that features multiple additional sectors, many of which undergo phase transitions that produce gravitational waves. We examine the cosmological history of this framework and then calculate novel gravitational wave signatures characteristic to only models with multiple decoupled sectors. These unique profiles, when compared to the sensitivity of future gravitational wave interferometers, could be a smoking gun for non-standard cosmologies. In the case where one of these hidden sectors instead contains a dark matter candidate which couples weakly with the SM, we propose novel searching methods to be employed at particle colliders. These confining hidden sectors at the GeV scale can lead to novel collider signatures including those termed emerging jets with large numbers of displaced vertices. The triggers at the LHC experiments were not designed with this type of new physics in mind, and triggering can be challenging. We show that the efficiency and the total event rate at current triggers can be significantly improved by considering additional sources of radiation. We also explore possible new triggers that employ hit counts in different tracker layers as input into a machine learning algorithm. We show that these new triggers can have reasonably low background rates and sensitive to a wide range of new physics parameters even when trained on a single model.