Professor Daniela Bortoletto

Abstract:

We outline the experimental concept and key scientific capabilities of AION (Atom Interferometer Observatory and Network), a proposed UK-based experimental programme using cold strontium atoms to search for ultra-light dark matter, to explore gravitational waves in the mid-frequency range between the peak sensitivities of the LISA and LIGO/Virgo/ KAGRA/INDIGO/Einstein Telescope/Cosmic Explorer experiments, and to probe other frontiers in fundamental physics. AION would complement other planned searches for dark matter, as well as probe mergers involving intermediate mass black holes and explore early universe cosmology. AION would share many technical features with the MAGIS experimental programme in the US, and synergies would flow from operating AION in a network with this experiment, as well as with other atom interferometer experiments such as MIGA, ZAIGA and ELGAR. Operating AION in a network with other gravitational wave detectors such as LIGO, Virgo and LISA would also offer many synergies.

Abstract:

Measuring the Higgs trilinear self-coupling $\lambda_{hhh}$ is experimentally demanding but fundamental for understanding the shape of the Higgs potential. We present a comprehensive analysis strategy for the HL-LHC using di-Higgs events in the four $b$-quark channel ($hh \to 4b$), extending current methods in several directions. We perform deep learning to suppress the formidable multijet background with dedicated optimisation for BSM $\lambda_{hhh}$ scenarios. We compare the $\lambda_{hhh}$ constraining power of events using different multiplicities of large radius jets with a two-prong structure that reconstruct boosted $h \to bb$ decays. We show that current uncertainties in the SM top Yukawa coupling $y_t$ can modify $\lambda_{hhh}$ constraints by $\sim 20\%$. For SM $y_t$, we find prospects of $-0.8 < \lambda_{hhh} / \lambda_{hhh}^\text{SM} < 6.6$ at 68% CL under simplified assumptions for 3000~fb$^{-1}$ of HL-LHC data. Our results provide a careful assessment of di-Higgs identification and machine learning techniques for all-hadronic measurements of the Higgs self-coupling and sharpens the requirements for future improvement.