Faint Galaxies in the Megaparsec-Scale Environments of Hyperluminous QSOs at Redshifts 2 < z < 3

This thesis presents detailed observational studies of the extended distributions of gas, galaxies, and dark matter around hyperluminous quasars (HLQSOs) at high redshift. Taken together, these works aim to coherently describe the relationships between these massive, accreting black holes and their environments: the nature of the regions that give rise to such massive black holes, the effect of HLQSO radiation on their surrounding galaxies and gas, and the ability of both galaxies and black holes to shed new light on the formation and evolution of the other.

Chapter 2 focuses on the continuum-color-selected galaxies drawn from the Keck Baryonic Structure Survey (KBSS). The KBSS is a uniquely deep spectroscopic survey of star-forming galaxies in the same volumes of space as 15 HLQSOs at 2.5 < z < 2.9. The three-dimensional distribution of these galaxies among themselves and the nearby HLQSOs is used to infer the extent to which these black holes are associated with overdense peaks in the dark matter and galaxy distribution as quantified by clustering statistics. In conjunction with recent dark-matter simulations, these data provide the first estimates of the host dark-matter halo masses for HLQSOs, providing new insight into the formation and evolution of the most massive black holes at high redshift.

Chapter 3 describes the first results from a new survey (KBSS-Lyα) conducted for this thesis. The KBSS-Lyα survey uses narrowband imaging to identify Lyα-emitters (LAEs) in the ~Mpc regions around eight of the KBSS HLQSOs. Many of these LAEs show the effect of reprocessed HLQSO radiation in their emission through the process known as Lyα fluorescence. In this chapter, these fluorescent LAEs are used to generate a coarse map of the average HLQSO ionizing emission on Mpc scales, thereby setting the first direct constraints of the lifetime and angular distribution of activity for a population of these uniquely luminous black holes.

Chapter 4 contains a more detailed description of the KBSS-Lyα survey itself and the detailed properties of the star-forming and fluorescent objects selected therein. Using imaging and spectroscopic data covering rest-frame UV and optical wavelengths, including spectra from the new near-infrared spectrometer MOSFIRE, we characterize this population of nascent galaxies in terms of their kinematics, enrichment, gas properties, and luminosity distribution while comparing and contrasting them with previously-studied populations of continuum-selected galaxies and LAEs far from the effects of HLQSO emission.

At the conclusion of this thesis, I briefly present future directions for the continuation of this research. In Appendix A, I provide background information on the instrumentation used in this thesis, including my own contributions to MOSFIRE.

Search for Higgs Boson Production Beyond the Standard Model Using the Razor Kinematic Variables in pp Collisions at √s=8 TeV and Optimization of Higgs Boson Identification Using a Quantum Annealer

In the first part of this thesis we search for beyond the Standard Model physics through the search for anomalous production of the Higgs boson using the razor kinematic variables. We search for anomalous Higgs boson production using proton-proton collisions at center of mass energy √s=8 TeV collected by the Compact Muon Solenoid experiment at the Large Hadron Collider corresponding to an integrated luminosity of 19.8 fb^-1.

In the second part we present a novel method for using a quantum annealer to train a classifier to recognize events containing a Higgs boson decaying to two photons. We train that classifier using simulated proton-proton collisions at √s=8 TeV producing either a Standard Model Higgs boson decaying to two photons or a non-resonant Standard Model process that produces a two photon final state.

The production mechanisms of the Higgs boson are precisely predicted by the Standard Model based on its association with the mechanism of electroweak symmetry breaking. We measure the yield of Higgs bosons decaying to two photons in kinematic regions predicted to have very little contribution from a Standard Model Higgs boson and search for an excess of events, which would be evidence of either non-standard production or non-standard properties of the Higgs boson. We divide the events into disjoint categories based on kinematic properties and the presence of additional b-quarks produced in the collisions. In each of these disjoint categories, we use the razor kinematic variables to characterize events with topological configurations incompatible with typical configurations found from standard model production of the Higgs boson.

We observe an excess of events with di-photon invariant mass compatible with the Higgs boson mass and localized in a small region of the razor plane. We observe 5 events with a predicted background of 0.54 ± 0.28, which observation has a p-value of 10^-3 and a local significance of 3.35σ. This background prediction comes from 0.48 predicted non-resonant background events and 0.07 predicted SM higgs boson events. We proceed to investigate the properties of this excess, finding that it provides a very compelling peak in the di-photon invariant mass distribution and is physically separated in the razor plane from predicted background. Using another method of measuring the background and significance of the excess, we find a 2.5σ deviation from the Standard Model hypothesis over a broader range of the razor plane.

In the second part of the thesis we transform the problem of training a classifier to distinguish events with a Higgs boson decaying to two photons from events with other sources of photon pairs into the Hamiltonian of a spin system, the ground state of which is the best classifier. We then use a quantum annealer to find the ground state of this Hamiltonian and train the classifier. We find that we are able to do this successfully in less than 400 annealing runs for a problem of median difficulty at the largest problem size considered. The networks trained in this manner exhibit good classification performance, competitive with the more complicated machine learning techniques, and are highly resistant to overtraining. We also find that the nature of the training gives access to additional solutions that can be used to improve the classification performance by up to 1.2% in some regions.