An investigation of techniques for the measurement and interpretation of cosmic ray isotopic abundances

An instrument, the Caltech High Energy Isotope Spectrometer Telescope (HEIST), has been developed to measure isotopic abundances of cosmic ray nuclei in the charge range 3 ≤ Z ≤ 28 and the energy range between 30 and 800 MeV/nuc by employing an energy loss -- residual energy technique. Measurements of particle trajectories and energy losses are made using a multiwire proportional counter hodoscope and a stack of CsI(TI) crystal scintillators, respectively. A detailed analysis has been made of the mass resolution capabilities of this instrument.

Landau fluctuations set a fundamental limit on the attainable mass resolution, which for this instrument ranges between ~.07 AMU for z~3 and ~.2 AMU for z~2b. Contributions to the mass resolution due to uncertainties in measuring the path-length and energy losses of the detected particles are shown to degrade the overall mass resolution to between ~.1 AMU (z~3) and ~.3 AMU (z~2b).

A formalism, based on the leaky box model of cosmic ray propagation, is developed for obtaining isotopic abundance ratios at the cosmic ray sources from abundances measured in local interstellar space for elements having three or more stable isotopes, one of which is believed to be absent at the cosmic ray sources. This purely secondary isotope is used as a tracer of secondary production during propagation. This technique is illustrated for the isotopes of the elements O, Ne, S, Ar and Ca.

The uncertainties in the derived source ratios due to errors in fragmentation and total inelastic cross sections, in observed spectral shapes, and in measured abundances are evaluated. It is shown that the dominant sources of uncertainty are uncorrelated errors in the fragmentation cross sections and statistical uncertainties in measuring local interstellar abundances.

These results are applied to estimate the extent to which uncertainties must be reduced in order to distinguish between cosmic ray production in a solar-like environment and in various environments with greater neutron enrichments.

Self-healing Techniques for RF and mm-Wave Transmitters and Receivers

With continuing advances in CMOS technology, feature sizes of modern Silicon chip-sets have gone down drastically over the past decade. In addition to desktops and laptop processors, a vast majority of these chips are also being deployed in mobile communication devices like smart-phones and tablets, where multiple radio-frequency integrated circuits (RFICs) must be integrated into one device to cater to a wide variety of applications such as Wi-Fi, Bluetooth, NFC, wireless charging, etc. While a small feature size enables higher integration levels leading to billions of transistors co-existing on a single chip, it also makes these Silicon ICs more susceptible to variations. A part of these variations can be attributed to the manufacturing process itself, particularly due to the stringent dimensional tolerances associated with the lithographic steps in modern processes. Additionally, RF or millimeter-wave communication chip-sets are subject to another type of variation caused by dynamic changes in the operating environment. Another bottleneck in the development of high performance RF/mm-wave Silicon ICs is the lack of accurate analog/high-frequency models in nanometer CMOS processes. This can be primarily attributed to the fact that most cutting edge processes are geared towards digital system implementation and as such there is little model-to-hardware correlation at RF frequencies.

All these issues have significantly degraded yield of high performance mm-wave and RF CMOS systems which often require multiple trial-and-error based Silicon validations, thereby incurring additional production costs. This dissertation proposes a low overhead technique which attempts to counter the detrimental effects of these variations, thereby improving both performance and yield of chips post fabrication in a systematic way. The key idea behind this approach is to dynamically sense the performance of the system, identify when a problem has occurred, and then actuate it back to its desired performance level through an intelligent on-chip optimization algorithm. We term this technique as self-healing drawing inspiration from nature's own way of healing the body against adverse environmental effects. To effectively demonstrate the efficacy of self-healing in CMOS systems, several representative examples are designed, fabricated, and measured against a variety of operating conditions.

We demonstrate a high-power mm-wave segmented power mixer array based transmitter architecture that is capable of generating high-speed and non-constant envelope modulations at higher efficiencies compared to existing conventional designs. We then incorporate several sensors and actuators into the design and demonstrate closed-loop healing against a wide variety of non-ideal operating conditions. We also demonstrate fully-integrated self-healing in the context of another mm-wave power amplifier, where measurements were performed across several chips, showing significant improvements in performance as well as reduced variability in the presence of process variations and load impedance mismatch, as well as catastrophic transistor failure. Finally, on the receiver side, a closed-loop self-healing phase synthesis scheme is demonstrated in conjunction with a wide-band voltage controlled oscillator to generate phase shifter local oscillator (LO) signals for a phased array receiver. The system is shown to heal against non-idealities in the LO signal generation and distribution, significantly reducing phase errors across a wide range of frequencies.

Computationally-guided thermostabilization of the primary endoglucanase from Hypocrea jecorina for cellulosic biofuel production

The creation of thermostable enzymes has wide-ranging applications in industrial, scientific, and pharmaceutical settings. As various stabilization techniques exist, it is often unclear how to best proceed. To this end, we have redesigned Cel5A (HjCel5A) from Hypocrea jecorina (anamorph Trichoderma reesei) to comparatively evaluate several significantly divergent stabilization methods: 1) consensus design, 2) core repacking, 3) helix dipole stabilization, 4) FoldX ΔΔG approximations, 5) Triad ΔΔG approximations, and 6) entropy reduction through backbone stabilization. As several of these techniques require structural data, we initially solved the first crystal structure of HjCel5A to 2.05 Å. Results from the stabilization experiments demonstrate that consensus design works best at accurately predicting highly stabilizing and active mutations. FoldX and helix dipole stabilization, however, also performed well. Both methods rely on structural data and can reveal non-conserved, structure-dependent mutations with high fidelity. HjCel5A is a prime target for stabilization. Capable of cleaving cellulose strands from agricultural waste into fermentable sugars, this protein functions as the primary endoglucanase in an organism commonly used in the sustainable biofuels industry. Creating a long-lived, highly active thermostable HjCel5A would allow cellulose hydrolysis to proceed more efficiently, lowering production expenses. We employed information gleaned during the survey of stabilization techniques to generate HjCel5A variants demonstrating a 12-15 °C increase in the temperature at which 50% of the total activity persists, an 11-14 °C increase in optimal operating temperature, and a 60% increase over the maximal amount of hydrolysis achievable using the wild type enzyme. We anticipate that our comparative analysis of stabilization methods will prove useful in future thermostabilization experiments.

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.