Light-Ion Production From Intermediate-Energy Heavy-Ion Interactions

As missions in space become longer, the dangers posed to astronauts become increasingly prevalent. One of the many dangers is an increasing radiation dose caused by the unique radiation present in space called Galactic Cosmic Radiation (GCR). The complexity of GCR usually requires the use of transport codes when testing new materials for use in space. Some of these codes use the coalescence model to provide fragmentation light-ion production cross section data when no tabulated data is available. The accuracy of this model depends on the availability of experimental proton, neutron, and light-ion production cross section data. Since there is little experimental data that is applicable to the coalescence model, the validity of the model over the wide range of energies and interactions that are present in GCR is uncertain. Described in this thesis is an experiment that provides the means of measuring double differential cross section data for protons, deuterons, and tritons for 39 different projectile/target systems with projectile energies ranging from 250 to 600 AMeV. Eight of these systems also provide cross section data for 3He and 4He. The cross sections were provided at energies ranging between 50 and 300 AMeV at the angles of 5, 10, 20, 30, 40, 60, and 80 degrees off the beam axis. As of this paper, no other reports or articles have included such an extensive set of both neutron and proton, as well as light-ion cross sections. The cross sections obtained from this experiment were used to calculate coalescence radii for each measured angle for each system. Coalescence radii were also determined over all angles to provide system-wide radii. The calculated radii varied from system to system, and ranged from 60 to 200 MeV/c. The radii tended to be larger with lower mass systems (either the projectile or target), and fell as the mass of the systems increased

Demand Curve Shifts in Multi-Unit Auctions: Evidence from a Laboratory Experiment

Basic economic theory predicts that a consumer's willingness to pay for a good is affected by the presence of complements and substitutes. In an auction setting, this theory implies that the presence of complements would increase bid prices for a good, while the presence of substitutes would decrease bid prices for a good. However, several experimental auction studies have sold complementary or substitutable products without regard for the effects these actions could have on bidding behavior. Using data from an experimental auction specifically designed to test the effect of complements and substitutes on bids, we used both unconditional tests and conditional tests where we derived demand flexibilities to analyze whether selling complementary and substitutable products has an effect on bids. Our results show that the availability of complementary and substitutable products affects bids in the expected directions. This finding has important implications for researchers who design experimental auctions.Research Methods/ Statistical Methods,

Characterization Of Real-World Particle Number Emissions During Re-Ignition Events From A 2010 Light-Duty Hybrid-Electric Vehicle

Despite the increasing popularity of hybrid-electric vehicles (HEVs), few studies have quantified their real-world particle emissions from internal combustion engine (ICE) re-ignition events (RIEVs). RIEVs have been known to occur under unstable combustion conditions which frequently result in particle number emission rates (PNERs) that exceed stabilized engine operation. Tailpipe total PN (5 to 560 nm diameter) emission rates (#/s) from a conventional vehicle (CV) and hybrid electric vehicle (HEV) 2010 Toyota Camry were quantified on a 50 km (32 mi) route over a variety of roadways in Chittenden County, Vermont using the Total On-board Tailpipe Emissions Measurement System (TOTEMS). While HEVs are known to have significant fuel conserving benefits compared to conventional vehicles, less is known about the relative emissions performance of HEVs. This study is the first to characterize RIEVs under a range of real-world driving conditions and to directly compare HEV and CV PNER during driving on different road sections. A total of 28 CV and 33 HEV sampling runs were conducted over an 18-month period under ambient temperatures ranging between -4 and 35 °C. A road classification based upon speed and intersection density divided the route into four different road sections: Freeway, Rural, Urban I and Urban II. Due to the distinct on-off cycling of the HEV ICE, a new operational mode framework (ICE OpMode) was developed to characterize shutdown, off, re-ignition and stabilized HEV ICE operation. Road section was found to affect overall ICE OpMode distribution, with HEV engine-off operation averaging 57%, 36% and 5% of total operation for combined Urban, Rural and Freeway road sections, respectively. Re-ignition frequency was found to range between 11 and 133 events per hour, with spatial density ranging between 0.1 and 5.6 events per kilometer of roadway. A total of 3212 re-ignition events were observed and recorded, and mean HEV PNER during RIEVs, on average, ranged between 2.4 and 4.4 times greater than that of HEV Stabilized operation. Approximately 65% of all re-ignition events resulted in a peak PNER exceeding the 95% percentile for all ICE-on activity in both vehicles (9.3 x 1011 #/s), known as a High Emission Event Record (HEER). RIEV operation made up only 7.4% of total ICE-on operation for both vehicles but accounted for 35.4% of all HEERs. Overall, total particles emitted during HEV operation associated with re-ignition events ranged from 5% for Freeway driving to 60% for Urban I driving. Comparisons between vehicles found an average of 37% and 7% fuel conserving benefits of the HEV during Urban I and Freeway driving, respectively. However, a different effect was found for PN emissions. During Urban I driving, where RIEVs were most frequent, on average HEV PNER was 2.3 times greater than overall mean CV PNER. For Freeway driving, where the HEV operated similar to a conventional vehicle, mean CV PNER was 2.4 times greater than mean HEV PNER. PNER from partial re-ignition events following an incomplete ICE shutdown (no period of prior engine off operation) were on average 1.65 times greater than those occurring when the ICE shutdown for at least one second. The typical fuel consumption benefits of HEVs in urban driving are associated with a tradeoff in PN emissions. The HEV ICE operating behavior has implications for the spatial distribution of PN hot-spots as well as the associated micro-scale modeling of alternative vehicle technology emissions. It is likely that building a model of HEV behavior based upon CV activity will be appropriate, with consideration of a hybridization factor and, as a result of these analyses, a re-ignition factor

Explorations in machine learning for interacting many-body systems.

Most interacting many-body systems in physics are not analytically solvable. Instead, numerical methods are needed for the study of these complex and high-dimensional problems. At present, there are many interesting problems in strongly correlated systems that remain unsolved with current methods. At the heart of this problem is finding an efficient representation that incorporates symmetries, correlations and general features. In the context of computer science, machine learning techniques have had astonishing success at reducing the dimensionality of data. The leading method is through the use of artificial neural networks. These networks have been enormously successful at sifting through vast amounts of data to find patterns and regularities. In a sense, neural networks are themselves a statistical system whose properties are adjusted to mimic the features of the data. By finding an effective low-dimensional representation of the data, machine learning has greatly subdued the curse of dimensionality found in many real-world problems. In this Thesis, we apply several machine learning techniques to the study of interacting many-body systems in classical and quantum statistical physics. We explore supervised classification of phases of matter with an emphasis on physical interpretation of the net- work. In doing so, we design a custom network architecture that possesses rotational symmetry as an inductive bias. We further investigate connections between the renormalization group and deep learning through applying a super-resolving neural network to the classical Ising model. Towards experimental efforts, we also repurpose generative machine learning to quantum state tomography for the calibration and testing of quantum devices. We conclude with a latent variable model inspired by near-term quantum algorithms. This maps to a variational Monte Carlo ansatz that produces samples efficiently for interacting quantum systems

SAR-Based Vibration Estimation Using the Discrete Fractional Fourier Transform

A vibration estimation method for synthetic aperture radar (SAR) is presented based on a novel application of the discrete fractional Fourier transform (DFRFT). Small vibrations of ground targets introduce phase modulation in the SAR returned signals. With standard preprocessing of the returned signals, followed by the application of the DFRFT, the time-varying accelerations, frequencies, and displacements associated with vibrating objects can be extracted by successively estimating the quasi-instantaneous chirp rate in the phase-modulated signal in each subaperture. The performance of the proposed method is investigated quantitatively, and the measurable vibration frequencies and displacements are determined. Simulation results show that the proposed method can successfully estimate a two-component vibration at practical signal-to-noise levels. Two airborne experiments were also conducted using the Lynx SAR system in conjunction with vibrating ground test targets. The experiments demonstrated the correct estimation of a 1-Hz vibration with an amplitude of 1.5 cm and a 5-Hz vibration with an amplitude of 1.5 mm