Electron Collisions with Atoms and Molecules

Measurements of the production of atomic and molecular metastable states are performed using a novel frozen solid layer detector. This includes examination of solid CO2 and N2O layers in metastable detection, measurements of N(2P) production from dissociation of N2, investigations into the suitability of solid neon layers for detection of S(1D) based on electron-impact dissociation studies of OCS and production of O(1S) and CO(a3Pi) in electron-impact dissociation of methanol. Novel solid layers for detection of metastable states were examined to determine which products they were sensitive to. First, CO2 layers were deposited and were found to be sensitive to both O(1S) and N2(a1Pi g) metastable states. The relative efficiency of these layers as a function of temperature and lifetime of the state formed from the impinging O(1S) atoms are reported. Mechanisms which may be responsible for the radiation through formation of CO3 or its ion are also suggested. The detection of metastable N2(a1Pi g) states from solid N2O layers is also reported. A study of N(2P) production after electron-impact dissociation of N2 was also performed. It was determined that two excitation channels contribute to production of this state. Time-of-flight and fragment kinetic energy spectra are presented for 100 eV impact energy. Excitation function data is also provided over the 0-200 eV range for one of these channels. Dissociation processes are proposed for both production channels which appear to be due to both a direct dissociation and pre-dissociation mechanism. Intermediary states are proposed for both channels. Investigations into the suitability of solid neon layers for the detection of S(1D) are performed through studies of electron-impact dissociation of OCS. It was found that while production of S(1D) is likely occurring, interactions with solid neon layers do not result in emission within the optical spectral range of our photo-multiplier tube. However, an ultraviolet emission from these layers was detected in these experiments. While the nature of this feature was not definitively identified, some possible processes which may be responsible are discussed. Future investigations to determine the source of the emission are proposed. In addition, likely production channels of S(1D) are suggested. Electron-impact dissociation of methanol was also performed. It was observed that both CO(a3Pi) and O(1S) metastable states are produced and detected with solid xenon layers. While production of CO(a3Pi) has been reported previously, this appears to be the first observation of O(1S) production from electron-impact of methanol. Time-of-flight and released kinetic energy spectra are presented for both features at 100 eV impact energy. Excitation functions are also presented for 10-90 eV impact energy for both states. The production of CO(a3Pi) coincides with the observations of previous studies and the measurement of the excitation cross section is extended from an energy of 21 to 90 eV. No new dissociation channels for this state were found. Meanwhile, production of O(1S) appears to occur through a direct dissociation mechanism causing breakage of the CO bond and formation of OH(B2Sigma-) which subsequently dissociates into O(1S) and H(2S)

Physics and Astrophysics of Strange Quark Matter

3-flavor quark matter (strange quark matter; SQM) can be stable or metastable for a wide range of strong interaction parameters. If so, SQM can play an important role in cosmology, neutron stars, cosmic ray physics, and relativistic heavy-ion collisions. As an example of the intimate connections between astrophysics and heavy-ion collision physics, this Chapter gives an overview of the physical properties of SQM in bulk and of small-baryon number strangelets; discusses the possible formation, destruction, and implications of lumps of SQM (quark nuggets) in the early Universe; and describes the structure and signature of strange stars, as well as formation and detection of strangelets in cosmic rays. It is concluded, that astrophysical and laboratory searches are complementary in many respects, and that both should be pursued to test the intriguing possibility of a strange ground state for hadronic matter, and (more generally) to improve our knowledge of the strong interactions.Comment: 45 pages incl. figures. To appear in "Hadrons in Dense Matter and Hadrosynthesis", Lecture Notes in Physics, Springer Verlag (ed. J.Cleymans

Protected states and metastable dynamics in superconducting circuits

The twin fields of superconducting circuits and circuit quantum electrodynamics now form the basis for a major part of the effort towards building a quantum computer. Yet many fundamental problems remain. These may range from very practical considerations, such as how to construct a qubit with a sufficiently long coherence time, to questions of how best to understand and model the complex nonlinear dynamics arising in superconducting circuits. In this thesis we take a broad look at these fields and explore many questions within them. We begin by studying critical slowing down in a dissipative phase transition of a coupled qubit-cavity system, before examining the underlying dynamics of switching between metastable states which causes this slowdown. We then examine an unexplained phenomenon of resonance narrowing in another qubit-cavity system and suggest it may also be related to metastable states. Finally, we examine a circuit which harnesses long range interactions, and present it as a promising candidate for building a qubit with a long coherence time

Machine learning in the analysis of biomolecular simulations

Machine learning has rapidly become a key method for the analysis and organization of large-scale data in all scientific disciplines. In life sciences, the use of machine learning techniques is a particularly appealing idea since the enormous capacity of computational infrastructures generates terabytes of data through millisecond simulations of atomistic and molecular-scale biomolecular systems. Due to this explosion of data, the automation, reproducibility, and objectivity provided by machine learning methods are highly desirable features in the analysis of complex systems. In this review, we focus on the use of machine learning in biomolecular simulations. We discuss the main categories of machine learning tasks, such as dimensionality reduction, clustering, regression, and classification used in the analysis of simulation data. We then introduce the most popular classes of techniques involved in these tasks for the purpose of enhanced sampling, coordinate discovery, and structure prediction. Whenever possible, we explain the scope and limitations of machine learning approaches, and we discuss examples of applications of these techniques.Peer reviewe