A history of the city of Somerville for the first four grades

Thesis (Ed.M.)--Boston Universit

Excess science accommodation capabilities and excess performance capabilities assessment for Mars Geoscience and Climatology Orbiter: Extended study

The excess science accommodation and excess performance capabilities of a candidate spacecraft bus for the Mars Geoscience and Climatology Orbiter MGCO mission are assessed. The appendices are included to support the conclusions obtained during this contract extension. The appendices address the mission analysis, the attitude determination and control, the propulsion subsystem, and the spacecraft configuration

The RHMC algorithm for theories with unknown spectral bounds

The Rational Hybrid Monte Carlo (RHMC) algorithm extends the Hybrid Monte Carlo algorithm for lattice QCD simulations to situations involving fractional powers of the determinant of the quadratic Dirac operator. This avoids the updating increment ($dt$) dependence of observables which plagues the Hybrid Molecular-dynamics (HMD) method. The RHMC algorithm uses rational approximations to fractional powers of the quadratic Dirac operator. Such approximations are only available when positive upper and lower bounds to the operator's spectrum are known. We apply the RHMC algorithm to simulations of 2 theories for which a positive lower spectral bound is unknown: lattice QCD with staggered quarks at finite isospin chemical potential and lattice QCD with massless staggered quarks and chiral 4-fermion interactions ($\chi$QCD). A choice of lower bound is made in each case, and the properties of the RHMC simulations these define are studied. Justification of our choices of lower bounds is made by comparing measurements with those from HMD simulations, and by comparing different choices of lower bounds.Comment: Latex(Revtex 4) 25 pages, 8 postscript figure

Nature of the spin liquid state of the Hubbard model on honeycomb lattice

Recent numerical work (Nature 464, 847 (2010)) indicates the existence of a spin liquid phase (SL) that intervenes between the antiferromagnetic and semimetallic phases of the half filled Hubbard model on a honeycomb lattice. To better understand the nature of this exotic phase, we study the quantum $J_1-J_2$ spin model on the honeycomb lattice, which provides an effective description of the Mott insulating region of the Hubbard model. Employing the variational Monte Carlo approach, we analyze the phase diagram of the model, finding a phase transition between antiferromagnet and an unusual $Z_2$ SL state at $J_2/J_1\approx 0.08$, which we identify as the SL phase of the Hubbard model. At higher $J_2/J_1\gtrsim 0.3$ we find a transition to a dimerized state with spontaneously broken rotational symmetry.Comment: 5 pages, 6 figure

Evidence that widespread star formation may be underway in G0.253+016, "The Brick"

Image cubes of differential column density as a function of dust temperature are constructed for Galactic Centre molecular cloud G0.253+0.016 ("The Brick") using the recently described PPMAP procedure. The input data consist of continuum images from the Herschel Space Telescope in the wavelength range 70-500 $\mu$m, supplemented by previously published interferometric data at 1.3 mm wavelength. While the bulk of the dust in the molecular cloud is consistent with being heated externally by the local interstellar radiation field, our image cube shows the presence, near one edge of the cloud, of a filamentary structure whose temperature profile suggests internal heating. The structure appears as a cool ($\sim 14$ K) tadpole-like feature, $\sim 6$ pc in length, in which is embedded a thin spine of much hotter ($\sim$ 40-50 K) material. We interpret these findings in terms of a cool filament whose hot central region is undergoing gravitational collapse and fragmentation to form a line of protostars. If confirmed, this would represent the first evidence of widespread star formation having started within this cloud.Comment: 5 pages, 4 figures; accepted for publication in MNRAS Letter

Non-equilibrium dynamic critical scaling of the quantum Ising chain

We solve for the time-dependent finite-size scaling functions of the 1D transverse-field Ising chain during a linear-in-time ramp of the field through the quantum critical point. We then simulate Mott-insulating bosons in a tilted potential, an experimentally-studied system in the same equilibrium universality class, and demonstrate that universality holds for the dynamics as well. We find qualitatively athermal features of the scaling functions, such as negative spin correlations, and show that they should be robustly observable within present cold atom experiments.Comment: 4 pages + 2 page supplemen

A Global Model of β−\beta^--Decay Half-Lives Using Neural Networks

Statistical modeling of nuclear data using artificial neural networks (ANNs) and, more recently, support vector machines (SVMs), is providing novel approaches to systematics that are complementary to phenomenological and semi-microscopic theories. We present a global model of $\beta^-$-decay halflives of the class of nuclei that decay 100% by $\beta^-$ mode in their ground states. A fully-connected multilayered feed forward network has been trained using the Levenberg-Marquardt algorithm, Bayesian regularization, and cross-validation. The halflife estimates generated by the model are discussed and compared with the available experimental data, with previous results obtained with neural networks, and with estimates coming from traditional global nuclear models. Predictions of the new neural-network model are given for nuclei far from stability, with particular attention to those involved in r-process nucleosynthesis. This study demonstrates that in the framework of the $\beta^-$-decay problem considered here, global models based on ANNs can at least match the predictive performance of the best conventional global models rooted in nuclear theory. Accordingly, such statistical models can provide a valuable tool for further mapping of the nuclidic chart.Comment: Proceedings of the 16th Panhellenic Symposium of the Hellenic Nuclear Physics Societ

Nuclear mass systematics by complementing the Finite Range Droplet Model with neural networks

A neural-network model is developed to reproduce the differences between experimental nuclear mass-excess values and the theoretical values given by the Finite Range Droplet Model. The results point to the existence of subtle regularities of nuclear structure not yet contained in the best microscopic/phenomenological models of atomic masses. Combining the FRDM and the neural-network model, we create a hybrid model with improved predictive performance on nuclear-mass systematics and related quantities.Comment: Proceedings for the 15th Hellenic Symposium on Nuclear Physic