Aaron Smith - CASS/UCSD

Aaron Smith
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Aaron Smith
San Diego
United States

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Mathematics - Probability (12)
Statistics - Computation (11)
Statistics - Theory (5)
Mathematics - Statistics (5)
Mathematics - Differential Geometry (3)
Statistics - Methodology (3)
Astrophysics of Galaxies (3)
Statistics - Applications (3)
Cosmology and Nongalactic Astrophysics (2)
Mathematics - Algebraic Topology (1)
Physics - Atomic Physics (1)
Astrophysics (1)
Computer Science - Data Structures and Algorithms (1)
Computer Science - Computational Complexity (1)
Mathematics - Dynamical Systems (1)
Mathematics - Analysis of PDEs (1)

Publications Authored By Aaron Smith

We briefly review the historical development of the ideas regarding the first supermassive black hole seeds, the physics of their formation and radiative feedback, recent theoretical and observational progress, and our outlook for the future. Read More

We introduce a Markov chain for sampling from the uniform distribution on a Riemannian manifold $\mathcal{M}$, which we call the $\textit{geodesic walk}$. We prove that the mixing time of this walk on any manifold with positive sectional curvature $C_{x}(u,v)$ bounded both above and below by $0 < \mathfrak{m}_{2} \leq C_{x}(u,v) \leq \mathfrak{M}_2 < \infty$ is $\mathcal{O}^*\left(\frac{\mathfrak{M}_2}{\mathfrak{m}_2}\right)$. In particular, this bound on the mixing time does not depend explicitly on the dimension of the manifold. Read More

The dynamical impact of Lyman-alpha (Ly{\alpha}) radiation pressure on galaxy formation depends on the rate and duration of momentum transfer between Ly{\alpha} photons and neutral hydrogen gas. Although photon trapping has the potential to multiply the effective force, ionizing radiation from stellar sources may relieve the Ly{\alpha} pressure before appreciably affecting the kinematics of the host galaxy or efficiently coupling Ly{\alpha} photons to the outflow. We present self-consistent Ly{\alpha} radiation-hydrodynamics simulations of high-$z$ galaxy environments by coupling the Cosmic Ly{\alpha} Transfer code (COLT) with spherically symmetric Lagrangian frame hydrodynamics. Read More

Performing Bayesian inference via Markov chain Monte Carlo (MCMC) can be exceedingly expensive when posterior evaluations invoke the evaluation of a computationally expensive model, such as a system of partial differential equations. In recent work [Conrad et al. JASA 2015, arXiv:1402. Read More

It is often possible to speed up the mixing of a Markov chain $\{ X_{t} \}_{t \in \mathbb{N}}$ on a state space $\Omega$ by \textit{lifting}, that is, running a more efficient Markov chain $\{ \hat{X}_{t} \}_{t \in \mathbb{N}}$ on a larger state space $\hat{\Omega} \supset \Omega$ that projects to $\{ X_{t} \}_{t \in \mathbb{N}}$ in a certain sense. In [CLP99], Chen, Lov{\'a}sz and Pak prove that for Markov chains on finite state spaces, the mixing time of any lift of a Markov chain is at least the square root of the mixing time of the original chain, up to a factor that depends on the stationary measure. Unfortunately, this extra factor makes the bound in [CLP99] very loose for Markov chains on large state spaces and useless for Markov chains on continuous state spaces. Read More

As it has become common to use many computer cores in routine applications, finding good ways to parallelize popular algorithms has become increasingly important. In this paper, we present a parallelization scheme for Markov chain Monte Carlo (MCMC) methods based on spectral clustering of the underlying state space, generalizing earlier work on parallelization of MCMC methods by state space partitioning. We show empirically that this approach speeds up MCMC sampling for multimodal distributions and that it can be usefully applied in greater generality than several related algorithms. Read More

Determining the total variation mixing time of Kac's random walk on the special orthogonal group $\mathrm{SO}(n)$ has been a long-standing open problem. In this paper, we construct a novel non-Markovian coupling for bounding this mixing time. The analysis of our coupling entails controlling the smallest singular value of a certain random matrix with highly dependent entries. Read More

Many modern applications collect large sample size and highly imbalanced categorical data, with some categories being relatively rare. Bayesian hierarchical models are well motivated in such settings in providing an approach to borrow information to combat data sparsity, while quantifying uncertainty in estimation. However, a fundamental problem is scaling up posterior computation to massive sample sizes. Read More

Throughout the epoch of reionization the most luminous Ly{\alpha} emitters are capable of ionizing their own local bubbles. The CR7 galaxy at $z \approx 6.6$ stands out for its combination of exceptionally bright Ly{\alpha} and HeII 1640 Angstrom line emission but absence of metal lines. Read More

Determining the mixing time of Kac's random walk on the sphere $\mathrm{S}^{n-1}$ is a long-standing open problem. We show that the total variation mixing time of Kac's walk on $\mathrm{S}^{n-1}$ is between $\frac{1}{2} \, n \log(n)$ and $200 \,n \log(n)$. Our bound is thus optimal up to a constant factor, improving on the best-known upper bound of $O(n^{5} \log(n)^{2})$ due to Jiang. Read More

Many finite-state reversible Markov chains can be naturally decomposed into "projection" and "restriction" chains. In this paper we provide bounds on the total variation mixing times of the original chain in terms of the mixing properties of these related chains. This paper is in the tradition of existing bounds on Poincare and log-Sobolev constants of Markov chains in terms of similar decompositions [JSTV04, MR02, MR06, MY09]. Read More

We study a kinetically constrained Ising process (KCIP) associated with a graph G and density parameter p; this process is an interacting particle system with state space $\{0,1\}^{G}$. The stationary distribution of the KCIP Markov chain is the Binomial($|G|, p$) distribution on the number of particles, conditioned on having at least one particle. The `constraint' in the name of the process refers to the rule that a vertex cannot change its state unless it has at least one neighbour in state `1'. Read More

We present the Cosmic Lyman-$\alpha$ Transfer code (COLT), a massively parallel Monte-Carlo radiative transfer code, to simulate Lyman-$\alpha$ (Ly$\alpha$) resonant scattering through neutral hydrogen as a probe of the first galaxies. We explore the interaction of centrally produced Ly$\alpha$ radiation with the host galactic environment. Ly$\alpha$ photons emitted from the luminous starburst region escape with characteristic features in the line profile depending on the density distribution, ionization structure, and bulk velocity fields. Read More

In many modern applications, difficulty in evaluating the posterior density makes performing even a single MCMC step slow. This difficulty can be caused by intractable likelihood functions, but also appears for routine problems with large data sets. Many researchers have responded by running approximate versions of MCMC algorithms. Read More

We analyze the computational efficiency of approximate Bayesian computation (ABC), which approximates a likelihood function by drawing pseudo-samples from the associated model. For the rejection sampling version of ABC, it is known that multiple pseudo-samples cannot substantially increase (and can substantially decrease) the efficiency of the algorithm as compared to employing a high-variance estimate based on a single pseudo-sample. We show that this conclusion also holds for a Markov chain Monte Carlo version of ABC, implying that it is unnecessary to tune the number of pseudo-samples used in ABC-MCMC. Read More

We construct a new framework for accelerating Markov chain Monte Carlo in posterior sampling problems where standard methods are limited by the computational cost of the likelihood, or of numerical models embedded therein. Our approach introduces local approximations of these models into the Metropolis-Hastings kernel, borrowing ideas from deterministic approximation theory, optimization, and experimental design. Previous efforts at integrating approximate models into inference typically sacrifice either the sampler's exactness or efficiency; our work seeks to address these limitations by exploiting useful convergence characteristics of local approximations. Read More

Adaptive Markov chains are an important class of Monte Carlo methods for sampling from probability distributions. The time evolution of adaptive algorithms depends on past samples, and thus these algorithms are non-Markovian. Although there has been previous work establishing conditions for their ergodicity, not much is known theoretically about their finite sample properties. Read More

Let $X_{t}$ and $Y_{t}$ be two Markov chains, on state spaces $\Omega \subset \hat{\Omega}$. In this paper, we discuss how to prove bounds on the spectrum of $X_{t}$ based on bounds on the spectrum of $Y_{t}$. This generalizes work of Diaconis, Saloff-Coste, Yuen and others on comparison of chains in the case $\Omega = \hat{\Omega}$. Read More

Benford's law is frequently used to evaluate the likihood that data is misrepresentative. Typically statistical tests measure the likihood. Another method of employing Benford's law is to compare the frequency of leading digits to the probabilities of leading digits over a subset of the natural numbers. Read More

A measure-preserving dynamical system can be approximated by a Markov shift with a bistochastic matrix. This leads to using empirical stochastic matrices to measure and estimate properties of stirring protocols. Specifically, the second largest eigenvalue can be used to statistically decide if a stirring protocol is weak-mixing, ergodic, or nonergodic. Read More

Cosmological data have provided new constraints on the number of neutrino species and the neutrino mass. However these constraints depend on assumptions related to the underlying cosmology. Since a correlation is expected between the number of effective neutrinos N_{eff}, the neutrino mass \sum m_\nu, and the curvature of the universe \Omega_k, it is useful to investigate the current constraints in the framework of a non-flat universe. Read More

This paper presents and explores a theory of \emph{multiholomorphic maps}. This group of ideas generalizes the theory of pseudoholomorphic curves in a direction suggested by consideration of the kinds of compatible geometric structures that appear in the realm of special holonomy as well as some of the topological and analytic considerations that are essential to pseudoholomorphic invariants. The first part presents the geometric framework of compatible $n$-triads, from which follows naturally the definition of a multiholomorphic mapping. Read More

We use a non-Markovian coupling and small modifications of techniques from the theory of finite Markov chains to analyze some Markov chains on continuous state spaces. The first is a Gibbs sampler on narrow contingency tables, the second a gen- eralization of a sampler introduced by Randall and Winkler. Read More

We determine the mixing time of a simple Gibbs sampler on the unit simplex, confirming a conjecture of Aldous. The upper bound is based on a two-step coupling, where the first step is a simple contraction argument and the second step is a non-Markovian coupling. We also present a MCMC-based perfect sampling algorithm based on our proof which can be applied with Gibbs samplers that are harder to analyze. Read More

Many experiments involving cold and ultracold atomic gases require very precise control of magnetic fields that couple to and drive the atomic spins. Examples include quantum control of atomic spins, quantum control and quantum simulation in optical lattices, and studies of spinor Bose condensates. This makes accurate cancellation of the (generally time dependent) background magnetic field a critical factor in such experiments. Read More

We describe an $A_\infty$-quasi-equivalence of dg-categories between the first authors' $\mathcal{P}_{\mathcal{A}}$ ---the category of category of prefect $A^0$-modules with flat $\Z$-connection, corresponding to the de Rham dga $\mathcal{A}$ of a compact manifold $M$--- and the dg-category of \emph{infinity-local systems} on $M$ ---homotopy coherent representations of the smooth singular simplicial set of $M$, $\Pinf$. We understand this as a generalization of the Riemann--Hilbert correspondence to $\Z$-connections ($\Z$-graded superconnections in some circles). In one formulation an infinity-local system is simplicial map between the simplicial sets ${\pi}_{\infty}M$ and a repackaging of the dg-category of cochain complexes by virtue of the simplicial nerve and Dold-Kan. Read More

The Solar Mass Ejection Imager (SMEI) views nearly every point on the sky once every 102 minutes and can detect point sources as faint as R~10th magnitude. Therefore, SMEI can detect or provide upper limits for the optical afterglow from gamma-ray bursts in the tens of minutes after the burst when different shocked regions may emit optically. Here we provide upper limits for 58 bursts between 2003 February and 2005 April. Read More