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Guy Avraham

Publications and source records attributed to Guy Avraham.

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The O(4)-breaking bubble

False vacuum decay in field theory is thought to be dominated by Coleman's O(4)-symmetric bounce, the minimum action nontrivial solution of the imaginary time equations of motion. Beyond the bounce, non-constructive existence proofs of O(4)-breaking solutions are available in the mathematics literature, but the solutions themselves, and their physics, have remained unknown. Considering the simple, bounded-below, scalar field potential $V(\phi)=\frac{m^2}{2}\phi^2-\frac{\lambda}{4}\phi^4+\frac{g}{6}\phi^6$, we construct a nonradial solution explicitly: two bubble-tubes of opposite sign wrapping orthogonal rings, invariant under ${\rm O}(2)\times{\rm O}(2)$ rotations combined with a parity that exchanges the rings. The solution admits valid Cauchy data for real time evolution from a $t=0$ slice, and supports an odd number of unstable deformation modes.

hep-th

Moving outside the lab: The viability of conducting sensorimotor learning studies online

Collecting data online via crowdsourcing platforms has proven to be a very efficient way to recruit a large and diverse sample. Studies of motor learning, however, have been largely confined to the lab due to the need for special equipment to record movement kinematics and, as such, are typically only accessible to specific participants (e.g., college students). As a first foray to make motor learning studies accessible to a larger and more diverse audience, we developed an online, web-based platform (OnPoint) to collect kinematic data, serving as a template for researchers to create their own online sensorimotor control and learning experiments. As a proof-of-concept, we asked if fundamental motor learning phenomena discovered in the lab could be replicated online. In a series of three experiments, we observed a close correspondence between the results obtained online with those previously reported from research conducted in the laboratory. This web-based platform paired with online crowdsourcing can serve as a powerful new method for the study of motor control and learning.

q-bio.NC