Tevatron Discovery Potential for Fourth Generation Neutrinos: Dirac, Majorana and Everything in Between

We analyze the power of the Tevatron dataset to exclude or discover fourth generation neutrinos. In a general framework, one can have mixed left- and right-handed neutrinos, with Dirac and Majorana neutrinos as extreme cases. We demonstrate that a single Tevatron experiment can make powerful statements across the entire mixing space, extending LEP's mass limits of 60-80 GeV up to 150-175 GeV, depending on the mixing.Comment: 4 pages, pdflate

Mono-everything: Combined limits on dark matter production at colliders from multiple final states

Searches for dark matter production at particle colliders are complementary to direct-detection and indirect-detection experiments and especially powerful for small masses, mχ<100  GeV. An important collider dark matter signature is due to the production of a pair of these invisible particles with the initial-state radiation of a standard model particle. Currently, collider searches use individual and nearly orthogonal final states to search for initial-state jets, photons or massive gauge bosons. We combine these results across final states and across experiments to give the strongest current collider-based limits in the context of effective field theories and map these to limits on dark matter interactions with nuclei and to dark matter self-annhiliation

Searching for Z′Z' bosons decaying to gluons

The production and decay of a new heavy vector boson, a chromophilic $Z'$ vector boson, is described. The chromophilic $Z'$ couples only to two gluons, but its two-body decays are absent, leading to a dominant decay mode of $Z'\rightarrow q\bar{q}g$. The unusual nature of the interaction predicts a cross-section which grows with $m_{Z'}$ for a fixed coupling and an accompanying gluon with a coupling that rises with its energy. We study the $t\bar{t}g$ decay mode, proposing distinct reconstruction techniques for the observation of an excess and for the measurement of $m_{Z'}$. We estimate the sensitivity of current experimental datasets.Comment: For submission to PR

Information barrier functional requirements

for the purpose of this paper, the authors have used the term functional requirement to indicate a required task rather than the recommended method for accomplishing this task. The creation of effective information barrier technology will proceed as a series of steps: (1) IB conceptual Description; (2) IB Functional Requirements (this document--ongoing); (3) IB hardware and software specification; (4) IB hardware and software construction; and (5) IB implementation. This functional requirements document is not intended to supplant or supersede the conceptual description; rather, these functional requirements are intended to be used along with the earlier description to help generate hardware and software requirements

Neuroevolutionary reinforcement learning for generalized control of simulated helicopters

This article presents an extended case study in the application of neuroevolution to generalized simulated helicopter hovering, an important challenge problem for reinforcement learning. While neuroevolution is well suited to coping with the domain’s complex transition dynamics and high-dimensional state and action spaces, the need to explore efficiently and learn on-line poses unusual challenges. We propose and evaluate several methods for three increasingly challenging variations of the task, including the method that won first place in the 2008 Reinforcement Learning Competition. The results demonstrate that (1) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering, (2) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models, (3) due to the difficulty of learning reliable models, model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and (4) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks