Tuesdays 10:30 - 11:30 | Fridays 11:30 - 12:30
Showing votes from 2018-01-23 11:30 to 2018-01-26 12:30 | Next meeting is Friday Aug 15th, 11:30 am.
The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demands that the astronomical community update its followup paradigm. Alert-brokers -- automated software system to sift through, characterize, annotate and prioritize events for followup -- will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate and retrospective classification of alerts. The first takes the form of variable vs transient categorization, the second, a multi-class typing of the combined variable and transient dataset, and the third, a purity-driven subtyping of a transient class. While several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress towards adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.
We perform a quantitative comparison between N-body simulations and the Schr\"odinger-Poisson system in 1+1 dimensions. In particular, we study halo formation with different initial conditions. We observe the convergence of various observables in the Planck constant h-bar and also test virialization. We discuss the generation of higher order cumulants of the particle distribution function which demonstrates that the Schr\"odinger-Poisson equations should not be perceived as a generalization of the dust model with quantum pressure but rather as one way of sampling the phase space of the Vlasov-Poisson system -- just as N-body simulations. Finally, we quantitatively recover the scaling behavior of the halo density profile from N-body simulations.
With the realization that dRGT has difficulties to provide dynamical cosmological solutions, the interest of current research in massive gravity has significantly decreased. The reason for that is not least because it is commonly believed that the dRGT theory is the unique way to describe a massive spin-2 field without ghosts. In this work, we argue that dRGT is very likely not unique by deriving a new massive gravity theory and providing indications for the absence of Ostrogradsky instabilities. For the construction of the theory, we use a disformal transformation of the metric tensor in the dRGT action. By analyzing the decoupling limit, we show that the resulting scalar-tensor theory lives inside the class of beyond-Horndeski Lagrangians as long as the transformation of the metric remains purely disformal. This proves the absence of ghosts in this decoupling limit and hints at their absence in the whole theory. Furthermore, we consider a more general case, in which we allow the conformal factor in the disformal transformation to be different from unity, and discuss the absence of ghosts in this decoupling limit.