Combining Theory and Practice in the Teaching of Science Institutions: A Collaborative Program between the City University of New York and the American Museum of Natural History

The National Science Education Standards contain several mandates that share the use of alternative and creative experiences in the teaching of science at all levels. An important feature of these standards is the call for learning settings and environments different from the traditional classroom in order to enhance student interest and participation in the learning process. New York City is rich in institutions that are ideal for the implementation of effective science teaching through the use of informal resources. This article uses the American Museum of Natural History as a prime example of this

LGBTQ Grantmakers 2008 Report Card on Racial Equity

A research study examining how a subset of lesbian, gay, bisexual, transgender and queer (LGBTQ) grantmakers addresses racial equity in grantmaking, governing documents, policies and practices, demographics and leadership, and strategic communications

Efficient Joint Sampling of Impact Parameters and Transit Depths in Transiting Exoplanet Light Curves

When fitting transiting exoplanet lightcurves, it is usually desirable to have ranges and/or priors for the parameters which are to be retrieved that include our degree of knowledge (or ignorance) in the routines which are being used. In Markov Chain Monte Carlo (MCMC) routines, for example, these enter as prior distributions. These can either represent our current knowledge of the distribution of such parameters (e.g., based on their observed values) or physically plausible parameters ranges to be sampled. Among the parameters that are constrained by transiting exoplanet lightcurves, there are two which are of much physical significance: the impact parameter of the orbit, $b = (a/R_*)\cos i$, and the planet-to-star radius ratio, $p = R_p/R_s$ (which defines the transit depth, $\delta = p^2$). These two are natural parameters to extract and constrain as they usually have well defined limits. A common set of "uninformative" priors used for those two parameters are uniform priors. However, this poses a sampling problem especially important for grazing orbits: given that we sample a value $p_i$ from the prior on $p$, the only physically plausible values for $b$ to be sampled given $p_i$ are those that satisfy $b < 1 + p_i$. If we simply reject the sample if the sampled value of b is greater than $1 + p_i$, then we will reject points from a significant portion of the prior area depending on its size. It is desirable, thus, to have an algorithm that efficiently samples values from the physically plausible zone in the $(b,p)$ plane. Here we present such an algorithm.Comment: 3 pages, 1 figur