Pierre-and-Marie-Curie University

About: Pierre-and-Marie-Curie University is a education organization based out in Paris, France. It is known for research contribution in the topics: Population & Raman spectroscopy. The organization has 34448 authors who have published 56139 publications receiving 2392398 citations.

Rates and Properties of Type Ia Supernovae as a Function of Mass and Star Formation in Their Host Galaxies

Abstract: We show that Type Ia supernovae (SNe Ia) are formed within both very young and old stellar populations, with observed rates that depend on the stellar mass and mean star formation rates (SFRs) of their host galaxies. Models in which the SN Ia rate depends solely on host galaxy stellar mass are ruled out with >99% confidence. Our analysis is based on 100 spectroscopically confirmed SNe Ia, plus 24 photometrically classified events, all from the Supernova Legacy Survey (SNLS) and distributed over 0.2 < z < 0.75. We estimate stellar masses and SFRs for the SN Ia host galaxies by fitting their broadband spectral energy distributions with the galaxy spectral synthesis code PEGASE.2. We show that the SN Ia rate per unit mass is proportional to the specific SFR of the parent galaxies—more vigorously star-forming galaxies host more SNe Ia per unit stellar mass, broadly equivalent to the trend of increasing SN Ia rate in later type galaxies seen in the local universe. Following earlier suggestions for a simple "two-component" model approximating the SN Ia rate, we find bivariate linear dependencies of the SN Ia rate on both the stellar masses and the mean SFRs of the host systems. We find that the SN Ia rate can be well represented as the sum of 5.3 ± 1.1 × 10 to the -14 SNe yr to the -1 M(.)to the -1 and 3.9 ± 0.7 × 10 to the -4 SNe yr to the -1 (M(.) yr to the -1)to the -1 of star formation. We also demonstrate a dependence of distant SN Ia light-curve shapes on star formation in the host galaxy, similar to trends observed locally. Passive galaxies, with no star formation, preferentially host faster declining/dimmer SNe Ia, while brighter events are found in systems with ongoing star formation.

Correlation and variable importance in random forests

Abstract: This paper is about variable selection with the random forests algorithm in presence of correlated predictors. In high-dimensional regression or classification frameworks, variable selection is a difficult task, that becomes even more challenging in the presence of highly correlated predictors. Firstly we provide a theoretical study of the permutation importance measure for an additive regression model. This allows us to describe how the correlation between predictors impacts the permutation importance. Our results motivate the use of the recursive feature elimination (RFE) algorithm for variable selection in this context. This algorithm recursively eliminates the variables using permutation importance measure as a ranking criterion. Next various simulation experiments illustrate the efficiency of the RFE algorithm for selecting a small number of variables together with a good prediction error. Finally, this selection algorithm is tested on the Landsat Satellite data from the UCI Machine Learning Repository.

Nitrogen retention in rivers: model development and application to watersheds in the northeastern U.S.A.

Abstract: A regression model (RivR-N) was developed that predicts the proportion of N removed from streams and reservoirs as an inverse function of the water displacement time of the water body (ratio of water body depth to water time of travel). When applied to 16 drainage networks in the eastern U.S., the RivR-N model predicted that 37% to 76% of N input to these rivers is removed during transport through the river networks. Approximately half of that is removed in 1st through 4th order streams which account for 90% of the total stream length. The other half is removed in 5th order and higher rivers which account for only about 10% of the total stream length. Most N removed in these higher orders is predicted to originate from watershed loading to small and intermediate sized streams. The proportion of N removed from all streams in the watersheds (37-76%) is considerably higher than the proportion of N input to an individual reach that is removed in that reach (generally <20%) because of the cumulative effect of continued nitrogen removal along the entire flow path in downstream reaches. This generally has not been recognized in previous studies, but is critical to an evaluation of the total amount of N removed within a river network. At the river network scale, reservoirs were predicted to have a minimal effect on N removal. A fairly modest decrease (<10 percentage points) in the N removed at the river network scale was predicted when a third of the direct watershed loading was to the two highest orders compared to a uniform loading.