Nucleosynthesis of Zinc and Iron-Peak Elements in Pop III Type II Supernovae : Comparison with abundances of Very Metal-Poor Halo Stars

We calculate nucleosynthesis in core-collapse explosions of massive Pop III stars, and compare the results with abundances of metal-poor halo stars to constrain the parameters of Pop III supernovae. We focus on iron-peak elements and, in particular, we try to reproduce the large [Zn/Fe] observed in extremely metal-poor stars. The interesting trends of the observed ratios [Zn, Co, Mn, Cr, V/Fe] can be related to the variation of the relative mass of the complete and incomplete Si-burning regions in supernova ejecta. We find that [Zn/Fe] is larger for deeper mass-cuts, smaller neutron excess, and larger explosion energies. The large [Zn/Fe] and [O/Fe] observed in the very metal-poor halo stars suggest deep mixing of complete Si-burning material and a significant amount of fall-back in Type II supernovae. Furthermore, large explosion energies (E_51 >~ 2 for M ~ 13 Msun and E_51 >~ 20 for M >~ 20 Msun) are required to reproduce [Zn/Fe] ~ 0.5. The observed trends of the abundance ratios among the iron-peak elements are better explained with this high energy (``Hypernova'') models rather than the simple ``deep'' mass-cut effect, because the overabundance of Ni can be avoided in the hypernova models. We also present the yields of pair-instability supernova explosions of M = 130 - 300 Msun stars, and discuss that the abundance features of very metal-poor stars cannot be explained by pair-instability supernovae.Comment: 32 pages, 19 figures, 18 tables. To appear in the Astrophysical Journal 2002, 565. Table 18 of yields of Pop III Pair-Instability Supernovae is replaced with a new on

Correlation length of hydrophobic polyelectrolyte solutions

The combination of two techniques (Small Angle X-ray Scattering and Atomic Force Microscopy) has allowed us to measure in reciprocal and real space the correlation length $\xi$ of salt-free aqueous solutions of highly charged hydrophobic polyelectrolyte as a function of the polymer concentration $C_p$, charge fraction $f$ and chain length $N$. Contrary to the classical behaviour of hydrophilic polyelectrolytes in the strong coupling limit, $\xi$ is strongly dependent on $f$. In particular a continuous transition has been observed from $\xi \sim C_p^{-1/2}$ to $\xi\sim C_p^{-1/3}$ when $f$ decreased from 100% to 35%. We interpret this unusual behaviour as the consequence of the two features characterising the hydrophobic polyelectrolytes: the pearl necklace conformation of the chains and the anomalously strong reduction of the effective charge fraction.Comment: 7 pages, 5 figures, submitted to Europhysics Letter

The Detectability of Pair-Production Supernovae at z < 6

Nonrotating, zero metallicity stars with initial masses 140 < M < 260 solar masses are expected to end their lives as pair-production supernovae (PPSNe), in which an electron-positron pair-production instability triggers explosive nuclear burning. Interest in such stars has been rekindled by recent theoretical studies that suggest primordial molecular clouds preferentially form stars with these masses. Since metal enrichment is a local process, the resulting PPSNe could occur over a broad range of redshifts, in pockets of metal-free gas. Using the implicit hydrodynamics code KEPLER, we have calculated a set of PPSN light curves that addresses the theoretical uncertainties and allows us to assess observational strategies for finding these objects at intermediate redshifts. The peak luminosities of typical PPSNe are only slightly greater than those of Type Ia, but they remain bright much longer (~ 1 year) and have hydrogen lines. Ongoing supernova searches may soon be able to limit the contribution of these very massive stars to < 1% of the total star formation rate density out to z=2 which already provides useful constraints for theoretical models. The planned Joint Dark Energy Mission satellite will be able to extend these limits out to z=6.Comment: 12 pages, 6 figures, ApJ in press; slightly revised version, a few typos correcte

Radiant Barrier Insulation Performance in Full Scale Attics with Soffit and Ridge Venting

There is a limited data base on the full scale performance of radiant barrier insulation in attics. The performance of RBS have been shown to be dependent on attic ventilation characteristics. Tests have been conducted on a duplex located in Florida with soffit and ridge venting to measure attic performance. The unique features of these experiments are accurate and extensive instrumentation with heat flow meters, field verification of HFM calibration, extensive characterization of the installed ceiling insulation, ventilation rate measurements and extensive temperature instrumentation. The attics are designed to facilitate experimental changes without damaging the installed insulation. RBS performance has been measured for two natural ventilation levels for soffit and ridge venting. Previously, no full scale data have been developed for these test configurations. Test data for each of the test configurations was acquired for a minimum of two weeks with some acquired over a five week period. The Rl9 insulation performed as expected

Last Layer Marginal Likelihood for Invariance Learning

Data augmentation is often used to incorporate inductive biases into models. Traditionally, these are hand-crafted and tuned with cross validation. The Bayesian paradigm for model selection provides a path towards end-to-end learning of invariances using only the training data, by optimising the marginal likelihood. We work towards bringing this approach to neural networks by using an architecture with a Gaussian process in the last layer, a model for which the marginal likelihood can be computed. Experimentally, we improve performance by learning appropriate invariances in standard benchmarks, the low data regime and in a medical imaging task. Optimisation challenges for invariant Deep Kernel Gaussian processes are identified, and a systematic analysis is presented to arrive at a robust training scheme. We introduce a new lower bound to the marginal likelihood, which allows us to perform inference for a larger class of likelihood functions than before, thereby overcoming some of the training challenges that existed with previous approaches