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Abstract:

ABSTRACT The kilonova (KN) associated with the binary neutron star (BNS) merger GW170817 is the only known electromagnetic counterpart to a gravitational wave source. Here we produce a sequence of radiative transfer models (using tardis) with updated atomic data, and compare them to accurately calibrated spectra. We use element compositions from nuclear network calculations based on a realistic hydrodynamical simulation of a BNS merger. We show that the blue spectrum at +1.4 d after merger requires a nucleosynthetic trajectory with a high electron fraction. Our best-fitting model is composed entirely of first r-process peak elements (Sr and Zr) and the strong absorption feature is reproduced well by Sr ii absorption. At this epoch, we set an upper limit on the lanthanide mass fraction of $X_{{\small LN}} \lesssim 5 \times 10^{-3}$. In contrast, all subsequent spectra from +2.4 to 6.4 d require the presence of a modest amount of lanthanide material ($X_{{\small LN}} \simeq 0.05^{+0.05}_{-0.02}$), produced by a trajectory with Ye = 0.29. This produces lanthanide-induced line blanketing below 6000 Å, and sufficient light r-process elements to explain the persistent strong feature at ∼0.7–1.0 $\mu$m (Sr ii). The composition gives good matches to the observed data, indicating that the strong blue flux deficit results in the near-infrared (NIR) excess. The disjoint in composition between the first epoch and all others indicates either ejecta stratification, or the presence of two distinct components of material. This further supports the ‘two-component’ KN model, and constrains the element composition from nucleosynthetic trajectories. The major uncertainties lie in availability of atomic data and the ionization state of the expanding material.

Abstract:

Covariance matrices are important tools for obtaining reliable parameter constraints. Advancements in cosmological surveys lead to larger data vectors and, consequently, increasingly complex covariance matrices, whose number of elements grows as the square of the size of the data vector. The most straightforward way of comparing these matrices, in terms of their ability to produce parameter constraints, involves a full cosmological analysis, which can be very computationally expensive. Using the concept and construction of compression schemes, which have become increasingly popular, we propose a fast and reliable way of comparing covariance matrices. The basic idea is to focus only on the portion of the covariance matrix that is relevant for the parameter constraints and quantify, via a fast Monte Carlo simulation, the difference of a second candidate matrix from the baseline one. To test this method, we apply it to two covariance matrices that were used to analyse the cosmic shear measurements for the Dark Energy Survey Year 1. We found that the uncertainties on the parameters change by 2.6 per cent, a figure in agreement with the full cosmological analysis. While our approximate method cannot replace a full analysis, it may be useful during the development and validation of codes that estimate covariance matrices. Our method takes roughly 100 times less CPUh than a full cosmological analysis.