In this work, we perform a full-spectrum fitting of 350 massive and passive galaxies selected as cosmic chronometers from the LEGA-C ESO public survey to derive their stellar ages, metallicities, and star formation histories. We extensively test our results by assessing their dependence on the possible contribution of dust, calibration of noise and signal, and use of photometric data in addition to spectral information; we also identify indicators of the correct convergence of the results, including the shape of the posterior distributions, the analysis of specific spectral features, and the correct reproduction of the observed spectrum. We derive a clear age–redshift trend compatible with the aging in a standard cosmological model showing a clear downsizing pattern, with more massive galaxies being formed at higher redshift (z _f ∼ 2.5) with respect to less massive ones (z _f ∼ 2). From these data, we measure the differential aging of this population of cosmic chronometers to derive a new measurement of the Hubble parameter, obtaining H ( z = 0.8 ) = 113.1 ± 15.1 ( stat . ) − 11.3 + 29.1 ( syst . ) . This analysis allows us to compare for the first time the differential ages of cosmic chronometers measured on the same sample with two completely different methods, the full-spectrum fit (this work) and the analysis of Lick indices, known to correlate with the age and metallicity of the stellar populations. Albeit an understood offset in the absolute ages, the differential ages have proven to be extremely compatible between the two methods, despite the very different data, assumptions, and models considered, demonstrating the robustness of the method.

We use simulated Hubble parameter data in the redshift range 0 ≤ z ≤ 2 to explore the role and power of observational H(z) data in constraining cosmological parameters of the ΛCDM model. The error model of the simulated data is empirically constructed from available measurements and scales linearly as z increases. By comparing the median figures of merit calculated from simulated data sets with that of current Type Ia supernova (SNIa) data, we find that as many as 64 further independent measurements of H(z) are needed to match the parameter constraining power of SNIa. If the error of H(z) could be lowered to 3%, the same number of future measurements would be needed, but then the redshift coverage would only be required to reach z = 1. We also show that accurate measurements of the Hubble constant H 0 can be used as priors to increase the H(z) data's figure of merit.

Constraining neutrino mass remains an elusive challenge in modern physics. Precision measurements are expected from several upcoming cosmological probes of large-scale structure. Achieving this goal relies on an equal level of precision from theoretical predictions of neutrino clustering. Numerical simulations of the non-linear evolution of cold dark matter and neutrinos play a pivotal role in this process. We incorporate neutrinos into the cosmological N-body code CUBEP ³ M and discuss the challenges associated with pushing to the extreme scales demanded by the neutrino problem. We highlight code optimizations made to exploit modern high performance computing architectures and present a novel method of data compression that reduces the phase-space particle footprint from 24 bytes in single precision to roughly 9 bytes. We scale the neutrino problem to the Tianhe-2 supercomputer and provide details of our production run, named TianNu, which uses 86% of the machine (13 824 compute nodes). With a total of 2.97 trillion particles, TianNu is currently the world’s largest cosmological N-body simulation and improves upon previous neutrino simulations by two orders of magnitude in scale. We finish with a discussion of the unanticipated computational challenges that were encountered during the TianNu runtime.

This paper is a review on the observational Hubble parameter data that have gained increasing attention in recent years for their illuminating power on the dark side of the universe: the dark matter, dark energy, and the dark age. Currently, there are two major methods of independent observational H(z) measurement, which we summarize as the “differential age method” and the “radial BAO size method.” Starting with fundamental cosmological notions such as the spacetime coordinates in an expanding universe, we present the basic principles behind the two methods. We further review the two methods in greater detail, including the source of errors. We show how the observational H(z) data present itself as a useful tool in the study of cosmological models and parameter constraint, and we also discuss several issues associated with their applications. Finally, we point the reader to a future prospect of upcoming observation programs that will lead to some major improvements in the quality of observational H(z) data.

The Search for Extraterrestrial Intelligence (SETI) attempts to address the possibility of the presence of technological civilizations beyond the Earth. Benefiting from high sensitivity, large sky coverage, and an innovative feed cabin for China’s Five-hundred-meter Aperture Spherical radio Telescope (FAST), we performed SETI’s first observations with FAST’s newly commissioned 19 beam receiver; we report preliminary results in this paper. Using the data stream produced by the SERENDIP VI real-time multibeam SETI spectrometer installed at FAST, as well as its off-line data processing pipelines, we identify and remove four kinds of radio frequency interference (RFI): zone, broadband, multibeam, and drifting, utilizing the Nebula SETI software pipeline combined with machine-learning algorithms. After RFI mitigation, the Nebula pipeline identifies and ranks interesting narrowband candidate ET signals, scoring candidates by the number of times candidate signals have been seen at roughly the same sky position and same frequency, signal strength, proximity to a nearby star or object of interest, along with several other scoring criteria. We show four example candidate groups that demonstrate this RFI mitigation and candidate selection. This preliminary testing on FAST data helps to validate our SETI instrumentation techniques as well as our data processing pipeline.