Evolution Of Power Spectral Density Research Articles

The diversity and variability of star formation histories in models of galaxy evolution

ABSTRACT Understanding the variability of galaxy star formation histories (SFHs) across a range of time-scales provides insight into the underlying physical processes that regulate star formation within galaxies. We compile the SFHs of galaxies at z = 0 from an extensive set of models, ranging from cosmological hydrodynamical simulations (Illustris, IllustrisTNG, Mufasa, Simba, EAGLE), zoom simulations (FIRE-2, g14, and Marvel/Justice League), semi-analytic models (Santa Cruz SAM) and empirical models (UniverseMachine), and quantify the variability of these SFHs on different time-scales using the power spectral density (PSD) formalism. We find that the PSDs are well described by broken power laws, and variability on long time-scales (≳1 Gyr) accounts for most of the power in galaxy SFHs. Most hydrodynamical models show increased variability on shorter time-scales (≲300 Myr) with decreasing stellar mass. Quenching can induce ∼0.4−1 dex of additional power on time-scales >1 Gyr. The dark matter accretion histories of galaxies have remarkably self-similar PSDs and are coherent with the in situ star formation on time-scales >3 Gyr. There is considerable diversity among the different models in their (i) power due to star formation rate variability at a given time-scale, (ii) amount of correlation with adjacent time-scales (PSD slope), (iii) evolution of median PSDs with stellar mass, and (iv) presence and locations of breaks in the PSDs. The PSD framework is a useful space to study the SFHs of galaxies since model predictions vary widely. Observational constraints in this space will help constrain the relative strengths of the physical processes responsible for this variability.

A complete system to generate electrical noise with arbitrary power spectral density

Reliability and performance of electronic devices are significantly dependent on their noise rejection capability that is usually investigated since their early production stage as well as during the scheduled maintenance. To this aim, white noise sources are mostly available on the market and usually to the purpose, whereas the use of generators capable of producing coloured noise (actually, very uncommon) should be advisable in most of applications. If arbitrary waveform generators (AWGs) are taken into account, either transient signals in single shot generation mode or periodic signals in continuous generation mode can only be produced due to the finite available memory; unfortunately, both modes are not suitable to emulate noise. To overcome the considered limitations, this work presents the design and implementation of a coloured noise generator that offers the possibility of tailoring the noise spectral content to the desired application. As it can be expected, performance of the generator changes according to different hardware selections; with specific regard to the proposed implementation, it performs as good as state of art solutions in terms of both bandwidth and flexibility. It is composed of a digital section implemented on field programmable gate arrays (FPGA) and a digital-to-analogue converter (DAC) mandated to generate the desired analogue output. The bandwidth of the generated noise can be selected up to a maximum of 50MHz while the evolution of power spectral density versus frequency can be defined with a resolution equal to 0.4% of the bandwidth. Thanks to suitable digital signal processing techniques, spurious components laying outside the selected bandwidth are hardly attenuated.

Fractal structure and predictive strategy of the daily extreme temperature residuals at Fabra Observatory (NE Spain, years 1917–2005)

A compilation of daily extreme temperatures recorded at the Fabra Observatory (Catalonia, NE Spain) since 1917 up to 2005 has permitted an exhaustive analysis of the fractal behaviour of the daily extreme temperature residuals, DTR, defined as the difference between the observed daily extreme temperature and the daily average value. The lacunarity characterises the lag distribution on the residual series for several thresholds. Hurst, H, and Hausdorff, Ha, exponents, together with the exponent β of the decaying power law, describing the evolution of power spectral density with frequency, permit to characterise the persistence, antipersistence or randomness of the residual series. The self-affine character of DTR series is verified, and additionally, they are simulated by means of fractional Gaussian noise, fGn. The reconstruction theorem leads to the quantification of the complexity (correlation dimension, μ*, and Kolmogorov entropy, κ) and predictive instability (Lyapunov exponents, λ, and Kaplan-Yorke dimension, D KY) of the residual series. All fractal parameters are computed for consecutive and independent segments of 5-year lengths. This strategy permits to obtain a high enough number of fractal parameter samples to estimate time trends, including their statistical significance. Comparisons are made between results of predictive algorithms based on fGn models and an autoregressive autoregressive integrated moving average (ARIMA) process, with the latter leading to slightly better results than the former. Several dynamic atmospheric mechanisms and local effects, such as local topography and vicinity to the Mediterranean coast, are proposed to explain the complex and instable predictability of DTR series. The memory of the physical system (Kolmogorov entropy) would be attributed to the interaction with the Mediterranean Sea.