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We describe the Period detection and Identification Pipeline Suite (pips) – a new, fast, and statistically robust platform for period detection and analysis of astrophysical time-series data. PIPS is an open-source Python package that provides various pre-implemented methods and a customizable framework for automated, robust period measurements with principled uncertainties and statistical significance calculations. In addition to detailing the general algorithm that underlies PIPS , this paper discusses one of PIPS ' central and novel features, the Fourier-likelihood periodogram, and compares its performance to existing methods. The resulting improved performance implies that one can construct deeper, larger, and more reliable sets of derived properties from various observations, including all-sky surveys. We present a comprehensive validation of PIPS against artificially generated data, which demonstrates the reliable performance of our algorithm for a class of periodic variable stars (RR Lyrae stars). [ABSTRACT FROM AUTHOR]

A string of recent studies has debated the exact form and physical origin of an evolutionary trend between the peak luminosity of Type Ia supernovae (SNe Ia) and the properties of the galaxies that host them. We shed new light on the discussion by presenting an analysis of ∼200 low-redshift SNe Ia in which we measure the separation of Hubble residuals (HR; as probes of luminosity) between two host-galaxy morphological types. We show that this separation can test the predictions made by recently proposed models, using an independently and empirically determined distribution of each morphological type in host-property space. Our results are partially consistent with the new HR–age slope, but we find significant scatter in the predictions from different galaxy catalogues. The inconsistency in age illuminates an issue in the current debate that was not obvious in the long-discussed mass models: HR–host-property models are strongly dependent on the methods employed to determine galaxy properties. While our results demonstrate the difficulty in constructing a universal model for age as a proxy for host environment, our results indeed identify evolutionary trends between mass, age, morphology, and HR values, encouraging (or requiring, if such trends are to be accounted for in cosmological studies) further investigation. [ABSTRACT FROM AUTHOR]

Much of the cosmological utility thus far extracted from Type Ia supernovae (SNe Ia) relies on the assumption that SN Ia peak luminosities do not evolve significantly with the age (local or global) of their stellar environments. Two recent studies have provided conflicting results in evaluating the validity of this assumption, with one finding no correlation between Hubble residuals (HR) and stellar environment age, while the other claims a significant correlation. In this Letter we perform an independent reanalysis that rectifies issues with the statistical methods employed by both of the aforementioned studies. Our analysis follows a principled approach that properly accounts for regression dilution and critically (and unlike both prior studies) utilizes the Bayesian-model-produced SN environment age estimates (posterior samples) instead of point estimates. Moreover, the posterior is used as an informative prior in the regression. We find the Pearson correlation between the HR and local (global) age to be in excess of 4σ (3σ). Assuming there exists a linear relationship between HR and local (global) age, we find a corresponding slope of −0.035 ± 0.007 mag Gyr−1 (−0.036 ± 0.007 mag Gyr−1). We encourage further use of our approach to examine HR and host environment correlations, as well as experiments in correcting for luminosity evolution in SN Ia standardization. [ABSTRACT FROM AUTHOR]