Hintze Centre for Astrophysical Surveys

Abstract:

<jats:title>ABSTRACT</jats:title> <jats:p>Early-type galaxies (ETGs) are known to harbour dense spheroids of stars but scarce star formation (SF). Approximately a quarter of these galaxies have rich molecular gas reservoirs yet do not form stars efficiently. We study here the ETG NGC 524, with strong shear suspected to result in a smooth molecular gas disc and low star-formation efficiency (SFE). We present new spatially resolved observations of the 12CO(2-1)-emitting cold molecular gas from the Atacama Large Millimeter/sub-millimeter Array (ALMA) and of the warm ionized-gas emission lines from SITELLE at the Canada–France–Hawaii Telescope. Although constrained by the resolution of the ALMA observations (≈37 pc), we identify only 52 GMCs with radii ranging from 30 to 140 pc, a low mean molecular gas mass surface density 〈Σgas〉 ≈ 125 M⊙ pc−2 and a high mean virial parameter 〈αobs, vir〉 ≈ 5.3. We measure spatially resolved molecular gas depletion times (τdep ≡ 1/SFE) with a spatial resolution of ≈100 pc within a galactocentric distance of 1.5 kpc. The global depletion time is ≈2.0 Gyr but τdep increases towards the galaxy centre, with a maximum τdep, max ≈ 5.2 Gyr. However, no pure H ii region is identified in NGC 524 using ionized-gas emission-line ratio diagnostics, so the τdep inferred are in fact lower limits. Measuring the GMC properties and dynamical states, we conclude that shear is the dominant mechanism shaping the molecular gas properties and regulating SF in NGC 524. This is supported by analogous analyses of the GMCs in a simulated ETG similar to NGC 524.</jats:p>

Abstract:

Context. Optimal frequency identification in astronomical datasets is crucial for variable star studies, exoplanet detection, and astero-seismology. Traditional period-finding methods often rely on specific parametric assumptions, employ binning procedures, or overlook the regression nature of the problem, limiting their applicability and precision. Aims. We introduce a universal- nonparametric kernel regression method for optimal frequency determination that is generalizable, efficient, and robust across various astronomical data types. Methods. FINKER uses nonparametric kernel regression on folded datasets at different frequencies, selecting the optimal frequency by minimising squared residuals. This technique inherently incorporates a weighting system that accounts for measurement uncertainties and facilitates multi-band data analysis. We evaluated our method’s performance across a range of frequencies pertinent to diverse data types and compared it with an established period-finding algorithm, conditional entropy. Results. The method demonstrates superior performance in accuracy and robustness compared to existing algorithms, requiring fewer observations to reliably identify significant frequencies. It exhibits resilience against noise and adapts well to datasets with varying complexity.