The non-universality of the low-mass end of the IMF is robust against the choice of SSP model

We perform a direct comparison of two state-of-the art single stellar population (SSP) models that have been used to demonstrate the non-universality of the low-mass end of the Initial Mass Function (IMF) slope. The two public versions of the SSP models are restricted to either solar abundance patterns or solar metallicity, too restrictive if one aims to disentangle elemental enhancements, metallicity changes and IMF variations in massive early-type galaxies (ETGs) with star formation histories different from the solar neighborhood. We define response functions (to metallicity and \alpha-abundance) to extend the parameter space of each set of models. We compare these extended models with a sample of Sloan Digital Sky Survey (SDSS) ETGs spectra with varying velocity dispersions. We measure equivalent widths of optical IMF-sensitive stellar features to examine the effect of the underlying model assumptions and ingredients, such as stellar libraries or isochrones, on the inference of the IMF slope down to ~0.1 solar masses. We demonstrate that the steepening of the low-mass end of the Initial Mass Function (IMF) based on a non-degenerate set of spectroscopic optical indicators is robust against the choice of the stellar population model. Although the models agree in a relative sense (i.e. both imply more bottom-heavy IMFs for more massive systems), we find non-negligible differences on the absolute values of the IMF slope inferred at each velocity dispersion by using the two different models. In particular, we find large inconsistency in the quantitative predictions of IMF slope variations and abundance patterns when sodium lines are used. We investigate the possible reasons for these inconsistencies.Comment: 16 pages, 9 figures, 2 tables, accepted for publication on Ap

Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction

Applying dimensionality reduction (DR) to large, high-dimensional data sets can be challenging when distinguishing the underlying high-dimensional data clusters in a 2D projection for exploratory analysis. We address this problem by first sharpening the clusters in the original high-dimensional data prior to the DR step using Local Gradient Clustering (LGC). We then project the sharpened data from the high-dimensional space to 2D by a user-selected DR method. The sharpening step aids this method to preserve cluster separation in the resulting 2D projection. With our method, end-users can label each distinct cluster to further analyze an otherwise unlabeled data set. Our `High-Dimensional Sharpened DR' (HD-SDR) method, tested on both synthetic and real-world data sets, is favorable to DR methods with poor cluster separation and yields a better visual cluster separation than these DR methods with no sharpening. Our method achieves good quality (measured by quality metrics) and scales computationally well with large high-dimensional data. To illustrate its concrete applications, we further apply HD-SDR on a recent astronomical catalog.Comment: This paper has been accepted for Information Visualization. Copyright may be transferred without notice, after which this version may no longer be accessibl