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

A Fast Alpha-tree Algorithm for Extreme Dynamic Range Pixel Dissimilarities

The α-tree algorithm is a useful hierarchical representation technique which facilitates comprehension of imagessuch as remote sensing and medical images. Most α-tree algorithms make use of priority queues to process image edgesin a correct order, but because traditional priority queues areinefficient in α-tree algorithms using extreme-dynamic-rangepixel dissimilarities, they run slower compared with other relatedalgorithms such as component tree. In this paper, we proposea novel hierarchical heap priority queue algorithm that canprocess α-tree edges much more efficiently than other stateof-the-art priority queues. Experimental results using 48-bitSentinel-2A remotely sensed images and randomly generatedimages have shown that the proposed hierarchical heap priorityqueue improved the timings of the flooding α-tree algorithm byreplacing the heap priority queue with the proposed queue: 1.68times in 4-N and 2.41 times in 8-N on Sentinel-2A images, and2.56 times and 4.43 times on randomly generated images

A Fast Alpha-tree Algorithm for Extreme Dynamic Range Pixel Dissimilarities

The α-tree algorithm is a useful hierarchical representation technique which facilitates comprehension of imagessuch as remote sensing and medical images. Most α-tree algorithms make use of priority queues to process image edgesin a correct order, but because traditional priority queues areinefficient in α-tree algorithms using extreme-dynamic-rangepixel dissimilarities, they run slower compared with other relatedalgorithms such as component tree. In this paper, we proposea novel hierarchical heap priority queue algorithm that canprocess α-tree edges much more efficiently than other stateof-the-art priority queues. Experimental results using 48-bitSentinel-2A remotely sensed images and randomly generatedimages have shown that the proposed hierarchical heap priorityqueue improved the timings of the flooding α-tree algorithm byreplacing the heap priority queue with the proposed queue: 1.68times in 4-N and 2.41 times in 8-N on Sentinel-2A images, and2.56 times and 4.43 times on randomly generated images