B. Duffy | Searcharxiv

arXiv2022

The APEX Large CO Heterodyne Orion Legacy Survey (ALCOHOLS). I. Survey overview

The Orion molecular cloud complex harbours the nearest GMCs and site of high-mass star formation. Its YSO populations are thoroughly characterized. The region is therefore a prime target for the study of star formation. Here, we verify the performance of the SuperCAM 64 pixel heterodyne array on APEX. We give a descriptive overview of a set of wide-field CO(3-2) spectral cubes obtained towards the Orion GMC complex, aimed at characterizing the dynamics and structure of the extended molecular gas in diverse regions of the clouds, ranging from very active sites of clustered star formation in Orion B to comparatively quiet regions in southern Orion A. We present a 2.7 square degree (130pc$^2$) mapping survey in the CO(3-2) transition, obtained using SuperCAM on APEX at an angular resolution of 19'' (7600AU or 0.037pc at a distance of 400pc), covering L1622, NGC2071, NGC2068, OriB9, NGC2024, and NGC2023 in Orion B, and the southern part of the L1641 cloud in Orion A. We describe CO integrated emission and line moment maps and position-velocity diagrams and discuss a few sub-regions in some detail. Evidence for expanding bubbles is seen with lines splitting into double components, most prominently in NGC2024, where we argue that the bulk of the molecular gas is in the foreground of the HII region. High CO(3-2)/CO(1-0) line ratios reveal warm CO along the western edge of Orion B in the NGC2023/NGC2024 region facing the IC434 HII region. Multiple, well separated radial velocity components seen in L1641-S suggest that it consists of a sequence of clouds at increasingly larger distances. We find a small, spherical cloud - the 'Cow Nebula' globule - north of NGC2071. We trace high velocity line wings for the NGC2071-IR outflow and the NGC2024 CO jet. The protostellar dust core FIR4 (rather than FIR5) is the true driving source of the NGC2024 monopolar outflow.

astro-ph.GA↗

arXiv2013

Measuring Visual Complexity of Cluster-Based Visualizations

Handling visual complexity is a challenging problem in visualization owing to the subjectiveness of its definition and the difficulty in devising generalizable quantitative metrics. In this paper we address this challenge by measuring the visual complexity of two common forms of cluster-based visualizations: scatter plots and parallel coordinatess. We conceptualize visual complexity as a form of visual uncertainty, which is a measure of the degree of difficulty for humans to interpret a visual representation correctly. We propose an algorithm for estimating visual complexity for the aforementioned visualizations using Allen's interval algebra. We first establish a set of primitive 2-cluster cases in scatter plots and another set for parallel coordinatess based on symmetric isomorphism. We confirm that both are the minimal sets and verify the correctness of their members computationally. We score the uncertainty of each primitive case based on its topological properties, including the existence of overlapping regions, splitting regions and meeting points or edges. We compare a few optional scoring schemes against a set of subjective scores by humans, and identify the one that is the most consistent with the subjective scores. Finally, we extend the 2-cluster measure to k-cluster measure as a general purpose estimator of visual complexity for these two forms of cluster-based visualization.

cs.AI↗