Morphological Dependence of MIR Properties of SDSS Galaxies in the Spitzer SWIRE Survey

We explore the correlation between morphological types and mid-infrared (MIR) properties of an optically flux-limited sample of 154 galaxies from the Forth Data Release (DR4) of Sloan Digital Sky Survey (SDSS), cross-correlated with Spitzer SWIRE (Spitzer Wide-Area InfraRed Extragalactic Survey) fields of ELAIS-N1, ELAIS-N2 and Lockman Hole. Aperture photometry is performed on the SDSS and Spitzer images to obtain optical and MIR properties. The morphological classifications are given based on both visual inspection and bulge-disk decomposition on SDSS g- and r-band images. The average bulge-to-total ratio (B/T) is a smooth function over different morphological types. Both the 8um(dust) and 24um(dust) luminosities and their relative luminosity ratios to 3.6um (MIR dust-to-star ratios) present obvious correlations with both the Hubble T-type and B/T. The early-type galaxies notably differ from the late-types in the MIR properties, especially in the MIR dust-to-star ratios. It is suggested that the MIR dust-to-star ratio is an effective way to separate the early-type galaxies from the late-type ones. Based on the tight correlation between the stellar mass and the 3.6um luminosity, we have derived a formula to calculate the stellar mass from the latter. We have also investigated the MIR properties of both edge-on galaxies and barred galaxies in our sample. Since they present similar MIR properties to the other sample galaxies, they do not influence the MIR properties obtained for the entire sample.Comment: Accepted for publication by AJ. 18 pages, 14 figures, and 4 table

Urania: Visualizing Data Analysis Pipelines for Natural Language-Based Data Exploration

Exploratory Data Analysis (EDA) is an essential yet tedious process for examining a new dataset. To facilitate it, natural language interfaces (NLIs) can help people intuitively explore the dataset via data-oriented questions. However, existing NLIs primarily focus on providing accurate answers to questions, with few offering explanations or presentations of the data analysis pipeline used to uncover the answer. Such presentations are crucial for EDA as they enhance the interpretability and reliability of the answer, while also helping users understand the analysis process and derive insights. To fill this gap, we introduce Urania, a natural language interactive system that is able to visualize the data analysis pipelines used to resolve input questions. It integrates a natural language interface that allows users to explore data via questions, and a novel data-aware question decomposition algorithm that resolves each input question into a data analysis pipeline. This pipeline is visualized in the form of a datamation, with animated presentations of analysis operations and their corresponding data changes. Through two quantitative experiments and expert interviews, we demonstrated that our data-aware question decomposition algorithm outperforms the state-of-the-art technique in terms of execution accuracy, and that Urania can help people explore datasets better. In the end, we discuss the observations from the studies and the potential future works

An Hα Imaging Survey of All (Ultra)luminous Infrared Galaxies at Decl. ≥ -30 in the GOALS Sample

This paper presents the result of Hα imaging for luminous and ultraluminous infrared galaxies. It is a complete subsample of the Great Observatories All-sky LIRG Survey (GOALS) with decl. ≥ -30 , and consists of 148 galaxies with log(L IR/L ) ≥ 11.0. All the Hα images were carried out using the 2.16 m telescope at the Xinglong Station of the National Astronomy Observatories, Chinese Academy of Sciences (NAOC), during the year from 2006 to 2009. We obtained the pure Hα luminosity for each galaxy and corrected the luminosity for [N ii] emission, filter transmission, and extinction. We also classified these galaxies based on their morphology and interaction. We found that the distribution of star-forming regions in these galaxies is related to this classification. As the merging process advanced, these galaxies tended to have a more compact distribution of star-forming regions, higher L IR, and warmer IR-color (f 60/f 100). These results imply that the degree of dynamical disturbance plays an important role in determining the distribution of a star-forming region

Stellar Mass Estimation Based on IRAC Photometry for Spitzer SWIRE-field Galaxies

We analyze the feasibility to estimate the stellar mass of galaxies by mid-infrared luminosities based on a large sample of galaxies cross-identified from $Spitzer$ SWIRE fields and SDSS spectrographic survey. We derived the formulae to calculate the stellar mass by using IRAC 3.6$\mu$m and 4.5$\mu$m luminosities. The mass-to-luminosity ratios of IRAC 3.6$\mu$m and 4.5$\mu$m luminosities are more sensitive to star formation history of galaxies than other factors, such as the intrinsic extinction, metallicity and star formation rate. To remove the affection from star formation history, we used g-r color to recalibrate the formulae and obtain a better result. It must be more careful to estimate the stellar mass of low metallicity galaxies using our formulae. Due to the emission from dust heated by hottest young stars, luminous infrared galaxies present higher IRAC 4.5 $\mu$m luminosity compared to IRAC 3.6 $\mu$m luminosity. For most of type-II AGNs, the nuclear activity can not enhance 3.6$\mu$m and 4.5$\mu$m luminosities compared with normal galaxies. The star formation in our AGN-hosting galaxies is also very weak, almost all of which are early-type galaxies.Comment: 33page(include 14 figures); accepted by RA

An increase in early cancer detection rates at a single cancer center: Experiences from Sun Yat-sen University Cancer Center

Cancer has become a major fatal disease in China. The relatively lower early detection rates for multiple cancer types have been one of the main reasons for a relatively lower cancer curative rate in China compared with the developed countries. To investigate trends in the early cancer detection rate over the past 5 years in a major city of China, 45,260 patients with newly diagnosed cancers of the nasopharynx, lung, thyroid, colorectum, liver, breast, uteral cervix, stomach, esophagus, blood, and kidney from 2016 to 2020 at Sun Yat-sen University Cancer Center were evaluated. The early detection rate (stage I disease) for all cancer types in combination significantly increased from 14.4 to 23.07%. Among the studied cancer types, a significant increase in stage I cancers was proportionally seen in cancers of the lung, thyroid, colorectum, and uterine cervix. While for cancers of the liver and stomach, a significant proportional increment was only observed when combining stage I and stage II diseases. No significant alteration in early cancer detection of the nasopharynx, breast, esophagus, blood, or kidney was observed. Three limitations of this present study include relatively small cohorts of cancer patients, relatively short observation periods, and limited sample representativeness. Further efforts are anticipated to validate our findings with larger patient cohorts from different parts of China and enhance early cancer detection rates by promoting public awareness, applying better health care policies, and improving insurance coverage and medical resources