adsabs/AstroCLIMB huggingface dataset:
Paper: A MIRI Search for Planets and Dust around WD 2149+021
Authors: Sabrina Poulsen, John Debes, Misty Cracraft, Susan E. Mullally, William T. Reach, Mukremin Kilic, Fergal Mullally, Loic Albert, Katherine Thibault, J. J. Hermes, Thomas Barclay, Elisa V. Quintana
Figure caption: Dust emission limits estimated for WD 2149+021. The solid black line shows the estimated MIRI dust mass limit assuming a mass in 1 μm grains, derived from the F2100W upper limits to a flux excess. The yellow line corresponds to the total mass accreted by WD 2149+021 in the time it is been known to possess Ca metal absorption lines. The solid blue and red lines represent the expected equilibrium disk mass of dust assuming a steady-state collisional cascade (see Equation (5)) for typical planetesimal sizes of 100 km (blue) and 1 km (red)). The dashed lines are the expected masses in 1 μm grains only.
Paper: A MIRI Search for Planets and Dust around WD 2149+021
Authors: Sabrina Poulsen, John Debes, Misty Cracraft, Susan E. Mullally, William T. Reach, Mukremin Kilic, Fergal Mullally, Loic Albert, Katherine Thibault, J. J. Hermes, Thomas Barclay, Elisa V. Quintana
Figure caption: Top left: F560W image of WD 2149+021. Top right: F560W with a reference PSF scaled and subtracted from the image. Bottom left: F1500W image of WD 2149+021. Bottom right: F1500W image with a reference PSF scaled and subtracted from the image. Our observations nearly hit the background sensitivity limit close to the star in both filters.
Paper: A MIRI Search for Planets and Dust around WD 2149+021
Authors: Sabrina Poulsen, John Debes, Misty Cracraft, Susan E. Mullally, William T. Reach, Mukremin Kilic, Fergal Mullally, Loic Albert, Katherine Thibault, J. J. Hermes, Thomas Barclay, Elisa V. Quintana
Figure caption: Color image of WD 2149+021 created by combining the F770W, F1500W, and F2100W filters. Visible directly above the white dwarf is a false-positive candidate, eliminated due to its extended shape and incorrect color.
Paper: A MIRI Search for Planets and Dust around WD 2149+021
Authors: Sabrina Poulsen, John Debes, Misty Cracraft, Susan E. Mullally, William T. Reach, Mukremin Kilic, Fergal Mullally, Loic Albert, Katherine Thibault, J. J. Hermes, Thomas Barclay, Elisa V. Quintana
Figure caption: F1500W flux vs. F2100W/F560W flux ratios for the field point sources in the field surrounding WD 2149+021. We compare these objects to the expected colors and fluxes for two planets (2 M Jup and 5 M Jup) that have 1 R ⊕ tidally heated exomoons with varying coverage fractions of 1500 K hot spots. Significantly heated moons can potentially change the observed position of a Jovian planet candidate in this plot by having an excess of shorter-wavelength emission.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: We chose the r, g, and i bands to generate a color image. Panel (a) shows the raw data, panel (b) shows the data after the asinh transformation, panel (c) displays the restoration results, and panel (d) shows the restored image after the asinh grayscale transformation. As depicted in these panels, the incorporation of image restoration and grayscale transformation steps significantly enhances the visibility of image details.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: The flowchart of the image restoration step, which contains an image restoration neural network (RESTORE) and an image blurring neural network (PSF).
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: As depicted in this figure, our method initiates the feature extraction process using the Swin transformer. These extracted features are subsequently compiled into a feature map, which is then utilized for box regression and classification.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: In panel (a), the detection results of the clear image after grayscale transformation are depicted. Panel (b) illustrates the PR curve of the noisy image after grayscale transformation, and panel (c) displays the PR curve of noisy images after grayscale transformation and image restoration. Compared to the PR curve shown in Figure 8, the image preprocessing method enhances the detection capabilities of our algorithm and mitigates the effects caused by noise and blur.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: Panel (a) presents the PR curve of detection results from clear, unprocessed images, while panel (b) displays the detection results from noisy, unprocessed images. As shown in these panels, noise and blur will introduce very strong effects to detection results, and we need to design some algorithms to reduce these effects. In these figures, we can find that if we set smaller IOU, the AP will be larger, which means that we can obtain higher recall rate and precision rate at the cost of lower position regression accuracy.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: The diagram illustrates the computation diagram of self-attention with a shifting window. In Layerl, each window is averaged, and the self-attention is calculated independently for each window. In Layerl+1, the windows are shifted systematically, generating new windows that maintain connectivity between the windows when calculating attention.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: The utilization of a fixed-size bounding box may affect detection outcomes, especially when dealing with large-sized strong-lensing systems. Illustrated in this figure, the blue box denotes a specific strong-lensing object, but the bounding box only partially encloses it. As a result, the IOU score becomes excessively low, hindering the accurate detection of this particular strong-lensing event.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: The strong-lensing system highlighted within the blue box in panel (a) exhibits dimensions of 16 × 17 pixels and an S/N of 15.88. However, due to its small size, our algorithm was unable to detect it. The strong-lensing system indicated by the blue box in panel (b) has dimensions of 25 × 25 pixels and an S/N of 10.42. Despite its larger size, its relatively low S/N prevented our algorithm from detecting it as well.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: The presence of bright central galaxy light can pose challenges in detecting strong lensing, as illustrated by the blue bounding box in panel (a). Panel (b) depicts several bright stars outlined with green bounding boxes, which were detected as strong-lensing systems. This could be attributed to their visual similarity to actual strong-lensing systems.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: The figure illustrates an efficient batch processing approach used for self-attention in the context of shifting window partitioning.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: This figure illustrates the learning curve observed throughout the entire training and validation process. We can find that the model achieves convergence at approximately 80 epochs.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: The specific structure and data flow of the Swin transformer. The input image undergoes linear embedding, patch merging, Layer Norm, global pooling, and fully connected layers to obtain the final output.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: The results of our method in identifying strong-lensing systems in Euclid media images. We have individually examined and categorized all these images based on their morphology, classifying them into strong-lensing systems of varying quality. The top three rows exhibit high-quality instances of strong-lensing systems, whereas the fourth row displays strong-lensing systems of moderate quality. The fifth row reveals some FP detection results. The coordinates of these identified targets in different Euclid media images are highlighted in red.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: Panels (a), (b), and (c) depict the confusion matrix of our detection algorithm using confidence score threshold values of 0, 0.5, and 0.9 on the simulated data. As demonstrated in these panels, our algorithm performs well in detecting strong-lensing systems, especially considering the significant influence of bright central galaxies and low S/Ns on many of these systems. Additionally, it is important to highlight that we can achieve a satisfactory balance of precision and recall rates for further analysis by selecting a confidence score threshold of 0.5.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: The images selected for detecting strong-lensing systems follow the distribution outlined in Huang et al. (2020). It is important to highlight that only a small subset of these images are utilized in this paper. Further elaboration on this issue will be provided in our future papers.
Paper: CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Hu Zou, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Figure caption: The figure demonstrates several detection candidates obtained from real observation data by our method. It can be observed that our model accurately detects and identifies strong-lensing systems at arbitrary positions in the image.
Paper: Frequencies of Warm Debris Disks Based on Point Source Catalogs of Spitzer, WISE, and Gaia
Authors: Toshiyuki Mizuki, Munetake Momose, Masataka Aizawa, Hiroshi Kobayashi
Figure caption: Sample selection and the number of stars reduced by each criterion are shown as a flowchart: (a) nearby sources selected from Gaia archive (Section 2.1), (b) sources with mid-infrared fluxes (Section 2.2), (c) sources within the defined regions of main-sequence stars (Section 2.4), (d) main-sequence stars with the effective temperature of 3000–15,000 K (Section 3.3), (e) main-sequence stars whose photospheric fluxes can be detected with 3σ confidence levels to the detection limits at mid-infrared wavelength (Section 3.5), and (f) stars that are bright enough to avoid the contamination of background objects (Section 4.2).
Paper: Frequencies of Warm Debris Disks Based on Point Source Catalogs of Spitzer, WISE, and Gaia
Authors: Toshiyuki Mizuki, Munetake Momose, Masataka Aizawa, Hiroshi Kobayashi
Figure caption: The left panel shows the distribution of the sample where infrared excess was searched for in the color–magnitude diagram. The number density of stars is shown as a two-dimensional histogram in color. The triangles indicate the stars hosting debris disks that have already been detected and identified as “Prime” infrared excess stars in Cotten & Song (2016). Dashed lines show PARSEC V1.2S isochrones with ages of 10 Myr, 100 Myr, and 1 Gyr (Bressan et al. 2012). In this work, the location where main-sequence stars are present is defined by thick black lines. The right panel shows the possible range of isochronal ages (t *,upper(lower)) for the sample as a function of stellar mass, where the dashed line represents the classical timescale of main-sequence phases as t MS* ∼ t ⊙(M */M ⊙)(L ⊙/L *) and t ⊙ ∼ 10 Gyr.
Paper: Frequencies of Warm Debris Disks Based on Point Source Catalogs of Spitzer, WISE, and Gaia
Authors: Toshiyuki Mizuki, Munetake Momose, Masataka Aizawa, Hiroshi Kobayashi
Figure caption: Reduced chi-square obtained from SED fitting for 2,528,171 stars as a function of the stellar effective temperature. The diamonds indicate the effective temperature bins with the predefined size of 500 K and the typical 1σ scatter of the reduced chi-square, for the horizontal and vertical extent, respectively. If the number of stars in a bin is less than 100, the bin is merged with its neighbors until the number exceeds the threshold. The solid and dashed lines show the best-fit solution with a polynomial function and the acceptable ranges of the 1σ confidence level for the sample candidates in Section 3.3.
Paper: Frequencies of Warm Debris Disks Based on Point Source Catalogs of Spitzer, WISE, and Gaia
Authors: Toshiyuki Mizuki, Munetake Momose, Masataka Aizawa, Hiroshi Kobayashi
Figure caption: The stellar effective temperatures are compared with those in RAVE DR5, LAMOST DR5, and StarHorse from left to right. The y-axis indicates the fractional difference between the effective temperatures obtained by the SED fitting in this work and those of previous studies. The diamonds indicate the effective temperature bins with the predefined size of 500 K and the typical 1σ scatter of the reduced chi-square, for the horizontal and vertical extent, respectively. If the number of stars in a bin is less than 100, the bin is merged with its neighbors until the number exceeds the threshold.
Paper: Frequencies of Warm Debris Disks Based on Point Source Catalogs of Spitzer, WISE, and Gaia
Authors: Toshiyuki Mizuki, Munetake Momose, Masataka Aizawa, Hiroshi Kobayashi
Figure caption: An example of a star with infrared excess according to our analysis, in which only photometric data with filled symbols are fitted to determine stellar parameters. The crossed-out symbol indicates the data ignored by the iteration in the fitting (Section 3.2). The bottom panel shows the ratio between the observed and calculated fluxes for stellar radiation.
Paper: Frequencies of Warm Debris Disks Based on Point Source Catalogs of Spitzer, WISE, and Gaia
Authors: Toshiyuki Mizuki, Munetake Momose, Masataka Aizawa, Hiroshi Kobayashi
Figure caption: Left shows the frequencies of debris disks for the comparison among three wavelengths, based on all the corrections and criteria in Sections 4 and 5.1. Right represents the detection limits of infrared excess associated with a solar-type star of T eff = 6000 K. Colors correspond to 12, 22, and 24 μm as references for WISE/W3, W4, and SEIP/M1, respectively. The dashed line indicates the detection limit at 4.6 μm in the W2 band, hot disks brighter than those that are not identified as infrared excesses in this work (see Section 5.4). The disk is considered a blackbody at a temperature of T bb.
Paper: Frequencies of Warm Debris Disks Based on Point Source Catalogs of Spitzer, WISE, and Gaia
Authors: Toshiyuki Mizuki, Munetake Momose, Masataka Aizawa, Hiroshi Kobayashi
Figure caption: Disk frequencies are presented as a function of stellar effective temperature. The colored lines show the frequency of debris disks based on all the corrections and criteria in Sections 4 and 5, and the black lines show the excess rate with no correction. The gray diamonds indicate the effective temperature bins and the 1σ uncertainty of the disk frequencies obtained by previous studies (Su et al. 2006; Trilling et al. 2008; Wu et al. 2013; Patel et al. 2014), for the horizontal and vertical extent, respectively. Due to the limited information of the sample in previous studies, particularly stars without disks, except for the intermediate-mass stars of MIPS ch1, we set the bins of effective temperature as follows; BA type for T eff = 7400–15,000 K, F type for 6000–7400 K, G type for 5300–6000 K, K type for 3900–5300 K, and M type for 3000–3900 K.
Paper: Frequencies of Warm Debris Disks Based on Point Source Catalogs of Spitzer, WISE, and Gaia
Authors: Toshiyuki Mizuki, Munetake Momose, Masataka Aizawa, Hiroshi Kobayashi
Figure caption: The significance of the excess, the correction coefficient to match the models to the observed fluxes using the average excess in each T eff bin, and the significance of the corrected excess represented as χ, c, and ﹩\bar{\chi }﹩ in Equations (2)–(4), respectively, are shown as functions of the stellar effective temperature. The diamonds indicate the effective temperature bins with the predefined size of 200 K and the typical 1σ scatter of the reduced chi-square, for the horizontal and vertical extent, respectively. If the number of stars in a bin is less than 100, the bin is merged with its neighbors until the number exceeds the threshold. The best-fit polynomial solutions for the diamonds are also shown.
Paper: Frequencies of Warm Debris Disks Based on Point Source Catalogs of Spitzer, WISE, and Gaia
Authors: Toshiyuki Mizuki, Munetake Momose, Masataka Aizawa, Hiroshi Kobayashi
Figure caption: (Left) The number of background sources within an aperture based on Equation (8) for W3, W4, and M1. The number densities of WISE are based on AKARI observations (see Section 4.2). (Middle) Cumulative distribution function of n bkg. (Right) The total number of background sources for stars with temperatures of 5000–6000 K based on Equation (10). The number of observed infrared excesses is also shown as dashed lines.