adsabs/AstroCLIMB huggingface dataset:
Paper: Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog
Authors: Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang
Figure caption: Evaluation metrics for classification of stars, quasars, and galaxies at different output probability thresholds. The panels display F1-score (top left), ﹩{\mathrm{Frac}}_{\mathrm{thresh}}﹩ (top right), Precision (bottom left), and Recall (bottom right), with blue, red, and green lines representing the metrics for stars, quasars, and galaxies, respectively.
Paper: Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog
Authors: Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang
Figure caption: Confusion matrix for the internal testing data set. Each panel has predicted categories (star, quasar, galaxy) on the vertical axis and true categories from SDSS on the horizontal axis. The top panel shows the number of targets predicted in each category, while the bottom panel shows these numbers normalized as percentages.
Paper: Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog
Authors: Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang
Figure caption: Schematic of the MNN architecture, consisting of 18,257,155 neurons. Each blue or purple rectangle represents a layer, and each light pink rectangle represents two identical ResNet blocks. The SED branch inputs photometry from nine KiDS bands and eight adjacent band colors, and the image branch processes a 41 × 41 pixels r-band image. The output layer provides classification probabilities for stars, quasars, or galaxies.
Paper: Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog
Authors: Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang
Figure caption: Redshift distribution for the GAMA testing data set. The black solid line shows the distribution for all targets, while the blue dotted line, red dashed line, and green solid line represent the distributions for targets predicted as stars, quasars, and galaxies by the MNN, respectively.
Paper: Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog
Authors: Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang
Figure caption: Training and validation loss (top) and accuracy (bottom) as a function of epochs.
Paper: Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog
Authors: Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang
Figure caption: The r vs. R1/2 for objects in our catalog. The contours represent the density distribution of stars (blue), QSOs (red), and galaxies (green). The dashed line (black) at 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩41 indicates the R1/2 corresponding to the average KiDS r-band seeing of 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩7, assuming a Gaussian PSF where R1/2 ≈ 0.5887 × FWHM.
Paper: Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog
Authors: Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang
Figure caption: Examples of spectra, SEDs, and images for different categories. The left panels show the SDSS spectra (black solid line) and the SEDs from KiDS' nine bands (red points). The SEDs agree well with the spectra for star and QSO, while galaxy photometric points tend to be higher due to the larger KiDS aperture compared to the SDSS fiber. The right panels show color-composite images from the KiDS gri bands.
Paper: Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog
Authors: Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang
Figure caption: Color–color diagrams (g − r vs. r − i) for KiDS and SDSS data sets. From top to bottom, the panels represent stars, QSOs, and galaxies. The left panels show objects with r ≤ 22, while the right panels correspond to objects with 22 < r ≤ 23. The red shaded regions indicate the crossmatched data between SDSS and KiDS, and the green contours represent all KiDS data within the corresponding magnitude range.
Paper: Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog
Authors: Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang
Figure caption: gri color-composite images of some misclassified targets. The vertical axis represents the spectral classification labels from SDSS, and the horizontal axis represents the predictions made by the MNN.
Paper: Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog
Authors: Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang
Figure caption: Distribution of r-band magnitude (top panel) and maximum output probability (bottom panel) for each type of object in the value-added catalog. Each panel plots the distribution for targets predicted as stars (blue dotted line), quasars (red dashed line), and galaxies (green solid line).
Paper: Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog
Authors: Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang
Figure caption: F1-scores for stars, quasars, and galaxies as a function of training data set size. The total training data set size ranges from 5000 to 100,000. For each training run, the same validation and testing data sets are used to ensure consistency during the training and evaluation processes.
Paper: Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog
Authors: Hai-Cheng Feng, Rui Li, Nicola R. Napolitano, Sha-Sha Li, J. M. Bai, Yue Dong, Ran Li, H. T. Liu, Kai-Xing Lu, Zhi-Wei Pan, Mario Radovich, Huan-Yuan Shan, Jian-Guo Wang, Wen-Zhe Xi, Ling-Hua Xie, Zun-Li Yuan, Yang-Wei Zhang
Figure caption: The same as Figure 3, but showing evaluation metrics for classification using only the MLP branch.
Paper: Revisiting the Dynamical Model for the Evolution of Hot Spots and Its Cosmological Application
Authors: Yalong Nan, Shuo Cao, Tao An, Haiyan Zhang, Wuzheng Guo, Ailing Zeng, Shiming Wen, Ruobing Ding
Figure caption: Angular diameter distances DA(z) (left panel) and CDDR parameter η(z) (right panel) from the simulated CSO2.0 sample. The evolution of η(z) with redshifts is also added, which is reconstructed with an ANN.
Paper: Revisiting the Dynamical Model for the Evolution of Hot Spots and Its Cosmological Application
Authors: Yalong Nan, Shuo Cao, Tao An, Haiyan Zhang, Wuzheng Guo, Ailing Zeng, Shiming Wen, Ruobing Ding
Figure caption: Fits to CSO2.0 sample calibrated by the Pantheon+SH0ES SNe Ia (D. Brout et al. 2022; A. G. Riess et al. 2022) (with the validity of CDDR). (a) The best-fit results for the linear size and the luminosity. (b) The best-fit results for the linear size and the hot spot radius. (c) The best-fit results for the hot spot radius and the luminosity. (d) The best-fit results for the hot spot velocity and linear size.
Paper: Revisiting the Dynamical Model for the Evolution of Hot Spots and Its Cosmological Application
Authors: Yalong Nan, Shuo Cao, Tao An, Haiyan Zhang, Wuzheng Guo, Ailing Zeng, Shiming Wen, Ruobing Ding
Figure caption: Upper panel: Gaussian process reconstructed DL(z) using the Pantheon+SH0ES SNe Ia sample (D. Brout et al. 2022; A. G. Riess et al. 2022). Lower panel: Gaussian process reconstructed DA(z) using the galaxy cluster sample (E. De Filippis et al. 2005).
Paper: Revisiting the Dynamical Model for the Evolution of Hot Spots and Its Cosmological Application
Authors: Yalong Nan, Shuo Cao, Tao An, Haiyan Zhang, Wuzheng Guo, Ailing Zeng, Shiming Wen, Ruobing Ding
Figure caption: Upper panel: the relative error of the angular separation size. Middle panel: the quantile–quantile plot of the fractional uncertainty of the flux density. Lower panel: the quantile–quantile plot of the fractional uncertainty of the hot spot radius.
Paper: Revisiting the Dynamical Model for the Evolution of Hot Spots and Its Cosmological Application
Authors: Yalong Nan, Shuo Cao, Tao An, Haiyan Zhang, Wuzheng Guo, Ailing Zeng, Shiming Wen, Ruobing Ding
Figure caption: Fits to simulated CSO2.0 sample. (a) The relation between the linear size and the luminosity. (b) The relation between the linear size and the hot spot radius. (c) The relation between the hot spot radius and the luminosity. (d) The relation between the hot spot velocity and linear size.
Paper: Revisiting the Dynamical Model for the Evolution of Hot Spots and Its Cosmological Application
Authors: Yalong Nan, Shuo Cao, Tao An, Haiyan Zhang, Wuzheng Guo, Ailing Zeng, Shiming Wen, Ruobing Ding
Figure caption: Fits to low-redshift CSO2.0 sample calibrated by the Pantheon+SH0ES SNe Ia (D. Brout et al. 2022; A. G. Riess et al. 2022) and galaxy cluster sample (E. De Filippis et al. 2005). (a) The best-fit results for the linear size and the luminosity. (b) The best-fit results for the linear size and the hot spot radius. (c) The best-fit results for the hot spot radius and the luminosity. (d) The best-fit results for the hot spot velocity and linear size.
Paper: Revisiting the Dynamical Model for the Evolution of Hot Spots and Its Cosmological Application
Authors: Yalong Nan, Shuo Cao, Tao An, Haiyan Zhang, Wuzheng Guo, Ailing Zeng, Shiming Wen, Ruobing Ding
Figure caption: Upper panel: the redshift distribution of the simulated CSO2.0 sample. Middle panel: the flux density distribution of the simulated CSO2.0 sample. Lower panel: the angular separation size distribution of the simulated CSO2.0 sample.
Paper: Revisiting the Dynamical Model for the Evolution of Hot Spots and Its Cosmological Application
Authors: Yalong Nan, Shuo Cao, Tao An, Haiyan Zhang, Wuzheng Guo, Ailing Zeng, Shiming Wen, Ruobing Ding
Figure caption: 2D KDE of redshift and luminosity for CSO2.0 sample.
Paper: Revisiting the Dynamical Model for the Evolution of Hot Spots and Its Cosmological Application
Authors: Yalong Nan, Shuo Cao, Tao An, Haiyan Zhang, Wuzheng Guo, Ailing Zeng, Shiming Wen, Ruobing Ding
Figure caption: Differences in the distances between two hot spots in the S and X bands.
Paper: The Karl G. Jansky Very Large Array Local Group L-Band Survey (LGLBS)
Authors: Eric W. Koch, Adam K. Leroy, Erik W. Rosolowsky, Laura Chomiuk, Julianne J. Dalcanton, Nickolas M. Pingel, Sumit K. Sarbadhicary, Snežana Stanimirović, Fabian Walter, Haylee N. Archer, Alberto D. Bolatto, Michael P. Busch, Hongxing Chen, Ryan Chown, Harrisen Corbould, Serena A. Cronin, Jeremy Darling, Thomas Do, Jennifer Donovan Meyer, Cosima Eibensteiner, Deidre Hunter, Rémy Indebetouw, Preshanth Jagannathan, Amanda A. Kepley, Chang-Goo Kim, Shin-Jeong Kim, Timea O. Kovacs, Joshua Marvil, Eric J. Murphy, Claire E. Murray, Jürgen Ott, D.J. Pisano, Mary Putman, Daniel R. Rybarczyk, Julia Roman-Duval, Karin Sandstrom, Eva Schinnerer, Evan D. Skillman, Adam Smercina, Ioana Stelea, Jay Strader, Jiayi Sun, Devisree Tallapaneni, Elizabeth Tarantino, Vicente Villanueva, Daniel R. Weisz, Thomas G. Williams, Tony Wong
Figure caption: H I  line width maps of the four LGLBS dwarf galaxies, as measured with the effective width, defined as ﹩\sigma ={I}_{{\rm{HI}}}/\sqrt{2\pi }\,{T}_{{\rm{peak}}}﹩. Each map is shown at a common physical resolution of 120 pc. Regions with recent massive star formation in NGC 6822 and IC 10 tend to show larger velocity dispersions, while IC 1613 and WLM with lower recent star formation rates tend to have lower dispersions.