adsabs/AstroCLIMB huggingface dataset:
Paper: FArSide Trained Active Region Recognition (FASTARR): A Machine Learning Approach
Authors: Amr Hamada, Mitchell Creelman, Kiran Jain, Charles Lindsey
Figure caption: FASTARR architecture inspired by U-Net configuration (O. Ronneberger et al. 2015). The input is the GONG phase map of size 256 × 256 pixels. U-Net is based on the encoder-decoder principle. The encoder, the left part of the figure, is composed of five convolution blocks (dashed green boxes). The red arrows indicate max-pooling layers with a pool size of 2 × 2 pixels. Each convolutional block contains two convolutional layers and ReLU activation function. The convolutional layers in blocks double their number of filters for each depth level from 64 at the start to 1024 at the bottom of the encoder part. The decoder, the right part of the figure, contains four deconvolutional blocks (dashed blue boxes). The green arrows indicate up-sampling layers with a pool size of 2 × 2 pixels. Deconvolution blocks consist of two transposed convolution layers and the ReLU activation function. Deconvolutional layers in blocks contain a number of filters progressively halving from 1024 to 64. Gray horizontal arrows indicate concatenation layers (skip connections), which concatenate feature maps within the same level. The output layer consists of the convolutional layer with the sigmoid activation function. The FASTARR output is a segmentation far-side AR mask with a size of 256 × 256 pixels.
Paper: FArSide Trained Active Region Recognition (FASTARR): A Machine Learning Approach
Authors: Amr Hamada, Mitchell Creelman, Kiran Jain, Charles Lindsey
Figure caption: The postprocessing of the seven FASTARR-ARs outputs (Figure 8(c)) on 2014 June 17, at 12:00 UT. (a) The phase area occupied by each region is extracted, and (b) the corresponding gradient map is calculated. (c) The histogram plot shows the distribution of the phases within each region. A fixed phase threshold on the x-axis is used to show the localization of each region with respect to the most negative value.
Paper: FArSide Trained Active Region Recognition (FASTARR): A Machine Learning Approach
Authors: Amr Hamada, Mitchell Creelman, Kiran Jain, Charles Lindsey
Figure caption: Performance comparison of FASTARR-G and FASTARR-E using receiver operating characteristic (ROC) curves (left) and precision-recall (PR) curves (right). ROC curves show the variation between the true positive rate (TPR) and false positive rate (FPR) across different classification thresholds. The diagonal dashed line at the ROC curve represents the random chance (AUC = 0.5). PR curves show the variation between precision and recall, highlighting the performance of both models in identifying positive cases. FASTARR-G consistently outperforms FASTARR-E in both metrics, indicating better classification performance.
Paper: FArSide Trained Active Region Recognition (FASTARR): A Machine Learning Approach
Authors: Amr Hamada, Mitchell Creelman, Kiran Jain, Charles Lindsey
Figure caption: Example of data preparation process on (a) the GONG phase map, (b) GONG AR mask, (c) synchronic EUV map, and (d) EUV AR mask on at 2012 July 30, 00:00 UT. Data preparation consists of alignment to far-side meridional (second row), cropping to the equatorial edges (third row), and resizing to 256 × 256, interpolating the missing polar pixels (fourth row).
Paper: FArSide Trained Active Region Recognition (FASTARR): A Machine Learning Approach
Authors: Amr Hamada, Mitchell Creelman, Kiran Jain, Charles Lindsey
Figure caption: Overview of the proposed FASTARR method for identifying and tracking far-side solar ARs. The workflow begins with (a) GONG phase maps as an input, paired with corresponding either far-side (b) GONG AR or (c) EUV AR masks for training the model. The FASTARR model is then tested using (d) new GONG phase maps to make (e) a prediction for the far-side AR. EUV ground truth (f) is used for reference. The training data set is highlighted within the dashed box. The training example maps are from 2012 January 19, 00:00 UT, and the testing one from 2014 June 21, 00:00 UT.
Paper: FArSide Trained Active Region Recognition (FASTARR): A Machine Learning Approach
Authors: Amr Hamada, Mitchell Creelman, Kiran Jain, Charles Lindsey
Figure caption: Example of a FASTARR-G data augmentation process on the GONG phase map on 2014 March 2, 00:00 UT and the corresponding GONG AR mask. Using this process, for one input training image we obtained five additional output images, resulting in a total of six images per original input.
Paper: FArSide Trained Active Region Recognition (FASTARR): A Machine Learning Approach
Authors: Amr Hamada, Mitchell Creelman, Kiran Jain, Charles Lindsey
Figure caption: Example of the two training data sets on 2014 March 2, 00:00 UT. The FASTARR-E data set consists of (a) a helioseismic phase map and (b) an EUV AR mask constructed from (c) the far-side EUV observation. The FASTARR-G data set consists of (d) the same helioseismic phase map and (e) the corresponding GONG AR mask.
Paper: FArSide Trained Active Region Recognition (FASTARR): A Machine Learning Approach
Authors: Amr Hamada, Mitchell Creelman, Kiran Jain, Charles Lindsey
Figure caption: Same as Figure 5, but with the FASTARR-E data augmentation process, with an EUV AR mask.
Paper: FArSide Trained Active Region Recognition (FASTARR): A Machine Learning Approach
Authors: Amr Hamada, Mitchell Creelman, Kiran Jain, Charles Lindsey
Figure caption: Comparison of far-side AR identification, using the FASTARR-G and FASTARR-E models. (a) The first column shows the helioseismic phase maps from 2014 February 11 to 2014 February 23. (b) The second column shows the output maps of FASTARR-G ARs, bounded by green boxes. (c) The third column shows the output maps of FASTARR-E ARs, bounded by blue boxes. (d) The fourth column shows the reference far-side EUVI/304 Å observations, where all the bright ground-truth ARs are edged by the black contours. The green and blue contours refer to FASTARR-G and FASTARR-E, respectively. ARs 11967 and 11968 are clearly identified in the FASTARR-G and FASTARR-E models as they transit across the Sun’s far side. All maps are presented in heliographic coordinates, with the west limb appearing on the right side and the east limb on the left.
Paper: The First Insight-HXMT Localization Catalog of Short-duration Gamma-Ray Bursts: The First Six Years
Authors: Shuo Xiao, Yan-Ting Zhang, Yue Wang, Yan-Qiu Zhang, Shao-Lin Xiong, Shuang-Nan Zhang, Shi-Jie Zheng, Jin-Peng Zhang, Peng Zhang, Cheng-Kui Li, Xiao-Bo Li, Shu-Xu Yi, Zhen Zhang, Bing Li, Hua Feng, Cong-Zhan Liu, Ming-Yu Ge, Xu-Fang Li, Zheng-Wei Li, Yu-Peng Xu, Xiang-Yang Wen, Jian-Chao Sun, Lian Tao, Qi-Bin Yi, Ce Cai, Yi Zhao, Wang-Chen Xue, Jia-Cong Liu, Chao Zheng, Chen-Wei Wang, Wen-Jun Tan, You-Li Tuo, Li-Ming Song, Fang-Jun Lu, Ti-Pei Li
Figure caption: Left panel: the distribution of localization accuracy improvements by the joint localization of multiple satellites compared to that by GBM alone. The horizontal coordinate is defined as the region area in square degrees localized by joint localization divided by that obtained by GBM alone. Right panel: the distribution of the improvement in joint localization accuracy by the participation of HXMT. The horizontal coordinate is defined as the region area in square degrees of the joint localization including HXMT participation divided by that of joint localization without HXMT.
Paper: The First Insight-HXMT Localization Catalog of Short-duration Gamma-Ray Bursts: The First Six Years
Authors: Shuo Xiao, Yan-Ting Zhang, Yue Wang, Yan-Qiu Zhang, Shao-Lin Xiong, Shuang-Nan Zhang, Shi-Jie Zheng, Jin-Peng Zhang, Peng Zhang, Cheng-Kui Li, Xiao-Bo Li, Shu-Xu Yi, Zhen Zhang, Bing Li, Hua Feng, Cong-Zhan Liu, Ming-Yu Ge, Xu-Fang Li, Zheng-Wei Li, Yu-Peng Xu, Xiang-Yang Wen, Jian-Chao Sun, Lian Tao, Qi-Bin Yi, Ce Cai, Yi Zhao, Wang-Chen Xue, Jia-Cong Liu, Chao Zheng, Chen-Wei Wang, Wen-Jun Tan, You-Li Tuo, Li-Ming Song, Fang-Jun Lu, Ti-Pei Li
Figure caption: Left panel: the distribution of uncertainties in time delays between pairs of satellites. The red dotted line is the fitted log-Gaussian function obtained with a mean of 7.5 ms. The blue dashed histograms represent the participation of only the LEO satellites HXMT, GBM and GECAM, while the yellow dashed histograms represent the participation of KW and SPI-ACS. Right panel: the distribution of half-widths (1σ) of the localized annuli obtained by each two satellites; it is worth noting here that the irregular shape of the distribution is due to the large difference in distance between the different satellites.
Paper: The First Insight-HXMT Localization Catalog of Short-duration Gamma-Ray Bursts: The First Six Years
Authors: Shuo Xiao, Yan-Ting Zhang, Yue Wang, Yan-Qiu Zhang, Shao-Lin Xiong, Shuang-Nan Zhang, Shi-Jie Zheng, Jin-Peng Zhang, Peng Zhang, Cheng-Kui Li, Xiao-Bo Li, Shu-Xu Yi, Zhen Zhang, Bing Li, Hua Feng, Cong-Zhan Liu, Ming-Yu Ge, Xu-Fang Li, Zheng-Wei Li, Yu-Peng Xu, Xiang-Yang Wen, Jian-Chao Sun, Lian Tao, Qi-Bin Yi, Ce Cai, Yi Zhao, Wang-Chen Xue, Jia-Cong Liu, Chao Zheng, Chen-Wei Wang, Wen-Jun Tan, You-Li Tuo, Li-Ming Song, Fang-Jun Lu, Ti-Pei Li
Figure caption: A robustness test for time-delay localization errors with precisely localized bursts observed by Insight-HXMT. Credible regions vs. the fraction of the true location within that credible region, that is, for a given confidence level, we calculated the fraction of GRBs when the true location is inside the corresponding cumulative probability contours of the localization. The black dotted line represents a perfect, one-to-one relation.
Paper: The First Insight-HXMT Localization Catalog of Short-duration Gamma-Ray Bursts: The First Six Years
Authors: Shuo Xiao, Yan-Ting Zhang, Yue Wang, Yan-Qiu Zhang, Shao-Lin Xiong, Shuang-Nan Zhang, Shi-Jie Zheng, Jin-Peng Zhang, Peng Zhang, Cheng-Kui Li, Xiao-Bo Li, Shu-Xu Yi, Zhen Zhang, Bing Li, Hua Feng, Cong-Zhan Liu, Ming-Yu Ge, Xu-Fang Li, Zheng-Wei Li, Yu-Peng Xu, Xiang-Yang Wen, Jian-Chao Sun, Lian Tao, Qi-Bin Yi, Ce Cai, Yi Zhao, Wang-Chen Xue, Jia-Cong Liu, Chao Zheng, Chen-Wei Wang, Wen-Jun Tan, You-Li Tuo, Li-Ming Song, Fang-Jun Lu, Ti-Pei Li
Figure caption: Upper panel: the annulus obtained from the time-delay localization for GRB 210605A (trigger time UTC 2021-06-05T05:08:58.39) by HXMT-GBM, Konus (Wind)-GBM, HXMT-Konus (Wind), SPI-ACS-GBM, and HXMT-SPI-ACS. Lower panel: the final localization map obtained by combining their HEALPix results. Our localization result is consistent with that of IPN (green box) reported by D. Svinkin et al. (2021).
Paper: The First Insight-HXMT Localization Catalog of Short-duration Gamma-Ray Bursts: The First Six Years
Authors: Shuo Xiao, Yan-Ting Zhang, Yue Wang, Yan-Qiu Zhang, Shao-Lin Xiong, Shuang-Nan Zhang, Shi-Jie Zheng, Jin-Peng Zhang, Peng Zhang, Cheng-Kui Li, Xiao-Bo Li, Shu-Xu Yi, Zhen Zhang, Bing Li, Hua Feng, Cong-Zhan Liu, Ming-Yu Ge, Xu-Fang Li, Zheng-Wei Li, Yu-Peng Xu, Xiang-Yang Wen, Jian-Chao Sun, Lian Tao, Qi-Bin Yi, Ce Cai, Yi Zhao, Wang-Chen Xue, Jia-Cong Liu, Chao Zheng, Chen-Wei Wang, Wen-Jun Tan, You-Li Tuo, Li-Ming Song, Fang-Jun Lu, Ti-Pei Li
Figure caption: The distributions of localization region size by GBM alone (dotted line) and the joint multiple satellites including HXMT (solid line).
Paper: Intelligent Forecasting for Solar Flares Using Magnetograms from SDO/SHARP, SDO/HMI, and ASO-S/FMG
Authors: Xuebao Li, Hongwei Ye, Yanfang Zheng, Ting Li, Jiaben Lin, Shunhuang Zhang, Pengchao Yan, Yongshang Lv, Noraisyah Mohamed Shah, Xuefeng Li, Xiaotian Wang, Yingbo Liu, Rui Wang, Jinfang Wei, Changtian Xiang, Honglei Jin
Figure caption: Distribution of samples and ARs in the training, validation, and testing sets within one of the 10 original CV data sets. The distributions of the remaining nine data sets are identical to the one shown. The number on the left side of the slash indicates the sample count of magnetograms in the data set, while the number on the right side of the slash denotes the AR count in the data set. These correspond to the “Sample Count” and “Active Region Count” axes in the figure, respectively.
Paper: Intelligent Forecasting for Solar Flares Using Magnetograms from SDO/SHARP, SDO/HMI, and ASO-S/FMG
Authors: Xuebao Li, Hongwei Ye, Yanfang Zheng, Ting Li, Jiaben Lin, Shunhuang Zhang, Pengchao Yan, Yongshang Lv, Noraisyah Mohamed Shah, Xuefeng Li, Xiaotian Wang, Yingbo Liu, Rui Wang, Jinfang Wei, Changtian Xiang, Honglei Jin
Figure caption: The sample and AR distributions in training, validation, and testing sets from the 10 filtered CV data sets. Each training set in the 10 filtered CV data sets is the same as the corresponding one in the 10 original CV data sets. The samples in each validation and testing set contain only a single NOAA AR. The two numbers separated by the slash correspond to the “Sample Count” and “Active Region Count” axes in the figure, respectively.
Paper: Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web
Authors: Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong
Figure caption: Stellar mass as a function of redshift for the parent sample of this study. The stellar-mass completeness, given by Mcomp(z)/M⊙ = −1.51 × 106(1 + z) + 6.81 × 107(1 + z)2, is indicated with the solid yellow line. Background gray dots represent the parent sample selected from the COSMOS2020 catalog (see Section 2.2).
Paper: Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web
Authors: Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong
Figure caption: Comparisons between the original and preprocessed galaxy images for six different morphological types. In each case, images are arranged in a sequence from left to right: original image, denoised image, and image after the polar coordinate transformation.
Paper: Revised Point-spread Functions for the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory
Authors: Stefan J. Hofmeister, Daniel W. Savin, Michael Hahn
Figure caption: Schematic for the calibration of the mesh parameters. The numbers correspond to steps in the calibration procedure as described in Section 3.2.2. Red path: We subtract from the solar flare image (a) a prior nonflare image. This results in (b) a background-subtracted flare image where the flare and the associated diffraction patterns are clearly visible. This observed diffraction pattern is later used for the evaluation (i). Blue path: Deconvolving the background-subtracted flare image (b) with the PSF from the published mesh parameters (c) and subsequently setting all intensities outside the flare to zero results in an image that contains the approximated true flare intensities of the isolated flare without the diffraction pattern (d). Convolving a mask whose open area encloses the diffraction arms of the PSF (e) with this isolated flare image (d) results in a mask enclosing the approximate location of the observed diffraction pattern (f). This mask for the observed diffraction pattern will later be used for the evaluation (i). Orange path: Starting with a test PSF (g), we first approximate the true flare intensities using this test PSF and isolate the flare (d). Then, we simulate the flare diffraction pattern by convolving the isolated flare with the test PSF (h). We evaluate the accuracy of the test PSF by comparing the simulated diffraction pattern with the observed diffraction pattern, using the mask enclosing the observed diffraction pattern (i). We iterate the procedure of the yellow path with a random walk in the test PSF parameter space to find the mesh parameters that best describe the observed diffraction pattern.
Paper: Revised Point-spread Functions for the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory
Authors: Stefan J. Hofmeister, Daniel W. Savin, Michael Hahn
Figure caption: Flow diagram for the calibration procedure of the diffuse scattered light portion of the PSF. The input includes (a) a lunar-transit image, (b) the PSF describing the diffraction pattern of the meshes, and (c) a discretization of the diffuse portion of the PSF, where each segment corresponds to one PSF coefficient to fit. We first derive a PSF-deconvolved image (d) and a mask containing the lunar-occulted and the solar on-disk pixels (e). By setting the occulted pixels in the deconvolved image to zero, we obtain an approximation of the true image (f). We determine the location of specific occulted pixels, which we will use to fit the PSF, and call this mask the selection mask (g). Using the PSF discretization from (c), the approximated true image from (f), and the selection mask from (g), we set up a system of equations, which describes the scattered light (h). By solving this system of equations, we obtain an approximation for the diffuse scattered portion of the PSF. Combining the diffuse scattered portion with the PSF diffraction pattern, we obtain a new approximation for the PSF (i). We use this PSF to derive a better approximation for the true image, and iterate this procedure until the fit has converged (j).
Paper: Revised Point-spread Functions for the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory
Authors: Stefan J. Hofmeister, Daniel W. Savin, Michael Hahn
Figure caption: Representative evaluation of the AIA 193 Å fit using the 2010 October 7 lunar transit. (a) Fitted PSF from the long-exposure images. (b) Fitted PSF coefficients along the dashed gray line in (a). The blue solid line shows the unparameterized fit result from the long-exposure images and the blue dashed line the fit result using a parameterization of two Lorentzians. The red solid line revises the fit using the short-exposure images, and the red dashed line parameterizes the new fit by two power laws. (c) Observed lunar-transit image. (d) Intensity profile along the dashed gray line in (c). The orange line shows the observed intensities, and the purple line the observed intensities smoothed by a 5 × 5 pixel kernel. The black line and the shaded areas show the median, 1σ, and 2σ uncertainties for the simulated scattered light.
Paper: Revised Point-spread Functions for the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory
Authors: Stefan J. Hofmeister, Daniel W. Savin, Michael Hahn
Figure caption: Results for the diffuse scattered light for all AIA channels, derived from 100 runs per AIA channel. (a) Amount of scattered light. Each of the vertical gray and white areas mark channels on the same AIA telescope. For each AIA channel, the color-shaded areas show the probability distribution for the scattered light as derived from all lunar occultations. The horizontal bar corresponds to the median value. The dark and light gray boxes are the 1σ and 2σ ranges, respectively, of the probability distributions. (b), (d), and (f) Fitted PSF weights for the 171, 131, and 335 Å channels. (c), (e), and (g) The corresponding amount of light that is scattered farther than a given distance vs. the distance.
Paper: Revised Point-spread Functions for the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory
Authors: Stefan J. Hofmeister, Daniel W. Savin, Michael Hahn
Figure caption: Observed and fitted diffraction pattern intensities for several flares. Flare observed on 2015 May 5 in AIA 193 Å; intensity profiles along the diffraction arms at (a) 310° and (b) 320° are shown. Flare observed on 2023 March 3 in AIA 131 Å; intensity profiles along the diffraction arms at (c) 220° and (d) 130° are shown. Flare observed on 2014 February 25 in AIA (e) 94 Å and (f) 304 Å; intensity profiles along the diffraction arm at 130° are shown.
Paper: Revised Point-spread Functions for the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory
Authors: Stefan J. Hofmeister, Daniel W. Savin, Michael Hahn
Figure caption: Flare observed by AIA 304 Å on 2023 March 3. Left: original image. Right: deconvolved image assuming a PSF core with a width of σ = 0.63 px. Green arrows point to reconstruction artifacts, i.e., pixels with negative values, due to too large of a chosen PSF core width.
Paper: Revised Point-spread Functions for the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory
Authors: Stefan J. Hofmeister, Daniel W. Savin, Michael Hahn
Figure caption: Issues in the calibration of AIA. (a) Different residual offsets in the four quadrants of the AIA image. (b) Ghost image: the solar limb is visible in the occulted region. In addition, the corners of the image are affected by vignetting.
Paper: Revised Point-spread Functions for the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory
Authors: Stefan J. Hofmeister, Daniel W. Savin, Michael Hahn
Figure caption: Continuation of Figure 10 but for the AIA 193, 211, 94, and 304 Å channels.
Paper: Revised Point-spread Functions for the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory
Authors: Stefan J. Hofmeister, Daniel W. Savin, Michael Hahn
Figure caption: Revised PSFs for the seven EUV AIA channels: (a) AIA 94 Å, (b) AIA 131 Å, (c) AIA 171 Å, (d) AIA 193 Å, (e) AIA 211 Å, (f) AIA 304 Å, and (g) AIA 335 Å.
Paper: Revised Point-spread Functions for the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory
Authors: Stefan J. Hofmeister, Daniel W. Savin, Michael Hahn
Figure caption: DEM reconstructions for various regions. First row: 2016 October 30 on-disk to off-limb region, reconstructed from the AIA 171 and 193 Å channels. The solid and dashed lines in the right panel give the EM and temperature evolution, respectively, within the green box in the left panel. Second row: 2016 October 30 quiet-Sun region, reconstructed from the AIA 171, 193, and 211 Å channels. Third row: 2016 October 30 active region, reconstructed from all six coronal AIA channels. Fourth row: 2022 April 20 flare, reconstructed from all six coronal AIA channels. The DEMs in panels (d), (f), and (h) are the average DEMs of the region outlined in green in the corresponding panels (c), (e), and (g). In the right panels, the blue lines give the DEM reconstructions from the original images, and the orange, green, and red lines give the DEM reconstructions from the deconvolved images using our new, the AIA team, and the Poduval PSFs, respectively.
Paper: Cassiopeia A’s Reverse Shock and Its Effects on the Expanding SN Ejecta 
Authors: Robert A. Fesen, Dan Milisavljevic, Daniel Patnaude, Roger A. Chevalier, John C. Raymond, McKinley Brumback, Kathryn E. Weil
Figure caption: High-resolution JWST [Fe II] F162M images of ejecta showing fine-scale ejecta knot morphologies along Cas A’s NE and NW regions (top panels) and its southern limb (bottom panel). North is up, east is to the left.
Paper: Cassiopeia A’s Reverse Shock and Its Effects on the Expanding SN Ejecta 
Authors: Robert A. Fesen, Dan Milisavljevic, Daniel Patnaude, Roger A. Chevalier, John C. Raymond, McKinley Brumback, Kathryn E. Weil
Figure caption: Comparison of Cas A’s large-scale optical (HST), radio (VLA), infrared (Spitzer), and X-ray (Chandra) emissions. The dashed black circles are 160″ in radius and centered on the remnant’s expansion center (J. R. Thorstensen et al. 2001; α(J2000) = 23h23m27.77s, δ(J2000) = ﹩58^\circ 4{8}^{{\prime} }49\mathop{.}\limits^{^{\prime\prime} }4﹩) marked as a small cross in the optical and X-ray images. The dashed circles show the extent of the remnant’s outermost radio and infrared emissions and the location of the forward shock, best seen in the X-rays. The solid red circles are 110″ in radius and shifted 5″ north and 13″ west to match the expansion center defined by the remnant’s bright radio emission shell (M. C. Anderson & L. Rudnick 1995; α(J2000) = 23h23m25.854s, δ(J2000) = ﹩58^\circ 4{8}^{{\prime} }54\mathop{.}\limits^{^{\prime\prime} }3﹩). North is up, east is to the left.