adsabs/AstroCLIMB huggingface dataset:
Paper: Accelerating the Fast Radio Burst Search: Data Set and Methods
Authors: Xuerong Guo, Han Wang, Yifan Xiao, Huaxi Chen, Yinan Ke, ChenChen Miao, Pei Wang, Di Li, Chenwu Jin, Ling He, Yi Feng, Yongkun Zhang, Jiaying Xu, Guangyong Chen
Figure caption: Candidate identification.
Paper: Accelerating the Fast Radio Burst Search: Data Set and Methods
Authors: Xuerong Guo, Han Wang, Yifan Xiao, Huaxi Chen, Yinan Ke, ChenChen Miao, Pei Wang, Di Li, Chenwu Jin, Ling He, Yi Feng, Yongkun Zhang, Jiaying Xu, Guangyong Chen
Figure caption: Distributed architecture of data processing.
Paper: Accelerating the Fast Radio Burst Search: Data Set and Methods
Authors: Xuerong Guo, Han Wang, Yifan Xiao, Huaxi Chen, Yinan Ke, ChenChen Miao, Pei Wang, Di Li, Chenwu Jin, Ling He, Yi Feng, Yongkun Zhang, Jiaying Xu, Guangyong Chen
Figure caption: Methods for evaluation metrics on different FRB sources.
Paper: A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations
Authors: Peng Jia, Jiaxin Li, Liang Cao, Shaonan Tian, Yushan Li, Yang Xu, Liping Xin, Jianyan Wei
Figure caption: The structure of the multimodal neural network used for classification of optical transient candidates.
Paper: A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations
Authors: Peng Jia, Jiaxin Li, Liang Cao, Shaonan Tian, Yushan Li, Yang Xu, Liping Xin, Jianyan Wei
Figure caption: The workflow of the MLOps for optical transient classification.
Paper: A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations
Authors: Peng Jia, Jiaxin Li, Liang Cao, Shaonan Tian, Yushan Li, Yang Xu, Liping Xin, Jianyan Wei
Figure caption: The image-preprocessing pipeline includes GPU-based image quality evaluation, image alignment, image differencing, target detection, and photometry algorithms.
Paper: A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations
Authors: Peng Jia, Jiaxin Li, Liang Cao, Shaonan Tian, Yushan Li, Yang Xu, Liping Xin, Jianyan Wei
Figure caption: Three examples of optical transients obtained by our method. From top to bottom, we show images of an unknown optical transient, an asteroid, and a stellar flare event. For each optical transient, we show the template image (left), science image (middle), and difference image (right). The unknown optical transient was observed over 13 frames. Although it shares characteristics with a cosmic-ray artifact, we classify it as an optical transient. The second object, identified as an asteroid, was detected for 76 frames and exhibited a relative shift of 15 pixels. Finally, the third event, lasting for 16 frames with a maximum magnitude difference of 0.97, was classified as a stellar flare.
Paper: A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations
Authors: Peng Jia, Jiaxin Li, Liang Cao, Shaonan Tian, Yushan Li, Yang Xu, Liping Xin, Jianyan Wei
Figure caption: The F1 score is analyzed both prior to and following the fine-tuning of the neural network. This figure illustrates a decrease in the F1 score; however, after the fine-tuning stage, an improvement in performance was achieved.
Paper: A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations
Authors: Peng Jia, Jiaxin Li, Liang Cao, Shaonan Tian, Yushan Li, Yang Xu, Liping Xin, Jianyan Wei
Figure caption: This figure illustrates the relative contributions of the image, PSF, and metadata reasoning paths for various optical transient candidates. True transients typically show high contributions from image and optical reasoning (e.g., panels (a) and (b), appearing in 13 and 58 consecutive frames, respectively). Metadata reasoning helps suppress false positives that resemble true transients (panel (d)) and effectively rejects other dissimilar artifacts (panels (e) and (f)).
Paper: A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations
Authors: Peng Jia, Jiaxin Li, Liang Cao, Shaonan Tian, Yushan Li, Yang Xu, Liping Xin, Jianyan Wei
Figure caption: The R.A. and decl. for the different optical transients. The number of flares occurring at (215.016786, 49.971478) is two, occurring at 15:08 on 2020 April 12 and 14:27 on 2020 April 27. The number of flares occurring at (206.381065, 51.135421) is two, occurring at 14:01 on 2020 April 12 and 14:03 on 2020 April 30.
Paper: A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations
Authors: Peng Jia, Jiaxin Li, Liang Cao, Shaonan Tian, Yushan Li, Yang Xu, Liping Xin, Jianyan Wei
Figure caption: Visualization interface. On the left is the optical transient candidate image, including the scientific image (left), the reference image (middle), and the difference image (right); the candidate information is on the right.
Paper: A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations
Authors: Peng Jia, Jiaxin Li, Liang Cao, Shaonan Tian, Yushan Li, Yang Xu, Liping Xin, Jianyan Wei
Figure caption: Magnitude variation and duration time of different optical transients. Panel (a) displays the duration times of the different the optical transients, while panel (b) shows the magnitude variations of the different optical transients.
Paper: A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations
Authors: Peng Jia, Jiaxin Li, Liang Cao, Shaonan Tian, Yushan Li, Yang Xu, Liping Xin, Jianyan Wei
Figure caption: The ROC curve and the confusion matrix in the validation set.
Paper: A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations
Authors: Peng Jia, Jiaxin Li, Liang Cao, Shaonan Tian, Yushan Li, Yang Xu, Liping Xin, Jianyan Wei
Figure caption: The loss curve during the training stage.
Paper: A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations
Authors: Peng Jia, Jiaxin Li, Liang Cao, Shaonan Tian, Yushan Li, Yang Xu, Liping Xin, Jianyan Wei
Figure caption: Five difference images that are misclassified as optical transients.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: The distributions of jet emission duration (tjet,z) and the magnetar wind emission duration (twind,z) in the rest frame (left panel), as well as the distribution of the plateau luminosity Lp (right panel). The red dashed lines are the best Gaussian fits. We adopt z = 1 for the LGRBs without redshift measurement in our sample.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: Numerical results for dipole radiation luminosity by considering the R and I evolution effects in choice of different NS EoSs, baryonic masses, radiation efficiencies, as well as the dominant energy loss term.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: Joint BAT+XRT light curve of GRB 111008A. The black dots are BAT data extrapolated to the XRT band (0.3–10 keV), and the blue dots are XRT data. The solid red line is the fit with a smooth triple power-law function, and the vertical dashed line is the separation of the jet emission phase and magnetar wind emission phase.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: Lp as a function of tb/(1 + z) (left panel) or tjet/(1 + z) (right panel) for the LGRBs in our sample. The red solid and dashed lines are the least-squares linear fit and its 95% confidence level, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: Comparisons of the derived magnetar physical parameter Bp histograms between the R and I evolution correction and the constant R and I in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: The cumulative distributions for the derived magnetar physical parameter Bp between the R and I evolution correction and the constant R and I in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: The cumulative distributions for the derived magnetar physical parameter P0 between the R and I evolution correction and the constant R and I in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: Comparisons of the derived magnetar physical parameter P0 histograms between the R and I evolution correction and the constant R and I in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: The correlations between the Bp and P0 in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5. The red solid and red dashed lines are the best-fitting results and the 95% confidence level, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: Left panel: the distributions of isotropic jet prompt gamma-ray/X-ray energy (Ejet,iso), corrected jet prompt gamma-ray/X-ray energy (Ejet), and X-ray energy release of the magnetar wind (Ewind) for the LGRBs in our sample. Right panel: the distribution of the energy partition ratio R and ﹩R^{\prime} ﹩ for the LGRBs in our sample. The red dashed lines are the best Gaussian fits.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: The correlations between the isotropic jet energy Ejet,iso and the magnetar wind energy Ewind (left panel), as well as between the corrected jet energy Ejet and the magnetar wind energy Ewind (right panel). The red solid line and dashed lines are the least-square fit and the 95% confidence level of the fits, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: The correlations between the ϵ and Bp in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5. The red solid and red dashed lines are the best-fitting results and the 95% confidence level, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: The cumulative distributions for the derived magnetar physical parameter ϵ between the R and I evolution correction and the constant R and I in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: The correlations between the P0 and Ejet,iso (black circles), and Ejet (gray circles) in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5. The red solid and red dashed lines are the best-fitting results and the 95% confidence level for P0−Ejet,iso, respectively. The gray solid and gray dashed lines are the best-fitting results and the 95% confidence level for P0−Ejet, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: The correlations between the Bp and Ejet,iso (black circles), and Ejet (gray circles) in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5. The red solid and red dashed lines are the best-fitting results and the 95% confidence level for P0−Ejet,iso, respectively. The gray solid and gray dashed lines are the best-fitting results and the 95% confidence level for P0−Ejet, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: The correlations between the ϵ and P0 in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5. The red solid and red dashed lines are the best-fitting results and the 95% confidence level, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: Comparisons of the derived magnetar physical parameter ϵ histograms between the R and I evolution correction and the constant R and I in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: Detectability test of GW signals with aLIGO and ET detectors for our GW-dominated GRBs with z measurement in four samples of EoSs with Mb = 2.0M⊙ and ηX = 0.5. The black solid line is the sensitivity limits for ET, and the red and green solid lines are the sensitivity limits for aLIGO-Hanford and aLIGO-Livingston, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: Similar to Figure 22, but for our GW-dominated GRBs without z measurement in four samples of EoSs with Mb = 2.0M⊙ and ηX = 0.5. The different color dashed and dashed–dotted lines represent z = 1 and z = 0.1, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: Left panel: the X-ray luminosity light curves of GW-dominated GRBs with z measurement in our sample. Right panel: the X-ray luminosity light curves of GW-dominated GRBs without z measurement in our sample, and we adopt z = 1 to estimate the luminosity of these GRBs.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: The GW characteristic amplitude of GRB 170607A in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: The GRB luminosity thresholds of Swift/XRT, EP/WXT, EP/FXT, SVOM/MXT in the flux limit ﹩{F}_{{{\rm{th}}}}^{{{\rm{XRT}}}}=2\times 1{0}^{-12}\,{{\rm{erg}}}\,{{{\rm{s}}}}^{-1}\,{{{\rm{cm}}}}^{-2}﹩, ﹩{F}_{{{\rm{th}}}}^{{{\rm{WXT}}}}=1\times 1{0}^{-10}\,{{\rm{erg}}}\,{{{\rm{s}}}}^{-1}\,{{{\rm{cm}}}}^{-2}﹩, ﹩{F}_{{{\rm{th}}}}^{{{\rm{FXT}}}}=1\times 1{0}^{-11}\,{{\rm{erg}}}\,{{{\rm{s}}}}^{-1}\,{{{\rm{cm}}}}^{-2}﹩, ﹩{F}_{{{\rm{th}}}}^{{{\rm{MXT}}}}=7\times 1{0}^{-11}\,{{\rm{erg}}}\,{{{\rm{s}}}}^{-1}\,{{{\rm{cm}}}}^{-2}﹩, respectively. The data of the X-ray plateau in our sample are shown with red dots, and the thresholds of different instruments are marked by different colored lines to examine the detection probability of magnetar dipole radiation.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: EM+GW-dominated case: the corner plots and best-fitting results of GRB 180626A in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: EM-dominated case: the corner plots and best-fitting results of GRB 050922B in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: GW-dominated case: the corner plots and best-fitting results of GRB 091208B in four samples of EoSs with Mb = 2.0 M⊙,  2.5 M⊙, and ηX = 0.1,  0.5, respectively.
Paper: Constraining Magnetar Parameters by Taking into Account the Evolutionary Effects of Radius and Moment of Inertia with Swift/XRT Data
Authors: Lin Lan, He Gao, Shunke Ai, Wen-Jin Xie, Yong Yuan, Long Li, Li-Ping Xin, Jian-Yan Wei
Figure caption: The best-fitting results of our entire GRB sample in AP3 EoS with Mb = 2.0 M⊙ and ηX = 0.5.
Paper: DarkAI: Reconstructing the Density, Velocity, and Tidal Fields of Dark Matter from a DESI-like Bright Galaxy Sample
Authors: Feng Shi, Zitong Wang, Xiaohu Yang, Yizhou Gu, Chengliang Wei, Ming Li, Jiaxin Han, Zhejie Ding, Huiyuan Wang, Youcai Zhang, Wensheng Hong, Yirong Wang, Xiao-dong Li
Figure caption: The footprint of the DESI Legacy Imaging Survey DR9. The red contour indicates the region selected for reconstruction. The dashed black line represents the Galactic plane, excluding galaxies with ∣b∣ < 25 in Galactic coordinates.
Paper: DarkAI: Reconstructing the Density, Velocity, and Tidal Fields of Dark Matter from a DESI-like Bright Galaxy Sample
Authors: Feng Shi, Zitong Wang, Xiaohu Yang, Yizhou Gu, Chengliang Wei, Ming Li, Jiaxin Han, Zhejie Ding, Huiyuan Wang, Youcai Zhang, Wensheng Hong, Yirong Wang, Xiao-dong Li
Figure caption: Normalized redshift distribution. The red dashed line shows the distribution for our mock DESI sample. The blue solid line shows the DESI galaxy sample, constructed by crossmatching photometric data from DESI LS DR9 with Year 1 spectroscopic observations.
Paper: DarkAI: Reconstructing the Density, Velocity, and Tidal Fields of Dark Matter from a DESI-like Bright Galaxy Sample
Authors: Feng Shi, Zitong Wang, Xiaohu Yang, Yizhou Gu, Chengliang Wei, Ming Li, Jiaxin Han, Zhejie Ding, Huiyuan Wang, Youcai Zhang, Wensheng Hong, Yirong Wang, Xiao-dong Li
Figure caption: Comparison of galaxy clustering between mock samples and DESI BGS measurements. The left panel displays the multipole moments of the 2PCF, while the right panel shows the corresponding power spectra. The lines represent results from the mock samples, while the symbols denote the DESI BGS measurements.
Paper: DarkAI: Reconstructing the Density, Velocity, and Tidal Fields of Dark Matter from a DESI-like Bright Galaxy Sample
Authors: Feng Shi, Zitong Wang, Xiaohu Yang, Yizhou Gu, Chengliang Wei, Ming Li, Jiaxin Han, Zhejie Ding, Huiyuan Wang, Youcai Zhang, Wensheng Hong, Yirong Wang, Xiao-dong Li
Figure caption: The projected field distributions in a slice of 1564 × 1564 × 9.16 h−1 Mpc. From left to right, the panels correspond to the galaxy overdensity, dark matter density, and velocity magnitude ∣v∣, respectively.
Paper: DarkAI: Reconstructing the Density, Velocity, and Tidal Fields of Dark Matter from a DESI-like Bright Galaxy Sample
Authors: Feng Shi, Zitong Wang, Xiaohu Yang, Yizhou Gu, Chengliang Wei, Ming Li, Jiaxin Han, Zhejie Ding, Huiyuan Wang, Youcai Zhang, Wensheng Hong, Yirong Wang, Xiao-dong Li
Figure caption: Upper left: loss function for training (δg, δm) using 3000 epochs. The dark solid and blue dashed lines represent the loss functions from the training and validation samples, respectively. Upper right: comparison of the density distributions for the validation samples. The black solid line represents the result of the target validation samples. The different colored lines represent the predicted fields at epochs 200, 1000, 2000, and 3000, respectively. Lower left: grid-to-grid density relation between the reconstructed density field 1 + δrec and the true field 1 + δtrue. The contour in each case represents the threshold encompassing 99% of the grid cells. The dashed lines indicate the ideal one-to-one correspondence. The different colored curves correspond to different epochs, as indicated. Lower right: the evolution of the factor fsc, defined as the product of the scatter variance and 1 minus the correlation scope between δrec and δtrue, as a function of epoch.