adsabs/AstroCLIMB huggingface dataset:
Paper: Investigating 16 Open Clusters in the Kepler/K2–Gaia DR3 Field. I. Membership, Binary Systems, and Rotation
Authors: Liu Long, Shaolan Bi, Jinghua Zhang, Xianfei Zhang, Liyun Zhang, Zhishuai Ge, Tanda Li, Xunzhou Chen, YaGuang Li, LiFei Ye, TianCheng Sun, JianZhao Zhou
Figure caption: CMDs of 16 OCs. The black dots are single stars. The magenta diamonds are stars flagged as candidate astrometric binaries with RUWE ≤ 1.4. The green pluses are stars flagged as candidate photometric binaries. The orange crosses are stars flagged as candidate spectroscopic binaries. The blue circles are stars flagged as candidate BSSs.
Paper: Two-stage Hierarchical Framework for Solar Flare Prediction
Authors: Hao Deng, Yuting Zhong, Hong Chen, Jun Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo
Figure caption: F1 scores of baseline methods before and after boosting with HPF, where the horizontal axis is the time interval of the training set; the number is the ratio of the imbalance ratio between the training set and the test set.
Paper: Two-stage Hierarchical Framework for Solar Flare Prediction
Authors: Hao Deng, Yuting Zhong, Hong Chen, Jun Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo
Figure caption: Feature distribution of flaring-quiet samples in flaring ARs and nonflaring ARs.
Paper: Two-stage Hierarchical Framework for Solar Flare Prediction
Authors: Hao Deng, Yuting Zhong, Hong Chen, Jun Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo
Figure caption: Temporal distribution of solar flares detected by Konus-Wind in trigger mode (Pal'shin et al. 2014; Tsvetkova 2021).
Paper: Two-stage Hierarchical Framework for Solar Flare Prediction
Authors: Hao Deng, Yuting Zhong, Hong Chen, Jun Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo
Figure caption: Diagram of data splitting. The blue and red/green parts indicate the year of observations in the training set and the test set respectively.
Paper: Two-stage Hierarchical Framework for Solar Flare Prediction
Authors: Hao Deng, Yuting Zhong, Hong Chen, Jun Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo
Figure caption: Workflow of the proposed hierarchical prediction framework (HPF).
Paper: The Minimum Variation Timescales of X-Ray Bursts from SGR J1935+2154
Authors: Shuo Xiao, Jiao-Jiao Yang, Xi-Hong Luo, Shao-Lin Xiong, Yuan-Hong Qu, Shuang-Nan Zhang, Wang-Chen Xue, Xiao-Bo Li, You-Li Tuo, Ai-Jun Dong, Ru-Shuang Zhao, Shi-Jun Dang, Lun-Hua Shang, Qing-Bo Ma, Ce Cai, Jin Wang, Ping Wang, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Ming-Yu Ge, Shi-Jie Zheng, Li-Ming Song, Wen-Xi Peng, Xiang-Yang Wen, Xin-Qiao Li, Zheng-Hua An, Xin Xu, Yue Wang, Chao Zheng, Yan-Qiu Zhang, Jia-Cong Liu, Bin Zhang, Wei Xie, Jian-Chao Feng, De-Hua Wang, Qi-Jun Zhi
Figure caption: An illustration of MVT calculation. Top panel: the 5 ms binned light curve in 8–100 keV of a relatively weak burst (T 0 = UTC 2019-11-04T07:20:33.720) from SGR J1935+2154 observed by the GBM Na I #0, 1, 2, 9, and 10; the orange vertical lines are the locations of the change points obtained by the BB algorithm, and the color depth represents the probability. Bottom panel: approximate probability distribution for the locations of each change point. By calculating the 68% confidence interval between two shortest time interval change points (red lines), the estimated MVT obtained is ﹩{12.0}_{-1.9}^{+2.3}﹩ ms, which is approximated as half the duration of the shortest pulse, which is from T0-﹩{41.1}_{-0.1}^{+2.1}﹩ ms to T0-﹩{17.1}_{-1.9}^{+1.0}﹩ ms in the light curve.
Paper: The Minimum Variation Timescales of X-Ray Bursts from SGR J1935+2154
Authors: Shuo Xiao, Jiao-Jiao Yang, Xi-Hong Luo, Shao-Lin Xiong, Yuan-Hong Qu, Shuang-Nan Zhang, Wang-Chen Xue, Xiao-Bo Li, You-Li Tuo, Ai-Jun Dong, Ru-Shuang Zhao, Shi-Jun Dang, Lun-Hua Shang, Qing-Bo Ma, Ce Cai, Jin Wang, Ping Wang, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Ming-Yu Ge, Shi-Jie Zheng, Li-Ming Song, Wen-Xi Peng, Xiang-Yang Wen, Xin-Qiao Li, Zheng-Hua An, Xin Xu, Yue Wang, Chao Zheng, Yan-Qiu Zhang, Jia-Cong Liu, Bin Zhang, Wei Xie, Jian-Chao Feng, De-Hua Wang, Qi-Jun Zhi
Figure caption: Distribution of the MVTs (Δt mvt) for bursts from SGR J1935+2154 observed by GBM, GECAM, and HXMT, respectively. The horizontal coordinates of the left panel and right panel are linearly uniform and logarithmically uniform, respectively. The vertical coordinate of the right panel displays the bin’s count divided by the total number of counts and the bin width (i.e., density).
Paper: The Minimum Variation Timescales of X-Ray Bursts from SGR J1935+2154
Authors: Shuo Xiao, Jiao-Jiao Yang, Xi-Hong Luo, Shao-Lin Xiong, Yuan-Hong Qu, Shuang-Nan Zhang, Wang-Chen Xue, Xiao-Bo Li, You-Li Tuo, Ai-Jun Dong, Ru-Shuang Zhao, Shi-Jun Dang, Lun-Hua Shang, Qing-Bo Ma, Ce Cai, Jin Wang, Ping Wang, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Ming-Yu Ge, Shi-Jie Zheng, Li-Ming Song, Wen-Xi Peng, Xiang-Yang Wen, Xin-Qiao Li, Zheng-Hua An, Xin Xu, Yue Wang, Chao Zheng, Yan-Qiu Zhang, Jia-Cong Liu, Bin Zhang, Wei Xie, Jian-Chao Feng, De-Hua Wang, Qi-Jun Zhi
Figure caption: Comparison between the MVTs estimated by BB (this work) and Haar wavelet analysis (Golkhou et al. 2015) for a common sample of Fermi/GBM GRBs; the latter results are from Golkhou et al. (2015). The red dotted line is the equivalent line.
Paper: The Minimum Variation Timescales of X-Ray Bursts from SGR J1935+2154
Authors: Shuo Xiao, Jiao-Jiao Yang, Xi-Hong Luo, Shao-Lin Xiong, Yuan-Hong Qu, Shuang-Nan Zhang, Wang-Chen Xue, Xiao-Bo Li, You-Li Tuo, Ai-Jun Dong, Ru-Shuang Zhao, Shi-Jun Dang, Lun-Hua Shang, Qing-Bo Ma, Ce Cai, Jin Wang, Ping Wang, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Ming-Yu Ge, Shi-Jie Zheng, Li-Ming Song, Wen-Xi Peng, Xiang-Yang Wen, Xin-Qiao Li, Zheng-Hua An, Xin Xu, Yue Wang, Chao Zheng, Yan-Qiu Zhang, Jia-Cong Liu, Bin Zhang, Wei Xie, Jian-Chao Feng, De-Hua Wang, Qi-Jun Zhi
Figure caption: Left panel: MVT (Δt mvt) vs. the duration (T bb or T90) for SGR J1935+2154 (red, yellow, and green dots), SGRB (blue dots), and LGRB (dark blue dots) in the full energy bands (i.e., 8–100 keV for SGR J1935+2154 and 8–1000 keV for GRB). The red dotted line is the 2 s that is usually considered the division between SGRB and LGRB; the black dotted line represents MVT equal to half the duration, which implies that the burst is single pulse. The MVTs of GRBs are from Golkhou et al. (2015). Right panel: MVT (Δt mvt) vs. the duration (T bb or T90) for SGR J1935+2154 in 15–25 keV; the green star represents the burst associated with FRB 200428 observed by HXMT/ME.
Paper: The Minimum Variation Timescales of X-Ray Bursts from SGR J1935+2154
Authors: Shuo Xiao, Jiao-Jiao Yang, Xi-Hong Luo, Shao-Lin Xiong, Yuan-Hong Qu, Shuang-Nan Zhang, Wang-Chen Xue, Xiao-Bo Li, You-Li Tuo, Ai-Jun Dong, Ru-Shuang Zhao, Shi-Jun Dang, Lun-Hua Shang, Qing-Bo Ma, Ce Cai, Jin Wang, Ping Wang, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Ming-Yu Ge, Shi-Jie Zheng, Li-Ming Song, Wen-Xi Peng, Xiang-Yang Wen, Xin-Qiao Li, Zheng-Hua An, Xin Xu, Yue Wang, Chao Zheng, Yan-Qiu Zhang, Jia-Cong Liu, Bin Zhang, Wei Xie, Jian-Chao Feng, De-Hua Wang, Qi-Jun Zhi
Figure caption: Top panel: the 5 ms binned light curve in 15–25 keV of the burst (T 0 = UTC 2020-04-28T14:34:24.150) associated with FRB 200428 from SGR J1935+2154 observed by the HXMT/ME. Bottom panel: approximate probability distribution for the locations of each change point. By calculating the 68% confidence interval between two shortest time interval change points (red lines), the estimated MVT obtained is ﹩{66.8}_{-23.4}^{+33.9}﹩ ms, which is approximated as half the duration of the shortest pulse, which is from T0+﹩{380.3}_{-0.3}^{+0.1}﹩ ms to T0+﹩{514.0}_{-23.4}^{+33.8}﹩ ms in the light curve. Note that the MVT obtained for the fourth and fifth change points is consistent with the above result within the error. But the shortest block between the second and third change points overlap with probability 2 × 10−5, which is greater than the threshold 3 × 10−7.
Paper: The Minimum Variation Timescales of X-Ray Bursts from SGR J1935+2154
Authors: Shuo Xiao, Jiao-Jiao Yang, Xi-Hong Luo, Shao-Lin Xiong, Yuan-Hong Qu, Shuang-Nan Zhang, Wang-Chen Xue, Xiao-Bo Li, You-Li Tuo, Ai-Jun Dong, Ru-Shuang Zhao, Shi-Jun Dang, Lun-Hua Shang, Qing-Bo Ma, Ce Cai, Jin Wang, Ping Wang, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Ming-Yu Ge, Shi-Jie Zheng, Li-Ming Song, Wen-Xi Peng, Xiang-Yang Wen, Xin-Qiao Li, Zheng-Hua An, Xin Xu, Yue Wang, Chao Zheng, Yan-Qiu Zhang, Jia-Cong Liu, Bin Zhang, Wei Xie, Jian-Chao Feng, De-Hua Wang, Qi-Jun Zhi
Figure caption: Left panel: consistency check of the MVTs measured by GBM and GECAM; the four blue error bars represent bursts (UTCs 2021-09-12T16:52:07.950, 2021-09-18T22:58:52.150, 2022-01-05T07:06:40.800, and 2022-01-12T01:03:46.900) that deviate obviously from the equivalent line (i.e., the red dashed line). We find that they have significantly lower signal-to-noise ratios on the GECAM than on the GBM, resulting in shorter pulses that are not observed by the former. Right panel: the error in MVT vs. MVT.
Paper: The Minimum Variation Timescales of X-Ray Bursts from SGR J1935+2154
Authors: Shuo Xiao, Jiao-Jiao Yang, Xi-Hong Luo, Shao-Lin Xiong, Yuan-Hong Qu, Shuang-Nan Zhang, Wang-Chen Xue, Xiao-Bo Li, You-Li Tuo, Ai-Jun Dong, Ru-Shuang Zhao, Shi-Jun Dang, Lun-Hua Shang, Qing-Bo Ma, Ce Cai, Jin Wang, Ping Wang, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Ming-Yu Ge, Shi-Jie Zheng, Li-Ming Song, Wen-Xi Peng, Xiang-Yang Wen, Xin-Qiao Li, Zheng-Hua An, Xin Xu, Yue Wang, Chao Zheng, Yan-Qiu Zhang, Jia-Cong Liu, Bin Zhang, Wei Xie, Jian-Chao Feng, De-Hua Wang, Qi-Jun Zhi
Figure caption: The MVT vs. energy band for the burst UTC 2020-04-27T18:31:41.080. The light curve in 8–100 keV has a higher signal-to-noise ratio and therefore a smaller MVT (red error bar).
Paper: The Minimum Variation Timescales of X-Ray Bursts from SGR J1935+2154
Authors: Shuo Xiao, Jiao-Jiao Yang, Xi-Hong Luo, Shao-Lin Xiong, Yuan-Hong Qu, Shuang-Nan Zhang, Wang-Chen Xue, Xiao-Bo Li, You-Li Tuo, Ai-Jun Dong, Ru-Shuang Zhao, Shi-Jun Dang, Lun-Hua Shang, Qing-Bo Ma, Ce Cai, Jin Wang, Ping Wang, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Ming-Yu Ge, Shi-Jie Zheng, Li-Ming Song, Wen-Xi Peng, Xiang-Yang Wen, Xin-Qiao Li, Zheng-Hua An, Xin Xu, Yue Wang, Chao Zheng, Yan-Qiu Zhang, Jia-Cong Liu, Bin Zhang, Wei Xie, Jian-Chao Feng, De-Hua Wang, Qi-Jun Zhi
Figure caption: The 0.5 ms binned light curves and MVTs of an burst (T 0 = UTC 2020-04-27T18:31:41.080) from SGR J1935+2154 observed by the GBM NaI # 0, 1, 3, 6, 7, and 9 in four energy bands. The MVT for different energy ranges is consistent within the error, except that 8–100 keV has a higher signal-to-noise ratio and therefore a smaller MVT (see Figure 8). The orange vertical lines are the locations of the two change points where the MVT was obtained.
Paper: The Minimum Variation Timescales of X-Ray Bursts from SGR J1935+2154
Authors: Shuo Xiao, Jiao-Jiao Yang, Xi-Hong Luo, Shao-Lin Xiong, Yuan-Hong Qu, Shuang-Nan Zhang, Wang-Chen Xue, Xiao-Bo Li, You-Li Tuo, Ai-Jun Dong, Ru-Shuang Zhao, Shi-Jun Dang, Lun-Hua Shang, Qing-Bo Ma, Ce Cai, Jin Wang, Ping Wang, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Ming-Yu Ge, Shi-Jie Zheng, Li-Ming Song, Wen-Xi Peng, Xiang-Yang Wen, Xin-Qiao Li, Zheng-Hua An, Xin Xu, Yue Wang, Chao Zheng, Yan-Qiu Zhang, Jia-Cong Liu, Bin Zhang, Wei Xie, Jian-Chao Feng, De-Hua Wang, Qi-Jun Zhi
Figure caption: Left panel: the cumulative curves of MVTs for different energy bands and 1σ ranges. Right panel: the 50th percentile of MVTs vs. energy band; the fitted yellow line is obtained by Markov Chain Monte Carlo.
Paper: The Minimum Variation Timescales of X-Ray Bursts from SGR J1935+2154
Authors: Shuo Xiao, Jiao-Jiao Yang, Xi-Hong Luo, Shao-Lin Xiong, Yuan-Hong Qu, Shuang-Nan Zhang, Wang-Chen Xue, Xiao-Bo Li, You-Li Tuo, Ai-Jun Dong, Ru-Shuang Zhao, Shi-Jun Dang, Lun-Hua Shang, Qing-Bo Ma, Ce Cai, Jin Wang, Ping Wang, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Ming-Yu Ge, Shi-Jie Zheng, Li-Ming Song, Wen-Xi Peng, Xiang-Yang Wen, Xin-Qiao Li, Zheng-Hua An, Xin Xu, Yue Wang, Chao Zheng, Yan-Qiu Zhang, Jia-Cong Liu, Bin Zhang, Wei Xie, Jian-Chao Feng, De-Hua Wang, Qi-Jun Zhi
Figure caption: The Lorentz factor of jet and the shock radius are constrained by MVT for the usual bursts and the burst associated with FRB 200428 based on the GRB-like model, respectively. The shaded area is the 1σ confidence interval.
Paper: The Minimum Variation Timescales of X-Ray Bursts from SGR J1935+2154
Authors: Shuo Xiao, Jiao-Jiao Yang, Xi-Hong Luo, Shao-Lin Xiong, Yuan-Hong Qu, Shuang-Nan Zhang, Wang-Chen Xue, Xiao-Bo Li, You-Li Tuo, Ai-Jun Dong, Ru-Shuang Zhao, Shi-Jun Dang, Lun-Hua Shang, Qing-Bo Ma, Ce Cai, Jin Wang, Ping Wang, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Ming-Yu Ge, Shi-Jie Zheng, Li-Ming Song, Wen-Xi Peng, Xiang-Yang Wen, Xin-Qiao Li, Zheng-Hua An, Xin Xu, Yue Wang, Chao Zheng, Yan-Qiu Zhang, Jia-Cong Liu, Bin Zhang, Wei Xie, Jian-Chao Feng, De-Hua Wang, Qi-Jun Zhi
Figure caption: Distribution of the MVTs (Δt mvt) for bursts from SGR J1935+2154, SGRBs, and LGRBs. The results of GRBs are from Golkhou et al. (2015).
Paper: The Minimum Variation Timescales of X-Ray Bursts from SGR J1935+2154
Authors: Shuo Xiao, Jiao-Jiao Yang, Xi-Hong Luo, Shao-Lin Xiong, Yuan-Hong Qu, Shuang-Nan Zhang, Wang-Chen Xue, Xiao-Bo Li, You-Li Tuo, Ai-Jun Dong, Ru-Shuang Zhao, Shi-Jun Dang, Lun-Hua Shang, Qing-Bo Ma, Ce Cai, Jin Wang, Ping Wang, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Ming-Yu Ge, Shi-Jie Zheng, Li-Ming Song, Wen-Xi Peng, Xiang-Yang Wen, Xin-Qiao Li, Zheng-Hua An, Xin Xu, Yue Wang, Chao Zheng, Yan-Qiu Zhang, Jia-Cong Liu, Bin Zhang, Wei Xie, Jian-Chao Feng, De-Hua Wang, Qi-Jun Zhi
Figure caption: Distribution of the MVTs (Δt mvt) for bursts from SGR J1935+2154 in 8–100 keV, 8–15 keV, 15–25 keV, 25–40 keV, and 40–100 keV observed by GBM, respectively.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: Schematic diagram of CoLFI. The initial parameters here can be set freely, and the parameter space to be learned will be updated after each estimation.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: One-dimensional and two-dimensional marginalized distributions with 1σ and 2σ contours of H 0, Ωb h 2, Ωc h 2, τ, A s, and n s constrained from ﹩{C}_{{\ell }}^{\mathrm{TT}}﹩ from Planck-2015.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: One-dimensional and two-dimensional marginalized distributions with 1σ and 2σ contours of H 0, Ωb h 2, Ωc h 2, τ, A s, n s, and M  B  constrained from Planck-2015 CMB spectra (﹩{C}_{{\ell }}^{\mathrm{TT}}﹩, ﹩{C}_{{\ell }}^{\mathrm{TE}}﹩, and ﹩{C}_{{\ell }}^{\mathrm{EE}}﹩) and Pantheon SN Ia.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: Two sampling methods of parameter space. The left panel stands for sampling in a hypercube uniformly, while the right panel refers to sampling in a hyperellipsoid uniformly by considering covariance between parameters. The red contours stand for the 1σ and 2σ contours of the posterior distribution, and the gray points refer to samples generated in the corresponding parameter space.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: The network structure used for parameter estimation. The structure of the left panel is for one data set, while the multibranch network of the right panel is for multiple data sets {D 1, D 2, D 3, ..., D  n } that are from different experiments.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: One-dimensional and two-dimensional marginalized distributions with 1σ and 2σ contours of w, Ωm, and μ  c  constrained from Pantheon SN Ia.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: Losses of the training set and validation set. There are 3000 samples in the training set and 500 samples in the validation set.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: An example of CoLFI that learns a mapping between the data space of measurements and the cosmological parameter space.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: Mean deviations between the [MNN, MDN, and ANN] results and the fiducial values as a function of the number of hidden layers, the number of training samples, the number of epochs, and the activation function. The areas here indicate the 1σ error range.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: Best-fit values and 1σ errors of H 0 as a function of the number of estimations. Here, the H 0 is constraint with Planck-2015 ﹩{C}_{{\ell }}^{\mathrm{TT}}﹩. The initial H 0 here is the initial setting of parameter space when using CoLFI (see Figure 3). The black solid line with the gray area is the result of the MCMC method. Each estimation corresponds to an estimate in Figure 3.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: Posterior distributions of H 0, Ωb h 2, Ωc h 2, τ, A s, n s, and ∑m  ν  constrained from Planck-2015 ﹩{C}_{{\ell }}^{\mathrm{TT}}﹩ with 1σ and 2σ contours. Three Gaussian components are used for the MDN method.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: Mean deviation of parameters and relative deviation of errors between the (i + 1)th posterior and the ith posterior as a function of ΔH 0 (Equation (36)).
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: Mean deviations between the MNN and ANN results and the MCMC results as a function of the standard deviation of the coefficient A (see Section 2.4.2). Parameters are constrained using the Planck-2015 ﹩{C}_{{\ell }}^{\mathrm{TT}}﹩.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: Mean deviations between the MNN results and the MCMC results as a function of the number of training samples for the two sampling methods mentioned in Section 2.4.1.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: Deviations between the ANN results and the MCMC results as a function of the standard deviation of the coefficient A (see Section 2.4.2). The cosmological parameter here is constrained using the Pantheon SN Ia data.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: One-dimensional distributions of Ωm constrained from Pantheon SN Ia. Here, w is manually set to −0.5, and one beta component is used for the MDN method.
Paper: CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
Authors: Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia, Amare Abebe, Aroonkumar Beesham
Figure caption: Two-dimensional marginalized distributions with 1σ and 2σ contours of w and Ωm constrained from Pantheon SN Ia.
Paper: A Value-added COSMOS2020 Catalog of Physical Properties: Constraining Temperature-dependent Initial Mass Function
Authors: Vadim Rusakov, Charles L. Steinhardt, Albert Sneppen
Figure caption: The number of objects in bins of IMF temperature and lookback time, normalized in lookback-time bins Δt  i , for the total (left), star-forming (middle), and quiescent sample (right). Dashed lines show best-fit exponentials fit in the space of temperature and redshift (T, z) using individual non-normalized data. Uncertainties in redshift are based on 1σ width of p(z) solutions from EAZY (median binned errors range between σ  z  ∼ 0.01 and 0.27). The errors in IMF temperature are computed using the likelihood based on chi-squared of the SED solutions, and are very loose (see the text for details; median binned errors range between ﹩{\sigma }_{{T}_{{\rm{IMF}}}}﹩ ∼ 3–12 K). Most of the objects have IMF temperature above that of the analogs in the local Universe forming a continuum of bottom-lighter IMFs. On average, the best-fit IMF temperature increases with redshift, as in Steinhardt et al. (2022b) and Sneppen et al. (2022). Finally, the temperature and corresponding IMF behavior are distinct between active and passive galaxy populations, where the former is almost exclusively responsible for the temperature increase in the total sample. This result for star-forming and quiescent galaxies is consistent with the observed trends in temperature of cold dust in similar redshift ranges (see the text for references).
Paper: A Value-added COSMOS2020 Catalog of Physical Properties: Constraining Temperature-dependent Initial Mass Function
Authors: Vadim Rusakov, Charles L. Steinhardt, Albert Sneppen
Figure caption: Difference in stellar mass and SFR between the “best-fit IMF sample” ﹩({{ \mathcal M }}_{T},{\mathrm{SFR}}_{T})﹩ and the standard properties from COSMOS2020 ﹩({ \mathcal M },\mathrm{SFR})﹩ at 0 < z < 2. The properties ﹩({{ \mathcal M }}_{T},{\mathrm{SFR}}_{T})﹩ are taken at the best-fit IMF temperature T, while ﹩({ \mathcal M },\mathrm{SFR})﹩ are based on the local IMF, analogous to T ∼ 20 K. The standard COSMOS2020 properties were converted from Chabrier to Kroupa IMF for a consistent comparison, scaling properties by 1.06 (e.g., ﹩{{ \mathcal M }}_{{ \mathcal K }}=1.06{{ \mathcal M }}_{{ \mathcal C }}﹩, using the conversion from Zahid et al. 2012). The separation of galaxies into star-forming and quiescent was done in the UVJ color space of the best-fit IMF colors. The UVJ classification is consistent between two catalogs with 98.2% agreement. The contours outline 42,545 active and 8416 passive galaxies. Clearly, as the best-fit IMF is almost always bottom-lighter than in the Milky Way, the masses and SFRs shift to lower values. The median differences of the total sample correspond to ﹩\mathrm{log}({{ \mathcal M }}_{T}/{ \mathcal M })\approx -0.21﹩ (1/1.6 linear change) and ﹩\mathrm{log}({\mathrm{SFR}}_{T}/\mathrm{SFR})\approx -0.40﹩ (1/2.5 linear change). The offset is, on average, higher for star-forming galaxies with the highest SFR, while quiescent galaxies experience little to no change in stellar mass and a similar change in SFR. The contours in both plots are consistent and extend to the same number levels (outer contours cover 98% of the respective total numbers).