adsabs/AstroCLIMB huggingface dataset:
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption:  BB auto- and cross-spectra band powers D  ℓ  between the AliCPT 95 and 150 GHz maps and the bands of WMAP (23 GHz) and Planck (217 and 353 GHz), in units of the CMB temperature ﹩\mu {{\rm{K}}}_{\mathrm{CMB}}^{2}﹩. In all cases the dots show the simulated data, while the three solid lines show the expectation values from TF “p3” (blue), “p10” (red), and “p16” (green), each of them using the median to predict the band powers. The error bars correspond to the 1σ standard deviations, given by the diagonal components of the covariance matrix ﹩{ \mathcal M }﹩ for each frequency pair. The χ 2 (for 9 degrees of freedom in each case) for a comparison of the data with different models are listed in each plot. The agreement with the spectra and error bars as well as the χ 2 is therefore a validation of these models and the TF pipeline.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Power spectra (left) and the posterior distribution (right) of the tensor-to-scalar ratio r obtained using the GLS pipeline for the sky subpatch labeled as “region A.” Left: the blue markers represent the power spectrum without noise or leakage bias correction, whereas the orange markers illustrate the final power spectrum after debiasing for both noise and leakage bias. For comparison, the noise bias is represented by the green dashed line, while the red dashed line indicates the leakage bias. Right: the solid red line represents the input r-value of 0.023 for DC1 data, while the dashed black line illustrates the mean value (r = 0.021) of the posterior distribution (blue shaded) derived from the likelihood analysis under a positive prior on r.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Left: point-source mask for DC1 obtained from HFI 100 GHz temperature map with the outlier method. Right: the “UNP” mask for polarized foreground and noise with inverse noise variance weights.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: CMB TT (top left), EE (top right), and TE (bottom) power spectra with 1σ errors derived from 50 simulations of CMB and noise and propagated through the ABS (red) and NILC (blue) pipelines in the range of 20 ≤ ℓ ≤ 1024 with bin width Δℓ = 30. The optimal variance defined in Equation (C2) is shown as an orange dashed line, while the cosmic variance is shown with a green dashed line. The errors from both ABS and NILC pipelines are very similar, with NILC having slightly smaller errors for ℓ > 700.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Summary of binary masks used for foreground cleaning in this study, including the DC1 observation patch (red), the “20 μK” mask (yellow) used in the T- and E-mode analysis from the ABS and NILC pipelines, and the “UNP” mask (dark green) used in the GLS and cILC pipelines for B-mode analysis, as well as the “ABSapo” mask (green) used in the ABS and TF pipelines for B-mode analysis. In practice, 6° C2, 4° C2, and inverse noise variance weighted apodization schemes have been applied to these binary masks of “20 μK,” “ABSapo,” and “UNP,” respectively.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: The filtering matrix ﹩{F}_{{\ell }{\ell }^{\prime} }﹩ in the TF analysis, estimated through a least-squares fitting to the simulated noise power spectra before and after filtering. Due to the limited number of simulations, the estimation is restricted to those belonging to the same or neighboring ℓ-bins.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Left: the NILC-cleaned TOD-filtered T map for the DC1 case at 11′ resolution. Right: the NILC-cleaned TOD-filtered E map for the DC1 case at 11′ resolution. Note that the large- and intermediate-scale power is missing due to the TOD filtering of the data.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Cosine needlet bands used for the NILC analysis of T and E maps.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Cross-power spectra (left) and posterior distribution (right) of r, estimated from the cross-power spectrum computed by the cILC pipeline. Left: the orange bars depict the mean and error bars of the cross BB power spectrum between two half-mission maps, corrected for leakage bias. Dashed curves represent the leakage bias (green), foreground residual (red), and point-source residual (blue). Right: the solid red line represents the input r-value of 0.023 for DC1 data, while the dashed black line illustrates the mean value (r = 0.025) of the posterior distribution (blue shaded).
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Uncertainties of the power spectrum for half-mission and full-mission cases using the cILC (solid) and GLS (dashed) pipelines. The auto-spectrum uncertainty for the full-mission case is indicated by green curves, while the cross-spectrum uncertainty for the half-mission case is represented by cyan curves. Their ratio agrees with the expected value of ﹩\sqrt{2}﹩.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Constraints on r, β  s , and β  d  from the “EE+cosmo” run, compared with the “p10” analysis.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Same as Figure 20, but with the GLS pipeline on the data splits. The posterior r is r = 0.023 ± 0.018.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Cosmological parameter constraints from the “EE+cosmo” run. Contours represent CLs of 68.3%, 95.4%, and 99.7% from the inside out.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Comparison of foreground parameters between “EE+cosmo,” “p10,” and “p16.”
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: On-sky angular distribution of M-type stars. The left panel depicts the sky angular distribution of M dwarf stars observed in the LAMOST survey. Active stars are denoted by red points, while blue points represent inactive stars. The right panel shows a comparable distribution of M dwarf stars from the SDSS survey.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: Comparison of stellar activity ratios in LAMOST DR9 and SDSS DR7 data. In the comparison between LAMOST DR9 data (685,849 stars) and SDSS DR7 data (70,842 stars), differences in stellar activity ratios are observed. Blue bars represent LAMOST data, green bars indicate SDSS data, and black bars depict stars observed by both telescopes. The right panel displays the results using the EWHβ spectral line.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: Distribution of the number of M-type stars and the total number of spectra in different spectral subtypes. The left panel illustrates the number distribution of M dwarf stars across various spectral subtypes. The right panel provides the total number of spectra for M dwarf stars.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: Correlation between chromospheric activity indicators ﹩{R}_{{\rm{H}}\alpha }^{{\prime} }﹩ and Rossby number (Ro). The red line represents the fitted line indicating the relationship between these parameters.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: Relationship between brightness variation and EWHα . This figure illustrates the relationship between the amplitude variation of the light-curve data and equivalent width of Hα (EWHα .) The red straight line represents the fitted line, and the top values indicate the number distribution across the observed data.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: Relationship between equivalent width of effective temperature (T eff) and Hα (EWHα) in M stars from the LAMOST DR9 and Gaia data.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: The distribution of the number of stars based on exposure counts, and the distribution of the number of stars within intervals between consecutive observations. The left panel displays the number distribution of exposures, the inset on the upper right is a cumulative chart showing the proportion of exposures in the total sample, while the right panel displays the statistical count of intervals between adjacent observations of stars.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: Two-dimensional distribution of active stars based on Hα and Hβ lines. The top-left panel illustrates the two-dimensional distribution of the ratio of active stars to the total number of stars in cylindrical Galactocentric coordinates (R and Z), where the color indicates the active fraction. The top-right panel represents a similar two-dimensional distribution based on the Hβ line as the chromospheric activity indicator, exhibiting consistency with the Hα results. The panels below illustrate the relationship between activity scores and uncertainty within each region. This uncertainty is represented by the total length of the 68% confidence interval derived from the binomial distribution. Yellow regions indicate areas of greater uncertainty. The lower-left panel depicts the uncertainty in activity scores based on the Hα spectral line, while the lower-right panel depicts the uncertainty based on the Hβ spectral line.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: Two-dimensional active fractions of M stars based on the Hα line. The figure presents two-dimensional graphs depicting the active fraction of M dwarf stars in different spectral subtypes, ranging from M0 to M5, based on the Hα line as chromospheric activity indicators. Each panel corresponds to a different M subtype, showing that the active fraction decreases as the vertical height above the Galactic disk increases, with colors indicating this trend from redder to bluer.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: This figure illustrates the relationship between the active fraction and the distances from 0 to 1000 pc, where the y-axis scales are different between the panels. The intervals are set at 50 pc, and the plot encompasses different stellar spectral subtypes from M0 to M5. The blue and red points denote results based on the Hα and Hβ lines, respectively. The error bars represent the confidence level associated with the confidence intervals.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: This figure displays the active fractions of active M dwarf stars relative to various M spectral subtypes within 15 distinct regions of vertical height from the Galactic disk. The trend in active fractions from M0 to M7 showcases an increase from M0 to M5, followed by a decrease from M5 to M7, attributed to the significant role of height in determining chromospheric activity. The slope and amplitude of active fractions exhibit variations across different distances. In addition, the error bars represent the confidence level associated with the confidence intervals.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: Two-dimensional active fractions of M0 to M5 stars based on the Hβ line. This figure mirrors Figure 11, presenting two-dimensional graphs illustrating the active fraction of M0 to M5 stars. The data is based on the Hβ line as the chromospheric activity indicator. Similar to the Hα results, the active fraction decreases as the vertical height above the Galactic disk increases, with colors indicating this trend from redder to bluer.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: Hα variation of selected examples. The left panel displays spectra in the Hα range, and the right panel shows EW variation of Hα in different observations, indicating significant variations in the Hα line. Additionally, the numbers on the x-axes labels in the right panels represent the initial Julian dates of observation for the stars. Therefore, in the right panels, we set the initial Julian dates of the first observations as 0 on the x-axes.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: Two-dimensional distribution of active stars based on the Hα line. The figure illustrates the two-dimensional distribution of the ratio of active stars to the total number of stars in cylindrical Galactocentric coordinates (R and Z), where the color indicates the active fraction. The left panel displays a two-dimensional distribution map of active stars based on SDSS DR7 data, while the right panel shows a two-dimensional distribution map of active stars based on the combined data of the SDSS DR7 and LAMOST DR9 surveys.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: Spectra and Hα emission variation in different spectral subtypes (M0 to M7). Spectra of M dwarf stars ranging from M0 to M7 are presented, with the lower panels (rows 2, 4, and 6) depicting the detailed variations in Hα emission for different observation times.
Paper: Physical Parameters and Magnetic Activity of M-type Stars Based on the LAMOST DR9, SDSS, and TESS Surveys
Authors: Wenjie Li, Li-yun Zhang, Tianhao Su, Xianming L. Han, Prabhakar Misra, Qingfeng Pi
Figure caption: The relationship between the activity fraction and the Galactocentric radii R on M dwarfs with spectral subtypes ranging from M0 to M4. Blue and red dots represent the Hα and Hβ lines, respectively. Additionally, the error bars represent the confidence level associated with the confidence intervals.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: Demonstration of the one-dimensional filter ﹩{F}_{{\nu }_{\mathrm{ref}}\nu }﹩ defined in Equation (16) (purple solid line) at frequency ν, and the true relative beam ﹩{B}_{\nu }^{\mathrm{rel}}﹩ (black dashed line) injected in the simulation. Here, ν ref is the referenced frequency chosen to be 700.0 MHz. Here, we illustrate only beam Model-I, as the results are very similar to those for Model-II. To evaluate the impact from intrinsic foreground variation, we demonstrate the filter for the simplified “freq−indep” noise model in the upper panel and for the realistic “freq−dep” model in the lower panel. For better understanding, we also decompose the filter into separate contributions, including the intrinsic temperature factor ﹩{F}_{{\nu }_{\mathrm{ref}}\nu }^{T}﹩ defined in Equation (20) (blue dashed–dotted lines), and the relative beam ﹩{B}_{\nu }^{\mathrm{rel}}﹩. ﹩{F}_{{\nu }_{\mathrm{ref}}\nu }^{n}﹩ is the normalized filter ﹩{F}_{{\nu }_{\mathrm{ref}}\nu }﹩ (red solid line), given by ﹩{F}_{{\nu }_{\mathrm{ref}}\nu }^{n}({\boldsymbol{u}})={F}_{{\nu }_{\mathrm{ref}}\nu }({\boldsymbol{u}})/{F}_{{\nu }_{\mathrm{ref}}\nu }({\boldsymbol{u}}=0)﹩.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: Demonstration of our noisy beam Model-I, i.e., the three-dimensional random noise model, for a 100 m single-dish telescope: Panel (a) displays the noisy beam, B  ν ( u ) (Equation (29)), at 800.2 MHz in Fourier space. Panel (b) showcases the beam fluctuation δ B  ν ( u ), with a color scheme representing positive values in red and negative values in blue. Panel (c) illustrates the frequency variation of the one-dimensional beam, spanning 700 to 900 MHz. Panel (d) presents the one-dimensional beams at 800.2 MHz for five different values of the noise amplitude ε = (0%, 2%, 4%, 6%, 8%). Panel (e) visualizes the sidelobes in configuration space generated by an inverse Fourier transform of the beam fluctuation δ B  ν ( u ). The normalization is applied such that the fluctuation is zero at the center. The dashed lines indicate the size of the Gaussian main beam θ FWHM at 800.2 MHz.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: Demonstration of SVD foreground removal with frequency-dependent beam noise. The upper panel displays the eigenvalues for various beam models. Here “ν-ind Gauss” refers to the frequency-independent Gaussian beam, while “ν-dep Gauss” represents the frequency-dependent Gaussian model. The notation “ν-ind noise” is used for the noisy Gaussian beam without frequency variation, and finally “beam n = ε%” denotes the frequency-dependent random noise with noise amplitude of ε%. As demonstrated, a noisy yet frequency-independent beam does not affect the eigenvalues. In contrast, the influence of a frequency-dependent noisy beam is heavily dependent on the amplitude of the noise. The lower panel illustrates the cross-correlation ratio with the H I signals after removal of the first three SVD modes. The color and line scheme here mirrors that of the upper panel. Clearly, the frequency-dependent beam noises introduce confusion and result in a larger foreground residual, leading to a substantially lower cross correlation. As shown, even with just a 0.1% level of beam noise, there is a notable reduction in the cross-correlation ratio between the cleaned map and the true H I signal.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: The one-dimensional beam fluctuation in Fourier space, with different colored lines indicating three distinct frequencies around 800 MHz. The upper panel illustrates the three-dimensional random noise model, i.e., Model-I, with ε = 0.4%. As observed, in this case, fluctuations are entirely random across frequencies, potentially leading to a more pronounced impact on the foreground removal. The lower panel presents the same plot for Model-II with ε = 1.6%, where the noise originates physically within the telescope’s spatial configuration. From the plot, it is evident that the noise appears similar at low u values for nearby frequencies and only starts to diverge at larger u.