adsabs/AstroCLIMB huggingface dataset:
Paper: CLASS Data Pipeline and Maps for 40 GHz Observations through 2022
Authors: Yunyang Li, 云炀 李, Joseph R. Eimer, Keisuke Osumi, John W. Appel, Michael K. Brewer, Aamir Ali, Charles L. Bennett, Sarah Marie Bruno, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Kevin L. Denis, Rolando Dünner, Francisco Espinoza, Thomas Essinger-Hileman, Pedro Fluxá Rojas, Kathleen Harrington, Jeffrey Iuliano, John Karakla, Tobias A. Marriage, Nathan J. Miller, Sasha Novack, Carolina Núñez, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Janet L. Weiland, Edward J. Wollack, Zhilei Xu, 智磊 徐, Lingzhen Zeng
Figure caption: The effect of template iteration in correcting the large angular scale power bias. The top panels show the signal power spectra for EE and BB. Green curves are the input synchrotron power spectrum model from Planck Collaboration et al. (2020c), and the associated sample variance for a 75% sky coverage. The colored curves show the estimated signal spectra, averaged over 2000 simulations, at each template iteration. The error bars are the standard error of the mean and are only shown for the last iteration for visual clarity. The bottom panels show the residual spectra between the output spectra and the input spectra normalized by the input amplitude, in the same color scheme. The correction converged quickly at high ℓ where the corresponding time streams are noise dominated; at low-ℓ the iteration converged by the fifth iteration, with a bias below 2.5% for ℓ ≤ 5, much smaller compared to the sample variance.
Paper: CLASS Data Pipeline and Maps for 40 GHz Observations through 2022
Authors: Yunyang Li, 云炀 李, Joseph R. Eimer, Keisuke Osumi, John W. Appel, Michael K. Brewer, Aamir Ali, Charles L. Bennett, Sarah Marie Bruno, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Kevin L. Denis, Rolando Dünner, Francisco Espinoza, Thomas Essinger-Hileman, Pedro Fluxá Rojas, Kathleen Harrington, Jeffrey Iuliano, John Karakla, Tobias A. Marriage, Nathan J. Miller, Sasha Novack, Carolina Núñez, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Janet L. Weiland, Edward J. Wollack, Zhilei Xu, 智磊 徐, Lingzhen Zeng
Figure caption: CLASS 40 GHz polarization maps in equatorial coordinates under Mollweide projection. The linear (Stokes Q/U) and circular polarization (Stokes V) are the final products of the data pipeline. The maps are smoothed to 2° resolution to enhance the large-scale features. The gray-shaded regions are not surveyed. The color scale is linear below 5 μK and logarithmic above to show the structure in the map where bright synchrotron radiation dominates. Due to the designed VPM throw, the noise levels are similar in the Q, U, and V maps; therefore, the fluctuations in the V map approximately represent the noise in the Q/U maps. The apparent signals in the Q/U maps are explored in E23.
Paper: CLASS Data Pipeline and Maps for 40 GHz Observations through 2022
Authors: Yunyang Li, 云炀 李, Joseph R. Eimer, Keisuke Osumi, John W. Appel, Michael K. Brewer, Aamir Ali, Charles L. Bennett, Sarah Marie Bruno, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Kevin L. Denis, Rolando Dünner, Francisco Espinoza, Thomas Essinger-Hileman, Pedro Fluxá Rojas, Kathleen Harrington, Jeffrey Iuliano, John Karakla, Tobias A. Marriage, Nathan J. Miller, Sasha Novack, Carolina Núñez, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Janet L. Weiland, Edward J. Wollack, Zhilei Xu, 智磊 徐, Lingzhen Zeng
Figure caption: CLASS 40 GHz hits maps and cross-linking map. The upper triangle of the plot shows the hits maps in units of the integration time per N side = 128 pixel (0.46 deg2). This is proportional to the inverse-variance map assuming constant white noise in the raw data around the VPM modulation frequencies. The bottom-left corner shows the cross-linking map in arbitrary units. Higher values reflect even coverage of the scanning direction and therefore suppression of the scanning-related low-frequency noise.
Paper: CLASS Data Pipeline and Maps for 40 GHz Observations through 2022
Authors: Yunyang Li, 云炀 李, Joseph R. Eimer, Keisuke Osumi, John W. Appel, Michael K. Brewer, Aamir Ali, Charles L. Bennett, Sarah Marie Bruno, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Kevin L. Denis, Rolando Dünner, Francisco Espinoza, Thomas Essinger-Hileman, Pedro Fluxá Rojas, Kathleen Harrington, Jeffrey Iuliano, John Karakla, Tobias A. Marriage, Nathan J. Miller, Sasha Novack, Carolina Núñez, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Janet L. Weiland, Edward J. Wollack, Zhilei Xu, 智磊 徐, Lingzhen Zeng
Figure caption: Geometry of the offset of array center from the mount boresight showing the symbols used in the equations in Appendix A.5. Here, β is the boresight angle, r is the spherical distance from the mount boresight to array center, Δx and Δy are the coordinates of array center with respect to the mount boresight in the receiver coordinate system, and Z M  and Z A  are the zenith angles of the mount boresight and array center, respectively.
Paper: CLASS Data Pipeline and Maps for 40 GHz Observations through 2022
Authors: Yunyang Li, 云炀 李, Joseph R. Eimer, Keisuke Osumi, John W. Appel, Michael K. Brewer, Aamir Ali, Charles L. Bennett, Sarah Marie Bruno, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Kevin L. Denis, Rolando Dünner, Francisco Espinoza, Thomas Essinger-Hileman, Pedro Fluxá Rojas, Kathleen Harrington, Jeffrey Iuliano, John Karakla, Tobias A. Marriage, Nathan J. Miller, Sasha Novack, Carolina Núñez, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Janet L. Weiland, Edward J. Wollack, Zhilei Xu, 智磊 徐, Lingzhen Zeng
Figure caption: The transfer functions for linear polarization EE/BB and circular polarization VV. The effects of the demodulation low-pass filter are shown in pink, and the diagonal components of the mapping transfer matrix F  ℓℓ  are in green. Together with the beam window function (Xu et al. (2020), orange curve, they make up the total transfer function of the signal.
Paper: CLASS Data Pipeline and Maps for 40 GHz Observations through 2022
Authors: Yunyang Li, 云炀 李, Joseph R. Eimer, Keisuke Osumi, John W. Appel, Michael K. Brewer, Aamir Ali, Charles L. Bennett, Sarah Marie Bruno, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Kevin L. Denis, Rolando Dünner, Francisco Espinoza, Thomas Essinger-Hileman, Pedro Fluxá Rojas, Kathleen Harrington, Jeffrey Iuliano, John Karakla, Tobias A. Marriage, Nathan J. Miller, Sasha Novack, Carolina Núñez, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Janet L. Weiland, Edward J. Wollack, Zhilei Xu, 智磊 徐, Lingzhen Zeng
Figure caption: Wind direction distribution at the Atacama site as measured by the CLASS weather station over the 6 yr survey. The wind direction distribution is shown as the histogram in the outer and inner ring for the Austral winter (June to August) and summer (December to February), respectively. The histograms are color-coded by the mean wind speed in each bin.
Paper: Systematic Uncertainties of Atomic Data in Photoionization Modeling
Authors: R. Ballhausen, T. R. Kallman, L. Gu, F. Paerels
Figure caption: Additional optical depth for each of the 500 lines from the Gaussian components against the optical depth of the model line without the Gaussian component. Black data points show positive additional optical depth (i.e., the absorption line becomes stronger), and red shows negative additional optical depth (i.e., the absorption line becomes weaker). The gray dashed line marks Δτ = τ, and the blue dashed line is a linear regression to the τ vs. Δτ relation. The 90% upper limits are shown by a downward pointing error with the limit given at the base of the arrow. Of the 500 lines, 390 are upper limits, and 110 are detections.
Paper: Systematic Uncertainties of Atomic Data in Photoionization Modeling
Authors: R. Ballhausen, T. R. Kallman, L. Gu, F. Paerels
Figure caption: (a) Close-up of the HEG (red) and MEG (blue) 10.5–11.0 Å spectra of the NGC 3783 observation 2090 fitted with a five-component warmabs model (black). (b) C-residuals for the baseline model. (c) Same data as in (a) but with the warmabs model modified by the additional Gaussian lines. (d) Additional optical depth Δτ that is added or removed from each bin by the Gaussian components. (e) C-residuals for the modified model. As shown here and described in Section 5.1, including additional Gaussians—equivalent to modifying A-values for the strongest 500 lines—leads only to minor to moderate improvement of the fit statistics and residuals.
Paper: Systematic Uncertainties of Atomic Data in Photoionization Modeling
Authors: R. Ballhausen, T. R. Kallman, L. Gu, F. Paerels
Figure caption: Distribution of C-residuals (square root of C-statistic per bin, signed according to data model) for our different trial models (black), a simulated spectrum based on our best-fit model (cyan), and a set of calibration observations of PKS 2155−304 (orange).
Paper: Systematic Uncertainties of Atomic Data in Photoionization Modeling
Authors: R. Ballhausen, T. R. Kallman, L. Gu, F. Paerels
Figure caption: Scatter plot of ion column densities for Si and Fe obtained calculated by xstar/warmabs. Uncertainties are purely statistical and on a 90% confidence level. from direct fitting vs. the equilibrium charge state distribution.
Paper: Systematic Uncertainties of Atomic Data in Photoionization Modeling
Authors: R. Ballhausen, T. R. Kallman, L. Gu, F. Paerels
Figure caption: Fractional uncertainty on the ionization parameter in simulated spectra as a function of signal-to-noise ratio per resolution element at the center of the O VIII Lyα line (for R = 1000) for the standard warmabs model (black) and the version including model uncertainties (red).
Paper: Systematic Uncertainties of Atomic Data in Photoionization Modeling
Authors: R. Ballhausen, T. R. Kallman, L. Gu, F. Paerels
Figure caption: Top: simulated MEG +1 spectrum with 109 s exposure, based on our NGC 3783 continuum model with a single oxygen absorber with ﹩\mathrm{log}\xi ﹩ = 1 and N H = 1021 cm−2. The gray shaded region shows the total uncertainty associated with each bin. Bottom: fractional uncertainty of each bin for the Poisson uncertainty (red) and the model uncertainty (blue).
Paper: Systematic Uncertainties of Atomic Data in Photoionization Modeling
Authors: R. Ballhausen, T. R. Kallman, L. Gu, F. Paerels
Figure caption: Normalized cumulative histogram of the 500 line modifications. Black data points show positive additional optical depth (i.e., the absorption line becomes stronger), and red shows negative additional optical depth (i.e., the absorption line becomes weaker).
Paper: Systematic Uncertainties of Atomic Data in Photoionization Modeling
Authors: R. Ballhausen, T. R. Kallman, L. Gu, F. Paerels
Figure caption: Normalized distribution of absolute contributions of the 500 strongest model lines.
Paper: Systematic Uncertainties of Atomic Data in Photoionization Modeling
Authors: R. Ballhausen, T. R. Kallman, L. Gu, F. Paerels
Figure caption: Scatter plot of ion column densities for C, N, O, Ne, Mg, S, Ar, and Ca obtained from direct fitting vs. xstar/warmabs. Uncertainties are purely statistical and on a 90% confidence level.
Paper: A Universal Equation to Predict Ωm from Halo and Galaxy Catalogs
Authors: Helen Shao, Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Romain Teyssier, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Ulrich P. Steinwandel, Elena Hernández-Martínez, Klaus Dolag, Christopher C. Lovell, Lehman H. Garrison, Eli Visbal, Mihir Kulkarni, Lars Hernquist, Tiago Castro, Mark Vogelsberger
Figure caption: We test the analytic equations that were trained for halo catalogs of N-body simulations on thousands of galaxies from 6 different hydrodynamic simulation sets, Astrid, IllustrisTNG, Magneticum, SB28, Simba, and SWIFT-EAGLE, to predict the value of Ωm and plot the predicted against truth for each simulation. To conserve space, we only present results for the tests performed on catalogs constructed with a stellar mass threshold of 4 × m * where m * is a fixed mass for an individual stellar particle as described in Section 3.1, but we reach similar accuracies for catalogs constructed with other mass cuts. We also include only 50 randomly selected catalogs for each simulation set for the clarity of the figures, but the reported metrics were computed for all simulations in the suites. Note that, for the bottom right panel, which depicts the predictions for the SWIFT-EAGLE simulation set, we use simulations that are generated with the same value of Ωm = 0.3. Thus, we plot the difference between the truth and the prediction on the y-axis for these catalogs. As depicted in Figure 6, a large fraction of the catalogs, particularly for the ASTRID, IllustrisTNG, and SB28 simulations, contain galaxy number densities that are outside the range of the number densities exhibited by the halo catalogs used during training of the network and equations. Hence, in this plot, we remove these outliers and find that the mean relative errors of the predictions significantly decrease (see Figure 6 for comparison). These results exhibit a relatively high accuracy with mean errors that average around ϵ ∼ 9.4%, comparable to the accuracies obtained by our companion paper de Santi et al. (2023) with model trained on galaxy properties. This further demonstrates the robustness of the equations as well as their ability to use halo properties to extrapolate to galaxy distributions. This is a surprising result given the various astrophysical processes exhibited by the hydrodynamic simulations and the complex mapping between galaxies and halos. We note that the presented errorbars for all simulations are the inferred posterior standard deviation values obtained by the model trained and tested on galaxy catalogs discussed in de Santi et al. (2023), since the equations predict only the first moment of the posterior.
Paper: A Universal Equation to Predict Ωm from Halo and Galaxy Catalogs
Authors: Helen Shao, Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Romain Teyssier, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Ulrich P. Steinwandel, Elena Hernández-Martínez, Klaus Dolag, Christopher C. Lovell, Lehman H. Garrison, Eli Visbal, Mihir Kulkarni, Lars Hernquist, Tiago Castro, Mark Vogelsberger
Figure caption: Top: we train a GNN model with a compressed latent space to perform likelihood-free inference for the cosmological parameter Ωm. The inputs to the model are halo catalogs from Gadget that only carry information about halo positions and peculiar velocity moduli. Once trained, we test the model on halo catalogs from different N-body and hydrodynamic simulations as indicated in the legend. We note that simulations of the same type, either N-body or hydrodynamic, are run with the same initial conditions, cosmology (and fiducial astrophysics for the hydrodynamic simulations). For each simulation, we generate 5 catalogs. Each halo catalog contains all halos with masses above Nm  p , where m  p  is the particle mass, and N can be 100, 200, 300, 400, or 500 (see legend). The y-axis represents the difference between the truth and the inference. As can be seen, this model exhibits surprising extrapolation properties and is robust to all simulation codes despite only containing one message-passing layer and two latent features. Bottom: same as above but for the analytic equations obtained using symbolic regression and modified to preserve rotational and translational symmetries in the data, as described in Section 3.3. As can be seen, the formulae maintain the robustness of the GNN model and achieve a very similar accuracy compared to the GNN.
Paper: A Universal Equation to Predict Ωm from Halo and Galaxy Catalogs
Authors: Helen Shao, Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Romain Teyssier, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Ulrich P. Steinwandel, Elena Hernández-Martínez, Klaus Dolag, Christopher C. Lovell, Lehman H. Garrison, Eli Visbal, Mihir Kulkarni, Lars Hernquist, Tiago Castro, Mark Vogelsberger
Figure caption: This figure follows the format of Figure 5. Here, each scatterpoint (representing one galaxy catalog) is colored according to the number of galaxies the catalog contains. The colorbar depicts the range of galaxy number density present in the catalogs for the corresponding stellar mass threshold of each column. As it can be seen, a significant portion of the galaxy catalogs from simulations such as Astrid, Illustris-TNG, and SB28 contains much smaller or larger galaxy number densities than the number densities seen during training, which were within the range of (1000, 6000). These catalogs account for the the relatively larger errors in these predictions, which is expected because the halo number density acts as an uninformative prior during the training of the GNN and equations. When we omit these outlier catalogs, we obtain smaller scatter in the results, as shown in Figure 5.
Paper: A Universal Equation to Predict Ωm from Halo and Galaxy Catalogs
Authors: Helen Shao, Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Romain Teyssier, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Ulrich P. Steinwandel, Elena Hernández-Martínez, Klaus Dolag, Christopher C. Lovell, Lehman H. Garrison, Eli Visbal, Mihir Kulkarni, Lars Hernquist, Tiago Castro, Mark Vogelsberger
Figure caption: We trained a GNN using halo catalogs generated with the halo finder ROCKSTAR to infer Ωm, and approximated the learned model with analytic equations using symbolic regression. The top plots show the accuracy of the model and the analytic approximations when evaluated on halo catalogs from the N-body Gadget simulations using a different halo finder SUBFIND constructed with the varying minimum particle thresholds as described earlier. It is overall able to accurately extrapolate to the different halo finder with ∼8% mean relative error across the different catalogs. On the other hand, while the analytic expressions have a slightly larger error of ∼9%, they do not exhibit the noticeable biases present in the predictions from the GNN, demonstrating the known improved extrapolation properties of analytic expressions over neural networks. The bottom plots depict the same test as above but for halo catalogs from the IllustrisTNG hydrodynamic simulations.
Paper: A Universal Equation to Predict Ωm from Halo and Galaxy Catalogs
Authors: Helen Shao, Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Romain Teyssier, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Ulrich P. Steinwandel, Elena Hernández-Martínez, Klaus Dolag, Christopher C. Lovell, Lehman H. Garrison, Eli Visbal, Mihir Kulkarni, Lars Hernquist, Tiago Castro, Mark Vogelsberger
Figure caption: We train a GNN with a low-dimensional latent space to infer Ωm from catalogs of the Gadget N-body simulations using the halo relative positions and velocity moduli. We then evaluate this model on different N-body simulations, Abacus, CUBEP3M, PKDGrav3, and Ramses, using catalogs created with particle thresholds indicated next to the plots. As can be seen, the model is able to extrapolate well to different N-body codes and is able to predict with similar accuracy compared to that of the halo catalogs from  Gadget.
Paper: A Universal Equation to Predict Ωm from Halo and Galaxy Catalogs
Authors: Helen Shao, Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Romain Teyssier, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Ulrich P. Steinwandel, Elena Hernández-Martínez, Klaus Dolag, Christopher C. Lovell, Lehman H. Garrison, Eli Visbal, Mihir Kulkarni, Lars Hernquist, Tiago Castro, Mark Vogelsberger
Figure caption: This plot shows the predictions of the GNN trained on halo catalogs from Gadget N-body simulations being tested on galaxies from six different hydrodynamic simulations as listed for each row. To construct the galaxy catalogs, we follow the procedure discussed in Section 3.1 and use four different stellar mass thresholds, which are labeled for each column. For clarity, we plot the predictions for 50 randomly selected catalogs in each panel. As can be seen, the GNN is unable to accurately predict the values of Ωm as all the predictions exhibit a bias deviating from the true values. This is common across all simulations, which is expected given that there is a nontrivial connection between halo and galaxy distributions. However, as explained in Section 4.2.2, the analytic equations that approximate the GNN can be easily tuned to avoid this error.
Paper: A Universal Equation to Predict Ωm from Halo and Galaxy Catalogs
Authors: Helen Shao, Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Romain Teyssier, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Ulrich P. Steinwandel, Elena Hernández-Martínez, Klaus Dolag, Christopher C. Lovell, Lehman H. Garrison, Eli Visbal, Mihir Kulkarni, Lars Hernquist, Tiago Castro, Mark Vogelsberger
Figure caption: These bar plots follow the same format as Figure 3 except we plot the percent error on the y-axes, defined as the ratio of the absolute difference between the truth and predicted to the truth. The five bars of each color represent the different catalogs created for each simulation and are ordered based on the minimum mass thresholds used to construct that catalog, following the same order presented in Figure 3. We note that, while the percent errors obtained by the analytic expressions for certain simulations, such as CubeP3M, appear seemingly larger compared to the other codes, this disparity in one simulation does not reflect the accuracy of the equations because we have performed tests on hundreds of simulations from each code and obtained similar accuracies for them (see Figure 8.)
Paper: A Universal Equation to Predict Ωm from Halo and Galaxy Catalogs
Authors: Helen Shao, Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Romain Teyssier, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Ulrich P. Steinwandel, Elena Hernández-Martínez, Klaus Dolag, Christopher C. Lovell, Lehman H. Garrison, Eli Visbal, Mihir Kulkarni, Lars Hernquist, Tiago Castro, Mark Vogelsberger
Figure caption: This follows the format as Figure 7 but for the analytic equations discussed in Section D. The equations were found using symbolic regression and modified using physical principles to preserve the rotational and translational symmetries of the data. As can be seen, the equations maintain the accuracy and robustness exhibited by the GNN in Figure 7, indicating that the formulae offer good approximations to the model.
Paper: A Universal Equation to Predict Ωm from Halo and Galaxy Catalogs
Authors: Helen Shao, Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Romain Teyssier, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Ulrich P. Steinwandel, Elena Hernández-Martínez, Klaus Dolag, Christopher C. Lovell, Lehman H. Garrison, Eli Visbal, Mihir Kulkarni, Lars Hernquist, Tiago Castro, Mark Vogelsberger
Figure caption: Similar to Figures 7 and 8, we test the GNN and the analytic equations on halo catalogs generated from the SIMBA and IllustrisTNG hydrodynamic simulations. For clarity, we plot the predictions for 50 randomly selected catalogs in each panel. It can be seen that both models remain robust to the additional astrophysical effects present in these simulations, indicating that they are employing a possibly fundamental relation between the relevant halo properties and Ωm. Moreover, the analytic equations are able to capture this as their accuracies for all hydrodynamic simulations, which are similar to those of the GNN.