adsabs/AstroCLIMB huggingface dataset:
Paper: CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network
Authors: Ye-Peng Yan, Si-Yu Li, Guo-Jian Wang, Zirui Zhang, Jun-Qing Xia
Figure caption: Recovered CMB polarization map and its corresponding angular power spectrum for each case outlined in Table 2.
Paper: Equations of State, Thermodynamics, and Miscibility Curves for Jovian Planet and Giant Exoplanet Evolutionary Models
Authors: Roberto Tejada Arevalo, Yubo Su, Ankan Sur, Adam Burrows
Figure caption: 1M J models computed with APPLE (A. Sur et al. 2024). The top left and bottom left panels show the temperature profile evolution with and without helium immiscibility (solid and dashed lines) as a function of pressure and mass, respectively. Each color represents a different time in the evolution in Gyr, ranging from 0.02 to 5 Gyr. We use the L0911 miscibility curve (shown as dotted lines) shifted for this example by ΔT = + 1000 K. The adjustment to the miscibility curve to the local conditions of the profile is shown as a function of age, and the insert zooms in to the region experiencing it. The miscibility curve moves closer to the profiles as the local pressure increases with age. The top right panel presents the entropy profiles. The entropy profiles drop to low values of ∼1 k  b  baryon−1 in the “heavy-element” core, which is 10 M ⊕ in these models. This evolution example contains 16.5 M ⊕ in heavy elements. The inset is a zoom-in to distinguish the helium-rain/no-helium-rain differences. The bottom right panel shows the evolution of the total helium mass fraction profile. This particular model has been computed approximately to match the observed total helium mass fraction of Jupiter’s atmosphere (U. von Zahn et al. 1998) at its current age of ∼4.56 Gyr, shown in magenta.
Paper: Equations of State, Thermodynamics, and Miscibility Curves for Jovian Planet and Giant Exoplanet Evolutionary Models
Authors: Roberto Tejada Arevalo, Yubo Su, Ankan Sur, Adam Burrows
Figure caption: Deviations from thermodynamic consistency indicated by Equations (9) and (10) for the CMS19+HG23 EOS (top row), the CD21 EOS (middle row), and the SCvH95 EOS (bottom row) at constant Y = 0.272. Zero indicates thermodynamic consistency. The greatest errors in the CMS19+HG23 EOS appear at low entropy near ∼0.01 Mbar near the metallic hydrogen phase transition. The low-entropy region corresponds to a temperature range of ∼2000 K ≤ T ≤ 4600 K at these pressures and helium mass fraction abundance. The inconsistency regions in the CMS19+HG23 EOS are largely due to the lack of nonideal internal energy corrections, and the inconsistency regions of the SCvH95 EOS are due to the interpolation from the molecular and metallic phases to the plasma phase (see Section 3.3 of SCvH95). Such interpolated regions have been highlighted in red squares. The CD21 EOS provides internal energy values consistent with the nonideal entropy contributions, making it more thermodynamically consistent than the rest.
Paper: Equations of State, Thermodynamics, and Miscibility Curves for Jovian Planet and Giant Exoplanet Evolutionary Models
Authors: Roberto Tejada Arevalo, Yubo Su, Ankan Sur, Adam Burrows
Figure caption: Selection of derivative plots for the CMS19+HG23 EOS at a constant Y = 0.272. The derivatives at constant entropy are plotted in the top row, and those at constant pressure are plotted in the bottom row. The entropy values approximately encompass standard gas giant planet evolution, and the selected pressures and temperatures for χ  T /χ  ρ  and the specific heat at constant volume surround the phase transition regions, highlighted in Figure 6. Note that for a nonrelativistic ideal gas with no internal degrees of freedom, Γ1 = 5/3.
Paper: Equations of State, Thermodynamics, and Miscibility Curves for Jovian Planet and Giant Exoplanet Evolutionary Models
Authors: Roberto Tejada Arevalo, Yubo Su, Ankan Sur, Adam Burrows
Figure caption: Entropy-composition derivatives at constant Y = 0.272 of the CMS19+HG23 EOS (solid lines) and the SCvH95 EOS (dashed lines) as a function of density at constant (ρ, P) (left) and (P,T) (right). The derivative relevant to the Ledoux condition, (∂S/∂Y) ρ,P , changes sign from negative to positive for densities greater than ∼0.1 g cm−3. This transition occurs at temperatures >1000 K for both EOSs. The positive value of ∂S/∂Y∣ ρ,P  indicates that Ledoux-unstable regions of thermodynamic space can exhibit a negative entropy gradient, dS/dr (see text).
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: The star-forming sequence predicted by our forward model. The red line shows the median of our predictions of ﹩{\mathrm{log}}_{10}(\mathrm{SFR})﹩, with the red shaded regions showing the 68% and 95% intervals of the conditional distribution at a given mass (estimated in a rolling mass window). The gray shaded region shows the estimated mass completeness limit in each redshift bin (see footnote 14). All subsequent Figures follow the same plotting scheme unless noted. The blue dashed lines show the inferred SFS from Leja et al. (2022). Note that the Leja et al. (2022) predictions are for the logarithm of the mean SFR.
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: Upper-left panel: predicted stellar metallicity vs. mass relation. Upper-right panel: median predicted mass–metallicity relation in redshift bins of width Δz = 0.4. Lower-left and -right panels: the same, but for gas metallicity. Gray bands indicate where the COSMOS sample becomes incomplete (see footnote 14).
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: Color–color diagrams comparing the COSMOS data (blue) to predictions from our trained forward model (red), in the observed data-space (i.e., with photometric noise and calibration included in the model predictions to ensure like-for-like comparison with the data). Contours show 68% and 95% levels. We show a subset of key colors following Weaver et al. (2022).
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: Comparison of the marginal color distributions predicted by our model (red), vs. the COSMOS data (blue). Comparison is shown in the observed data-space, i.e., for r < 25 selected galaxies and with photometric noise and calibration included (to ensure like-for-like comparison with the data). We show a subset of key colors following Weaver et al. (2022).
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: Left panel: predicted diffuse dust attenuation as a function of SFR (left) and stellar mass (middle), and the index of the dust attenuation law as a function of dust attenuation (right).
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: Predicted galaxy stellar mass functions in redshift bins of widths Δz = 0.4. Our forward model predictions are shown in red (dotted in the incomplete region), with the LePhare mass distributions shown as blue histograms. To ensure a like-for-like comparison with LePhare, we show the mass function for galaxies with an r-band magnitude cut r < 25. Gray shaded regions indicate the region where the selected sample plotted begins to become incomplete (see footnote 14).
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: Fundamental metallicity relation (FMR) showing median predicted gas metallicity conditional on stellar mass in bins of ﹩{\mathrm{log}}_{10}(\mathrm{sSFR})﹩.
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: Comparison of the marginal distributions for the magnitudes predicted by our model (red), vs. the COSMOS data (blue). Comparison is shown in the observed data-space, i.e., for r < 25 selected galaxies and with photometric noise and calibration included (to ensure like-for-like comparison with the data). We show a subset of the 26 bands, spanning the full wavelength range.
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: Photometric redshift distribution predicted by our forward model (red curve), with estimated uncertainties due to cosmic variance (red envelope). The blue histogram shows redshift estimates for COSMOS using LePhare (from Weaver et al. 2022).
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: Comparison of pop-cosmos fits with (left column) and without (right column) emission-line calibration, for three population-level quantities most impacted by emission-line calibration: the fundamental metallicity relation (FMR; top row), gas ionization–sSFR relation (middle row), and the relationship between the AGN luminosity and optical depth (bottom row).
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: Predicted dependence of gas ionization on sSFR. The blue line shows the relation from Kaasinen et al. (2018).
Paper: ALMA-IMF. XIV. Free–Free Templates Derived from H41α and Ionized Gas Content in 15 Massive Protoclusters
Authors: Roberto Galván-Madrid, Daniel J. Díaz-González, Frédérique Motte, Adam Ginsburg, Nichol Cunningham, Karl M. Menten, Mélanie Armante, Mélisse Bonfand, Jonathan Braine, Timea Csengeri, Pierre Dell'Ova, Fabien Louvet, Thomas Nony, Rudy Rivera-Soto, Patricio Sanhueza, Amelia M. Stutz, Friedrich Wyrowski, Rodrigo H. Álvarez-Gutiérrez, Tapas Baug, Sylvain Bontemps, Leonardo Bronfman, Manuel Fernández-López, Antoine Gusdorf, Atanu Koley, Hong-Li Liu, Javiera Salinas, Allison P. M. Towner, Anthony P. Whitworth
Figure caption: Dynamical FWHM line width (ΔV dyn) of the H41α emission vs. the logarithm of their equivalent diameter (top panel) and of the ionizing photon rate (bottom panel). The plot shows the H II regions in the ALMA-IMF protoclusters listed in Table 2, except for H II region 2 in G351.77, whose total line width is consistent with being purely thermal. The speed of sound of ionized gas at the assumed T  e  = 7000 K is c  s  = 9.82 km s−1. Considering a 20% error in T e, the shaded area marks the expected range of H II region expansion velocities between ﹩{c}_{s}({T}_{e}-{\sigma }_{{T}_{e}})﹩ and ﹩2{c}_{s}({T}_{e}+{\sigma }_{{T}_{e}})﹩. Outliers are marked.
Paper: ALMA-IMF. XIV. Free–Free Templates Derived from H41α and Ionized Gas Content in 15 Massive Protoclusters
Authors: Roberto Galván-Madrid, Daniel J. Díaz-González, Frédérique Motte, Adam Ginsburg, Nichol Cunningham, Karl M. Menten, Mélanie Armante, Mélisse Bonfand, Jonathan Braine, Timea Csengeri, Pierre Dell'Ova, Fabien Louvet, Thomas Nony, Rudy Rivera-Soto, Patricio Sanhueza, Amelia M. Stutz, Friedrich Wyrowski, Rodrigo H. Álvarez-Gutiérrez, Tapas Baug, Sylvain Bontemps, Leonardo Bronfman, Manuel Fernández-López, Antoine Gusdorf, Atanu Koley, Hong-Li Liu, Javiera Salinas, Allison P. M. Towner, Anthony P. Whitworth
Figure caption: Fits to H41α (ν 0 = 92.034434 GHz) and He41α (ν 0 = 92.071938 GHz) recombination line emission in the 12 protoclusters with detection. This figure shows the H II regions not shown in Figure 5, from those listed in Table 2. The fitted parameters and derived abundances are listed in Table 3.
Paper: ALMA-IMF. XIV. Free–Free Templates Derived from H41α and Ionized Gas Content in 15 Massive Protoclusters
Authors: Roberto Galván-Madrid, Daniel J. Díaz-González, Frédérique Motte, Adam Ginsburg, Nichol Cunningham, Karl M. Menten, Mélanie Armante, Mélisse Bonfand, Jonathan Braine, Timea Csengeri, Pierre Dell'Ova, Fabien Louvet, Thomas Nony, Rudy Rivera-Soto, Patricio Sanhueza, Amelia M. Stutz, Friedrich Wyrowski, Rodrigo H. Álvarez-Gutiérrez, Tapas Baug, Sylvain Bontemps, Leonardo Bronfman, Manuel Fernández-López, Antoine Gusdorf, Atanu Koley, Hong-Li Liu, Javiera Salinas, Allison P. M. Towner, Anthony P. Whitworth
Figure caption:                         Comparison of the original 1.3 mm continuum image convolved to the angular resolution of the H41α cube (left panel), free–free estimation map at 1.3 mm (center), and the corresponding pure-dust estimation map (right). The images shown are prior to primary-beam correction. The color normalization is a power law with a 0.33 exponent. The displayed areas are the same as in Figure 2 and correspond to where free–free emission was detected.
Paper: CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network
Authors: Ye-Peng Yan, Si-Yu Li, Guo-Jian Wang, Zirui Zhang, Jun-Qing Xia
Figure caption: Recovered noisy CMB map and its corresponding angular power spectrum for varying the number of frequency bands in the input data. The recovered QQ power spectra presented in the analysis are obtained after the denoising procedure. Each case is listed in Table 3.
Paper: CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network
Authors: Ye-Peng Yan, Si-Yu Li, Guo-Jian Wang, Zirui Zhang, Jun-Qing Xia
Figure caption: Recovered CMB polarization Q maps and their angular power spectra for the partial-sky observation data with the performance of CMB-S4 experiment. Here, the network model is designed to remove the foregrounds and noise. The simulated maps utilized in this analysis comprise the beam-convolved CMB map without noise map. The recovered maps correspond to the pure CMB maps reconstructed using the CMBFSCNN algorithm.
Paper: CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network
Authors: Ye-Peng Yan, Si-Yu Li, Guo-Jian Wang, Zirui Zhang, Jun-Qing Xia
Figure caption: The recovery of EE and lensing B-model power spectra from test set, where the simulation of the synchrotron radiation and thermal dust radiation is based on the s2 and d4 model. Here, the CNN model is trained in the training set, which includes sky maps of the synchrotron radiation s2 model and the dust radiation d4 model.
Paper: CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network
Authors: Ye-Peng Yan, Si-Yu Li, Guo-Jian Wang, Zirui Zhang, Jun-Qing Xia
Figure caption: The recovered noisy CMB polarization maps for the Experiment 2. The simulation of the synchrotron radiation and thermal dust radiation in the test set is based on the s2 and d4 model.
Paper: CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network
Authors: Ye-Peng Yan, Si-Yu Li, Guo-Jian Wang, Zirui Zhang, Jun-Qing Xia
Figure caption: The recovery of EE and lensing B-model power spectra from the recovered pure CMB Q/U maps as depicted in Figures 9.
Paper: CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network
Authors: Ye-Peng Yan, Si-Yu Li, Guo-Jian Wang, Zirui Zhang, Jun-Qing Xia
Figure caption: The recovery of EE and lensing B-model power spectra from the recovered CMB Q/U maps as depicted in Figures 13.
Paper: CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network
Authors: Ye-Peng Yan, Si-Yu Li, Guo-Jian Wang, Zirui Zhang, Jun-Qing Xia
Figure caption: The recovered noisy CMB polarization maps for the Experiment 1. The simulation of the synchrotron radiation in the test set is based on the s2 model.
Paper: CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network
Authors: Ye-Peng Yan, Si-Yu Li, Guo-Jian Wang, Zirui Zhang, Jun-Qing Xia
Figure caption: The recovery of EE and lensing B-model power spectra from the recovered CMB Q/U maps as depicted in Figures 11.
Paper: CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network
Authors: Ye-Peng Yan, Si-Yu Li, Guo-Jian Wang, Zirui Zhang, Jun-Qing Xia
Figure caption: The recovery of EE and lensing B-model power spectra using NILC method.
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: Left: logical flow of our forward modeling (simulation) framework described in Section 3. Galaxy parameters are drawn from the population model (parameterized as a score-based diffusion model; Section 3.2); calibrated model photometry are calculated assuming the SPS model (Section 3.3) and calibration models (Equation (2)); photometric uncertainties are drawn from the uncertainty model (Section 3.4); photometric noise is drawn and added given the noise model (Equation (4)); and finally selection is applied (Section 2). Note that the three stochastic steps (the population model, uncertainty model, and noise draws) are parameterized as bijective transforms from a base density (unit normal for the population and uncertainty model, and Student’s-t for the noise-model draws; see Section 4). Each red and blue point represents a galaxy in the mock and real data, respectively. Right: schematic illustration of our SBI framework, optimizing the optimal transport distance between simulated (red) and real (blue) data, by gradient descent. The forward modeling stages expanded in detail on the left are represented by the purple arrows in the right-hand block. Gradients of the simulator can be obtained via automatic differentiation, by keeping the input draws from the base density fixed in the forward simulations (see Section 4). Note that by performing inference in this way, we infer the population-level quantities (i.e., population-, uncertainty- and calibration-model parameters) directly, bypassing the need to perform any fits at the individual galaxy level.
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: One-dimensional (1D) and two-dimensional (2D) marginals of the SPS parameters, predicted by our galaxy population model. SPS parameters shown comprise the following (see also Table 1): stellar mass (M/M ⊙) and metallicity (Z/Z ⊙); specific star formation rate, sSFR; mass-weighted age t age/t univ(z), optical depths of the diffuse (τ 2) and birth-cloud (τ 1) dust screens; the index of the dust attenuation law n (relative to Calzetti et al. 2000); the fraction of the bolometric luminosity from AGN f AGN; the optical depth of the AGN torus τ AGN; the gas metallicity and ionization parameter Z gas and U gas; and redshift z. The SFR and age are derived quantities; we assume a nonparametric (piecewise-constant) model for the SFH (see Section 3.3).
Paper:  pop-cosmos: A Comprehensive Picture of the Galaxy Population from COSMOS Data
Authors: Justin Alsing, Stephen Thorp, Sinan Deger, Hiranya V. Peiris, Boris Leistedt, Daniel Mortlock, Joel Leja
Figure caption: The subset of 26 COSMOS bands use in this work. Broadband transmission curves are rescaled to have peak transmission of 1.0, intermediate bands (“IA/B...”) are scaled to peak at 1/3, and narrow bands (“NB...”) to peak at 0.2.
Paper: ALMA-IMF. XIV. Free–Free Templates Derived from H41α and Ionized Gas Content in 15 Massive Protoclusters
Authors: Roberto Galván-Madrid, Daniel J. Díaz-González, Frédérique Motte, Adam Ginsburg, Nichol Cunningham, Karl M. Menten, Mélanie Armante, Mélisse Bonfand, Jonathan Braine, Timea Csengeri, Pierre Dell'Ova, Fabien Louvet, Thomas Nony, Rudy Rivera-Soto, Patricio Sanhueza, Amelia M. Stutz, Friedrich Wyrowski, Rodrigo H. Álvarez-Gutiérrez, Tapas Baug, Sylvain Bontemps, Leonardo Bronfman, Manuel Fernández-López, Antoine Gusdorf, Atanu Koley, Hong-Li Liu, Javiera Salinas, Allison P. M. Towner, Anthony P. Whitworth
Figure caption: Physical properties of the H II regions in the ALMA-IMF protoclusters listed in Table 2, as a function of their equivalent diameter D. Top panel: electron density n  e , middle panel: emission measure EM, bottom panel: ionizing photon rate Q 0. The black lines and shaded areas show the respective best fits and 1σ confidence intervals. The better constrained linear relation is ﹩\mathrm{log}[{Q}_{0}/{{\rm{s}}}^{-1}]=(2.49\pm 0.18)\times \mathrm{log}[D/\mathrm{pc}]+(49.89\pm 0.23)﹩. The slope and intercept for ﹩\mathrm{log}({n}_{e})﹩ vs. ﹩\mathrm{log}(D)﹩ are −0.23 ± 0.08 and 3.70 ± 0.10. The slope and intercept for ﹩\mathrm{log}(\mathrm{EM})﹩ vs. ﹩\mathrm{log}(D)﹩ are 0.52 ± 0.17 and 7.39 ± 0.22 (1σ errors). H II regions that are outliers in at least one of their properties are labeled.
Paper: ALMA-IMF. XIV. Free–Free Templates Derived from H41α and Ionized Gas Content in 15 Massive Protoclusters
Authors: Roberto Galván-Madrid, Daniel J. Díaz-González, Frédérique Motte, Adam Ginsburg, Nichol Cunningham, Karl M. Menten, Mélanie Armante, Mélisse Bonfand, Jonathan Braine, Timea Csengeri, Pierre Dell'Ova, Fabien Louvet, Thomas Nony, Rudy Rivera-Soto, Patricio Sanhueza, Amelia M. Stutz, Friedrich Wyrowski, Rodrigo H. Álvarez-Gutiérrez, Tapas Baug, Sylvain Bontemps, Leonardo Bronfman, Manuel Fernández-López, Antoine Gusdorf, Atanu Koley, Hong-Li Liu, Javiera Salinas, Allison P. M. Towner, Anthony P. Whitworth
Figure caption:                         Fits to H41α (ν                   0 = 92.034434 GHz) and He41α (ν                   0 = 92.071938 GHz) recombination line emission in the 12 protoclusters with detection. This figure shows the brightest region for each protocluster. The rest of the fits are shown in Figure 11 in the Appendix. The fitted parameters and derived abundances are listed in Table 3.
Paper: ALMA-IMF. XIV. Free–Free Templates Derived from H41α and Ionized Gas Content in 15 Massive Protoclusters
Authors: Roberto Galván-Madrid, Daniel J. Díaz-González, Frédérique Motte, Adam Ginsburg, Nichol Cunningham, Karl M. Menten, Mélanie Armante, Mélisse Bonfand, Jonathan Braine, Timea Csengeri, Pierre Dell'Ova, Fabien Louvet, Thomas Nony, Rudy Rivera-Soto, Patricio Sanhueza, Amelia M. Stutz, Friedrich Wyrowski, Rodrigo H. Álvarez-Gutiérrez, Tapas Baug, Sylvain Bontemps, Leonardo Bronfman, Manuel Fernández-López, Antoine Gusdorf, Atanu Koley, Hong-Li Liu, Javiera Salinas, Allison P. M. Towner, Anthony P. Whitworth
Figure caption: Free–free estimation maps in Band 3 for protoclusters G008.67 (top left), G010.62 (top right), G012.80 (second left), G327.29 (second right), G333.60 (third left), G337.92 (third right), G351.77 (fourth left), G353.41 (fourth right), W43-MM2 (fifth left), W43-MM3 (fifth right), W51-E (bottom left), and W51-IRS2 (bottom right). The compact emission at the center of G327.29 is dominated by contamination from molecular lines (see Section 3.3).