adsabs/AstroCLIMB huggingface dataset:
Paper: Orientations of Dark Matter Halos in FIRE-2 Milky Way–mass Galaxies
Authors: Jay Baptista, Robyn Sanderson, Dan Huber, Andrew Wetzel, Omid Sameie, Michael Boylan-Kolchin, Jeremy Bailin, Philip F. Hopkins, Claude-André Faucher-Giguere, Sukanya Chakrabarti, Drona Vargya, Nondh Panithanpaisal, Arpit Arora, Emily Cunningham
Figure caption: Major-axis alignment to LMC as a function of the distance to the host. Different selection scale lengths for the DM halo are coded by color. The virial radius tends to be more closely aligned with the LMC than the inner halo, showing differential alignment to the LMC over time.
Paper: Orientations of Dark Matter Halos in FIRE-2 Milky Way–mass Galaxies
Authors: Jay Baptista, Robyn Sanderson, Dan Huber, Andrew Wetzel, Omid Sameie, Michael Boylan-Kolchin, Jeremy Bailin, Philip F. Hopkins, Claude-André Faucher-Giguere, Sukanya Chakrabarti, Drona Vargya, Nondh Panithanpaisal, Arpit Arora, Emily Cunningham
Figure caption: DM density plot of m12i with host minor axes overplotted at z = 0. The red arrow indicates the direction of the stellar disk/galaxy vector. The blue arrows indicate the minor axes of the halo, with the lengths scaled to the radii at which the principal axes were calculated. Each column shows a different projection of the halo. Brighter regions indicate higher densities of DM. Figure 13 replicates this density map for all galaxies.
Paper: Orientations of Dark Matter Halos in FIRE-2 Milky Way–mass Galaxies
Authors: Jay Baptista, Robyn Sanderson, Dan Huber, Andrew Wetzel, Omid Sameie, Michael Boylan-Kolchin, Jeremy Bailin, Philip F. Hopkins, Claude-André Faucher-Giguere, Sukanya Chakrabarti, Drona Vargya, Nondh Panithanpaisal, Arpit Arora, Emily Cunningham
Figure caption: The selection radius defines the enclosed spherical region where DM particles are chosen. The moment-of-inertia tensor is determined from the enclosed DM particles. Symmetry axes of the DM halo (labeled a, b, and c) are defined by the eigenvectors of the reduced moment-of-inertia tensor. The length of each symmetry axis is the square root of the corresponding eigenvalues. These eigenvectors and eigenvalues define the converged halo shape.
Paper: Orientations of Dark Matter Halos in FIRE-2 Milky Way–mass Galaxies
Authors: Jay Baptista, Robyn Sanderson, Dan Huber, Andrew Wetzel, Omid Sameie, Michael Boylan-Kolchin, Jeremy Bailin, Philip F. Hopkins, Claude-André Faucher-Giguere, Sukanya Chakrabarti, Drona Vargya, Nondh Panithanpaisal, Arpit Arora, Emily Cunningham
Figure caption: The same as Figure 4, but the orientations at each snapshot are calculated with respect to the stellar disk at the present day.
Paper: Orientations of Dark Matter Halos in FIRE-2 Milky Way–mass Galaxies
Authors: Jay Baptista, Robyn Sanderson, Dan Huber, Andrew Wetzel, Omid Sameie, Michael Boylan-Kolchin, Jeremy Bailin, Philip F. Hopkins, Claude-André Faucher-Giguere, Sukanya Chakrabarti, Drona Vargya, Nondh Panithanpaisal, Arpit Arora, Emily Cunningham
Figure caption: Angle between the major axis and the LMC analog position. The black line indicates the time when the satellite reaches pericenter and the red line indicates when t bursty occurs.
Paper: FRAMEx. IV. Mechanical Feedback from the Active Galactic Nucleus in NGC 3079
Authors: Luis C. Fernandez, Nathan J. Secrest, Megan C. Johnson, Travis C. Fischer
Figure caption: 5 GHz archival data points are hollow markers while our 6 GHz data are filled circles. Top: separation of the core components A and B from NGC 3079. Black dashed line indicates a broken linear fit with three slopes corresponding to the separate brightening. Bottom: integrated flux over time.
Paper: FRAMEx. IV. Mechanical Feedback from the Active Galactic Nucleus in NGC 3079
Authors: Luis C. Fernandez, Nathan J. Secrest, Megan C. Johnson, Travis C. Fischer
Figure caption: Posterior distribution of the projected offset between the nuclear components A and B in NGC 3079. Top: for our analysis of the data from Helmboldt et al. (2007). Middle: for our observation on 2020 January 27. Bottom: for our observation on 2020 May 19.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: A 2D histogram illustrating the correlation between the true ISRF and the dust temperature in the synthetic test data. The black dashed line represents the relationship predicted by Equation (1) (Bernard et al. 2010), where the dust emissivity index β is assumed to be 2, and the incident SED is that of the unattenuated local ISRF.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: Similar to Figure 5 but applied to synthetic dust images generated using different dust models. The black dotted line represents the one-to-one line, which represents perfect prediction. The blue dotted line represents the 10-to-one line, indicating underestimation by a factor of 10. The brown dotted line represents the one-to-10 line, indicating overestimation by a factor of 10.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: Similar to Figure 5 but specifically for simulations with an ISRF of G 0 = 17 (left panel) and 170 (right panel). The black dotted line represents the one-to-one line, which represents perfect prediction. The blue dotted line represents the 10-to-one line, indicating underestimation by a factor of 10.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: Three-band dust emission at 4.5 (blue), 24 (green), and 250 (red) μm in MonR2 (left panel) alongside the radiation field predicted by the diffusion model (right panel). The contour lines overlaid on the dust emission maps represent the intensity of the radiation field predicted by the diffusion model.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: Similar to Figure 5 but applied to synthetic dust images generated from new simulations with updated heating and cooling treatments, as well as two crossing times of turbulent driving. The black dotted line signifies the one-to-one line, indicating perfect predictions. The blue dotted line signifies the 10-to-one line, suggesting underestimation by a factor of 10. The brown dotted line represents the one-to-10 line, indicating overestimation by a factor of 10.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: A 2D histogram illustrating the correlation between the predictions of the diffusion model and the ground-truth values of the radiation fields.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: Predicted radiation field generated by the diffusion model, along with the corresponding three-band dust emission and ground-truth radiation field. The first three columns correspond to new simulations with an ISRF of G 0 = 17, while the last three columns represent simulations with an ISRF of G 0 = 170.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: Predicted radiation field produced by the diffusion model alongside the associated three-band dust emission and the actual radiation field for new simulations. These simulations incorporate updated heating and cooling treatments and experience two crossing times of turbulent driving, leading to well-developed turbulence and increased gas density before self-gravity is turned on.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: Predicted radiation field generated by the diffusion model (third and sixth columns), along with the corresponding three-band dust emission at 4.5 (blue), 24 (green), and 250 (red) μm (first and fourth columns) and the ground-truth radiation field (second and fifth columns).
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: The χ 2 values calculated between the synthetic and observed SEDs across various gas number density cutoffs during dust model selection and different evolutionary stages in various simulations. The upper panel displays the raw χ 2 values calculated between the synthetic and observed SEDs. In the lower panel, we compute the χ 2 values while incorporating a free scaling parameter during the calculation.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: The SEDs obtained by employing different cutoffs on gas number densities when selecting dust models and different dust models in the radiative transfer for different simulation snapshots. The blue dots represent the observed SEDs in MonR2, which have been rescaled by specific factors, as indicated in the lower right corner.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: The 2D histograms illustrating the correlation between the predicted ISRF and the dust emission intensity (4.5 μm on the left, 24 μm in the middle, and 250 μm on the right) in MonR2.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: Synthetic images at three different wavelengths (4.5, 24, and 250 μm) generated by employing different cutoffs (ρ cut) on gas number densities when selecting dust models and different dust models in the radiative transfer for different simulation snapshots.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: The 2D histograms illustrating the correlation between the predicted ISRF and the dust temperature (left panel) and the column density (right panel) in MonR2. The black dashed line in the left panel represents the predicted relation between the ISRF and dust temperature given in Equation (1).
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: Projected stellar radiation density and corresponding SEDs of different categories for a snapshot of simulations at 4.5 Myr without jets (third row of Figure 1).
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: Column density (left) and dust temperature (right) maps derived by Pokhrel et al. (2016). The contour lines represent the intensity of the radiation field predicted by the diffusion model.
Paper: Predicting the Radiation Field of Molecular Clouds Using Denoising Diffusion Probabilistic Models
Authors: Duo Xu, Stella S. R. Offner, Robert Gutermuth, Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins
Figure caption: The 2D histograms illustrating the correlation between the ISRF and the dust emission intensity (4.5 μm on the left, 24 μm in the middle, and 250 μm on the right) in the synthetic test data.
Paper: Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection
Authors: Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Figure caption: ROC curves obtained with the LOF method, for the four different experiments considered in Section 4, and for different levels of instrumental noise, as described in the legends.
Paper: Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection
Authors: Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Figure caption: Scatterplot in the plane of the log-mixing ratio ﹩\mathrm{log}({X}_{i})﹩ vs. the negative of the LOF score, of the normal instances (black plus symbols) and the anomalous instances (circles), color coded by the log of the relative concentration r  i . The left panels shows ideal results with no instrumental noise, while the middle and right panels include instrumental noise of 10 ppm and 30 ppm, respectively.
Paper: Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection
Authors: Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Figure caption: The same as Figure 10, but for the 1CSVM anomaly detection method discussed in Section 5.
Paper: Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection
Authors: Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Figure caption: Distributions of the negative of the LOF score for the normal and anomalous populations in Experiment 1, for different levels of instrumental noise.
Paper: Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection
Authors: Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Figure caption: Distributions of the negative of the LOF score for the normal and anomalous populations in Experiment 4, for different levels of instrumental noise.
Paper: Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection
Authors: Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Figure caption: The same as Figure 11, but for the 1CSVM anomaly detection method discussed in Section 5.
Paper: Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection
Authors: Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Figure caption: The same as Figure 9, but for the 1CSVM anomaly detection method discussed in Section 5.
Paper: Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection
Authors: Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Figure caption: Error rates as a function of the number of neighbors included in the LOF calculation. The result is for CH4 (Experiment 1) and a noise level of 30 ppm with a cut of −2.5 on the LOF score.
Paper: Spectral Templates Optimal for Selecting Galaxies at z > 8 with the JWST
Authors: Rebecca L. Larson, Taylor A. Hutchison, Micaela Bagley, Steven L. Finkelstein, L. Y. Aaron Yung, Rachel S. Somerville, Michaela Hirschmann, Gabriel Brammer, Benne W. Holwerda, Casey Papovich, Alexa M. Morales, Stephen M. Wilkins
Figure caption: Rest-frame ultraviolet region of the EAZY template set, redshifted to z = 10, which was used in our analysis to measure photometric redshifts. The red and orange lines show the latest standard EAZY template set (tweak_fsps_QSF_v12_v3), while the purple lines show the BPASS models we create here as described in Section 4.1. The blue lines show the BPASS + CLOUDY templates that have high nebular-line equivalent widths (EWs), as described in Section 4.2. As can be seen, these newly created templates are bluer than the standard set, better matching the expected colors of z > 8 galaxies. All templates are normalized to their flux density at 2.301 μm. We also include in this plot the template from Erb et al. (2010) used by Finkelstein et al. (2022a), which includes a high-EWLyα  emission line. This template was not used in our analysis as the new BPASS and CLOUDY templates satisfied the same color range, and our fits were not improved with its inclusion.
Paper: Spectral Templates Optimal for Selecting Galaxies at z > 8 with the JWST
Authors: Rebecca L. Larson, Taylor A. Hutchison, Micaela Bagley, Steven L. Finkelstein, L. Y. Aaron Yung, Rachel S. Somerville, Michaela Hirschmann, Gabriel Brammer, Benne W. Holwerda, Casey Papovich, Alexa M. Morales, Stephen M. Wilkins
Figure caption: Black histogram showing the distribution of F200W − F277W colors for the z > 8 galaxies from the SAM catalog of Yung et al. (2022). The solid vertical lines show the rest-UV color of the SED templates we used for photometric-redshift fitting, using the color calculated by integrating the templates through the JWST/NIRCAM F200W and F277W filters after placing them at z = 10. The bluest EAZY FSPS template only reaches a rest-UV color of −0.1, while the majority of the comparison high-redshift sample have bluer (more negative) colors. Finkelstein et al. (2022a) added a bluer template from Erb et al. (2010; green), but it is still redder than the majority of our simulated high-redshift galaxies. We created BPASS (purple) and BPASS + CLOUDY emission line (blue) templates and note that the BPASS + CLOUDY templates are redder in color than the BPASS only models due to their nebular continuum emission. This full template set can now reproduce the colors of all high-redshift galaxies in our simulated sample.
Paper: Spectral Templates Optimal for Selecting Galaxies at z > 8 with the JWST
Authors: Rebecca L. Larson, Taylor A. Hutchison, Micaela Bagley, Steven L. Finkelstein, L. Y. Aaron Yung, Rachel S. Somerville, Michaela Hirschmann, Gabriel Brammer, Benne W. Holwerda, Casey Papovich, Alexa M. Morales, Stephen M. Wilkins
Figure caption: Left: comparison of our recovered redshifts from EAZY vs. the input redshifts from the SAM. We ran our sample of 913,288 simulated galaxies from z = 0–10 through EAZY using only the included EAZY FSPS templates (red) and then again after adding our new SED template set (blue) as described in Section 4.3 and Figure 3. For this test, we set the errors in each filter equivalent to a 5σ depth of m = 30. Center: Δz (input redshift − recovered redshift) vs. input redshifts of our SAM galaxies from both EAZY runs. Right: median (dashed line) and median absolute deviation (solid line) of the Δz for both EAZY runs, where the inclusion of the new templates provides a significant improvement as both values are lower across the full redshift range. We calculate an outlier fraction, or catastrophic failures, as those where Δz > 0.2 × z (dotted line), which highlights the set of z > 4 galaxies that fit at lower redshifts when using only the original templates, but whose redshifts are accurately recovered after the inclusion of our new set of SED templates.
Paper: Spectral Templates Optimal for Selecting Galaxies at z > 8 with the JWST
Authors: Rebecca L. Larson, Taylor A. Hutchison, Micaela Bagley, Steven L. Finkelstein, L. Y. Aaron Yung, Rachel S. Somerville, Michaela Hirschmann, Gabriel Brammer, Benne W. Holwerda, Casey Papovich, Alexa M. Morales, Stephen M. Wilkins
Figure caption: Redshift distribution of the full semi-analytic model (SAM) catalog of 1,472,791 galaxies shown in green with a zoom-in on the 6578 z = 8–10 galaxies from the Cosmic Evolution Early Release Science (CEERS) Survey Simulated Data Product V3 (Yung et al. 2022). As real observations with the JWST are limited by our ability to detect objects in the images, we impose an initial S/N > 3 cut in F200W, where the noise is set to the 1σ expected CEERS depth (m 3σ  = 29.5;  Finkelstein et al. 2017). This paper utilizes the 913,288 galaxies (3084 at z > 8) that meet this initial criterion (purple).
Paper: Identifying Physical Structures in Our Galaxy with Gaussian Mixture Models: An Unsupervised Machine Learning Technique
Authors: M. Tiwari, R. Kievit, S. Kabanovic, L. Bonne, F. Falasca, C. Guevara, R. Higgins, M. Justen, R. Karim, Ü. Kavak, C. Pabst, M. W. Pound, N. Schneider, R. Simon, J. Stutzki, M. Wolfire, A. G. G. M. Tielens
Figure caption: Average spectra from all clusters shown in the domain maps of NGC 1977 (top), RCW 120 (middle), and RCW 49 (bottom). The color of every cluster shown here is the same as shown in Figures 3–5. The dotted lines indicate the mean of each GMM cluster (μ  k  in Equation (1)). Channels without dots are those discarded by the rms threshold. Large differences between the GMM means and the averaged spectra can be an indication of large variance within a single cluster.
Paper: Identifying Physical Structures in Our Galaxy with Gaussian Mixture Models: An Unsupervised Machine Learning Technique
Authors: M. Tiwari, R. Kievit, S. Kabanovic, L. Bonne, F. Falasca, C. Guevara, R. Higgins, M. Justen, R. Karim, Ü. Kavak, C. Pabst, M. W. Pound, N. Schneider, R. Simon, J. Stutzki, M. Wolfire, A. G. G. M. Tielens
Figure caption: GMM results of RCW 49: weights histogram (top) and domain map (bottom) displaying the identified clusters.
Paper: Identifying Physical Structures in Our Galaxy with Gaussian Mixture Models: An Unsupervised Machine Learning Technique
Authors: M. Tiwari, R. Kievit, S. Kabanovic, L. Bonne, F. Falasca, C. Guevara, R. Higgins, M. Justen, R. Karim, Ü. Kavak, C. Pabst, M. W. Pound, N. Schneider, R. Simon, J. Stutzki, M. Wolfire, A. G. G. M. Tielens
Figure caption: GMM results of NGC 1977: weights histogram (top) and domain map (bottom) displaying the identified clusters.
Paper: Identifying Physical Structures in Our Galaxy with Gaussian Mixture Models: An Unsupervised Machine Learning Technique
Authors: M. Tiwari, R. Kievit, S. Kabanovic, L. Bonne, F. Falasca, C. Guevara, R. Higgins, M. Justen, R. Karim, Ü. Kavak, C. Pabst, M. W. Pound, N. Schneider, R. Simon, J. Stutzki, M. Wolfire, A. G. G. M. Tielens
Figure caption: GMM results of RCW 120: weights histogram (top) and domain map (bottom) displaying the identified clusters.
Paper: Identifying Physical Structures in Our Galaxy with Gaussian Mixture Models: An Unsupervised Machine Learning Technique
Authors: M. Tiwari, R. Kievit, S. Kabanovic, L. Bonne, F. Falasca, C. Guevara, R. Higgins, M. Justen, R. Karim, Ü. Kavak, C. Pabst, M. W. Pound, N. Schneider, R. Simon, J. Stutzki, M. Wolfire, A. G. G. M. Tielens
Figure caption: Number of clusters identified by GMM (n output) vs. number of clusters given as input (n input) for NGC 1977, RCW 120, and RCW 49. Models were run 10, 50, and 10 times for each n input in NGC 1977, RCW 120, and RCW 49, respectively. Their mean n output is shown with blue, while the standard deviation is displayed in gray.
Paper: Identifying Physical Structures in Our Galaxy with Gaussian Mixture Models: An Unsupervised Machine Learning Technique
Authors: M. Tiwari, R. Kievit, S. Kabanovic, L. Bonne, F. Falasca, C. Guevara, R. Higgins, M. Justen, R. Karim, Ü. Kavak, C. Pabst, M. W. Pound, N. Schneider, R. Simon, J. Stutzki, M. Wolfire, A. G. G. M. Tielens
Figure caption: Velocity-integrated (within the ranges given in Table 1) intensity maps of [C II] toward NGC 1977, RCW 120, and RCW 49. The pink colored stars mark the main ionizing sources in these regions. These plots are made using regrided data cubes for the three sources based on the S/N requirements described in Section 5.