adsabs/AstroCLIMB huggingface dataset:
Paper: The Dependence of the Location and Pressure Variations of the Martian Magnetic Pileup Boundary on the Interplanetary Magnetic Field: An MHD Simulation Study
Authors: M. Wang, K. Xu, J. Y. Lu, M. X. Yin, H. Y. Sui, Z. J. Guan, J. Q. Zhang
Figure caption: IMF cone angle effect on the MPB subsolar standoff distance (r 0 in panel (a)), the solar wind pressure coefficient (k in panel (b)), and the magnetic compression factor (f in panel (c)) under the strongest Martian crustal magnetic field located on the dayside (longitude = 180° W, latitude = 0° N). The black triangles, squares, and circles stand for the IMF cone angles varied by IMF B  X  and westward B  Y  while B  Z  = 0 nT and B  t  = 5.0 nT. The red triangles, squares, and circles represent the IMF cone angles varied by IMF B  X  and southward B  Z  while B  Y  = 0 nT and B  t  = 5.0 nT.
Paper: The Dependence of the Location and Pressure Variations of the Martian Magnetic Pileup Boundary on the Interplanetary Magnetic Field: An MHD Simulation Study
Authors: M. Wang, K. Xu, J. Y. Lu, M. X. Yin, H. Y. Sui, Z. J. Guan, J. Q. Zhang
Figure caption: IMF direction effect on the pressure profiles along the Sun–Mars line on the dayside under the strongest Martian crustal magnetic field located on the dayside (longitude = 180° W, latitude = 0° N). The formats of the panels are the same as panel (a) of Figure 1. Panels (a), (b), and (c) stand for the upstream IMF conditions of B  X  = 5 nT (B  Y  = B  Z  = 0 nT), B  Y  = 5 nT (B  X  = B  Z  = 0 nT), and B  Z  = 5 nT (B  X  = B  Y  = 0 nT), respectively.
Paper: The Dependence of the Location and Pressure Variations of the Martian Magnetic Pileup Boundary on the Interplanetary Magnetic Field: An MHD Simulation Study
Authors: M. Wang, K. Xu, J. Y. Lu, M. X. Yin, H. Y. Sui, Z. J. Guan, J. Q. Zhang
Figure caption: IMF clock angle effect on the MPB subsolar standoff distance (r 0 in panel (a)), the solar wind pressure coefficient (k in panel (b)), and the magnetic compression factor (f in panel (c)) under the strongest Martian crustal magnetic field located on the dayside (longitude = 180° W, latitude = 0° N). The IMF clock angles are varied by IMF B  Y  and B  Z  while B  X  = 0 nT and B  t  = 5.0 nT.
Paper: The Dependence of the Location and Pressure Variations of the Martian Magnetic Pileup Boundary on the Interplanetary Magnetic Field: An MHD Simulation Study
Authors: M. Wang, K. Xu, J. Y. Lu, M. X. Yin, H. Y. Sui, Z. J. Guan, J. Q. Zhang
Figure caption: The influence of the subsolar longitude on the MPB subsolar standoff distance (r 0 in panel (a)), the solar wind pressure coefficient (k in panel (b)), and the magnetic compression factor (f in panel (c)) under the Parker IMF (B  X  = − 1.6776 nT, B  Y  = 2.4871 nT, and B  Z  = 0 nT).
Paper: Shadow Geometry of Kerr Naked Singularities
Authors: Bao Nguyen, Pierre Christian, Chi-kwan Chan
Figure caption: Deflection angle plots on the image plane for different spins a = 1.01, 1.18, 1.38, 1.61, 1.88, and 2.2 (vertical axis) and observational inclination angles i = 0°, 15°, 30°, 45°, 60°, and 75° (horizontal axis) for the numerical ray-tracing calculations described in Section 3.1. Redder colors represent higher deflection angles for light rays that return to the vicinity of the image plane. Bluer colors represent lower deflection angles for light rays that travel to the other side of the KNS. The spins are approximately chosen according to the relation a  n  = 0.01 + 10 n/14.7, where n = 0, 1, ..., 5, so that a is evenly spaced in the logarithmic scale. The plots show that for lower spins 1 < a ≲ 1.18, the shadow is closed for lower inclination angles. For a ≈ 1.18, the shadow opens its gap, and for greater spins a ≳ 1.18, the shadow vanishes for lower inclination angles. This topological change in the KNS shadow is consistent with the analytical results in Figure 2.
Paper: MFPIM: A Deep Learning Model Based on Multimodal Fusion Technology for Pulsar Identification
Authors: Yi Liu, Jing Jin, Hongyang Zhao, Xujie He, Yanan Guo
Figure caption: MFPIM. The inputs to MFPIM are profile, DM curves, sub-bands plot, and sub-integrations plot from top to bottom, with image sizes of 128, 128, 64 × 64, and 64 × 64, respectively. Each input takes a separate model and obtains a binary classification result and a loss. MFPIM then fuses a fully connected layer of each model to form a 32 × 1 feature vector and finally obtains the discrimination result.
Paper: MFPIM: A Deep Learning Model Based on Multimodal Fusion Technology for Pulsar Identification
Authors: Yi Liu, Jing Jin, Hongyang Zhao, Xujie He, Yanan Guo
Figure caption: Diagnostic plot of a pulsar candidate from the FAST data set. The sub-band plot (A) represents the superimposed pulse signals at different observed frequencies. The sub-integrations plot (B) represents the variation of the superimposed pulse signal at a specific observed frequency. The profile (C) represents the average pulse at all observed frequencies. The DM curve (D) represents the integral of the electron density between Earth and the pulsar.
Paper: MFPIM: A Deep Learning Model Based on Multimodal Fusion Technology for Pulsar Identification
Authors: Yi Liu, Jing Jin, Hongyang Zhao, Xujie He, Yanan Guo
Figure caption: F1 score and loss curves of MFPIM model on the training and validation sets of the FAST data set. The blue solid and dashed lines represent the model’s F1 score and loss values, respectively, on the training set, while the green solid and dashed lines represent the F1 score and loss values, respectively, of the validation set.
Paper: MFPIM: A Deep Learning Model Based on Multimodal Fusion Technology for Pulsar Identification
Authors: Yi Liu, Jing Jin, Hongyang Zhao, Xujie He, Yanan Guo
Figure caption: Training F1 score of Single-Modal Models: the DM Curve, Profile, Sub-Bands, and Sub-Integrations achieved training F1-scores of approximately 95%, 85%, 94%, and 90%, respectively.
Paper: MFPIM: A Deep Learning Model Based on Multimodal Fusion Technology for Pulsar Identification
Authors: Yi Liu, Jing Jin, Hongyang Zhao, Xujie He, Yanan Guo
Figure caption: Comparison of ROC curves and AUC between MFPIM and MFPIM-ResNet models on the FAST Data set.
Paper: MFPIM: A Deep Learning Model Based on Multimodal Fusion Technology for Pulsar Identification
Authors: Yi Liu, Jing Jin, Hongyang Zhao, Xujie He, Yanan Guo
Figure caption: Validation set comparison of Loss M  and Loss5: F1 score and loss curves. The blue solid and dashed lines represent the F1 score and loss values, respectively, of the model trained with Loss M , while the green solid and dashed lines represent the F1 score and loss values, respectively, of the model trained with only Loss5.
Paper: MFPIM: A Deep Learning Model Based on Multimodal Fusion Technology for Pulsar Identification
Authors: Yi Liu, Jing Jin, Hongyang Zhao, Xujie He, Yanan Guo
Figure caption: Channel attention module (CAM).
Paper: Submillimeter Observations of Magnetic Fields in Massive Star-forming Region W75N
Authors: Lingzhen Zeng, Qizhou Zhang, Felipe O. Alves, Tao-Chung Ching, Josep M. Girart, Junhao Liu
Figure caption: (a) 345 GHz continuum emission image of W75N. The synthesized beam marked in the lower left corner of the image is 3.″05 × 2.″83 at position angle (PA) = 77.°8. The intensity is shown in gray scales in units of Jy beam−1 with contour levels at [−5σ, 5σ, 10σ, 20σ, 40σ, 80σ, 160σ, and 320σ], where σ = 26.1 mJy beam−1. The previous detected compact sources are denoted by filled triangles. (b) Dense cores identified by CARTA are labeled as MM1, MM2, MM3, MM4, and MM[N]. The ellipses show the FWHMs from 2D Gaussian fits, while the blue color mask of the entire region is labeled as “all.”
Paper: Submillimeter Observations of Magnetic Fields in Massive Star-forming Region W75N
Authors: Lingzhen Zeng, Qizhou Zhang, Felipe O. Alves, Tao-Chung Ching, Josep M. Girart, Junhao Liu
Figure caption: W75N magnetic field orientation map. Polarized emission intensities are shown in gray scales in the units of Jy beam−1. Magnetic field orientations inferred from the linearly polarized emission with PI/σ PI > 3 are shown as red line segments. Intensity contour levels are the same as those in Figure 1.
Paper: Beyond BPT: A New Multidimensional Diagnostic Diagram for Classifying Power Sources Tested Using the SAMI Galaxy Survey
Authors: Victor D. Johnston, Anne M. Medling, Brent Groves, Lisa J. Kewley, Luca Cortese, Scott Croom, Ángel R. López-Sánchez, Henry Zovaro, Joss Bland-Hawthorn, Julia Bryant, Jon Lawrence, Matt Owers, Samuel Richards, Jesse van de Sande
Figure caption: Examples of ionization maps for GAMA 106717, 376478, and 9388000338 (left to right). The relative contribution of each driving power source is shown as a fraction of the total amount of ionization. For galaxies with either AGN- or shock-driven ionization, the corresponding layers are mapped according to the mixing sequence identified by our method. As our method cannot separate ionization sources within a single galaxy, one layer will have no contribution to the total ionization. For galaxies where non-star-forming ionization is detected but we cannot definitively determine between shocks, AGNs, and beam-smearing, a map of negative values is placed in both layers to denote this uncertainty.
Paper: Submillimeter Observations of Magnetic Fields in Massive Star-forming Region W75N
Authors: Lingzhen Zeng, Qizhou Zhang, Felipe O. Alves, Tao-Chung Ching, Josep M. Girart, Junhao Liu
Figure caption: Histogram of position angles of the magnetic field segments from Figure 2. The bin size is 10°, and position angles are measured east of north.
Paper: Submillimeter Observations of Magnetic Fields in Massive Star-forming Region W75N
Authors: Lingzhen Zeng, Qizhou Zhang, Felipe O. Alves, Tao-Chung Ching, Josep M. Girart, Junhao Liu
Figure caption: Polarization percentage vs. Stokes I plots. Polarization data points from Figure 2 are shown in log–log scale with error bars. The orange line shows the fitting results using the power-law model.
Paper: Simulation of Head-on Collisions between Filamentary Molecular Clouds Threaded by a Lateral Magnetic Field and Subsequent Evolution
Authors: Raiga Kashiwagi, Kazunari Iwasaki, Kohji Tomisaka
Figure caption: Comparison of the density profiles on the x- and y-axes for the radial collapse models with different initial line masses. A comparison of the three pairs of models, (β1M, β1L), (β01M, β01L), and (β001M, β001L), is performed. The vertical axis represents the relative density to the maximum density at the final epoch, ﹩\rho /{\rho }_{\max ,\mathrm{end}}﹩. The horizontal axis represents the distance normalized by the scale-height at the center of the filament, ﹩\tilde{L}={c}_{s}/{(4\pi G{\rho }_{\max ,\mathrm{end}})}^{1/2}﹩. Color represents the plasma beta value: red (β 0 = 1), green (β 0 = 0.1), and blue (β 0 = 0.01). The solid and dashed lines represent the models with intermediate (M) and massive (L) line masses. The black line in the left panel corresponds to the hydrostatic density profile of an isothermal sheet.
Paper: The Galaxy–Halo Connection of DESI Luminous Red Galaxies with Subhalo Abundance Matching
Authors: Angela M. Berti, Kyle S. Dawson, Wilber Dominguez
Figure caption: Mass accretion histories and z starve values of four randomly selected halos from the ﹩{z}_{\mathrm{sim}}=0.425﹩ snapshot of the MDPL2 simulation. The most massive halo (purple dashed–dotted line) has the earliest starvation redshift (z starve = 4.63), corresponding to when its mass first reaches the characteristic value M char = 1012 h −1 M ⊙. The other three halos also reach M char (at later redshifts), but in accordance with Equation (2), the redshift at which this occurs for each halo is not necessarily the same as its z starve value; e.g., the least massive halo (orange solid line) has z starve = 1.36, although its mass does not exceed M char until z ≲ 1. The shaded region below each curve corresponds to a halo’s mass accretion history before it reaches z starve.
Paper: The Galaxy–Halo Connection of DESI Luminous Red Galaxies with Subhalo Abundance Matching
Authors: Angela M. Berti, Kyle S. Dawson, Wilber Dominguez
Figure caption: Color–magnitude diagrams of the parent galaxy samples and DESI LRG target samples (overlaid white contours) in the 0.4 < z phot < 0.5 redshift bin. The left column shows optical (k-corrected r − z color vs. M  z  magnitude), while the right column shows IR (k-corrected r − W1 color vs. M  W1 magnitude). The top (bottom) row shows the distribution of optical (IR) LRGs in each color–magnitude space. The boxed regions labeled “A” and “B” are referenced in Section 4.1 below.
Paper: Simulation of Head-on Collisions between Filamentary Molecular Clouds Threaded by a Lateral Magnetic Field and Subsequent Evolution
Authors: Raiga Kashiwagi, Kazunari Iwasaki, Kohji Tomisaka
Figure caption: Line mass at the shocked region plotted against the maximum density. The y-axis is ﹩{\lambda }_{{\rho }_{\max }/100}﹩, which is the line mass counted over ﹩{\rho }_{\max }/100﹩ and normalized by λ crit. The x-axis shows the maximum density. The solid and dashed lines represent the intermediate and massive line mass models, respectively, and the different colors indicate models with β 0 = 1 (red), 0.1 (green), and 0.01 (blue), respectively. The initial relative velocity is fixed as V int = 1.
Paper: The Galaxy–Halo Connection of DESI Luminous Red Galaxies with Subhalo Abundance Matching
Authors: Angela M. Berti, Kyle S. Dawson, Wilber Dominguez
Figure caption: Distribution of k-corrected z-band (left column) and W1-band (right column) absolute magnitudes for the three redshift bins in our study: 0.4 < z phot < 0.5 (top), 0.5 < z phot < 0.6 (middle), and 0.6 < z phot < 0.7 (bottom). Each panel shows the distribution of all galaxies with z < 20.7 (solid gray), as well as the distributions of optical (hatched purple; see Equation (4a)) and IR (solid orange; see Equation (5a)) DESI LRG targets. The leftmost dashed black line in each panel is the absolute magnitude cut that defines each absolute magnitude–limited parent galaxy sample for our models. At fainter magnitudes (to the left of the cut in each panel), the DECaLS sample is incomplete. The three dotted black lines in each panel denote the luminosity bins used to constrain the model parameters (see Section 3.2 and Tables 2 and 3).
Paper: The Galaxy–Halo Connection of DESI Luminous Red Galaxies with Subhalo Abundance Matching
Authors: Angela M. Berti, Kyle S. Dawson, Wilber Dominguez
Figure caption: Luminosity-binned projected clustering of the W1-band absolute magnitude–limited parent galaxy samples and corresponding mock galaxy catalogs, with different colors and line styles representing different luminosity bins as denoted by the legend in each panel. Luminosity bins are offset by 0.15 dex for clarity. Each panel shows a different redshift bin. The shaded region at r p < 0.1 h −1 Mpc denotes measurements not used for modeling.
Paper: Simulation of Head-on Collisions between Filamentary Molecular Clouds Threaded by a Lateral Magnetic Field and Subsequent Evolution
Authors: Raiga Kashiwagi, Kazunari Iwasaki, Kohji Tomisaka
Figure caption: Same as Figure 2, but representing the models in which the collision direction (x-direction) is perpendicular to the global magnetic field (y-direction). This figure shows the cross-section of the colliding filaments on the x–y plane, and the plot area is restricted to ±4.0 from the box size (±5). The upper (a)–(c) and bottom (d)–(f) panels show the models of β1M’⊥ and β1S’⊥, respectively.
Paper: The Galaxy–Halo Connection of DESI Luminous Red Galaxies with Subhalo Abundance Matching
Authors: Angela M. Berti, Kyle S. Dawson, Wilber Dominguez
Figure caption: Same as Figure 4 but for the z-band parent galaxy samples and corresponding mock galaxy catalogs.
Paper: Calculation and Uncertainty of Fast Radio Burst Structure Based on Smoothed Data
Authors: Adrian T. Sutinjo, Danica R. Scott, Clancy W. James, Marcin Glowacki, Keith W. Bannister, Hyerin Cho, 혜린 조, Cherie K. Day, Adam T. Deller, Timothy P. Perrett, Ryan M. Shannon
Figure caption: Vector diagram representing Equation (5).
Paper: Calculation and Uncertainty of Fast Radio Burst Structure Based on Smoothed Data
Authors: Adrian T. Sutinjo, Danica R. Scott, Clancy W. James, Marcin Glowacki, Keith W. Bannister, Hyerin Cho, 혜린 조, Cherie K. Day, Adam T. Deller, Timothy P. Perrett, Ryan M. Shannon
Figure caption:                       ﹩{{\boldsymbol{\Lambda }}}_{{D}_{1}^{T}{D}_{1}}^{1/2}{{\boldsymbol{D}}}_{\mathrm{filter}}﹩ bandpass filter response for k  c  = 4, 15, and 100 for an O = 3 filter defined by Equation (16).
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: The differential number count of the continuum sources in the survey area. The black stepping curves display the differential number count of the NVSS catalog, while the red circle markers with solid lines display the results of our map. The green vertical dashed–dotted line indicates the flux limit of 5σ c ≈ 7 mJy beam−1, where σ c, shown with the green vertical dashed line, is the confusion limit due to the background unresolved sources. The gray vertical dashed line indicates the 30 μJy beam−1 flux limit of the map.
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: Map-domain flux measurements comparing. The flux measurements before and after correction with simulation are shown in green and red, respectively. Top panel: The x-axis gives the flux values from the NVSS catalog and the y-axis gives the flux measurements from the map. Bottom panel: The histogram statistic of the relative flux residual, i.e., Equation (44). All of the measurements use an aperture radius size of 1.5 arcmin.
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: The spectra of bright sources in the field. The red markers show the measured source spectrum. The flux is averaged within each 10 MHz frequency bin and the error bar indicates the flux rms in each frequency bin. The gap between 1150 and 1300 MHz is due to RFI contamination. The dashed line indicates the source spectrum model fit using flux measurements in the literature and the gray area indicates the spectrum model uncertainty.
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: Same as Figure 18 but for the residual maps. The gray-thin curves represent the histograms of different frequencies.
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: The histogram of relative flux residual between our measurements using the TOD and the NVSS catalog, i.e., Equation (44).
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: Map-domain flux measurements with different aperture radius sizes. Top panel: The x-axis is the flux values from the NVSS catalog and the y-axis is the flux measurements from the map. Bottom panel: The histogram statistic of the relative flux residual, i.e., Equation (44).
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: The flux–flux diagram compares the measurements using the TOD with those in the NVSS catalog.
Paper: Physics-driven Machine Learning for the Prediction of Coronal Mass Ejections’ Travel Times
Authors: Sabrina Guastavino, Valentina Candiani, Alessandro Bemporad, Francesco Marchetti, Federico Benvenuto, Anna Maria Massone, Salvatore Mancuso, Roberto Susino, Daniele Telloni, Silvano Fineschi, Michele, Piana
Figure caption: Distribution of absolute errors for the prediction of TT for all samples in test sets for each configuration. First row: results with configurations C1 and C4. Second row: results with configurations C2 and C5. Third row: results with configurations C2 and C5.
Paper: Physics-driven Machine Learning for the Prediction of Coronal Mass Ejections’ Travel Times
Authors: Sabrina Guastavino, Valentina Candiani, Alessandro Bemporad, Francesco Marchetti, Federico Benvenuto, Anna Maria Massone, Salvatore Mancuso, Roberto Susino, Daniele Telloni, Silvano Fineschi, Michele, Piana
Figure caption: Distributions of mean absolute errors for the prediction of TT on 100 realizations of training (red box plot), validation (blue box plot), and the test (green box plot) sets. The numbers in figures represent the mean value of the distributions of MAEs. First row: results with configurations C1 and C4. Second row: results with configurations C2 and C5. Third row: results with configurations C2 and C5.
Paper: Physics-driven Machine Learning for the Prediction of Coronal Mass Ejections’ Travel Times
Authors: Sabrina Guastavino, Valentina Candiani, Alessandro Bemporad, Francesco Marchetti, Federico Benvenuto, Anna Maria Massone, Salvatore Mancuso, Roberto Susino, Daniele Telloni, Silvano Fineschi, Michele, Piana
Figure caption: Neural network architectures in cascade: The first network N1 can be used to estimate the drag parameter C from CME data. The second network N2 is then employed to estimate the ToA of the considered CME at 1 au. Here, N = 200, M = 25, and P = 10 are the number of neurons in the first, fifth, and sixth hidden layers, respectively.
Paper: Physics-driven Machine Learning for the Prediction of Coronal Mass Ejections’ Travel Times
Authors: Sabrina Guastavino, Valentina Candiani, Alessandro Bemporad, Francesco Marchetti, Federico Benvenuto, Anna Maria Massone, Salvatore Mancuso, Roberto Susino, Daniele Telloni, Silvano Fineschi, Michele, Piana
Figure caption: Impact of features on network predictions according to permutation importance. The most important features are the wind speed, the CME speed, and the drag parameter C.
Paper: ALMA 0.5 kpc Resolution Spatially Resolved Investigations of Nuclear Dense Molecular Gas Properties in Nearby Ultraluminous Infrared Galaxies Based on HCN and HCO+ Three Transition Line Data
Authors: Masatoshi Imanishi, 昌俊 今西, Shunsuke Baba, 俊介 馬場, Kouichiro Nakanishi, 康一郎 中西, Takuma Izumi, 拓磨 泉
Figure caption: Same as Figure 9, but (a) the HCN-to-HCO+ abundance ratio of [HCN]/[HCO+] = 1 and 7, (b) the HCO+ column density of N HCO+  = 1 × 1015 cm−2 (a factor of 10 lower than that adopted in Figure 9), and (c) N HCO+  = 3 × 1016 cm−2 (a factor of 3 higher). The filled red circles are the observed HCN-to-HCO+ flux ratios of the same (U)LIRGs as plotted in Figure 9.