adsabs/AstroCLIMB huggingface dataset:
Paper: MC-BLOS: Automated Software for Determining the Line-of-sight Component of Magnetic Fields Associated with Molecular Clouds 
Authors: Mehrnoosh Tahani, John Ming Ngo, Jennifer Glover, Ryan Clairmont, Gabriel M. Zarazua, René Plume
Figure caption: Schematic illustration of the division of the cloud layer to estimate the electron column density. The molecular cloud (denoted by MC) is divided into layers along the line of sight. A telescope (left) observes through these layers and the rest of the Galactic path length. Assuming a symmetrical cloud, the electron column density is calculated by summing contributions from each layer up to the center of the cloud along the line of sight, reaching ﹩{A}_{V,\rm{MC}}/2﹩ in extinction, and then doubling this value to account for the symmetrical front half of the cloud. This approach enables more accurate accounting of varying electron abundances throughout the cloud’s depth.
Paper: MC-BLOS: Automated Software for Determining the Line-of-sight Component of Magnetic Fields Associated with Molecular Clouds 
Authors: Mehrnoosh Tahani, John Ming Ngo, Jennifer Glover, Ryan Clairmont, Gabriel M. Zarazua, René Plume
Figure caption: Final BLOS maps of the Orion A, Orion B, California, and Perseus molecular clouds using Default parameter set. The background color scale represents extinction (AV). Red (blue) circles indicate magnetic fields pointing away from (toward) the observer. The size of each circle is proportional to the absolute strength of BLOS at that location, without displaying the associated uncertainties. Green circles represent the reference points used to determine RMref.
Paper: The Study of the Circumnuclear Environment of Accreting Supermassive Black Holes with Realistic X-Ray Spectral Models
Authors: G. Dimopoulos, C. Ricci, S. Paltani
Figure caption: Two examples of column density profile for the RXTOPO model, considering two different torus configurations. In both panels, we show how the line-of-sight column density varies for different angles. The shaded areas that are also noted by the arrows mark the angle range in which the cone or torus intercept the photons that originate from the center. In the left panel, we considered a torus with CFtor = (r/R) = 0.6, whereas the right panel shows a torus with CFtor = 0.9.
Paper: The Study of the Circumnuclear Environment of Accreting Supermassive Black Holes with Realistic X-Ray Spectral Models
Authors: G. Dimopoulos, C. Ricci, S. Paltani
Figure caption: (Top) A visual illustration of the configuration of the RXTOPO model. In the center of the system is the SMBH with the corona placed on top of it. The torus can be found after the sublimation zone with a variable size. Perpendicular to the plane of the torus is the hollow polar cone. The dashed–dotted line (red) depicts the R and r that are used to calculate the covering factor of the torus. The components are not scaled. The angle θ is the observing angle of the system. ϕ is the opening angle of the torus and ξ∘ is the maximum angle of the polar hollow cone. (Bottom) A cutout generated using REFLEX showing a torus with CFtor = 0.6 and the polar hollow cone with 10° angular width.
Paper: The Study of the Circumnuclear Environment of Accreting Supermassive Black Holes with Realistic X-Ray Spectral Models
Authors: G. Dimopoulos, C. Ricci, S. Paltani
Figure caption: The line-of-sight density profile of the RXAGN1 model. The solid line represents the total line-of-sight column density, whereas the dashed line illustrates the contribution of the BLR. Left: The density profile of the RXAGN1 model for CFtor = 0.3. Right: The density profile of a larger torus with CFtor = 0.7.
Paper: The Study of the Circumnuclear Environment of Accreting Supermassive Black Holes with Realistic X-Ray Spectral Models
Authors: G. Dimopoulos, C. Ricci, S. Paltani
Figure caption: Different options available for the RXTOPO and RXAGN1 models as described in Section 3.1. The different lines represent: all the photons collected (ALL, gray solid line), reprocessed photons (either by scattering or fluorescence; RPRC, dashed red line), scattered photons (SCAT, dashed green line), fluorescent photons (FLUO, solid blue line), continuum photons (CNT, orange dashed line). Here, we give an example of the RXTOPO model with the following parameters: photon index Γ = 1.8, obs. angle = 65°, Torus ﹩{\mathrm{log}}\,({N}_{{\rm{H}}}^{{\rm{tor}}}/{{\rm{cm}}}^{-2})=24.0﹩, CFtor = 0.6, and cone ﹩{\mathrm{log}}\,({N}_{{\rm{H}}}^{{\rm{pol}}}/{{\rm{cm}}}^{-2})=22.4﹩.
Paper: The Study of the Circumnuclear Environment of Accreting Supermassive Black Holes with Realistic X-Ray Spectral Models
Authors: G. Dimopoulos, C. Ricci, S. Paltani
Figure caption: Cartoon illustrating the configuration of the RXAGN1 model. This model is an extension of the RXTOPO model, with the addition of an AD and a BLR. The dashed–dotted line (red) depicts the R and r that are used to calculate the covering factor of the torus. The objects are not scaled. The angle θ is the observing angle of the system. ϕ is the opening angle of the torus and ξ∘ is the maximum angle of the polar hollow cone.
Paper: The Study of the Circumnuclear Environment of Accreting Supermassive Black Holes with Realistic X-Ray Spectral Models
Authors: G. Dimopoulos, C. Ricci, S. Paltani
Figure caption: The four panels illustrate a few examples of the simulated spectra. In every panel are presented both the RXTOPO and RXAGN1 models. The top row shows two different versions of the torus with ﹩{\mathrm{log}}\,({N}_{{\rm{H}}}^{{\rm{tor}}}/{{\rm{cm}}}^{-2})=24.0﹩(left), 25.0(right), CFtor = 0.6 and Cone ﹩{\mathrm{log}}\,({N}_{{\rm{H}}}^{{\rm{pol}}}/{{\rm{cm}}}^{-2})=22.4﹩ observed at 45°. The bottom row is the same configuration but observed at 85 degrees.
Paper: Effects of Nonresonant Instability and Magnetic Reconnection on Ion Heating and Acceleration in Quasi-parallel Shock Waves
Authors: N. Bessho, J. Ng, L.-J. Chen, M. Hesse, L. B. Wilson, J. E. Stawarz
Figure caption: Particle tracing result for an ion that moves to the upstream region at Ωit = 20.31. The same format as in Figure 9 for panels (a)–(d), and (f)–(h). (e) Electric field Ez in the x–y plane at Ωit = 19.69.
Paper: Effects of Nonresonant Instability and Magnetic Reconnection on Ion Heating and Acceleration in Quasi-parallel Shock Waves
Authors: N. Bessho, J. Ng, L.-J. Chen, M. Hesse, L. B. Wilson, J. E. Stawarz
Figure caption: (a) Schematic of an ion-scale island and ion energization. The center of a circle with its radius R is located at (R, 0). Ions start at x = 0. Hall electric fields EH are blue arrows. The line y = y0 is the trajectory of an ion starting at (0, y0). The entrance position is at (x1, y0). When the ion reaches the position at (x, y0), the angle ϕ is between the red segment and the vertical dashed line. (b) Schematic of the formation of the motional electric fields (Ezm). Vst and Bst are the velocity and the magnetic field for the moving structure. Ions (red) can be accelerated by Ezm.
Paper: Effects of Nonresonant Instability and Magnetic Reconnection on Ion Heating and Acceleration in Quasi-parallel Shock Waves
Authors: N. Bessho, J. Ng, L.-J. Chen, M. Hesse, L. B. Wilson, J. E. Stawarz
Figure caption: Particle tracing result for an ion that enters the top ion-scale island at Ωit = 20.31. (a), (b) The particle trajectory (red) in the x–y plane. Black curves are magnetic field lines. The black dot is the position at Ωit = 19.06. The red dot is the position at Ωit = 20 (panel (a)) and the position at Ωit = 20.31 (panel (b)). (c) The particle trajectory in the x–z plane. (d) Work done by the electric field Ex (red curve for Wx), by Ey (blue curve for Wy), and by Ez (green curve for Wz). The total work is shown in black.
Paper: Effects of Nonresonant Instability and Magnetic Reconnection on Ion Heating and Acceleration in Quasi-parallel Shock Waves
Authors: N. Bessho, J. Ng, L.-J. Chen, M. Hesse, L. B. Wilson, J. E. Stawarz
Figure caption: Ion temperature Ti and the positions of energetic ions (magenta dots) in the 2D simulation at Ωit = 20.31. (a), (b) Energetic ions whose γ > 1.23. (c), (d) Energetic ions whose γ > 1.27.
Paper: Effects of Nonresonant Instability and Magnetic Reconnection on Ion Heating and Acceleration in Quasi-parallel Shock Waves
Authors: N. Bessho, J. Ng, L.-J. Chen, M. Hesse, L. B. Wilson, J. E. Stawarz
Figure caption: 3D PIC simulation results. (a) Ion temperature Ti in the shock transition region at Ωit = 17 (upper panel) and 18 (lower panel). (b) Ion temperature Ti (yellow and orange color) and magnetic field lines (green and gray) at Ωit = 18. The colored region corresponds to the black square shown in the lower panel of (a). The numbered small boxes (1 and 2) are where distribution functions are taken. (c) Ion distribution functions at box 1 and box 2.
Paper: Effects of Nonresonant Instability and Magnetic Reconnection on Ion Heating and Acceleration in Quasi-parallel Shock Waves
Authors: N. Bessho, J. Ng, L.-J. Chen, M. Hesse, L. B. Wilson, J. E. Stawarz
Figure caption: Particle tracing result for another ion that enters the top ion-scale island at Ωit = 20.31. The same format as in Figure 9 for panels (a)–(d). (e) Enlargement of the particle trajectory near the ion-scale island. Black field lines are at Ωit = 20.31. (f), (g), (h) Electric field Ex, Ey, and Ez exerted on the particle as a function of time.
Paper: Effects of Nonresonant Instability and Magnetic Reconnection on Ion Heating and Acceleration in Quasi-parallel Shock Waves
Authors: N. Bessho, J. Ng, L.-J. Chen, M. Hesse, L. B. Wilson, J. E. Stawarz
Figure caption: (a) Schematic of ion distribution functions in a low Ti region and a high Ti region where the density reduction of the cold incident ions occurs. (b) Ion distribution function with the increase of thermal spread of the hot reflected component.
Paper: Effects of Nonresonant Instability and Magnetic Reconnection on Ion Heating and Acceleration in Quasi-parallel Shock Waves
Authors: N. Bessho, J. Ng, L.-J. Chen, M. Hesse, L. B. Wilson, J. E. Stawarz
Figure caption: 3D PIC simulation results. The background color shows the ion fluid velocity Viz at Ωit = 17. Magnetic field lines are shown with the projection of ion temperature Ti. The numbered boxes (1 and 2) are the same positions of the boxes used in Figure 6(b) at Ωit = 18. The colored region corresponds to the black square shown in the lower panel of Figure 6(a).
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Spectrum of the L3γ spectral standard 2MASSW J2208136+292121 (green), along with 100 synthetic spectra in blue drawn from the spectrum and uncertainty. The orange line shows the downsampled 0.02 μm bins used as features and discussed in Section 3.1.6. We masked out the gray telluric regions at ∼1.35 and ∼1.9 μm. We removed data at the edges, bluer than 0.89 μm and redder than 2.45 μm as indicated by the red dashed line. In generating the synthetic spectra, we applied the original noise estimates near the spectral edges, while increasing the noise level at intermediate wavelengths to better reflect the uncertainties there. All flux and noises shown are normalized at [1.27, 1.28] μm.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: RF metrics for synthetic spectra optimization. Top panel: the relationship between noise scaling (fractional uncertainty on the flux) and classification accuracy (subtype and class), indicating an optimal noise scale of 5%. Bottom panel: the relationship between the number of synthetic training spectra, training time, and classification accuracy, which shows little improvement in accuracy but increasingly longer training times beyond 70,000 samples.
Paper: Spectral Properties of Globally Distributed ENA Fluxes across Diverse Regions of the Heliosphere
Authors: Thomas K. Kim, Daniel B. Reisenfeld, Eric J. Zirnstein, Paul H. Janzen, Asher S. Merrill, Sung Jun Noh, Nehpreet K. Walia, Yue Chen, Fan Guo, Dave Osthus, Lauren J. Beesley, Dae-Kyu Shin, Justyna M. Sokół, Maciej Bzowski, Marzena A. Kubiak, Czeslaw Porowski, Herbert O. Funsten
Figure caption: Similar to Figure 11 but showing a comparison between 1 au (left column) and heliosheath (right column) for central tail, port lobe, and starboard lobe.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Color–magnitude diagram showing absolute J magnitude (MJ) versus J-K color (after outliers removal) for SpeX Prism Library objects with adopted spectral types from M0 to T9. This plot confirms the consistency of our adopted spectral-typing method. Objects that deviate from the sequence, including giants, unresolved binaries, and YSOs, are identified and removed to ensure a homogeneous sample.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Stacked spectral type distribution of the 1548 spectra. Metallicity and gravity classes are indicated by different color bars. Despite having standards for sdM9.5, sdL3.5, sdL4, and esdM6.5, we do not have examples of these classes in the testing and validation set.
Paper: Spectral Properties of Globally Distributed ENA Fluxes across Diverse Regions of the Heliosphere
Authors: Thomas K. Kim, Daniel B. Reisenfeld, Eric J. Zirnstein, Paul H. Janzen, Asher S. Merrill, Sung Jun Noh, Nehpreet K. Walia, Yue Chen, Fan Guo, Dave Osthus, Lauren J. Beesley, Dae-Kyu Shin, Justyna M. Sokół, Maciej Bzowski, Marzena A. Kubiak, Czeslaw Porowski, Herbert O. Funsten
Figure caption: Similar to Figure 11 but showing a comparison between 1 au (left column) and heliosheath (right column) for upwind, north pole, and south pole.
Paper: Spectral Properties of Globally Distributed ENA Fluxes across Diverse Regions of the Heliosphere
Authors: Thomas K. Kim, Daniel B. Reisenfeld, Eric J. Zirnstein, Paul H. Janzen, Asher S. Merrill, Sung Jun Noh, Nehpreet K. Walia, Yue Chen, Fan Guo, Dave Osthus, Lauren J. Beesley, Dae-Kyu Shin, Justyna M. Sokół, Maciej Bzowski, Marzena A. Kubiak, Czeslaw Porowski, Herbert O. Funsten
Figure caption: Similar to Figure 10 but showing a comparison between 1 au (left column) and heliosheath (right column) for central tail, port lobe, and starboard lobe.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Conceptual workflow diagram for the ML models, detailing data gathering, preprocessing, training, validation, testing, and prediction stages, along with spectral sources and methods.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Grouped bar charts showing the performance before and after the removal of K band. Accuracies of spectral type, d/sd, sd, esd, INT-G, and VL-G are plotted against each ML model. Bars in blue correspond to models trained with K-band data, and those in orange correspond to models trained without. The figure illustrates that the exclusion of K band significantly degrades gravity classification accuracy while exerting a smaller effect on metallicity diagnostics.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Same as Figure 11 for the KNN model.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: SNR histogram of the KNN model’s predictions. The y-axis displays the log frequency, and the x-axis is the difference between the true label and the prediction (e.g., M0 = 0, L0 = 10; M0 − L0 = 0–10 would lead to a −10 classification error). Despite the expectation that high SNR spectra would be classified more accurately than low-SNR spectra, it is not obvious in our result. Instead, few high SNR objects were misclassified, likely because of the reasons explained in Section 4.4.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Mean ±1 subtype classification accuracy vs. SNR for our multioutput KNN classifier. Each data point (gray dot) is 100 synthetic spectra generated with Gaussian noise from Table 2. Each point shows the mean accuracy within a given SNR bin, with 5–95th percentiles indicated by the orange error bars. The blue violin shapes describe the kernel-density distribution of accuracies for the 100 synthetic spectra in each SNR bin, illustrating the full spread of results. Our result shows that classification accuracy improves with increasing SNR, and an SNR ≳ 60 could guarantee robust classification.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Confusion Matrix for the RF model in classifying spectral subtypes. All field and peculiar subtypes are included and grouped under their base type (i.e., L6γ goes under L6). The values are normalized across each row (actual label), i.e., each row sums to 1. This graph demonstrates the predicted label of the RF model versus the actual label defined in the data set.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Same as Figure 20, but for models trained with and without the H-band continuum features (1.47–1.67 μm). In contrast to the K-band analysis, removal of the H-band continuum yields only minor changes in d/sd and gravity classification accuracy.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Model performance of KNN, RF, and SVM for each subtype. We show how accuracy varies for KNN (blue line and circles), RF (orange line and diamonds), and SVM (green line and triangles). The x-axis shows the subtypes from M0–T9, with the number in brackets representing the number of sources within that subtype in the testing set. The performance of all three models suggests L dwarfs might be harder to classify, compared to M and T dwarfs.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Same as Figure 22, but for SVM.
Paper: Bondi-like Accretion Flow Dynamics: The Role of Gravitational Potential
Authors: Razieh Ranjbar, Héctor R. Olivares-Sánchez, Shahram Abbassi
Figure caption: Dominance regions of gravitational potentials. The magnitudes of the potentials for the BH (generalized Newtonian ϕG, Paczynski–Wiita ϕPW), DM halo, and galaxy stars are plotted at different length scales. The dotted–dashed gray line is their sum. In our normalized units (rs = c = 1), gravitational potentials are dimensionless. The small panel provides a zoomed-in view of the central region. Shaded regions are added to indicate where each potential dominates over the others. Blue shading is for the BH potential dominance. Gray shading is for the DM potential dominance.
Paper: The ODYSSEUS Survey. Characterizing Magnetospheric Geometries and Hotspot Structures in T Tauri Stars
Authors: Caeley V. Pittman, Catherine C. Espaillat, Connor E. Robinson, Thanawuth Thanathibodee, Sophia Lopez, Nuria Calvet, Zhaohuan Zhu, Frederick M. Walter, John Wendeborn, Carlo F. Manara, Justyn Campbell-White, Rik Claes, Min Fang, Antonio Frasca, Jorge F. Gameiro, Manuele Gangi, Jesus Hernández, Ágnes Kóspál, Karina Maucó, James Muzerolle, Michał Siwak, Łukasz Tychoniec, Laura Venuti
Figure caption: The leftmost panel shows the SEDs of the excess accretion shock luminosity for the entire sample. We have highlighted four CTTSs, ranging from SpT of G9 to M6, that demonstrate the range in both luminosity and shape of accretion excess spectra. Line colors indicate the mean energy flux density using the same color bar as Figure 3. The middle panel shows the percentage of the total accretion luminosity, Lacc,total that is in the Balmer jump region between 0.34 and 0.45 μm, and the rightmost panel shows percentage that is below 0.31 μm (and therefore accessible only from space). The bars are divided by stellar mass as indicated in the legend.
Paper: Bondi-like Accretion Flow Dynamics: The Role of Gravitational Potential
Authors: Razieh Ranjbar, Héctor R. Olivares-Sánchez, Shahram Abbassi
Figure caption: Global solutions for Bondi accretion, γ = 5/3,  Mhalo = MBH, a0 = 100. The blue line represents the case without DM potential (ϕDM = 0,  rc = 386.74). Dashed pink line (ϕDM ≠ 0,  rc = 1253.6). The galaxy’s stellar potential is ϕs ≠ 0. A solid dot indicates the position of the critical point. The vertical dotted lines correspond to the location of the Bondi radius rB.
Paper: The ODYSSEUS Survey. Characterizing Magnetospheric Geometries and Hotspot Structures in T Tauri Stars
Authors: Caeley V. Pittman, Catherine C. Espaillat, Connor E. Robinson, Thanawuth Thanathibodee, Sophia Lopez, Nuria Calvet, Zhaohuan Zhu, Frederick M. Walter, John Wendeborn, Carlo F. Manara, Justyn Campbell-White, Rik Claes, Min Fang, Antonio Frasca, Jorge F. Gameiro, Manuele Gangi, Jesus Hernández, Ágnes Kóspál, Karina Maucó, James Muzerolle, Michał Siwak, Łukasz Tychoniec, Laura Venuti
Figure caption: The same as Figure 2, but showing the results when only optical–NIR data are considered in the fit. NUV data are important for constraining the model fits across the full NUV–NIR range. When NUV data are ignored in the fit (indicated by the gray shaded region), the inferred model NUV fluxes can be (a) too high, (b) too low, and/or (c) the inferred model extinction corrections can be too strong. Panel (d) shows the ratio of Lacc,opt from the optical-only fit to Lacc,NUV from the full wavelength range, with a dashed gray line marking the line of equality.
Paper: Bondi-like Accretion Flow Dynamics: The Role of Gravitational Potential
Authors: Razieh Ranjbar, Héctor R. Olivares-Sánchez, Shahram Abbassi
Figure caption: Left panel: position of the critical point as a function of the temperature at the outer boundary Tout. Violet dots are cases without DM potential, and orange dots are cases with DM potential. Right panel: Mass accretion rate with zero angular momentum in units of Eddington accretion rate as a function of temperature at the outer boundary (Tout). Violet dots show the mass accretion rate without DM potential, and orange dots show the mass accretion rate with DM potential. All solutions correspond to a0 = 50,  Mhalo = MBH.