adsabs/AstroCLIMB huggingface dataset:
Paper: Revising the Giant Planet Mass–Metallicity Relation: Deciphering the Formation Sequence of Giant Planets
Authors: Yayaati Chachan, Jonathan J. Fortney, Kazumasa Ohno, Daniel Thorngren, Ruth Murray-Clay
Figure caption: A sample of models and parameter constraints for our mergers model with mean number of mergers μ = 1.5. In the left panel, we color the model predictions by the fitted values of nmerg. Solid straight lines show metal mass as a function of planet mass for a range of metallicities. The fitted core mass ﹩{M}_{{\rm{core}}}﹩ is roughly half the value from our fiducial model fits. The fraction of accreted material in metals fZ and astrophysical scatter σmult remain unchanged.
Paper: Revising the Giant Planet Mass–Metallicity Relation: Deciphering the Formation Sequence of Giant Planets
Authors: Yayaati Chachan, Jonathan J. Fortney, Kazumasa Ohno, Daniel Thorngren, Ruth Murray-Clay
Figure caption: Evolution models for a 3 MJup planet for different bulk metallicities assuming the metals are uniformly distributed inside the planet. The solid lines correspond to our updated evolution model. The models corresponding to the dashed lines are indicated in the panel titles. See the Figure 3 caption for more details.
Paper: Revising the Giant Planet Mass–Metallicity Relation: Deciphering the Formation Sequence of Giant Planets
Authors: Yayaati Chachan, Jonathan J. Fortney, Kazumasa Ohno, Daniel Thorngren, Ruth Murray-Clay
Figure caption: Violin plots for the metal mass and bulk metallicity of planets in our sample. The sample is split into six nearly equally sized bins. We take 100 samples for the bulk metallicity and metal mass for each planet and pool these samples together for all the planets in the mass bin to account for the individual- and population-level uncertainties in these quantities. The horizontal bar indicates the median of the population in each mass bin and the vertical line with caps indicates the 16th and 84th percentiles of the population.
Paper: Revising the Giant Planet Mass–Metallicity Relation: Deciphering the Formation Sequence of Giant Planets
Authors: Yayaati Chachan, Jonathan J. Fortney, Kazumasa Ohno, Daniel Thorngren, Ruth Murray-Clay
Figure caption: The probability distribution of the calculated metal mass for a sample of three planets. For HAT-P-18 b, the distribution provides an upper limit on its metal mass. TOI-1439 b’s distribution is truncated at its total planet mass while WASP-139 b’s metal distribution is nearly Gaussian. For planets such as HAT-P-18 b and TOI-1439 b, we fit a truncated normal distribution to the metal distribution and use it to calculate the model likelihood.
Paper: Revising the Giant Planet Mass–Metallicity Relation: Deciphering the Formation Sequence of Giant Planets
Authors: Yayaati Chachan, Jonathan J. Fortney, Kazumasa Ohno, Daniel Thorngren, Ruth Murray-Clay
Figure caption: The bulk metallicity and metal mass plotted against planet mass for our sample. The dashed line in the left panel marks solar metallicity (Z⊙ = 0.014). Solids lines in the right panel show metal mass as a function of planet mass for a range of metallicities.
Paper: Revising the Giant Planet Mass–Metallicity Relation: Deciphering the Formation Sequence of Giant Planets
Authors: Yayaati Chachan, Jonathan J. Fortney, Kazumasa Ohno, Daniel Thorngren, Ruth Murray-Clay
Figure caption: Fitted profiles for the metallicity of the incoming material Z(m) as a function of planet mass m.
Paper: On the Sensitivity of Different Galaxy Properties to Warm Dark Matter
Authors: Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
Figure caption: 1/mWDM predictions (y-axis) using the MLP + NF model, compared to the true 1/mWDM value of the WDM particle mass used in each simulation (x-axis). A one-to-one red line is shown to indicate where predictions match the true values. The left panel shows the results considering the 14 galaxy properties; the middle and right panels use only the gas mass Mg and total mass Mt, respectively, as single features. Error bars correspond to the 68% credible interval, computed as the [50th–16th, 84th–50th] percentiles of the posterior distribution.
Paper: On the Sensitivity of Different Galaxy Properties to Warm Dark Matter
Authors: Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
Figure caption: Feature importance analysis for predicting the warm dark matter (WDM) mass using histograms of various galaxy properties from all subhalos within a simulation box. The left panel displays the R2 values for the inference. The right panel illustrates the shape of individual sample inferences from a selected simulation, highlighting the constraining power of Mg, Mtot, and ﹩{V}_{{\rm{\max }}}﹩. The dashed black line marks the true value of WDM mass for this chosen simulation.
Paper: On the Sensitivity of Different Galaxy Properties to Warm Dark Matter
Authors: Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
Figure caption: Architectures used for inferring the WDM particle mass from the DREAMS simulations. Left panel: the input consists of global statistical descriptors of galaxy properties across the simulation box, such as the mean, standard deviation, or histogram bins. These features pass through multiple perceptron layers (two are shown for illustration), producing a latent representation that serves as input to the normalizing flow. Right panel: the graphs are constructed from the subhalos hosted by the halos. These graphs pass through multiple graph layers (two are shown), which produce a latent space representation. In both cases, the resulting latent vector is passed to the normalizing flow, which outputs the posterior distribution of the WDM mass conditioned on either the global descriptors or the halo-specific graph.
Paper: Revising the Giant Planet Mass–Metallicity Relation: Deciphering the Formation Sequence of Giant Planets
Authors: Yayaati Chachan, Jonathan J. Fortney, Kazumasa Ohno, Daniel Thorngren, Ruth Murray-Clay
Figure caption: Our bulk metallicity measurements with the formation sequence from R. Helled (2023; only available up to 1 MJup) overlaid on the population. The black line shows the median outcome and the gray region encompasses the range of formation tracks. The dashed horizontal line indicates solar metallicity Z⊙ = 0.014. The black diamonds show the bulk metallicity estimates for Saturn (MZ = 19.1 ± 1.0 M⊕; C. R. Mankovich & J. Fuller 2021) and Jupiter (MZ ∼ 21 ± 3 M⊕ for an unmodified EOS; S. Howard et al. 2023a).
Paper: Revising the Giant Planet Mass–Metallicity Relation: Deciphering the Formation Sequence of Giant Planets
Authors: Yayaati Chachan, Jonathan J. Fortney, Kazumasa Ohno, Daniel Thorngren, Ruth Murray-Clay
Figure caption: Metal mass MZ divided by the median fiducial model with ﹩{M}_{{\rm{core}}}=14.72﹩ M⊕ and fZ = 0.09 plotted against various stellar and planetary properties. Additional uncertainty from variations in the modeled values is not included. The horizontal dashed lines show the range of astrophysical scatter (1 ± σmult with σmult = 0.66). Planets with eccentricity values that are missing or equal to zero are assigned an eccentricity of 10−3 to display them in log scale. No correlations between these additional properties and the metal mass residuals are apparent.
Paper: Revising the Giant Planet Mass–Metallicity Relation: Deciphering the Formation Sequence of Giant Planets
Authors: Yayaati Chachan, Jonathan J. Fortney, Kazumasa Ohno, Daniel Thorngren, Ruth Murray-Clay
Figure caption: Mixing entropy that arises as a result of mixing water and H–He as a function of the H–He mass fraction (Y = 0.275). The different lines correspond to regions in a planet’s interior where H–He and water exist in different forms (molecular, atomic, and ionic).
Paper: On the Sensitivity of Different Galaxy Properties to Warm Dark Matter
Authors: Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
Figure caption: Histogram of 1024 unique WDM particle masses used in the varied cosmology simulation suite, ranging between 1.8 keV and 16 keV, sampled uniformly from an inverse distribution.
Paper: On the Sensitivity of Different Galaxy Properties to Warm Dark Matter
Authors: Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
Figure caption: Two graph examples built from galaxy catalogs (two different DREAMS-IllustrisTNG simulations). The purple nodes represent the galaxies of the halo and they are connected if their distance is smaller than the linking radius rlink.
Paper: On the Sensitivity of Different Galaxy Properties to Warm Dark Matter
Authors: Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
Figure caption: 1/mWDM predictions (y-axis) using the GNN + NF model, compared to the true 1/mWDM value of the WDM particle used to create each simulation (x-axis). A secondary axis at the top indicates the true mWDM value of the WDM. A one-to-one red line is shown to indicate where predictions match the true values. Node features consist of 14 subhalo properties, while global features correspond to the mean and standard deviation of these properties across all subhalos in each simulation. Error bars correspond to the 68% credible interval, computed as the [50th–16th, 84th–50th] percentiles of the posterior distribution.
Paper: On the Sensitivity of Different Galaxy Properties to Warm Dark Matter
Authors: Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
Figure caption: 1/mWDM predictions (y-axis) using the DeepSet + NF model, compared to the true 1/mWDM value of the WDM particle used to create each simulation (x-axis). A secondary axis at the top indicates the true mWDM value of the WDM. A one-to-one red line is shown to indicate where predictions match the true values. Node features consist of 14 subhalo properties, while global features correspond to the mean and standard deviation of these properties across all subhalos in each simulation. Error bars correspond to the 68% credible interval, computed as the [50th–16th, 84th–50th] percentiles of the posterior distribution.
Paper: On the Sensitivity of Different Galaxy Properties to Warm Dark Matter
Authors: Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
Figure caption: Left panel: GNN + NF model using the 14 node features and only the mean and standard deviation of the subhalo gas mass as global features. Right panel: GNN + NF model using the 14 node features and only the mean and standard deviation of the subhalo total mass as global features. In both panels the error bars correspond to the 68% credible interval, computed as the [50th–16th, 84th–50th] percentiles of the posterior distribution.
Paper: On the Sensitivity of Different Galaxy Properties to Warm Dark Matter
Authors: Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
Figure caption: 1/mWDM predictions (y-axis) using the MLP + NF model, compared to the true 1/mWDM value of the WDM particle mass used to create each simulation (x-axis). A one-to-one red line is shown to indicate where predictions match the true values. On the left panel only the gas mass of the subhalo Mg is used as a subhalo property, and the matter density of the simulation Ωm is included, while on the right panel Ωm is not included. Error bars correspond to the 68% credible interval, computed as the [50th–16th, 84th–50th] percentiles of the posterior distribution.
Paper: On the Sensitivity of Different Galaxy Properties to Warm Dark Matter
Authors: Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
Figure caption: In the x-axis the confidence level is presented, while in the y-axis the coverage probability. The top panels present the results when the mean and standard deviation of the 14 properties are used as global features (in the left panel the GNN + NF model, in the right panel the Deepset + NF model). The bottom panels present the results when the mean and standard deviation of the Mg (left panel) and Mt (right panel) are used as global features.
Paper: On the Sensitivity of Different Galaxy Properties to Warm Dark Matter
Authors: Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
Figure caption: In the x-axis the confidence level is presented, while in the y-axis the coverage probability for the models trained with MLP + NF. The top-left panel presents the results when the mean and standard deviation of the 14 properties are used, while the top right presents the case using only the mean and standard deviation of the Mt. The bottom panels present the results using the mean and standard deviation of the Mg with Ωm included (left panel) and not included (right panel).
Paper: On the Sensitivity of Different Galaxy Properties to Warm Dark Matter
Authors: Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
Figure caption: Feature importance analysis for predicting the warm dark matter (WDM) mass using histograms of various galaxy properties from selected subhalos within a simulation box. The left panel displays the R2 values for the inference using subhalos with more than 30 DM particles while the right panel displays using subhalos with more than 50 DM particles.
Paper: On the Sensitivity of Different Galaxy Properties to Warm Dark Matter
Authors: Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
Figure caption: Left panel: SHMF considering all the subhalos in the simulation (blue curve) and SHMF considering subhalos with more than 30 DM particles (green curve). Right panel: SHMF considering all the subhalos in the simulation (blue curve) and SHMF considering subhalos with more than 50 DM particles (pink curve). The dashed black lines represent the masses 30 × mp and 50 × mp, where mp is the DM mass resolution.
Paper: Quasiperiodic Oscillations in a Highly Magnetized Ultracompact X-Ray Binary 4U 1626-67
Authors: Zi-Yi Zhou, Long Ji, Ling- Da Kong, Sergey S. Tsygankov, Qing-Cang Shui, Lian Tao
Figure caption: The evolution of spectral parameters with the observed flux in the 4–79 keV band. Orange points represent spectral parameters from the QPO-phase-averaged spectra. Other points show QPO-phase-resolved results for four observations: 90901318002 (blue), 90901318006 (red), 90901318008 (green), and 90901318010 (cyan). For each observation, QPOs are divided into six phases as mentioned in Section 3.4. Black lines are linear fits to the QPO-phase-resolved spectral parameters.
Paper: Quasiperiodic Oscillations in a Highly Magnetized Ultracompact X-Ray Binary 4U 1626-67
Authors: Zi-Yi Zhou, Long Ji, Ling- Da Kong, Sergey S. Tsygankov, Qing-Cang Shui, Lian Tao
Figure caption: The magnetospheric radius Rm calculated from Equation (3) assuming k = 0.5. Different shadows indicate the distance range (5–13 kpc) we considered in this paper. The shadow widths correspond to the flux range (1.5–4.5 × 10−10 erg cm−2 s−1) calculated from the broadband spectral analysis. The solid and dot lines present two radii with the local Keplerian frequency νk = νNS + νQPO and νk = νNS − νQPO based on the beat frequency model, and their intersections with shadows are possible solutions.
Paper: Quasiperiodic Oscillations in a Highly Magnetized Ultracompact X-Ray Binary 4U 1626-67
Authors: Zi-Yi Zhou, Long Ji, Ling- Da Kong, Sergey S. Tsygankov, Qing-Cang Shui, Lian Tao
Figure caption: Left: PDSs of four NuSTAR observations of 4U 1626–67. Right: a representative PDS (black line) constructed for the observational ID 90901318002, together with PDSs of individual IMFs after the decomposition. The IMF1 (yellow line) corresponds to the QPO signal we considered. Here we assumed K = 5 and a = 1000 (see text).
Paper: Quasiperiodic Oscillations in a Highly Magnetized Ultracompact X-Ray Binary 4U 1626-67
Authors: Zi-Yi Zhou, Long Ji, Ling- Da Kong, Sergey S. Tsygankov, Qing-Cang Shui, Lian Tao
Figure caption: A possible configuration of 4U 1626–67, where black lines represent the magnetosphere. The red dashed line marks the corotational radius, and the blue doted line is the radius above which the strong propeller effect works. Outside the magnetospheric radius, there might be a region with a trapped disk influenced by the pileup of backflows.
Paper: Quasiperiodic Oscillations in a Highly Magnetized Ultracompact X-Ray Binary 4U 1626-67
Authors: Zi-Yi Zhou, Long Ji, Ling- Da Kong, Sergey S. Tsygankov, Qing-Cang Shui, Lian Tao
Figure caption: Left: normalized pulse profiles of 4U 1626–67 at six equally spaced QPO phases (labeled 1–6) from ObsID 9090131808. Right: fractional rms of pulse profiles measured at different QPO phases across observations as a function of the observed flux.
Paper: Quasiperiodic Oscillations in a Highly Magnetized Ultracompact X-Ray Binary 4U 1626-67
Authors: Zi-Yi Zhou, Long Ji, Ling- Da Kong, Sergey S. Tsygankov, Qing-Cang Shui, Lian Tao
Figure caption: The evolution of the fractional rms of QPO waveforms with energy in four observations of 4U 1626–67.
Paper: Quasiperiodic Oscillations in a Highly Magnetized Ultracompact X-Ray Binary 4U 1626-67
Authors: Zi-Yi Zhou, Long Ji, Ling- Da Kong, Sergey S. Tsygankov, Qing-Cang Shui, Lian Tao
Figure caption: The QPO waveform reconstructed by folding the 4–79 keV lightcurve according to the instantaneous phase obtaining from the HHT using the data from ObsID 90901318002. Here we divide the QPO cycles into 32 phase bins and present two QPO cycles for clarity.
Paper: Quasiperiodic Oscillations in a Highly Magnetized Ultracompact X-Ray Binary 4U 1626-67
Authors: Zi-Yi Zhou, Long Ji, Ling- Da Kong, Sergey S. Tsygankov, Qing-Cang Shui, Lian Tao
Figure caption: Results of the QPO-phase-resolved spectroscopy for four observations, i.e., 90901318002 (blue), 90901318006 (red), 90901318008 (green), and 90901318010(cyan). Gray dashed lines represent QPO waveforms in the energy band of 4–79 keV.
Paper: Quasiperiodic Oscillations in a Highly Magnetized Ultracompact X-Ray Binary 4U 1626-67
Authors: Zi-Yi Zhou, Long Ji, Ling- Da Kong, Sergey S. Tsygankov, Qing-Cang Shui, Lian Tao
Figure caption: A representative segment of the lightcurve for the obsID 90901318002 with a bin size 7.7 s.
Paper: Quasiperiodic Oscillations in a Highly Magnetized Ultracompact X-Ray Binary 4U 1626-67
Authors: Zi-Yi Zhou, Long Ji, Ling- Da Kong, Sergey S. Tsygankov, Qing-Cang Shui, Lian Tao
Figure caption: Left: a representative PDS of observation 90901318006 where the yellow dot line is the model for the fitting. Right: fractional rms values calculated by using the HHT (red points) and the conventional method based on PDSs (blue points).
Paper: Quasiperiodic Oscillations in a Highly Magnetized Ultracompact X-Ray Binary 4U 1626-67
Authors: Zi-Yi Zhou, Long Ji, Ling- Da Kong, Sergey S. Tsygankov, Qing-Cang Shui, Lian Tao
Figure caption: Left: reconstructed QPO waveforms by the VMD plus HHT technique assuming different K and a parameters. The data are adopted from the observational ID 90901318010 in the energy range of 4–79 keV. Right: the evolution of the fractional rms with energy, considering different K and a combinations.
Paper: Highly Polarized Intrinsic Emission and Its Orthogonal Counterpart in Vela X-1
Authors: WanYun Wu, Fei Xie, Long Ji, MingYu Ge, Fabio La Monaca
Figure caption: Results of the phase-resolved spectropolarimetric analysis, using the combined data set of two IXPE observations in the 2–8 keV energy band. (A) The overall pulse profiles. (B) The phase-resolved PD1 and PD2,3. The purple line shows Component 1, and the light coral line shows Component 2,3. (C) Same colors indicate the variation of Luminosity1 and Luminosity2,3. (D) Same colors indicate the phase-resolved PA1 and PA2,3. The black solid curve shows the best-fit RVM for simultaneous fitting of PA1 and PA2,3. The black dashed curve represents the best-fit RVM for separate fitting of PA1 and PA2,3.
Paper: Highly Polarized Intrinsic Emission and Its Orthogonal Counterpart in Vela X-1
Authors: WanYun Wu, Fei Xie, Long Ji, MingYu Ge, Fabio La Monaca
Figure caption: Top panel: the spectra of the combined data set of two IXPE observations. The red, black, and cyan colors represent the data from the three DUs, respectively. The black solid line, dotted line, dashed line, and dotted–dashed line represent Component 1, Component 2, Component 3, and the Gaussian component of our fitting model, respectively. The fitting residuals are shown in the bottom panel.
Paper: Highly Polarized Intrinsic Emission and Its Orthogonal Counterpart in Vela X-1
Authors: WanYun Wu, Fei Xie, Long Ji, MingYu Ge, Fabio La Monaca
Figure caption: Light curves from the two IXPE observations of Vela X-1. Time intervals (a)–(e) are used for time-resolved polarization analysis, respectively, corresponding to the MJD time intervals: (a) 59684.8–59687, (b) 59689.5–59690.5, (c) 59914–59915, (d) 59915–59917.2, and (e) 59917.2–59919.4.
Paper: Highly Polarized Intrinsic Emission and Its Orthogonal Counterpart in Vela X-1
Authors: WanYun Wu, Fei Xie, Long Ji, MingYu Ge, Fabio La Monaca
Figure caption: The polarization vectors of the combined data set of two IXPE observations. The blue contours show the polarization vectors of Component 1, while the red shows the joint polarization vectors of Component 2,3. For each set of vectors, the three contours from outward to inward correspond to the 68.30%, 95.45%, and 99.73% confidence levels, respectively. Due to the narrow uncertainty ranges, the three red contours are visually indistinguishable at this scale.
Paper: Highly Polarized Intrinsic Emission and Its Orthogonal Counterpart in Vela X-1
Authors: WanYun Wu, Fei Xie, Long Ji, MingYu Ge, Fabio La Monaca
Figure caption: Correlation of parameters in Table 2. The left, middle, and right panels, respectively, show PD2,3 vs. Luminosity2,3, NH,2 and NH,3.
Paper: Atomic and Molecular Gas in the Milky Way. I. Structure Decomposition
Authors: Xin Liu, Fujun Du
Figure caption: Workflow of the GDCluster algorithm. Black lines in each panel represent mock spectra from nine neighboring LoSs. Green lines in the first panel show the initially estimated Gaussian parameters. The central spectrum contains an isolated component (far right) that appears only in this spectrum and lacks a component (center) that is present in all neighboring spectra. In the second panel, the isolated component is removed and the missing component is added, illustrated by the orange and red dashed lines, respectively. In the third panel, Gaussian components belonging to the same cluster are shown in the same color across different LoSs.