adsabs/AstroCLIMB huggingface dataset:
Paper: HIFLOW: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics Using Normalizing Flows
Authors: Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt, David N. Spergel, Daniel Anglés-Alcázar, Shy Genel, Miles Cranmer, Greg L. Bryan, Romeel Davé, Rachel S. Somerville, Michael Eickenberg, Desika Narayanan, Shirley Ho, Sambatra Andrianomena
Figure caption: Similar to Figure 9, this figure shows a comparison between conditioning HIFLOW only on the cosmological parameters (blue) vs. on all six parameters (green, orange) as quoted in the legend above panels (﹩{{\rm{\Omega }}}_{m},{\sigma }_{8},{A}_{\mathrm{SN}1},{A}_{\mathrm{AGN}1},{A}_{\mathrm{SN}2},{A}_{{AG}2}﹩). In all cases, we see that conditioning solely on cosmology is able to recover the observed HIFLOW maps (red cross). As seen in Figure 4, the astrophysical parameters do not impact the HI distribution, and hence they add degeneracies that lead to posteriors far away from the target. This shows that the conditional HIFLOW on cosmology is able to successfully marginalize over the astrophysics at the field level, regardless of the feedback strength.
Paper: HIFLOW: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics Using Normalizing Flows
Authors: Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt, David N. Spergel, Daniel Anglés-Alcázar, Shy Genel, Miles Cranmer, Greg L. Bryan, Romeel Davé, Rachel S. Somerville, Michael Eickenberg, Desika Narayanan, Shirley Ho, Sambatra Andrianomena
Figure caption: Ratio between the conditional HIFLOW and CAMELS of the mean (left) and standard deviation (right) power over all the testing set as quoted in the legend. Solid lines show the average and shaded areas reflect the standard deviation over all the prior range. HIFLOW predicts the large scale with a higher accuracy than the small-scale power. In all cases, the HIFLOW is able to predict the true mean and standard deviation power within a factor of ≲2.
Paper: HIFLOW: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics Using Normalizing Flows
Authors: Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt, David N. Spergel, Daniel Anglés-Alcázar, Shy Genel, Miles Cranmer, Greg L. Bryan, Romeel Davé, Rachel S. Somerville, Michael Eickenberg, Desika Narayanan, Shirley Ho, Sambatra Andrianomena
Figure caption: Comparison between the conditional HIFLOW (blue) and CAMELS (red) for randomly selected values of Ω m  and σ 8 from the testing set. The mean and standard deviation power μ(P) and σ(P) are computed over the 15 maps. The σ(P) panels share the same cosmology as their immediate top μ(P) panels. In all cases, the HIFLOW is able to reproduce CAMELS within a factor of ≤2, scoring a very high R 2 > 90%.
Paper: HIFLOW: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics Using Normalizing Flows
Authors: Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt, David N. Spergel, Daniel Anglés-Alcázar, Shy Genel, Miles Cranmer, Greg L. Bryan, Romeel Davé, Rachel S. Somerville, Michael Eickenberg, Desika Narayanan, Shirley Ho, Sambatra Andrianomena
Figure caption: Comparison between CAMELS (red) and the unconditional HIFLOW (blue), in terms of the probability density distribution of the HI column density pixels (pdf, left) and power spectra of the HI maps (P, right). Top and bottom panels show the average μ(pdf) and μ(P) and standard deviation σ(pdf) and σ(P), respectively, over the 750 HI real maps from CAMELS testing set and the fake maps from HIFLOW. It is evident that HIFLOW recovers the expected pdf properties (average and standard deviation as a function of HI column density). While HIFLOW underpredicts the small-scale power by a factor of ∼2, it predicts the expected large-scale power very accurately. This effect is visible in Figure 2.
Paper: HIFLOW: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics Using Normalizing Flows
Authors: Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt, David N. Spergel, Daniel Anglés-Alcázar, Shy Genel, Miles Cranmer, Greg L. Bryan, Romeel Davé, Rachel S. Somerville, Michael Eickenberg, Desika Narayanan, Shirley Ho, Sambatra Andrianomena
Figure caption: Correlation between the target and predicted cosmology using a simple multilayer perceptron trained on HI power spectra from CAMELS. Results of testing with the power spectra from CAMELS (PCAMELS) and HIFLOW (PHIFlow) are shown in the top and bottom panels, respectively. Solid black lines represent the identity line (target vs. target). While trained on CAMELS, the scatter predicted by testing with HIFLOW is similar to the target scatter produced by testing with CAMELS as quoted by the RMSE values. This shows that the differences seen in the power spectra between CAMELS and HIFLOW (see Figures 5, 6, 7) have a minimal impact on parameter recovery.
Paper: The Location and Angle Distribution of Magnetic Reconnection in the Solar Corona
Authors: Kalman J. Knizhnik, Logan C. Cabral-Pelletier
Figure caption: Histograms of the lengths of the reconnecting field lines (top) and all the closed field lines (bottom). The median length is indicated by the red vertical line.
Paper: The Location and Angle Distribution of Magnetic Reconnection in the Solar Corona
Authors: Kalman J. Knizhnik, Logan C. Cabral-Pelletier
Figure caption: The reconnection locations along the loop from the bifurcation (top) and minimum separation (bottom) methods. The median reconnection location is shown with the red vertical line.
Paper: The Location and Angle Distribution of Magnetic Reconnection in the Solar Corona
Authors: Kalman J. Knizhnik, Logan C. Cabral-Pelletier
Figure caption: Scatter plots of the angles θ 1 and θ 2 (top) and fractional half-lengths ﹩{f}_{{L}_{1}}﹩ and ﹩{f}_{{L}_{2}}﹩ (bottom), as determined by the maximum bifurcation and minimum separation methods. The red line has a slope of 1 and we quote the Pearson coefficient for the relationship.
Paper: The Location and Angle Distribution of Magnetic Reconnection in the Solar Corona
Authors: Kalman J. Knizhnik, Logan C. Cabral-Pelletier
Figure caption: Inclination angle (degrees) of the photospheric magnetic field relative to the vertical. The blue circle is the PIL.
Paper: Comprehensive Analysis of the Neutrino Process in Core-collapsing Supernovae
Authors: Heamin Ko, Dukjae Jang, Myung-Ki Cheoun, Motohiko Kusakabe, Hirokazu Sasaki, Xingqun Yao, Toshitaka Kajino, Takehito Hayakawa, Masaomi Ono, Toshihiko Kawano, Grant J. Mathews
Figure caption: Time-evolving temperature profiles as a function of the Lagrange mass coordinate. The upper and lower panels show the same models as in Figure 1, respectively. The temperature unit is taken as T 9 = T/(109 K).
Paper: Comprehensive Analysis of the Neutrino Process in Core-collapsing Supernovae
Authors: Heamin Ko, Dukjae Jang, Myung-Ki Cheoun, Motohiko Kusakabe, Hirokazu Sasaki, Xingqun Yao, Toshitaka Kajino, Takehito Hayakawa, Masaomi Ono, Toshihiko Kawano, Grant J. Mathews
Figure caption: Time-evolving density profiles in the Lagrange mass coordinate. The upper and lower panels show the HKC18 (Blinnikov et al. 2000) and KCK19 (Kusakabe et al. 2019) hydrodynamics models, respectively. The time range is taken from about 0 to 7 s.
Paper: Stellar Energetic Particle Transport in the Turbulent and CME-disrupted Stellar Wind of AU Microscopii
Authors: Federico Fraschetti, Julián D. Alvarado-Gómez, Jeremy J. Drake, Ofer Cohen, Cecilia Garraffo
Figure caption: 2D histograms in spherical coordinates (in degrees) of the hitting points of 1 GeV kinetic energy protons injected at R  s  = 5 R ⋆ into the quiescent stellar wind solution constructed from the ZDI radial field map from Klein et al. (2021b) for σ 2 = 1. The polar angle is defined as ﹩\theta ^{\prime} =\theta +90^\circ ﹩, where θ is the latitude centered on the star. The panels correspond to spherical surfaces at R  b  (left) and R  c  (right). Here, L  c  = 10−5 au. The planet’s orbital plane corresponds here to a horizontal line at ﹩\theta ^{\prime} =90^\circ ﹩. The log-scale colorbar indicates the number of EPs within each 2° × 2° pixel. The same total number of EPs was injected in each case shown below.
Paper: Stellar Energetic Particle Transport in the Turbulent and CME-disrupted Stellar Wind of AU Microscopii
Authors: Federico Fraschetti, Julián D. Alvarado-Gómez, Jeremy J. Drake, Ofer Cohen, Cecilia Garraffo
Figure caption: Top row: for a stellar wind 90 minutes past the eruption of a 1036 erg kinetic energy CME, spherical-coordinates 2D EP histogram at radii of R = R  b /2 (top row), R = R  b  (mid row), and R = R  c  (bottom row) of the hitting points for 1 GeV kinetic energy protons, for σ 2 = 0.01 (left column) and σ 2 = 1 (right column), injected at R  s  = 5 R ⋆; here L  c  = 10−5 au. The quiescent stellar wind is constructed from the ZDI radial field map from Klein et al. (2021b). The x (y) axis indicates the azimuthal (polar) coordinate on that sphere. The log-scale colorbar counts logarithmically the number of EPs. Bottom row: same as the top row but for R = R  b .
Paper: Stellar Energetic Particle Transport in the Turbulent and CME-disrupted Stellar Wind of AU Microscopii
Authors: Federico Fraschetti, Julián D. Alvarado-Gómez, Jeremy J. Drake, Ofer Cohen, Cecilia Garraffo
Figure caption: Left: snapshot in a 175 R ⋆ field of view of the bi-lobed plasma density isosurface (n(x, t)/n  SS (x) = 10.0, where n(x, t) is the density at location x and time t and n  SS (x) is the steady-state density at that location) of a propagating CME within wind reconstructed from the ZDI maps of Klein et al. (2021b) at a time 90 minutes past the CME initiation. The sphere at R  b /2 is color coded by the B-field strength. The two cyan circles represent the orbits of planet b (solid) and c (dotted). The magenta lines indicate selected closed magnetic field lines. Open magnetic field lines are in green if one end is attached to the star surface and in black if none of the ends is tracked, respectively. Right: same as the left panel with a 60 R ⋆ field of view.
Paper: Stellar Energetic Particle Transport in the Turbulent and CME-disrupted Stellar Wind of AU Microscopii
Authors: Federico Fraschetti, Julián D. Alvarado-Gómez, Jeremy J. Drake, Ofer Cohen, Cecilia Garraffo
Figure caption: Top row: the total wind flow speed, U, (left) and magnitude of the unperturbed magnetic field, B 0, (right) in the quiescent wind solution driven by the ZDI map from Klein et al. (2021b) on the spherical surface at R = R  b /2. Middle row: same as the top row at R = R  b . Bottom row: same as the top row at R = R  c .
Paper: Stellar Energetic Particle Transport in the Turbulent and CME-disrupted Stellar Wind of AU Microscopii
Authors: Federico Fraschetti, Julián D. Alvarado-Gómez, Jeremy J. Drake, Ofer Cohen, Cecilia Garraffo
Figure caption: The same as Figure 2 except for a weaker turbulence of σ 2 = 0.01.
Paper: Stellar Energetic Particle Transport in the Turbulent and CME-disrupted Stellar Wind of AU Microscopii
Authors: Federico Fraschetti, Julián D. Alvarado-Gómez, Jeremy J. Drake, Ofer Cohen, Cecilia Garraffo
Figure caption: Top: in the 90 minutes post-CME snapshot based on the Klein et al. (2021b) magnetogram as in Figure 8, the strength of the unperturbed wind magnetic field B 0 projected on the spherical surface at R = R  b /2, with azimuthal (polar) coordinates degrees on that sphere, is indicated on the x (y) axis. Middle: same as the top left but at a radius of R = R  b . Bottom: same as the top left but at a radius of R = R  c .
Paper: Stellar Energetic Particle Transport in the Turbulent and CME-disrupted Stellar Wind of AU Microscopii
Authors: Federico Fraschetti, Julián D. Alvarado-Gómez, Jeremy J. Drake, Ofer Cohen, Cecilia Garraffo
Figure caption: Same as Figure 2 but for R  s  = 1.2 R ⋆ projected at R = 0.25 R  b  (left top panel), R = 0.5 R  b  (right top panel), at R = R  b  (left bottom panel), and at R = R  c  (right bottom panel).
Paper: Stellar Energetic Particle Transport in the Turbulent and CME-disrupted Stellar Wind of AU Microscopii
Authors: Federico Fraschetti, Julián D. Alvarado-Gómez, Jeremy J. Drake, Ofer Cohen, Cecilia Garraffo
Figure caption: Same as Figure 2, except with particles injected closer in, at R  s  = 2 R ⋆ = 1.04 × 1011 cm = 0.0069 au. The panels corresponds to spherical surfaces at distinct radii: 0.25 R  b  = 4.8 R ⋆ = 0.0165 au (top left), 0.5 R  b  = 0.033 au (top right), R  b  = 0.066 au (bottom left), and R  c  = 0.11 au (bottom right).
Paper: Stellar Energetic Particle Transport in the Turbulent and CME-disrupted Stellar Wind of AU Microscopii
Authors: Federico Fraschetti, Julián D. Alvarado-Gómez, Jeremy J. Drake, Ofer Cohen, Cecilia Garraffo
Figure caption: The same as Figure 2 except for E = 0.1 GeV.
Paper: HIFLOW: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics Using Normalizing Flows
Authors: Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt, David N. Spergel, Daniel Anglés-Alcázar, Shy Genel, Miles Cranmer, Greg L. Bryan, Romeel Davé, Rachel S. Somerville, Michael Eickenberg, Desika Narayanan, Shirley Ho, Sambatra Andrianomena
Figure caption: Random representative examples of diverse HI maps from the CAMELS testing set (real, right) and generated using the unconditional HIFLOW (fake, left). These maps cover an area of 25 × 25 h −1 cMpc with 64 pixels on a side, resulting in a resolution of ∼0.4 h −1 cMpc. The color scale in these maps show the column density range log10 N HI/cm−2 = 14–22.
Paper: HIFLOW: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics Using Normalizing Flows
Authors: Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt, David N. Spergel, Daniel Anglés-Alcázar, Shy Genel, Miles Cranmer, Greg L. Bryan, Romeel Davé, Rachel S. Somerville, Michael Eickenberg, Desika Narayanan, Shirley Ho, Sambatra Andrianomena
Figure caption: Diagram of the inference scheme by the conditional HIFLOW on the cosmological parameters (Ω m  and σ 8). Arrows indicate conditional dependence between variables, which constructs the flow between all MADEs in order to produce the joint density from the N-dimensional Gaussian distribution. The z variables denote the latent spaces in MADEs.
Paper: Detectability of Rotational Modulation in Kepler Sun-like Stars as a Function of Age
Authors: Kento Masuda
Figure caption: Results of injection-and-recovery tests for 5000 simulated stars. The plots show the biases in the recovered mass (left) and age (right) as a function of true stellar mass and age, where the median of the marginal posterior PDF is adopted as the “recovered” value. Each cell contains many stars with different metallicities and parallaxes. In each cell, we compute the median of the differences between the recovered and true masses/ages and show the value with different colors indicated in the right-hand scales (see Section 2.2 for details). The star symbols show the ages and masses of Kepler asteroseismic stars from the LEGACY and KEGAS projects (see Section 2.3).
Paper: Detectability of Rotational Modulation in Kepler Sun-like Stars as a Function of Age
Authors: Kento Masuda
Figure caption: Ages and masses of the CKS stars derived from (left) isochrone fitting only and from (right) isochrone and gyrochrone in Section 3. Filled circles show stars for which rotational modulation has been detected by Mazeh et al. (2015). These plots show the medians of the marginal posteriors, which are significantly biased in a large part of the parameter space (see Figure 2). For example, although no star appears to have ages ≲2 Gyr in the left panel, many of the stars around the lower edge in fact have large age uncertainties and are consistent with ages ≲2 Gyr. Therefore these plots are only for illustration; a more careful analysis fully taking into account the age and mass uncertainties will be performed in Section 5 to derive the fraction of stars with robust rotation periods as a function of mass and age.
Paper: Detectability of Rotational Modulation in Kepler Sun-like Stars as a Function of Age
Authors: Kento Masuda
Figure caption: Results of injection-and-recovery tests for individual stars. Typically masses are well determined, and age posteriors are not inconsistent with the truths, but simple summary statistics such as median often miss the mark; see also Figure 2, and Section 2.2 for details.
Paper: Detectability of Rotational Modulation in Kepler Sun-like Stars as a Function of Age
Authors: Kento Masuda
Figure caption: Amplitudes of photometric modulation versus ages for stars with different mass ranges shown in the title of each plot. Stars with robust period detection are shown with filled circles, for which ages and masses are from joint isochrone and gyrochrone fitting (Section 3.2); otherwise, the parameters are from isochrone-only fitting (Section 3.1). The ages shown here are medians of the marginal posterior PDFs. Gray dashed lines show the scaling ﹩{R}_{\mathrm{per}}\propto {t}_{\star }^{-2.3}﹩, which for ﹩{P}_{\mathrm{rot}}\propto {t}_{\star }^{1/2}﹩ corresponds to ﹩{R}_{\mathrm{per}}\propto {P}_{\mathrm{rot}}^{-4.6}﹩ derived in Masuda (2022) using a larger sample of Kepler stars. Thus, this is not a fit to the data shown here; see the text for details.
Paper: Detectability of Rotational Modulation in Kepler Sun-like Stars as a Function of Age
Authors: Kento Masuda
Figure caption: Test results for the uniform mass–age distribution in Section 4.3.1. Top-left panel: the mean of the posterior PDF for p occ as a function of mass and age. Top-right panel: the true distribution that was used to simulate the sample. Bottom-left panel: marginal distribution for stellar mass. The orange line and shading show the mean and standard deviation of the prediction, respectively. The gray dashed histogram shows the ground truth (i.e., top-right panel). The blue dashed histogram shows the distribution of the medians of marginal mass posterior in the sample. Bottom-right panel: same as the bottom left, but for age.
Paper: Detectability of Rotational Modulation in Kepler Sun-like Stars as a Function of Age
Authors: Kento Masuda
Figure caption: Comparison between our isochrone parameters and seismic parameters for Kepler seismic stars. They happen to have masses and ages for which isochrone fitting works particularly well (see also Figure 2). The vertical axes show the difference between the median of the posterior of the isochrone fit from the seismic value. The error bars only show the 68% interval of the isochrone fit.
Paper: Detectability of Rotational Modulation in Kepler Sun-like Stars as a Function of Age
Authors: Kento Masuda
Figure caption: Ages and metallicities of the CKS stars. The metallicity comes from the CKS, and ages are medians of the marginal posteriors from isochrone-only fitting (left) and from joint isochrone and gyrochrone fitting (right); see Section 3. Filled circles show stars for which rotational modulation has been detected by Mazeh et al. (2015).
Paper: Detectability of Rotational Modulation in Kepler Sun-like Stars as a Function of Age
Authors: Kento Masuda
Figure caption: Test results for the young low-mass stars (Section 4.3.3). See the caption of Figure 7.
Paper: Detectability of Rotational Modulation in Kepler Sun-like Stars as a Function of Age
Authors: Kento Masuda
Figure caption: Test results for the Gaussian distribution in Section 4.3.2. See the caption of Figure 7.
Paper: Orbital Decay in M82 X-2
Authors: Matteo Bachetti, Marianne Heida, Thomas Maccarone, Daniela Huppenkothen, Gian Luca Israel, Didier Barret, Murray Brightman, McKinley Brumback, Hannah P. Earnshaw, Karl Forster, Felix Fürst, Brian W. Grefenstette, Fiona A. Harrison, Amruta D. Jaodand, Kristin K. Madsen, Matthew Middleton, Sean N. Pike, Maura Pilia, Juri Poutanen, Daniel Stern, John A. Tomsick, Dominic J. Walton, Natalie Webb, Jörn Wilms
Figure caption: Neutron star dipolar magnetic field estimate assuming spin equilibrium as described in the text, comparing the models from Wang (1996; the area corresponds to values of 0.5 < ξ < 1) and from Chashkina et al. (2017; the area covers the range of viscosity parameter 0.01 < α  v  < 0.3), in four cases with a range of mass ratios and a different mass-loss fraction (α in Equation (B3)) from the donor. We show R co and R sph with vertical lines. Note that a lower mass ratio increases the estimated mass transfer. Also, the mass loss from the donor implies a larger mass transfer rate to justify the observed derivative. This explains the larger R sph. We highlight the region where 0.8 < R in/R co < 1, as expected from spin equilibrium. For the traditional model we took into account a change of accretion rate due to mass loss inside R sph. The estimated dipolar magnetic field is always above 1013 G for reasonable values of the parameters.
Paper: Orbital Decay in M82 X-2
Authors: Matteo Bachetti, Marianne Heida, Thomas Maccarone, Daniela Huppenkothen, Gian Luca Israel, Didier Barret, Murray Brightman, McKinley Brumback, Hannah P. Earnshaw, Karl Forster, Felix Fürst, Brian W. Grefenstette, Fiona A. Harrison, Amruta D. Jaodand, Kristin K. Madsen, Matthew Middleton, Sean N. Pike, Maura Pilia, Juri Poutanen, Daniel Stern, John A. Tomsick, Dominic J. Walton, Natalie Webb, Jörn Wilms
Figure caption: Example detection of pulsations, and orbital/spin parameter refinement, from ObsID 30101045002. The color map shows the Rayleigh search in a three-parameter grid, using spin frequency, spin first derivative, and the drift of the periastron passage T asc. The corner plot on the right, instead, shows the refinement of the results of the Rayleigh search, with the addition of pulse phase, fitted using the Bayesian method by Pletsch & Clark (2015). For example, if dF0-5 is −0.03 and the initial F0 was 0.725, this means that the best-fit frequency is 0.725 − 0.03 · 10−5 Hz.
Paper: Orbital Decay in M82 X-2
Authors: Matteo Bachetti, Marianne Heida, Thomas Maccarone, Daniela Huppenkothen, Gian Luca Israel, Didier Barret, Murray Brightman, McKinley Brumback, Hannah P. Earnshaw, Karl Forster, Felix Fürst, Brian W. Grefenstette, Fiona A. Harrison, Amruta D. Jaodand, Kristin K. Madsen, Matthew Middleton, Sean N. Pike, Maura Pilia, Juri Poutanen, Daniel Stern, John A. Tomsick, Dominic J. Walton, Natalie Webb, Jörn Wilms
Figure caption: Mass transfer rate toward the accretor (through L1), vs. donor mass, given the measured orbital period decay. We estimate the mass transfer rate for different mechanisms of mass loss and different accretor masses. Solid lines indicate conservative mass transfer, dashed lines indicate 90% isotropic mass loss from the donor, and dotted lines indicate 100% mass loss from the proximity of the accretor. The latter is only a small correction to the conservative case, while mass loss from the donor implies a much larger mass transfer rate in order to produce the observed orbital decay. Vertical bands on the left show the limit donor masses given the absence of eclipses, for different accretor masses (same color coding).
Paper: Orbital Decay in M82 X-2
Authors: Matteo Bachetti, Marianne Heida, Thomas Maccarone, Daniela Huppenkothen, Gian Luca Israel, Didier Barret, Murray Brightman, McKinley Brumback, Hannah P. Earnshaw, Karl Forster, Felix Fürst, Brian W. Grefenstette, Fiona A. Harrison, Amruta D. Jaodand, Kristin K. Madsen, Matthew Middleton, Sean N. Pike, Maura Pilia, Juri Poutanen, Daniel Stern, John A. Tomsick, Dominic J. Walton, Natalie Webb, Jörn Wilms
Figure caption: Roche-lobe radius in Eggleton approximation (Eggleton 1983) vs. mass for the donor star in M82 X-2. The donor has to lie around the black solid line in order to undergo Roche-lobe overflow. We overplot all donors from the HMXBs in Falanga et al. (2015) and all semidetached binary stars from Surkova & Svechnikov (2004) for comparison. For the HMXBs, we plot in black the Roche-lobe radius. Colors span the A (red) to O (blue) spectral types, and markers indicate different branches in the Hertzsprung–Russell diagram. The gray shaded area is excluded by the absence of eclipses. The black area is prohibited by the mass function and the necessity that ﹩\sin i\leqslant 1﹩.