adsabs/AstroCLIMB huggingface dataset:
Paper: Particle-in-Cell Simulations of Sunward and Anti-sunward Whistler Waves in the Solar Wind
Authors: Ilya V. Kuzichev, Ivan Y. Vasko, Anton V. Artemyev, Stuart D. Bale, Forrest S. Mozer
Figure caption: The results of all the 75 simulation runs performed at various values of core electron beta β  c , halo temperature anisotropy A  h , and core electron drift velocity u  c /v  A  (Table 1). Each of the panels (a)–(c) presents saturated amplitudes of parallel and antiparallel whistler waves observed in 25 simulation runs performed at β  c  = 0.3, 1, and 3. Note that we present the amplitude of a whistler wave provided that its initial linear growth rate is larger than 10−3 ω  ce ; otherwise, the computation time of ﹩5000{\omega }_{{ce}}^{-1}﹩ is insufficient for the whistler waves to saturate in our simulations.
Paper: Particle-in-Cell Simulations of Sunward and Anti-sunward Whistler Waves in the Solar Wind
Authors: Ilya V. Kuzichev, Ivan Y. Vasko, Anton V. Artemyev, Stuart D. Bale, Forrest S. Mozer
Figure caption: The results of a simulation run performed at β  c  = 1, A  h  = 1.3, and u  c /v  A  = −3. Panel (a) presents the magnitude of magnetic field ﹩\delta {\boldsymbol{B}}(x,t)=\delta {B}_{y}(x,t)\hat{y}+\delta {B}_{z}(x,t)\hat{z}﹩ perpendicular to background magnetic field ﹩{B}_{0}\hat{x}﹩. Panels (b) and (c) demonstrate the magnitude of magnetic fields δ  B  +(x, t) and δ  B  −(x, t) corresponding to whistler waves propagating parallel and antiparallel to the electron heat flux. The magnetic field fluctuations were decomposed into those propagating parallel and antiparallel to the electron heat flux using the Fourier transform (Section 2).
Paper: Particle-in-Cell Simulations of Sunward and Anti-sunward Whistler Waves in the Solar Wind
Authors: Ilya V. Kuzichev, Ivan Y. Vasko, Anton V. Artemyev, Stuart D. Bale, Forrest S. Mozer
Figure caption: Panel (a) presents the relative electron heat flux variations reached by the end of 75 simulation runs performed for various values of core electron beta β  c , halo temperature anisotropy A  h , and core electron drift velocity u  c /v  A  (Table 1). Panel (b) shows the electron heat flux variation vs. the saturated amplitude of parallel (red) and antiparallel (blue) whistler waves. Note that we only present results of those simulations runs where the whistler waves had initial growth rates larger than 10−3 ω  ce  and could saturate over the computation time.
Paper: Particle-in-Cell Simulations of Sunward and Anti-sunward Whistler Waves in the Solar Wind
Authors: Ilya V. Kuzichev, Ivan Y. Vasko, Anton V. Artemyev, Stuart D. Bale, Forrest S. Mozer
Figure caption: Each of the panels (a)–(c) presents saturated amplitudes of parallel and antiparallel whistler waves observed in 25 simulation runs performed at β  c  = 0.3, 1, and 3. The saturated amplitudes B  w /B 0 of the whistler waves are correlated with their initial linear growth rates γ/ω  ce , and the best power-law fits are indicated in the panels; the best-fit parameters are also presented in Table 2.
Paper: Particle-in-Cell Simulations of Sunward and Anti-sunward Whistler Waves in the Solar Wind
Authors: Ilya V. Kuzichev, Ivan Y. Vasko, Anton V. Artemyev, Stuart D. Bale, Forrest S. Mozer
Figure caption: Panel (a) presents the temporal evolution of averaged magnetic field magnitudes of parallel and antiparallel whistler waves observed in the simulation run shown in Figure 2; the magnetic field magnitudes were averaged over the simulation box, ﹩\langle \delta {B}_{\pm }\rangle ={\left[{L}^{-1}{\int }_{0}^{L}| \delta {{\boldsymbol{B}}}_{\pm }{| }^{2}\ {dx}\right]}^{1/2}﹩. Panel (b) presents the corresponding growth rates, ﹩\gamma (t)=d/{dt}\left[\langle \ \delta {B}_{\pm }\rangle \ \right]﹩.
Paper: Helioseismic Investigation of Quasi-biennial Oscillation Source Regions
Authors: Kiran Jain, Partha Chowdhury, Sushanta C. Tripathy
Figure caption: Morlet wavelet spectrum (left) and the global power spectrum (right) for 10.7 cm radio flux. Daily flux values are averaged over 36 days. The two vertical brown lines in the left panels delineate the boundaries between cycles 23 and 24 then cycles 24 and 25, while the dashed lines in the right panels represent the 95% confidence level.
Paper: Helioseismic Investigation of Quasi-biennial Oscillation Source Regions
Authors: Kiran Jain, Partha Chowdhury, Sushanta C. Tripathy
Figure caption: Morlet wavelet spectra (left) and the global power spectra (right) for 10.7 cm radio flux in cycle 23 (top) and cycle 24 (bottom). The dashed lines represent the 95% confidence level.
Paper: The Impact of Patchy Reionization on Ultrafaint Dwarf Galaxies
Authors: Jaeeun Kim, Myoungwon Jeon, Yumi Choi, Hannah Richstein, Elena Sacchi, Nitya Kallivayalil
Figure caption: The strength of patchy reionization in terms of hydrogen photoionization rate (left) and photoheating rate (right) exerted by a host galaxy onto a target UFD analog during the period from z = 7 to z = 3. The values derived from GR and PR runs are represented by solid black and colored lines, respectively. The PR values are, on average, 2 orders of magnitude lower than those of GR from the onset of the first star formation in UFD analogs (z ≳ 10) until around z ∼ 3. Since the contribution from star-forming galaxies to reionization is the only consideration, we switch to the GR implementation at a designated transition time, z t = 5.8. In the bottom panels, the same data is presented as in the upper panels, but with a transition redshift of z t = 5.8 applied. Below this redshift, the GR implementation is used, and this combination of GR and PR is referred to as PR going forward.
Paper: The Impact of Patchy Reionization on Ultrafaint Dwarf Galaxies
Authors: Jaeeun Kim, Myoungwon Jeon, Yumi Choi, Hannah Richstein, Elena Sacchi, Nitya Kallivayalil
Figure caption: The evolution of the simulated UFD analogs in the GR runs. Upper left: the total mass (long-dashed line) and gas mass (short-dashed line) of the UFD analogs. Upper right: the maximum hydrogen number density. Lower left: the star formation rate (SFR). Lower right: the cumulative star formation history (SFH). The quantities presented in all panels are computed using particles residing within the virial radius of the halos. The gray-shaded regions in each panel denote the period when a homogeneous UV background field is introduced at z = 7, with its strength gradually increasing up to the full value by z = 6. The evolution of each UFD analog realization is depicted using different colors and line types. Due to the effects of reionization and SN feedback, all simulated galaxies experience truncated star formation. The upper-right panel shows that there is no dense gas above the density threshold, n H,th = 100 cm−3, suggesting that no further star formation has occurred since the epoch of reionization.
Paper: The Impact of Patchy Reionization on Ultrafaint Dwarf Galaxies
Authors: Jaeeun Kim, Myoungwon Jeon, Yumi Choi, Hannah Richstein, Elena Sacchi, Nitya Kallivayalil
Figure caption: Cumulative SFHs (top panel) and the maximum hydrogen number density of the gas (bottom panel) of the simulated UFD analogs, comparing the runs using GR (left panel) and PR (right panel) implementations. The evolution of each UFD analog is represented by different colors and line types. The simulations with GR implementation indicate complete cessation of star formation due to reionization, whereas three runs (HALO2-PR, HALO4-PR, and HALO5-PR) using PR exhibit prolonged SFHs compared to those of GR runs. Among the runs adopting PR implementation, HALO5-PR shows the most extended SFH, forming stars until around z ∼ 4.45. The late star formation phase due to patchy reionization lasts for 280 Myr, 180 Myr, and 550 Myr in HALO2-PR, HALO4-PR, and HALO5-PR, respectively. The lower right panel indicates that halos with extended SFH tend to retain dense gas particles with a threshold density of n H, th = 100 cm−3, which can facilitate late star formation due to the weaker UV field compared to that of GR.
Paper: The Impact of Patchy Reionization on Ultrafaint Dwarf Galaxies
Authors: Jaeeun Kim, Myoungwon Jeon, Yumi Choi, Hannah Richstein, Elena Sacchi, Nitya Kallivayalil
Figure caption: The same as Figure 4, but it displays the metallicity for stars from all PR runs. The blue-shaded region in each panel indicates the period where the patchy reionization effect is applied, and the blue inverted triangles within this region represent the stars formed during the late star formation phase in all PR runs. It is interesting to note that the stars formed during the late phase of star formation are likely to exhibit metallicity levels similar to or even lower than those of stars with the highest metallicity formed prior to reionization. This is because even though the UV field from patchy reionization is not strong enough to completely stop star formation, it temporarily halts it between z = 7 and z = 6. During this time, metals in the gas tend to diffuse along with the gas due to patchy reionization. This leads to a decrease in the gas metallicity and the formation of stars with similar or lower metallicities.
Paper: The Impact of Patchy Reionization on Ultrafaint Dwarf Galaxies
Authors: Jaeeun Kim, Myoungwon Jeon, Yumi Choi, Hannah Richstein, Elena Sacchi, Nitya Kallivayalil
Figure caption: The metallicity evolution of stars as a function of their formation time in HALO2-GR (left panel) and HALO4-GR (right panel). All of the stars found within the virial radius of the halos at z = 0 are shown in the upper panels, while the bottom panels show only the in situ stars in the primary progenitor of a halo. Among all of the stars in the upper panels, we differentiate the in situ stars by coloring them in black. As depicted in the run, HALO2-GR, stars with relatively low metallicity ([Fe/H] < −2) usually form in progenitor halos that later merge with the primary halo. The evolution of the [Fe/H] in the primary halo shows a rising trend with redshift, as seen in the case of HALO2-GR. At z ≈ 13, the value of [Fe/H] is [Fe/H] ≈ −3, which increases to [Fe/H] ≈ −2 at z ≈ 6.
Paper: The Impact of Patchy Reionization on Ultrafaint Dwarf Galaxies
Authors: Jaeeun Kim, Myoungwon Jeon, Yumi Choi, Hannah Richstein, Elena Sacchi, Nitya Kallivayalil
Figure caption: The cumulative SFHs of the simulated galaxies are shown as the transition time decreases from z t = 5.8 to z t = 5.5. As the completion of reionization is delayed, the halos tend to form more stars through late star formation, and the duration of the late star formation generally increases. Consequently, the stars produced by late star formation contribute to an increase in stellar mass. For example, when the transition time is changed from z t = 5.8 to z t = 5.5, the stellar mass of Halo2-PR and Halo4-PR increases by up to 31% and 63%, respectively. Furthermore, our findings suggest that the extent of late star formation in the simulated galaxies is not only dependent on the transition time from PR to GR implementation but also on the nature of the late star formation, whether it occurs in episodic bursts or as a continuous process.
Paper: The Impact of Patchy Reionization on Ultrafaint Dwarf Galaxies
Authors: Jaeeun Kim, Myoungwon Jeon, Yumi Choi, Hannah Richstein, Elena Sacchi, Nitya Kallivayalil
Figure caption: The normalized metallicity distribution function of stars is shown for each simulated galaxy, with the range spanning from [Fe/H] = −3.9 to [Fe/H] = −1.1 and an interval of 0.2 dex. The orange (blue) histogram exhibits the simulated halo adopting the GR (PR) implementation. In each halo, the peak of the MDF of stars ranges from [Fe/H] = −2.5 to [Fe/H] = −2. The MDFs of halos with PR implementation, especially HALO2-PR, HALO4-PR, HALO5-PR, display a shift toward higher metallicity compared to those with GR. This shift is attributed to the extended star formation period in the PR runs, which leads to the formation of stars with higher metallicity.
Paper: The Impact of Patchy Reionization on Ultrafaint Dwarf Galaxies
Authors: Jaeeun Kim, Myoungwon Jeon, Yumi Choi, Hannah Richstein, Elena Sacchi, Nitya Kallivayalil
Figure caption: The fraction of stars formed within the progenitor halos of the UFD analog is shown in each panel as a function of halo masses at z = 7. Only progenitor halos that have formed stars contributing to more than 5% of the stellar mass of the UFD analog at z = 0 are displayed. The value in the upper corner of each panel represents the proportion of stars formed at z = 7 compared to the total number of stars formed by z = 0, implying that all stars formed at z = 7. The primary halo, the most massive halo at a given epoch, is depicted as a filled blue circle. This suggests that the number of progenitor halos contributing to the total stellar mass can vary depending on how fast the primary halo grows.
Paper: The Impact of Patchy Reionization on Ultrafaint Dwarf Galaxies
Authors: Jaeeun Kim, Myoungwon Jeon, Yumi Choi, Hannah Richstein, Elena Sacchi, Nitya Kallivayalil
Figure caption: Dark matter morphology at z = 0, with each panel depicting a smaller scale from left to right. Left: final snapshot from a dark-matter-only preliminary run that includes an MW-like halo, marked as a large white circle, and a UFD analog with masses of M vir ∼ 2 × 1012 M ⊙ and M vir ∼ 109 M ⊙ at z = 0, respectively. Middle: the zoom-in of the region surrounding the UFD analogs. Right: detailed look of HALO6-GR (see Table 1). The virial radius of the halo is ∼ 30 kpc at z = 0.
Paper: Validating Posteriors Obtained by an Emulator When Jointly Fitting Mock Data of the Global 21 cm Signal and High-z Galaxy UV Luminosity Function
Authors: J. Dorigo Jones, D. Rapetti, J. Mirocha, J. J. Hibbard, J. O. Burns, N. Bassett
Figure caption: Marginalized 1D posterior distributions when jointly fitting mock global 21 cm signal and UVLF data using the full ARES model, for three different 21 cm noise levels: 10 mK (optimistic), 25 mK (standard), and 250 mK (pessimistic). These eight parameters control the SFE and the UV and X-ray photon production in galaxies (see Table 1). Blue vertical lines indicate the input, or fiducial, parameter values used to generate the mock data (see Section 2.6). The dotted green histograms result from using PolyChord with σ 21 = 10 mK and match well the corresponding distributions obtained by using MultiNest. The noise on the mock UVLF being fit is the same as the error on the z=5.9 UVLF measurements from Bouwens et al. (2015), except for the posteriors for 10 mK shown here, for which we used twice the UVLF error to allow for a reasonable convergence time of the PolyChord run (see Section 3.1). The posteriors for 25 and 250 mK are the same as those in Figure 3 and B2 in Appendix B, respectively. Axis ranges are zoomed in from the full prior ranges given in Table 1.
Paper: Validating Posteriors Obtained by an Emulator When Jointly Fitting Mock Data of the Global 21 cm Signal and High-z Galaxy UV Luminosity Function
Authors: J. Dorigo Jones, D. Rapetti, J. Mirocha, J. J. Hibbard, J. O. Burns, N. Bassett
Figure caption: Top: emulation bias (number of standard deviations; see Equation (5)) between globalemu and ARES for different noise levels of the mock 21 cm data being jointly fit with the mock UVLF data. Generally, the emulation bias decreases as the 21 cm noise level increases. For σ 21 = 50 and 250 mK, the emulation biases are <1 for all eight parameters, as indicated by the horizontal black line. The emulation biases for γ lo, ﹩{T}_{\min }﹩, and f esc can be significantly higher than the rest for certain lower 21 cm noise levels. Bottom: true bias (Equation 6) between ARES and the fiducial parameter values, for the same joint fits. True bias is lowest at 25 mK (<1 for all parameters) and increases at high and low 21 cm noise levels owing to increased uncertainty and difficulty in sampling, respectively (see Section 3.1). As also discussed in the text, note that the high emulation bias on f esc at 5 mK is dominated by its high true bias.
Paper: Validating Posteriors Obtained by an Emulator When Jointly Fitting Mock Data of the Global 21 cm Signal and High-z Galaxy UV Luminosity Function
Authors: J. Dorigo Jones, D. Rapetti, J. Mirocha, J. J. Hibbard, J. O. Burns, N. Bassett
Figure caption: Marginalized 1D and 2D posterior distributions for eight astrophysical parameters in ARES when jointly fitting mock global 21 cm signal and UVLF data. These eight parameters control the SFE and the UV and X-ray photon production in galaxies (see Table 1). The red posterior is obtained using the ARES-trained globalemu network model, and the black posterior is obtained using the full ARES model. Blue vertical and horizontal lines indicate the input, or fiducial, parameter values used to generate the mock data being fit (see Table 1), which are calibrated to real observations of the UVLF (see Section 2.6). The statistical noise in the 21 cm data being fit is  σ  21 = 25 mK, which among the five tested we find gives the most accurate “true” ARES posteriors with respect to the fiducial parameter values and also highlights for which parameters globalemu obtains biased constraints (see also Figure 5). The UVLF data noise is the same as the error on the z = 5.9 UVLF measurements from Bouwens et al. (2015). Contour lines in the 2D histograms represent the 95% confidence levels, and density color maps are shown. Axis ranges are zoomed in with respect to the full prior ranges given in Table 1. See Table 2 for further details on each fit.
Paper: Validating Posteriors Obtained by an Emulator When Jointly Fitting Mock Data of the Global 21 cm Signal and High-z Galaxy UV Luminosity Function
Authors: J. Dorigo Jones, D. Rapetti, J. Mirocha, J. J. Hibbard, J. O. Burns, N. Bassett
Figure caption: Marginalized 1D and 2D posterior distributions obtained when fitting either mock global 21 cm signal data (red and black) or mock UVLF data (green). All is the same as in Figure 3, except that the statistical noise in the 21 cm data being fit is  σ  21 = 50 mK, and the axis ranges are the full prior ranges given in Table 1. See Table 2 for further details on each fit.
Paper: Validating Posteriors Obtained by an Emulator When Jointly Fitting Mock Data of the Global 21 cm Signal and High-z Galaxy UV Luminosity Function
Authors: J. Dorigo Jones, D. Rapetti, J. Mirocha, J. J. Hibbard, J. O. Burns, N. Bassett
Figure caption: Representative subset of the training set (10% out of 24,000 total) containing mock global 21 cm signals generated by ARES when varying eight astrophysical parameters. The full training set was used to train globalemu (see Table 1 for the parameter ranges). Shown in thick blue is the fiducial global 21 cm signal to which we add Gaussian-distributed noise at different levels to form the mock 21 cm data sets that we fit. The mock UVLF data that we add in our joint fits are also generated by ARES using the same fiducial parameter values (see Table 1) that were obtained via calibration to the Bouwens et al. (2015) z = 5.9 UVLF by Mirocha et al. (2017), as described in Section 2.6.
Paper: Validating Posteriors Obtained by an Emulator When Jointly Fitting Mock Data of the Global 21 cm Signal and High-z Galaxy UV Luminosity Function
Authors: J. Dorigo Jones, D. Rapetti, J. Mirocha, J. J. Hibbard, J. O. Burns, N. Bassett
Figure caption: Marginalized 1D and 2D posterior distributions for eight astrophysical parameters in ARES when jointly fitting mock global 21 cm signal and UVLF data. All is the same as in Figure 3, except that the noise in the 21 cm data being fit is  σ  21 = 50 mK.
Paper: Validating Posteriors Obtained by an Emulator When Jointly Fitting Mock Data of the Global 21 cm Signal and High-z Galaxy UV Luminosity Function
Authors: J. Dorigo Jones, D. Rapetti, J. Mirocha, J. J. Hibbard, J. O. Burns, N. Bassett
Figure caption: Top: representative subset of the test set (200 out of 2000) generated by ARES (“true” global signals; black dashed curves) and the corresponding subset of emulations from the globalemu network (solid red curves) trained on the ARES training set using the architecture [32, 32, 32]. Bottom: differences between the emulated and “true” signals in the top panel (i.e., emulation residuals), with color depicting the depth of the Cosmic Dawn (CD) trough of the respective signal. The horizontal dotted red line indicates the mean RMSE of 1.25 mK between the emulated and “true” signals in the full test set (see Section 2.5).
Paper: Dust Coagulation Reconciles Protoplanetary Disk Observations with the Vertical Shear Instability. I. Dust Coagulation and the VSI Dead Zone
Authors: Thomas Pfeil, Tilman Birnstiel, Hubert Klahr
Figure caption: Time evolution of VSI simulations based on the different dust models. Turbulent Mach numbers are shown as a function of time (radially and vertically Favre averaged) and as a function of height above the midplane (time averaged and radially Favre averaged). In models with larger particles, cooling times are generally longer, which results in lower growth rates and lower Mach number turbulence. The vertical profiles in the right panel change accordingly. Cooling times in models with larger maximum particle size increase more rapidly with height above the midplane, which also cuts off the VSI turbulence. Isothermal models typically have vertically increasing turbulent velocities. The three dashed horizontal lines show the Mach numbers corresponding to the three α values that we assumed for our coagulation models (see Equation (10)). Note that the conversion between turbulent Mach numbers and diffusivities assumes a perfect Kolmogorov turbulence spectrum (see discussion in Section 6), which is likely not given for the anisotropic VSI turbulence.
Paper: Dust Coagulation Reconciles Protoplanetary Disk Observations with the Vertical Shear Instability. I. Dust Coagulation and the VSI Dead Zone
Authors: Thomas Pfeil, Tilman Birnstiel, Hubert Klahr
Figure caption: Row a: RADMC-3D intensity maps of our VSI simulation with v fr = 400 cm s−1 and α = 10−3, seen edge-on. Rows b, c, and d show intensity maps calculated from analytic dust distribution that assume different diffusivities δ. The grain sizes are identical in all simulations. We convolve the images with a typical ALMA beam with FWHM of 35 mas for a distance of 100 pc shown as a gray circle. Hatched areas mark regions that have optical depth τ ≥ 1. Horizontal hatches correspond to areas for which the τ = 1 surface lies on the far side of the disk. Diagonally hatched regions mark τ = 1 surfaces that lie on the observer’s side of the disk. The right panels show minor-axis cuts through the images along the vertical lines in the intensity maps. Purple lines in all plots are the minor-axis cuts from the VSI simulation (panel (a)).
Paper: Dust Coagulation Reconciles Protoplanetary Disk Observations with the Vertical Shear Instability. I. Dust Coagulation and the VSI Dead Zone
Authors: Thomas Pfeil, Tilman Birnstiel, Hubert Klahr
Figure caption: Row a: RADMC-3D intensity maps of our VSI simulation with v fr = 200 cm s−1 and α = 10−3, seen edge-on. Rows b, c, and d show intensity maps calculated from analytic dust distribution that assume different diffusivities δ. The grain sizes are identical in all simulations. We convolve the images with a typical ALMA beam with FWHM of 35 mas for a distance of 100 pc shown as a gray circle. Hatched areas mark regions that have optical depth τ ≥ 1. Horizontal hatches correspond to areas for which the τ = 1 surface lies on the far side of the disk. Diagonally hatched regions mark τ = 1 surfaces that lie on the observer’s side of the disk. The right panels show minor-axis cuts through the images along the vertical lines in the intensity maps. Purple lines in all plots are the minor-axis cuts from the VSI simulation (panel (a)).