adsabs/AstroCLIMB huggingface dataset:
Paper: The Influence of Hydrogen Sulfide on the Optical Properties of Planetary Organic Hazes: Implications for Exoplanet Climate Modeling
Authors: Nathan W. Reed, Kevin T. Jansen, Zachary R. Schiffman, Margaret A. Tolbert, Eleanor C. Browne
Figure caption: (a) Contour plot of imaginary refractive index (k) values vs. real refractive index (n) values. The color of the contours indicates the MF value calculated for each combination of n and k. The red point represents the best n and k combination to minimize the MF. (b) The experimentally derived Q ext (blue squares) and Q abs (red squares) vs. aerosol particle diameter. The calculated Q ext (solid blue line) and Q abs (solid red line) fits from the Mie theory calculation are included. The data shown are from an example experiment of 0.1% CH4 and 5 ppmv H2S in N2 experiment at 405 nm.
Paper: The Influence of Hydrogen Sulfide on the Optical Properties of Planetary Organic Hazes: Implications for Exoplanet Climate Modeling
Authors: Nathan W. Reed, Kevin T. Jansen, Zachary R. Schiffman, Margaret A. Tolbert, Eleanor C. Browne
Figure caption: Illustration of flow-through system and aerosol flow in the PASCaRD (PAS coupled with CaRD) instrumentation. “MFC” is “mass flow controller,” “DMA” is “differential mobility analyzer,” “CaRD” is “cavity ring-down spectroscopy,” “PAS” is “photoacoustic spectroscopy,” and “CPC” is “condensation particle counter.”
Paper: The Influence of Hydrogen Sulfide on the Optical Properties of Planetary Organic Hazes: Implications for Exoplanet Climate Modeling
Authors: Nathan W. Reed, Kevin T. Jansen, Zachary R. Schiffman, Margaret A. Tolbert, Eleanor C. Browne
Figure caption: (a) Real refractive indices, n and (b) imaginary refractive indices, k, as a function of the S:C ratio of the initial gas mixture at both 405 nm (blue) and 532 (green) nm. Each point is the average of two different measurements. Error bars are described in Section 3.1. Dotted lines represent linear least-squares best-fit lines for the data.
Paper: Modeling the JWST High-redshift Galaxies with a General Formation Scenario and the Consistency with the ΛCDM Model
Authors: Yi-Ying Wang, Lei Lei, Guan-Wen Yuan, Yi-Zhong Fan
Figure caption: Posterior distributions of three SFE parameters for the best-fit UV LFs presented in Figure 3. The results are displayed in blue and red, differentiating between cases with and without consideration of dust attenuation. The contours are at the 68%, 95%, and 99% credible level. The values correspond to the 68% credible level.
Paper: S8 Tension in the Context of Dark Matter–Baryon Scattering
Authors: Adam He, Mikhail M. Ivanov, Rui An, Vera Gluscevic
Figure caption: Residual between the power spectrum for our fractional IDM model and the power spectrum for ΛCDM as a function of redshift, for different values of k. None of the curves continue oscillating past z ∼ 103, indicating that there is no evolution on these scales past recombination. This means that DM–baryon interactions do not have an effect at these redshifts; if they did, the code would need to be edited to account for them. This plot is generated assuming best-fit cosmological parameters from a Planck + BOSS + DES analysis of the f  χ  = 10% IDM model, with a DM particle mass m  χ  = 1 MeV and cross section σ 0 = 5.16 · 10−26 cm2.
Paper: S8 Tension in the Context of Dark Matter–Baryon Scattering
Authors: Adam He, Mikhail M. Ivanov, Rui An, Vera Gluscevic
Figure caption: 68% and 95% confidence-level marginalized posterior distributions of all cosmological parameters for ΛCDM from Planck (gray) and our m  χ  = 1 MeV, f  χ  = 10% DM–baryon interacting model (colored) from different combinations of Planck, BOSS, and DES data.
Paper: S8 Tension in the Context of Dark Matter–Baryon Scattering
Authors: Adam He, Mikhail M. Ivanov, Rui An, Vera Gluscevic
Figure caption: 68% and 95% confidence-level marginalized posterior distributions of all cosmological parameters for ΛCDM from Planck (gray) and our m  χ  = 1 MeV, f  χ  = 10% DM–baryon interacting model (colored) from different combinations of Planck, BOSS, and DES data. Same as Figure 5 but with posteriors for BOSS-only analyses.
Paper: S8 Tension in the Context of Dark Matter–Baryon Scattering
Authors: Adam He, Mikhail M. Ivanov, Rui An, Vera Gluscevic
Figure caption: 68% and 95% confidence-level marginalized posterior distributions of all cosmological parameters for ΛCDM from Planck (gray) and our m  χ  = 1 MeV, f  χ  = 10% DM–baryon interacting model (colored) from different combinations of Planck, BOSS, and DES data. Same as Figure 5 but with posteriors for BOSS + DES added.
Paper: S8 Tension in the Context of Dark Matter–Baryon Scattering
Authors: Adam He, Mikhail M. Ivanov, Rui An, Vera Gluscevic
Figure caption: 68% and 95% confidence-level marginalized posterior distributions of all cosmological parameters for our m  χ  = 1 MeV, f  χ  = 10% DM–baryon interacting model under a Planck, BOSS, and DES analysis and a log prior on σ 0.
Paper: S8 Tension in the Context of Dark Matter–Baryon Scattering
Authors: Adam He, Mikhail M. Ivanov, Rui An, Vera Gluscevic
Figure caption: 68% and 95% confidence-level marginalized posterior distributions of all cosmological parameters for our IDM model from a Planck + BOSS + DES analysis (gray) and for ΛCDM (colored) from different combinations of Planck, BOSS, and DES data.
Paper: Iron Isotope Constraints on the Structure of the Early Solar System
Authors: Yves Marrocchi, Maxime Piralla, François L. H. Tissot
Figure caption: Diagrams of μ 54Cr vs. μ 54Fe (a) and μ 50Ti vs. μ 54Fe (b) for the bulk silicate Earth (BSE, green, Ryugu samples (pale blue), NC (red), and C (blue) chondrites. Error bars correspond to group average and their respective standard errors (2SE) of the mean (95%). Ryugu/CI samples appear somehow disconnected from other CCs and were interpreted as suggesting the presence of an isotopic dichotomy in the early solar system (Hopp et al. 2022a). Data from Burkhardt et al. (2021), Hopp et al. (2022a, 2022b), and Schiller et al. (2020).
Paper: Iron Isotope Constraints on the Structure of the Early Solar System
Authors: Yves Marrocchi, Maxime Piralla, François L. H. Tissot
Figure caption: (a)—Average Fe concentration of carbonaceous chondrites as a function of the mass fraction of matrix. The regression corresponds to the best fits using weight errors and assuming a 10% error on the Fe concentration. The regression intercept allows estimating the Fe concentration of the nonmatrix component to be 24.38 ± 1.51 wt%. Iron concentration from Braukmüller et al. (2018). Matrix mass fraction from Hellmann et al. (2020). (b)—μ 54Fe vs. 1/Fe (×10−6) mixing line defined by carbonaceous chondrites. 1/Fe was calculated using abundances in μg/g. The Fe isotopic composition of the nonmatrix component (32.9 ± 13.3) was determined using the inferred Fe concentration. Error on this component was estimated using a Monte Carlo approach. The regression corresponds to the best fit using weighted errors.
Paper: Modeling the JWST High-redshift Galaxies with a General Formation Scenario and the Consistency with the ΛCDM Model
Authors: Yi-Ying Wang, Lei Lei, Guan-Wen Yuan, Yi-Zhong Fan
Figure caption: The comoving stellar mass density contained within galaxies that are more massive than M * at z ∼ 7.5 and z ∼ 8.9. The gray data points and lines are reproduced from Figure 2 in Boylan-Kolchin (2023). The solid blue lines are the best-fit ρ *( > M *) considering the best-fit result of SFE at z = 8 and z = 9, respectively. The slightly dark blue regions represent the 90% credible regions of the estimated ρ *, while the light blue regions represent the additional contributions of cosmic variance (CV). The purple regions show the updated observations from Labbé et al. (2023). Particularly, in the left panel, the orange region represents the cumulative stellar mass density excluding the single/most massive galaxy with ﹩\mathrm{log}({M}_{* }/{M}_{\odot })\sim 10.89﹩, suggesting that such a single galaxy dominates and magnifies ρ *( > M *) significantly. This most massive galaxy has not been confirmed or excluded by spectroscopic evidence because of no data up to now.
Paper: Modeling the JWST High-redshift Galaxies with a General Formation Scenario and the Consistency with the ΛCDM Model
Authors: Yi-Ying Wang, Lei Lei, Guan-Wen Yuan, Yi-Zhong Fan
Figure caption: The fitting results of the observed UV LFs at the redshift range of 4 ≤ z ≤ 9. The selected JWST galaxies are taken from McLure et al. (2013), Finkelstein et al. (2015), Bouwens et al. (2021), Harikane et al. (2022), Adams et al. (2023), Bouwens et al. (2023a), Donnan et al. (2023), Harikane et al. (2023b), Morishita & Stiavelli (2023), and Pérez-González et al. (2023). The solid blue lines depict the best fit to the entire data set. The light blue regions represent the corresponding 90% credible regions. The dashed lines illustrate the best fit of UV LFs but reshaped to the case without dust attenuation. The observational data points are marked in circles with error bars, and the upper limits are shown in lower triangles.
Paper: Modeling the JWST High-redshift Galaxies with a General Formation Scenario and the Consistency with the ΛCDM Model
Authors: Yi-Ying Wang, Lei Lei, Guan-Wen Yuan, Yi-Zhong Fan
Figure caption: The fit to the observations of UV LFs at the redshift range of 10 ≤ z ≤ 13. The selected JWST galaxies are taken from Finkelstein et al. (2022b), Naidu et al. (2022a), Adams et al. (2023), Bouwens et al. (2023a, 2023b), Donnan et al. (2023), Finkelstein et al. (2023), Harikane et al. (2023b), McLeod et al. (2023), Morishita & Stiavelli (2023), and Pérez-González et al. (2023). The blue and yellow regions represent the corresponding 90% credible regions of the estimated LFs with and without dust attenuation. The solid, dashed-gray lines represent qualitative cases of ﹩{{ \mathcal K }}_{\mathrm{UV}}﹩ and ϵ N, where the values of β and γ are fixed at the best fitting values. The observational data points are marked in circles with error bars, and the upper limits are shown in lower triangles.
Paper: Modeling the JWST High-redshift Galaxies with a General Formation Scenario and the Consistency with the ΛCDM Model
Authors: Yi-Ying Wang, Lei Lei, Guan-Wen Yuan, Yi-Zhong Fan
Figure caption: The evolution of peak SFE with redshift. The shaded pink region represents the range of maximum SFE values with a 68% credible uncertainty at each redshift. At z ≥ 10, the estimation of SFE is performed without considering the impact of dust attenuation.
Paper: Modeling the JWST High-redshift Galaxies with a General Formation Scenario and the Consistency with the ΛCDM Model
Authors: Yi-Ying Wang, Lei Lei, Guan-Wen Yuan, Yi-Zhong Fan
Figure caption: Posterior distributions of three SFE parameters for the best-fit UV LFs presented in Figure 1. The results for different redshifts are shown in red, blue, and orange, respectively. The contours are at the 68%, 95%, and 99% credible levels. The values correspond to the 68% credible level. It should be noticed that the parameter ranges are employed distinct for better visual effect.
Paper: Modeling the JWST High-redshift Galaxies with a General Formation Scenario and the Consistency with the ΛCDM Model
Authors: Yi-Ying Wang, Lei Lei, Guan-Wen Yuan, Yi-Zhong Fan
Figure caption: The estimated SFEs with a fixed prior and a Gaussian prior of M 1 are shown in red and blue, respectively. The solid lines and colored regions represent the best-fit value and 90% credible level of SFE. The similar logarithms of Bayes evidence ﹩\mathrm{ln}(Z)﹩ suggest that it is inconclusive to distinguish the better-fitting model.
Paper: Deciphering the Slow-rise Precursor of a Major Coronal Mass Ejection
Authors: X. Cheng, C. Xing, G. Aulanier, S. K. Solanki, H. Peter, M. D. Ding
Figure caption: Left) 3D rendering of one MFR field line (orange) and two sheared arcades (green) at t = 100t  A  that are separated by the MFR boundary as delineated by the black curve in the right panel. Right) Distribution of ﹩\mathrm{log}Q﹩ overlaid with the contours of ﹩\mathrm{log}Q\ =\ 2.7,3.0,5.0﹩ in purple, blue, and magenta, respectively, at the plane y = −0.06 perpendicular to the MFR axis. The positions of the MFR field line and sheared arcades passing the plane are represented by the dots in orange and green, respectively.
Paper: Deciphering the Slow-rise Precursor of a Major Coronal Mass Ejection
Authors: X. Cheng, C. Xing, G. Aulanier, S. K. Solanki, H. Peter, M. D. Ding
Figure caption: Model fitting for the temporal evolution of the height (left), velocity (middle), and acceleration (right) of the second MFR bundle. The dots in red show measured data points, with the vertical bars indicating the uncertainties. The curves in gray show the fitted results. The vertical dotted lines in the left and middle panels represent the onset time of the main acceleration phase derived by the model. The solid line in the right panel shows the onset directly from the acceleration-time profile.
Paper: Deciphering the Slow-rise Precursor of a Major Coronal Mass Ejection
Authors: X. Cheng, C. Xing, G. Aulanier, S. K. Solanki, H. Peter, M. D. Ding
Figure caption: Temporal evolution of the height and velocity of the MFR in the simulation. The height of the MFR is estimated by tracking an overlying field line that is located at the top of the MFR in the direction of the eruption. The vertical dashed lines mark the time interval in which the driving motion at the bottom boundary gradually decreases to zero by following a ramp function.
Paper: Deciphering the Slow-rise Precursor of a Major Coronal Mass Ejection
Authors: X. Cheng, C. Xing, G. Aulanier, S. K. Solanki, H. Peter, M. D. Ding
Figure caption: Temporal evolutions of the integrated vertical components of the Lorentz force, magnetic tension, magnetic pressure gradient, and the mass density within the MFR cross section as shown in Figure 7(a). The errors (vertical bars) are from the uncertainty in determining the MFR boundary.
Paper: Extreme Variability in a Long-duration Gamma-Ray Burst Associated with a Kilonova
Authors: P. Veres, P. N. Bhat, E. Burns, R. Hamburg, N. Fraija, D. Kocevski, R. Preece, S. Poolakkil, N. Christensen, M. A. Bizouard, T. Dal Canton, S. Bala, E. Bissaldi, M. S. Briggs, W. Cleveland, A. Goldstein, B. A. Hristov, C. M. Hui, S. Lesage, B. Mailyan, O. J. Roberts, C. A. Wilson-Hodge
Figure caption: Lightcurve of GRB 211211A (black) and the corresponding minimum variability timescale (red). Inset: zoomed lightcurve around the time of the shortest variation. Individual pulse models are indicated, with the shortest rise time highlighted in bold.
Paper: Extreme Variability in a Long-duration Gamma-Ray Burst Associated with a Kilonova
Authors: P. Veres, P. N. Bhat, E. Burns, R. Hamburg, N. Fraija, D. Kocevski, R. Preece, S. Poolakkil, N. Christensen, M. A. Bizouard, T. Dal Canton, S. Bala, E. Bissaldi, M. S. Briggs, W. Cleveland, A. Goldstein, B. A. Hristov, C. M. Hui, S. Lesage, B. Mailyan, O. J. Roberts, C. A. Wilson-Hodge
Figure caption: MVT values as a function of T 90 for all Fermi–GBM GRBs with well-measured T 90 and MVT. We highlight GRBs with T 90 > 5 s and MVT < 15 ms (red line) by displaying the GBM trigger number in the legend. GRB 211211A and GRB 090720B are indicated by red letters (B, K); blue letters (A, J) mark two possibly similar GRBs, but with higher MVT; other selected GRBs have gray letters.
Paper: Extreme Variability in a Long-duration Gamma-Ray Burst Associated with a Kilonova
Authors: P. Veres, P. N. Bhat, E. Burns, R. Hamburg, N. Fraija, D. Kocevski, R. Preece, S. Poolakkil, N. Christensen, M. A. Bizouard, T. Dal Canton, S. Bala, E. Bissaldi, M. S. Briggs, W. Cleveland, A. Goldstein, B. A. Hristov, C. M. Hui, S. Lesage, B. Mailyan, O. J. Roberts, C. A. Wilson-Hodge
Figure caption: Flux density evolution of the late emission, fit with a smoothly broken power law. The time axis is shifted by 9 s with respect to the trigger time to match the end of the main episode.
Paper: Extreme Variability in a Long-duration Gamma-Ray Burst Associated with a Kilonova
Authors: P. Veres, P. N. Bhat, E. Burns, R. Hamburg, N. Fraija, D. Kocevski, R. Preece, S. Poolakkil, N. Christensen, M. A. Bizouard, T. Dal Canton, S. Bala, E. Bissaldi, M. S. Briggs, W. Cleveland, A. Goldstein, B. A. Hristov, C. M. Hui, S. Lesage, B. Mailyan, O. J. Roberts, C. A. Wilson-Hodge
Figure caption:  T 90 distribution of 10 yr of GBM GRBs (von Kienlin et al. 2020). Fractions of short GRBs with T 90 > 10 s and T 90 > 30 s are indicated.
Paper: Extreme Variability in a Long-duration Gamma-Ray Burst Associated with a Kilonova
Authors: P. Veres, P. N. Bhat, E. Burns, R. Hamburg, N. Fraija, D. Kocevski, R. Preece, S. Poolakkil, N. Christensen, M. A. Bizouard, T. Dal Canton, S. Bala, E. Bissaldi, M. S. Briggs, W. Cleveland, A. Goldstein, B. A. Hristov, C. M. Hui, S. Lesage, B. Mailyan, O. J. Roberts, C. A. Wilson-Hodge
Figure caption: Lightcurve of GRB 090720B with similar short pulse + extended emission structure. GRB 090720B has a similarly short (∼2 ms) variability timescale as GRB 211211A.
Paper: S8 Tension in the Context of Dark Matter–Baryon Scattering
Authors: Adam He, Mikhail M. Ivanov, Rui An, Vera Gluscevic
Figure caption: Percent difference between the matter power spectrum for an IDM cosmology with DM–baryon scattering and collisionless cold DM (CDM) cosmology. The linear power spectrum is shown in green, and the total power spectrum is shown in orange. The lines are generated with the best-fit parameter values from a joint Planck + BOSS + the Dark Energy Survey (DES) analysis of the IDM model with a DM mass of 1 MeV and interacting fraction f  χ  = 10%. The shaded bands designate the uncertainty in reconstructed matter power spectrum that corresponds to a 1σ uncertainty around these best-fit parameter values. An increase in the interaction cross section and in the interacting fraction leads to a greater suppression in P(k), while the former also shifts the onset of suppression to larger scales.
Paper: S8 Tension in the Context of Dark Matter–Baryon Scattering
Authors: Adam He, Mikhail M. Ivanov, Rui An, Vera Gluscevic
Figure caption: Effect of varying the IDM fraction f  χ  on the linear matter power spectrum for a cosmology with DM–baryon scattering. The residuals of the linear spectra w.r.t. CDM are shown for f  χ  = 5%, 10%, 15%, and 100%. These spectra are generated with the best-fit parameter values from a joint Planck + BOSS + DES analysis of each fraction for a DM mass of m  χ  = 1 MeV. Fractions higher than ∼10% generate a strong power suppression in the range 0.2 ≲ k ≲ 2 h Mpc−1 for this choice of cross section and are thus ruled out by Lyα forest and Milky Way satellite data.
Paper: S8 Tension in the Context of Dark Matter–Baryon Scattering
Authors: Adam He, Mikhail M. Ivanov, Rui An, Vera Gluscevic
Figure caption: 68% and 95% confidence-level marginalized posterior distributions of relevant parameters for ΛCDM from Planck (black) and our fractional DM–baryon interacting model (colored) from different combinations of Planck, BOSS, and DES data. The gray bands show the DES measurement of S 8. The bottom right-hand panel shows a 2.6σ preference for nonzero interactions between DM and baryons under a combined Planck, BOSS, and DES analysis.
Paper: Iron Isotope Constraints on the Structure of the Early Solar System
Authors: Yves Marrocchi, Maxime Piralla, François L. H. Tissot
Figure caption: (a)—δ 128/126Te vs. ε 54Cr trend defined by all CCs. The dashed curve corresponds to the mixing hyperbola calculated by taking into account the respective Te and Cr concentrations and isotopic compositions of CI and the nonmatrix components (data from Hellmann et al. 2020; Burkhardt et al. 2021). (b)—δ 128/126Te vs. ε 54Fe trend established from all CCs. The mixing hyperbola is calculated from the Te and Fe concentrations and isotopic compositions.
Paper: Iron Isotope Constraints on the Structure of the Early Solar System
Authors: Yves Marrocchi, Maxime Piralla, François L. H. Tissot
Figure caption: Average Ni concentration of carbonaceous chondrites as a function of the mass fraction of matrix. The regression corresponds to the best fits using weight errors and assuming a 10% error on the Ni concentration. This suggests that the 54Fe anomalies in CCs are carried by Fe–Ni metal beads. Iron concentration from Braukmüller et al. (2018). Matrix mass fraction from Hellmann et al. (2020).
Paper: Deciphering the Slow-rise Precursor of a Major Coronal Mass Ejection
Authors: X. Cheng, C. Xing, G. Aulanier, S. K. Solanki, H. Peter, M. D. Ding
Figure caption: Early kinematics and initiation of pre-eruptive MFR. a) Slice-time plot of the AIA 131 Å difference images showing the evolution of two flux bundles of the pre-eruptive MFR. Diamonds and filled circles indicate their height-time measurements. b) The same as panel a, but for the AIA 171 Å passband representing the expanding overlying field. c) Temporal evolution of the heights of the MFR (red) and expanding overlying field (yellow). d) Temporal evolution of the velocities overplotted by the GOES SXR 1–8 Å flux (gray) and its time derivation (black). The uncertainty in velocity is mainly from that in height, which is estimated to be 2 Mm. The vertical slits in panels c and d indicate the onset of the CME impulsive acceleration and flare main phase, with their width denoting the uncertainty (1 minute). e) The radial component of HMI cylindrical equal-area (CEA) vector magnetogram. The white (black) indicates the magnetic field upward (downward). f) The decay index distribution with height above the PIL, which is an average of all height profiles of the decay index at the yellow dots, as shown in panel e. The bars in gray denote its uncertainty as derived by the standard deviation of height profiles. The dots in blue and yellow represent the initial and critical height (35 and 100 Mm) of the MFR axis, respectively. Their reference point is the midpoint of the line segment connecting the two footpoints of the cusp-shaped precursor loops. The dot in red points out the critical decay index of 1.5 for a ring current.
Paper: Deciphering the Slow-rise Precursor of a Major Coronal Mass Ejection
Authors: X. Cheng, C. Xing, G. Aulanier, S. K. Solanki, H. Peter, M. D. Ding
Figure caption: Pre-eruptive MFR and preflare emission. a) A composite of the AIA 131 Å (red) and 171 Å (green) images showing the pre-eruptive hot MFR and induced precursor loops (left) for the eruption on 2012 March 13. The AIA 131 Å difference image (subtracting the image at 15:50 UT) displaying zoom-in of the MFR (right). b) Temporal evolution of the GOES SXR 1–8 Å flux (red), temperature (blue), X-ray energy loss rate (yellow), integrated AIA 131 Å (cyan) and 304 Å (brown) intensity for NOAA AR 11429 with the field of view shown by the white box in panel a. The three dashed–dotted lines indicate the onset, peak, and end time of the main flare phase, respectively.
Paper: Deciphering the Slow-rise Precursor of a Major Coronal Mass Ejection
Authors: X. Cheng, C. Xing, G. Aulanier, S. K. Solanki, H. Peter, M. D. Ding
Figure caption: Lorentz force during the slow rise and early eruption of the MFR. a) Distributions of the vertical component of the Lorentz force density F  z  at the plane y = −0.06, as shown in Figure 6, at four different times. The boundaries of the MFR are delineated by the dashed lines; the contours of ﹩\mathrm{log}Q\,=\,3﹩ are shown by the curves in green. b–d) Temporal evolution of the MFR accelerations (﹩{a}_{{F}_{z}}﹩, ﹩{a}_{{({F}_{t})}_{z}}﹩, ﹩{a}_{{({F}_{p})}_{z}}﹩) contributed by the vertical components of the Lorentz force F  z , magnetic tension ﹩{({F}_{t})}_{z}﹩ and magnetic pressure gradient ﹩{({F}_{p})}_{z}﹩, respectively. Their errors indicated by vertical bars are mainly from the uncertainty in determining the MFR outer boundary, which is achieved by changing ﹩\mathrm{log}Q﹩ value from 2.4 to 3.6. The horizontal dotted lines indicate zero acceleration.