adsabs/AstroCLIMB huggingface dataset:
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: GW strains due to each neutrino species ﹩{\nu }_{e},{\bar{\nu }}_{e}﹩, and ν  μ  (a bundling of ν  μ , ﹩{\bar{\nu }}_{\mu }﹩, ν  τ , and ﹩{\bar{\nu }}_{\tau }﹩ neutrinos) as a function of time after bounce for the 9a (left) and 60 (right) progenitor models. The plus polarization is plotted with solid lines and the cross polarization is plotted with dashed lines. Between both progenitor models, the heavy neutrino model species ν  μ tends to have the larger strain across both polarizations, reaching close to 800 cm divided by distance for the 60 model, over an order of magnitude larger than the memory strain due to large-scale matter motions. The antineutrino species ﹩{\bar{\nu }}_{e}﹩ has the weakest strain across both progenitors and polarizations.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: (Left) Integrated Galactic density as a function of progenitor mass using the matter avg detection ranges. (Right) Integrated Galactic density as a function of progenitor mass using the “combined avg” detection ranges. Both plots are normalized to the total integrated density of the Galaxy. The fraction of the Galaxy in which aLIGO can detect the CCSN event roughly increases with progenitor mass. The left plot saturates to one at higher masses (>30 M ⊙), whereas the right plot saturates to one at around 12.25 M ⊙.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: Integrated Galactic density as a function of distance from Earth using the P. J. McMillan (2017) Galactic density model (from Equation (25)), normalized to the total integrated density of the Galaxy. Assuming Earth is located around 8.2 kpc from the Galactic center, there is a sharp increase in integrated density starting around 8 kpc when nearing the Galactic bulge and the density profile starts to plateau around 13 kpc once the integral passes to the other side of the Galactic bulge.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: The h + and h × matter strain of the 15.01 progenitor model in the x-direction with the low-frequency memory subtracted from the original signal. By isolating the high-frequency component of the signal due to turbulence, we observe that the memory-subtracted strain indeed returns to zero by the end of the simulation.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: Butterworth low-pass filter applied to the 15.01 progenitor model strain in the x-direction. The orange filtered curve isolates the low-frequency memory component of the strain due to large-scale matter motion which leads to a permanent alteration to the surrounding space.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: Milky Way density profile as described by P. J. McMillan (2017) as a function of Galactic radius R (in kiloparsec) for several values of vertical height z. The increase in density close to R = 0 is due to the bulge at the center of the Galaxy, and the effect of the bulge is less evident for larger values of z.
Paper: Characterization of the Western Pictor A Hotspot in Hard X-Rays with NuSTAR
Authors: Aamil Shaik, Eileen T. Meyer, Karthik Reddy, Sibasish Laha, Markos Georganopoulos
Figure caption: Plot showing the evolution of the power-law photon normalization at 1 keV, N 1 (top), the power-law spectral index, Γ (middle), and the integrated flux in the shared 3–8 keV band (bottom) over time. The error bars for the first two are determined via the Bayesian analysis, while the errors for the bottom plot are calculated via the XSPEC flux command. The best-fit values for the combined data of all epochs for NuSTAR FPMA (red), FPMB (blue), and Chandra (green) are plotted as dashed lines. The normalization shows slight changes over time with a significant offset between best-fit values between NuSTAR and Chandra. Despite large changes in the normalization between NuSTAR epochs 1 and 2, the uncertainties due to lower exposure limit the statistical significance of our variability analysis. In contrast, Γ shows little change over time and little variation between the different telescopes, maintaining a nearly constant spectrum (Γ ∼ 2) across all data.
Paper: Characterization of the Western Pictor A Hotspot in Hard X-Rays with NuSTAR
Authors: Aamil Shaik, Eileen T. Meyer, Karthik Reddy, Sibasish Laha, Markos Georganopoulos
Figure caption: SED of the western hotspot of Pictor A. Archival data taken from Meisenheimer et al. (1997; circles), Werner et al. (2012; squares), Isobe et al. (2017; triangles), and Isobe et al. (2020; diamonds) are plotted in orange. These archival data are consistent with a synchrotron spectrum modeled as a cutoff power law and a broken cutoff power law to account for excess IR emission, thought to be due to synchrotron emission from discrete zones with enhanced magnetic field strength (Isobe et al. 2020). We plot the unfolded X-ray spectral data points based on the best-fit models for NuSTAR (purple) and Chandra (green). We calculated the unfolded data points corrected for the instrument response and the galactic absorption using XSPEC's plot ufspec command, which scales the data by a factor equal to the ratio of the unfolded model flux and the folded model flux. The model predictions from the two observatories show a significant difference in the measured flux, which is further evidence of short-timescale variability.
Paper: Characterization of the Western Pictor A Hotspot in Hard X-Rays with NuSTAR
Authors: Aamil Shaik, Eileen T. Meyer, Karthik Reddy, Sibasish Laha, Markos Georganopoulos
Figure caption: (Top): photon flux vs. energy for the combined Chandra data. For plotting purposes only, the data are collected into 187 spectral bins across an energy range of 0.5–7 keV. Each point is the averaged spectrum across the 11 observations for that spectral bin, calculated using the total counts and exposure for that spectral bin. The solid green line plots the flux of the best-fit broken power-law model including the effects of galactic absorption. For visual purposes only, the background spectrum is subtracted. The shaded green contours around either line display the 2σ range in flux density of the posterior distribution. (Bottom): residuals of the best-fit power law model and posterior distribution as compared to the spectral data. As with the top plot, the solid green line plots the residuals between the best-fit model and the data and the shaded green contours plot the 2σ range of the posterior distribution.
Paper: Characterization of the Western Pictor A Hotspot in Hard X-Rays with NuSTAR
Authors: Aamil Shaik, Eileen T. Meyer, Karthik Reddy, Sibasish Laha, Markos Georganopoulos
Figure caption: (Top): photon flux density vs. energy for the combined NuSTAR epochs, shown separately for the FPMA (blue) and FPMB (red) modules. For plotting purposes only, the data are collected into 66 and 69 spectral bins for the FPMA and FPMB detectors, respectively, across an energy range of 3–20 keV. Each point is the averaged photon flux as calculated using the source counts and exposure for that spectral bin. The solid line plots the flux density of the folded best-fit power-law model including the effects of galactic absorption. The shaded contours around either line display the 2σ range in flux density of the posterior distribution. (Bottom): residuals of the best-fit power law model and posterior distribution as compared to the spectral data. As with the top plot, the solid lines plot the residuals between the best-fit model and the data and the shaded contours plot the 2σ range of the posterior distribution for both the FPMA and FPMB modules.
Paper: Characterization of the Western Pictor A Hotspot in Hard X-Rays with NuSTAR
Authors: Aamil Shaik, Eileen T. Meyer, Karthik Reddy, Sibasish Laha, Markos Georganopoulos
Figure caption: (Left): image of the various background components and total background spectrum for NuSTAR, taken from Wik et al. (2014). The model consists of an aperture background due to stray light leaking into the detector, an internal background consisting of a broken power law and a spectral line continuum, a background of scattered light, and a background due to unresolved X-ray sources in the FOV. (Right): image of the background fit completed using nuskybgd. The code fits background spectra from multiple annular regions which effectively encompass the entirety of the image which contains negligible source emission. The spectra from these different annuli are depicted as the red, green, and black spectra, which are binned to a minimum of 30 counts per bin, fit simultaneously, and used to constrain the spectral parameters and map the spatial variations of the background across the detector.
Paper: Characterization of the Western Pictor A Hotspot in Hard X-Rays with NuSTAR
Authors: Aamil Shaik, Eileen T. Meyer, Karthik Reddy, Sibasish Laha, Markos Georganopoulos
Figure caption: Final combined Chandra image of Pictor A using soft X-ray data from all epochs listed in Table 1. The images from each observation are aligned using PSF simulations and cross correlation of the bright core. The images are then exposure-corrected using exposure maps generated by the CIAO tool mkexpmap. The individual flux images are then averaged together, and the final image is smoothed by a Gaussian at a factor of 8 pixels (3.″92). The elliptical extraction region used for spectral analysis is encircled in magenta. The overlaid cyan contours are taken from the combined NuSTAR data shown in Figure 1. The color bar below shows the image flux scale in units of ph s−1 cm−2.
Paper: Characterization of the Western Pictor A Hotspot in Hard X-Rays with NuSTAR
Authors: Aamil Shaik, Eileen T. Meyer, Karthik Reddy, Sibasish Laha, Markos Georganopoulos
Figure caption: (Top): combined spectral data of all 11 Chandra and 8 NuSTAR FPMA and FPMB observations used in this study fit by a single model. The data are binned into 70 spectral bins across an energy range of 0.5–20 keV for visual purposes only. The solid black line plots the flux of the best-fit broken power-law model including the effects of galactic absorption and the contributions of background radiation. The shaded contours around the line display the 2σ range in flux density of the posterior distribution. (Bottom): residuals of the best-fit power law model and posterior distribution as compared to the spectral data. As with the top plot, the solid black line plots the best-fit residuals and the shaded contours plot the 2σ range of the posterior distribution.
Paper: Characterization of the Western Pictor A Hotspot in Hard X-Rays with NuSTAR
Authors: Aamil Shaik, Eileen T. Meyer, Karthik Reddy, Sibasish Laha, Markos Georganopoulos
Figure caption: LIRA-deconvolved images of Pictor A with the Chandra contours overlaid on the top. (a) shows the deconvolved image of the core region where no evidence of an inner jet is found indicating its emission is within the error limits of the PSF of the core and background. (b) shows the deconvolved image of the western hotspot. The observed structure is roughly consistent with the Chandra observations.
Paper: Characterization of the Western Pictor A Hotspot in Hard X-Rays with NuSTAR
Authors: Aamil Shaik, Eileen T. Meyer, Karthik Reddy, Sibasish Laha, Markos Georganopoulos
Figure caption: Image of source Pictor A and its western hotspot generated from NuSTAR epoch 1. The three green concentric circles define the annular background regions, which are used by the nuskybgd package to map and calculate NuSTAR's asymmetric background radiation. The first annulus is centered around the source and hotspot to exclude its source emission. The two outer annuli are defined such that their inner radii are equal to the outer radii of the preceding annulus to maximize the coverage of the image. The region sections which include pixels outside the detector’s FOV do not impact the spectral analysis as the exposure value is zero.
Paper: Properties of Solar Wind Current Sheets in the Martian Space Environment
Authors: Yangjun Chen, Mingyu Wu, Heying Wang, Peng E, Sudong Xiao, Guoqiang Wang, Xuesong Leng, Aimin Du, Tielong Zhang
Figure caption: Scatter diagrams of the current density ﹩{j}_{m}﹩ in (a) the upstream solar wind and (b) the Martian magnetosheath at the X–Y plane. The distributions of the current density ﹩{j}_{m}﹩ are shown in panels (c)–(f) as follows: (c) current density ﹩{j}_{m}﹩ of SCSs and MCSs in units of ﹩{\rm{nA}}\,{{\rm{m}}}^{-2};﹩ (d) ﹩{j}_{m}﹩ of MCSs at the dayside and nightside in units of ﹩\mathrm{nA}\,{{\rm{m}}}^{-2};﹩ (e) normalized current density ﹩{j}_{m}/{j}_{{V}_{A}}﹩; and (f) normalized current density ﹩{j}_{m}/{j}_{{V}_{T}}.﹩ In panels (c), (e), and (f), the cyan histograms represent CSs in the upstream solar wind, while the orange histograms represent CSs in the Martian magnetosheath. In (d), the dashed and dotted–dashed orange histograms display MCSs at the dayside and nightside magnetosheath, respectively.
Paper: Properties of Solar Wind Current Sheets in the Martian Space Environment
Authors: Yangjun Chen, Mingyu Wu, Heying Wang, Peng E, Sudong Xiao, Guoqiang Wang, Xuesong Leng, Aimin Du, Tielong Zhang
Figure caption: The histograms of ﹩{B}_{x}/{B}_{T}﹩ for SCSs (cyan), MCSs at the dayside magnetosheath (orange), and MCSs at the nightside magnetosheath (red). The cyan, orange, and red lines give the trend of variations of ﹩{B}_{x}/{B}_{T}﹩ in these three regions, respectively.
Paper: Electrostatic Waves and Electron Holes in Simulations of Low-Mach Quasi-perpendicular Shocks
Authors: Artem Bohdan, Aaron Tran, Lorenzo Sironi, Lynn B. Wilson
Figure caption: Overview of Run B at ﹩t=8.50{{\rm{\Omega }}}_{{\rm{i}}}^{-1}﹩. The shock travels from left to right and has its shock ramp at x ≈ 8λ si, which coincides with the largest density jump. (a) Magnetic field magnitude B/B 0. (b) Shock-normal magnetic fluctuation (B  x  − B  x0)/B 0, scaled to upstream field values B 0 and B  x0. (c) Parallel electric field, E ∥/E 0, measured with respect to local  B  and scaled to upstream motional electric field E 0. The inset waveform plot (magenta curve) shows the E ∥/E 0 waveform of a strong electron hole, measured along the magenta ray at x = 15λ si. The abscissa increases along  B  and toward ﹩+\hat{{\boldsymbol{x}}}﹩. (d) The y-averaged parallel electric field 〈E ∥/E 0〉 y  (blue) and magnetic fluctuation 〈B/B 0〉 y  − 1 (green). (e) Total (black) and y-averaged (blue) parallel electric field energy densities in arbitrary units. In panels (a)–(c), the black contours trace magnetic field lines.
Paper: Electrostatic Waves and Electron Holes in Simulations of Low-Mach Quasi-perpendicular Shocks
Authors: Artem Bohdan, Aaron Tran, Lorenzo Sironi, Lynn B. Wilson
Figure caption: Left panel: scaling of the mean electrostatic energy density ﹩\delta {E}_{\parallel }^{2}﹩ (Equation (1)) with v sh/c, for four regions in each of Runs A–E. The solid gray lines show numerical noise scaling ﹩{(\delta {E}_{\parallel }/{E}_{0})}^{2}\propto {({v}_{\mathrm{sh}}/c)}^{-2}﹩ like Equation (2); the dashed gray lines show ﹩{(\delta {E}_{\parallel }/{E}_{0})\propto ({v}_{\mathrm{sh}}/c)}^{-1}﹩. Right panel: scaling with mass ratio m i/m e based on Runs B, B400, B800, and B1836. The solid gray lines show numerical noise scaling ﹩{(\delta {E}_{\parallel }/{E}_{0})}^{2}\propto {({v}_{\mathrm{sh}}/c)}^{-2}\propto {m}_{{\rm{i}}}/{m}_{{\rm{e}}}﹩ appropriate for our simulation parameters. The electrostatic energy for Runs B800 and B1836 is dominated by numerical noise.
Paper: Electrostatic Waves and Electron Holes in Simulations of Low-Mach Quasi-perpendicular Shocks
Authors: Artem Bohdan, Aaron Tran, Lorenzo Sironi, Lynn B. Wilson
Figure caption: The electrostatic waves in Runs B and B1836 are measured in the colored regions: “Ramp” (blue), ”Near Precursor” (orange), “Far Precursor” (green), and “Control” (red). In panels (a)–(b), the y-averaged fluctuations δ B/B 0 (black) and δ n i/n 0 (orange dotted) show the region selection procedure for Run B. Panels (c)–(d) illustrate the same procedure for Run B1836. The “Near” and “Far Precursor” regions are magnetic troughs (wave half-cycles of low amplitude), with ∣δ B/B 0∣ just exceeding 0.1 and 0.01, respectively. The “Ramp” is the contiguous region where δ n i/n 0 < δ B/B 0 (corresponding to δ B ff > 0) at the sharpest magnetic field increase in panels (a) and (c). The “Control” region samples undisturbed upstream plasma.
Paper: Electrostatic Waves and Electron Holes in Simulations of Low-Mach Quasi-perpendicular Shocks
Authors: Artem Bohdan, Aaron Tran, Lorenzo Sironi, Lynn B. Wilson
Figure caption: Three electron hole trajectories in Run B, tracked between 8.0 and ﹩8.5\,{{\rm{\Omega }}}_{{\rm{i}}}^{-1}﹩. The E ∥/E 0 panels advance in time from left to right, comoving with the shock’s precursor wave train; the x- and y-axis coordinates are measured in the simulation frame. Individual electron holes are marked by the green, orange, and purple dots. The faint lines of corresponding color show their trajectories over time. The “Near Precursor” region shown is defined in Section 2.3.
Paper: Electrostatic Waves and Electron Holes in Simulations of Low-Mach Quasi-perpendicular Shocks
Authors: Artem Bohdan, Aaron Tran, Lorenzo Sironi, Lynn B. Wilson
Figure caption: One-dimensional parallel momentum distribution, integrated over p ⊥, of electrons in the Run B shock regions (a) “Ramp,” (b) “Near Precursor,” (c) “Far Precursor,” and (d) “Control.” Panels (e)–(h) are the same, but with a logarithm-scaled y-axis. The black line is the average of many simulation snapshots (t = 8.00 to ﹩8.50{{\rm{\Omega }}}_{{\rm{i}}}^{-1}﹩). The orange line is a best-fit bi-Gaussian distribution (Table 2).
Paper: Electrostatic Waves and Electron Holes in Simulations of Low-Mach Quasi-perpendicular Shocks
Authors: Artem Bohdan, Aaron Tran, Lorenzo Sironi, Lynn B. Wilson
Figure caption: Electrostatic power spectrum ﹩W({\boldsymbol{k}}={k}_{\parallel }\hat{{\boldsymbol{b}}})﹩, measured along the ray k ⊥ = 0, for the “Ramp” region in Runs A–E. The left column shows k ∥ scaled to λ se; the right column shows k ∥ scaled to λ De. The thick translucent lines are the “Control” region power spectra. The triangle and dotted vertical line together mark ﹩{k}_{\max }﹩ (Equation (3)). The dashed vertical line marks k damp, the damping wavelength set by the PIC current filtering.
Paper: Electrostatic Waves and Electron Holes in Simulations of Low-Mach Quasi-perpendicular Shocks
Authors: Artem Bohdan, Aaron Tran, Lorenzo Sironi, Lynn B. Wilson
Figure caption: The electrostatic power spectrum’s peak wavelength ﹩{\lambda }_{\max }\equiv 2\pi /{k}_{\max }﹩ scaled to λ se (blue dots) and λ De (red dots) for the “Near Precursor” (left column) and “Ramp” (right column) regions.
Paper: Electrostatic Waves and Electron Holes in Simulations of Low-Mach Quasi-perpendicular Shocks
Authors: Artem Bohdan, Aaron Tran, Lorenzo Sironi, Lynn B. Wilson
Figure caption: Like Figure 4, but showing numerical convergence with respect to the total number of particles per cell, counting both species. Runs A, B, and C are converged, based on the near-constant electrostatic energy density in the “Ramp” and “Near Precursor” regions; Runs D, E, and B400 are less or not converged. Runs B800 and B1836 are not converged and/or do not generate electrostatic fluctuations above numerical noise.
Paper: Where Have All the Sulfur Atoms Gone? Polycyclic Aromatic Hydrocarbon as a Possible Sink for the Missing Sulfur in the Interstellar Medium. I. The C–S Band Strengths
Authors: X. J. Yang, Lijun Hua, Aigen Li
Figure caption: Absorption cross sections for a S-containing PAH molecule of 48 C atoms (left panel) and its cation (right panel). As described in Equation (3), the absorption cross sections are obtained by adding the C–S vibrational bands of various S atoms (N S = 0, 1, 3, and 5) to that of astro-PAHs of B. T. Draine & A. Li (2007). Assuming the interstellar PAH population consumes a total C/H abundance of ﹩{\left[{\rm{C}}/{\rm{H}}\right]}_{\mathrm{PAH}}=50\,\mathrm{ppm}﹩ and has a single size of N C = 48 C atoms, then, N S S atoms correspond to a total S/H depletion of ﹩{\left[{\rm{S}}/{\rm{H}}\right]}_{\mathrm{PAH}}={\left[{\rm{C}}/{\rm{H}}\right]}_{\mathrm{PAH}}\times \left({N}_{{\rm{S}}}/{N}_{{\rm{C}}}\right)\approx {N}_{{\rm{S}}}\,\mathrm{ppm}﹩.
Paper: Where Have All the Sulfur Atoms Gone? Polycyclic Aromatic Hydrocarbon as a Possible Sink for the Missing Sulfur in the Interstellar Medium. I. The C–S Band Strengths
Authors: X. J. Yang, Lijun Hua, Aigen Li
Figure caption: Same as Figure 8 but for a large molecule with N C = 96.
Paper: Where Have All the Sulfur Atoms Gone? Polycyclic Aromatic Hydrocarbon as a Possible Sink for the Missing Sulfur in the Interstellar Medium. I. The C–S Band Strengths
Authors: X. J. Yang, Lijun Hua, Aigen Li
Figure caption: Fitting the average C–S vibrational spectra of neutrals (left panel) and cations (right panel) in terms of a linear background and several Drude and/or Gaussian functions. The central wavelengths (in microns) of the most prominent bands are marked. Also shown are the fitting residuals obtained by subtracting the fitted spectra from the original spectra.
Paper: Where Have All the Sulfur Atoms Gone? Polycyclic Aromatic Hydrocarbon as a Possible Sink for the Missing Sulfur in the Interstellar Medium. I. The C–S Band Strengths
Authors: X. J. Yang, Lijun Hua, Aigen Li
Figure caption: Average spectra of the C–S vibrations of all the target PASH neutrals (left panel) and cations (right panel) except DBT, DN[23b23d]T and their cations.
Paper: Radio Scrutiny of the X-Ray-weak Tail of Low-mass Active Galactic Nuclei: A Novel Signature of High-Eddington Accretion?
Authors: Jeremiah D. Paul, Richard M. Plotkin, W. N. Brandt, Christopher H. Ellis, Elena Gallo, Jenny E. Greene, Luis C. Ho, Amy E. Kimball, Daryl Haggard
Figure caption: (a) On a logarithmic scale, 5 GHz radio luminosity, L R, vs. 2–10 keV luminosity, L X (see Section 3.1). Diagonal dashed lines show constant ﹩{{ \mathcal R }}_{{\rm{X}}}={L}_{{\rm{R}}}/{L}_{{\rm{X}}}﹩ at 10−5, 10−4, and 10−3. Green filled symbols show our sample of G&H07 AGNs, and gray squares show the comparison sample from Yang et al. (2020). Detection limits are denoted with arrows. Throughout the remainder of this paper, circles show objects consistent with Seyfert-like Type 1 AGNs on the BPT diagram (Baldwin et al. 1981) of their narrow emission lines, and stars show objects consistent with composite galaxies (see Section 4.1.2). (b) ﹩\mathrm{log}{{ \mathcal R }}_{{\rm{X}}}﹩ vs. Δα ox (see Section 3.1). Vertical dashed lines mark values of f weak from 1 to 100 (leftward objects are X-ray-weaker). Symbol definitions are the same as in (a), except 1σ error bars are included for our sample. The solid orange line shows the linear anticorrelation fit to our sample (note this fit does not include the comparison sample). The green dot and diagonal bars in the upper right illustrate the potential influence of X-ray variability using the mock uncertainty scheme described in Section 3.1. The yellow shaded area shows the model prediction described in Section 4.2 (this is not the uncertainty for the orange linear fit). In both (a) and (b), the radio-intermediate object GH 214 is tagged by name (see Appendix).