adsabs/AstroCLIMB huggingface dataset:
Paper: The Interplay between the Disk and Corona of the Changing-look Active Galactic Nucleus 1ES 1927+654
Authors: Ruancun Li, Claudio Ricci, Luis C. Ho, Benny Trakhtenbrot, Erin Kara, Megan Masterson, Iair Arcavi
Figure caption: GALEX images. The left panel displays the original NUV-band image, where the green ellipse shows the stellar disk derived from 2D imaging decomposition (R. Li et al. 2022) and the blue ellipses show the mask generated through augmenting the SExtractor segments. The central panel gives the cleaned NUV-band image after interpolating near-aperture contamination pixels by the source isophote. The red ellipse is the photometry aperture, while the cyan elliptical annulus with the same shape is used to estimate the nearby sky level. The same aperture and annulus are used for the FUV-band image in the right panel.
Paper: The Interplay between the Disk and Corona of the Changing-look Active Galactic Nucleus 1ES 1927+654
Authors: Ruancun Li, Claudio Ricci, Luis C. Ho, Benny Trakhtenbrot, Erin Kara, Megan Masterson, Iair Arcavi
Figure caption: Evolution of the maximum temperature of the disk (red-filled circles) and blackbody (blue-filled squares). The stars at t = 0 and the horizontal dashed lines mark the luminosity of the individual subcomponents before the optical outburst (No. 0). The vertical dashed lines separate the four evolution epochs discussed in Section 4.1.
Paper: Modeling ALMA Observations of the Warped Molecular Gas Disk in the Red Nugget Relic Galaxy NGC 384
Authors: Jonathan H. Cohn, Maeve Curliss, Jonelle L. Walsh, Kyle M. Kabasares, Benjamin D. Boizelle, Aaron J. Barth, Karl Gebhardt, Kayhan Gültekin, David A. Buote, Jeremy Darling, Andrew J. Baker, Luis C. Ho
Figure caption: (Left) Contours of the HST F160W (H-band) image (black) with contours of the fiducial model’s dust-masked MGE overlaid (red). (Middle, top) Central 5″ of the HST H-band image. (Right, top) Central 5″ of the HST F814W (I-band) image, which shows a dust disk to the southwest of the nucleus. (Middle, bottom) Central 5″ of the HST H-band contours (black) and the dust-masked MGE (red), with gray shading showing the region that was masked during the MGE fit. The asymmetry in the H-band contours in the dust-masked region points to dust attenuation, and the lack of asymmetry in the red contours indicates the dust-masked MGE accounts for this attenuation. (Right, bottom) ALMA CO(2–1) emission (blue) with HST I − H contours (black) overlaid, showing that the CO(2–1) emission and dust disk are cospatial and approximately the same size (∼4.″2 in diameter). The ellipse in the lower left of the panel corresponds to the ALMA synthesized beam.
Paper: A Model of the C IV λ λ1548, 1550 Doublet Line in T Tauri Stars
Authors: Thanawuth Thanathibodee, Connor E. Robinson, Nuria Calvet, Catherine C. Espaillat, Caeley V. Pittman, Nicole Arulanantham, Kevin France, Hans Moritz Günther, Seok-Jun Chang, P. Christian Schneider
Figure caption: Atomic and radiative properties of the postshock and preshock flows with ﹩{ \mathcal F }={10}^{11}\,\mathrm{erg}\,{{\rm{s}}}^{-1}\,{\mathrm{cm}}^{-2}﹩ and M ⋆ = 0.37 M ☉, R ⋆ = 1.66 R ☉. The shock is marked with the vertical dashed line, and the distance is measured outwardly from the postshock-heated photosphere transition. The observer is viewing through the flow from the right toward the star on the left. In the postshock, the dashed lines show the quantities with the LTE level populations. The LTE populations in the preshock are much lower than the range presented here and are not shown. In the second row, the dotted gray line is the Planck function, plotted here for comparison. The Planck function for the postshock traces the same curve as the LTE source function (dashed line). The units for S  l , χ 0,l , and η 0,l  are erg s−1 cm−2 sr−1 Hz−1, cm−1, and erg s−1 cm−3 sr−1 Hz−1, respectively.
Paper: Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation
Authors: D. Brethauer, D. Kasen, R. Margutti, R. Chornock
Figure caption: Best-fitting models for atomic data set HULLAC, ATOMIC, and Autostructure models for the left, middle, and right, respectively. At each point on the model grid, the left, middle, and right symbol connected by a line represent the best-fitting model from each atomic data set in the same order as the columns (thus the “correct” symbol is on the left, middle, and right for the left, middle, and right columns, respectively). Color corresponds to the best-fitting X lan, while a circle, triangle, square, and pentagon represent a best-fitting velocity of 0.05, 0.1, 0.2, and 0.3c, respectively. Size of the marker correlates to the best-fitting mass. Atomic data sets HULLAC and ATOMIC fit each other with the highest accuracy, though at higher velocities, a 1 dex discrepancy in X lan emerges with atomic data set HULLAC underestimating X lan, and the highest X lan models disagree on mass and velocity estimates by 25%–40% and 0.1–0.2c, respectively. Atomic data sets HULLAC and ATOMIC have very similar fits to atomic data set Autostructure models as they (i) require much lower X lan and high velocities to match the IR of Autostructure models, (ii) tend to overestimate the velocity by 0.1–0.2c, and (iii) incorrectly estimate the mass by one grid step (20%–50%). When fitting other models with atomic data set Autostructure models, atomic data set Autostructure often overestimates the mass by as much as a factor of 3 at low velocities, and consistently struggles to fit the correct velocity at all.
Paper: A Model of the C IV λ λ1548, 1550 Doublet Line in T Tauri Stars
Authors: Thanawuth Thanathibodee, Connor E. Robinson, Nuria Calvet, Catherine C. Espaillat, Caeley V. Pittman, Nicole Arulanantham, Kevin France, Hans Moritz Günther, Seok-Jun Chang, P. Christian Schneider
Figure caption: Left: the overall geometry of the model. The star’s equatorial plane is the same as that of the disk. Axisymmetric accretion funnels originate from the disk and land perpendicular to the star’s surface. The vector toward the observer is defined to be in the yz plane with the colatitude i, i.e., the inclination of the star. Right: a zoomed-in, side-view illustration of the large-scale geometry of accretion flows landing on the surface of a star. The darker color corresponds to the highest-energy flux (﹩{{ \mathcal F }}_{h}﹩). The post/preshock are small regions at the base of the flows. Gray dotted lines are the center of the flow used to define the colatitude θ for a given flow.
Paper: A Model of the C IV λ λ1548, 1550 Doublet Line in T Tauri Stars
Authors: Thanawuth Thanathibodee, Connor E. Robinson, Nuria Calvet, Catherine C. Espaillat, Caeley V. Pittman, Nicole Arulanantham, Kevin France, Hans Moritz Günther, Seok-Jun Chang, P. Christian Schneider
Figure caption: The wavelength-dependent, 2D line properties for the same model as in Figure 4 are shown for ﹩\cos {\theta }_{0}=0.5﹩. The values in the upper row are the local properties of the lines, whereas the values in the lower row are cumulative and include the contribution from the continuum. The white dashed vertical lines mark the rest-frame line centers, and the horizontal lines mark the shock location. The red dotted lines show the shifted line-center wavelength ﹩\lambda ^{\prime} ={\lambda }_{0}(v(r)\,\cos {\theta }_{0}/c+1)﹩.
Paper: A Model of the C IV λ λ1548, 1550 Doublet Line in T Tauri Stars
Authors: Thanawuth Thanathibodee, Connor E. Robinson, Nuria Calvet, Catherine C. Espaillat, Caeley V. Pittman, Nicole Arulanantham, Kevin France, Hans Moritz Günther, Seok-Jun Chang, P. Christian Schneider
Figure caption: The macroscopic and microscopic properties of an accretion flow with ﹩{ \mathcal F }={10}^{11}\,\mathrm{erg}\,{{\rm{s}}}^{-1}\,{\mathrm{cm}}^{-2}﹩, M ⋆ = 0.37 M ☉, and R ⋆ = 1.66 R ☉. The horizontal axis indicates the distance from the transition between the heated photosphere and the postshock. The vertical dashed line marks the location of the shock. In the fourth row, the dashed lines indicate the fraction at each ionization state, assuming LTE, for comparison.
Paper: Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation
Authors: D. Brethauer, D. Kasen, R. Margutti, R. Chornock
Figure caption: Best-fitting light curve model for atomic data set Autostructure. atomic data set Autostructure is the only data set presented here that was capable of producing sufficiently long-lasting optical bands and not over-producing IR filters by more than a magnitude at early times, likely due to its treatment of light r-process elements.
Paper: A Model of the C IV λ λ1548, 1550 Doublet Line in T Tauri Stars
Authors: Thanawuth Thanathibodee, Connor E. Robinson, Nuria Calvet, Catherine C. Espaillat, Caeley V. Pittman, Nicole Arulanantham, Kevin France, Hans Moritz Günther, Seok-Jun Chang, P. Christian Schneider
Figure caption: The schematic view of an accretion column and its associated geometry. An observer is viewing the flow at an angle θ 0.
Paper: A Model of the C IV λ λ1548, 1550 Doublet Line in T Tauri Stars
Authors: Thanawuth Thanathibodee, Connor E. Robinson, Nuria Calvet, Catherine C. Espaillat, Caeley V. Pittman, Nicole Arulanantham, Kevin France, Hans Moritz Günther, Seok-Jun Chang, P. Christian Schneider
Figure caption: The change in radiative properties of the flow due to turbulence velocities. The units are the same as in Figure 4.
Paper: The COS CGM Compendium. V. The Dichotomy of O VI Associated with Low- and High-metallicity Cool Gas at z < 1
Authors: Sameer, Nicolas Lehner, J. Christopher Howk, Andrew J. Fox, John M. O'Meara, Benjamin D. Oppenheimer
Figure caption: Normalized PDFs of O VI column density for the (a) HM lower-ionization gas comprising of strong O VI absorbers, ﹩\mathrm{log}N﹩(O VI) ≥ 14 (in red) and weaker O VI absorbers, ﹩\mathrm{log}N﹩(O VI) < 14 (in blue); (b) LM lower-ionization gas excluding the one data point with ﹩\mathrm{log}N﹩(O VI) > 14; (c) star-forming (67 in number) and passive (36 in number) galaxies from T22 within R vir; (d) O VI-selected absorbers (22 in number) extending 200–600 kpc beyond the closest associated galaxies from the survey of S14. The contribution from the upper limits is shown with hatchings.
Paper: The COS CGM Compendium. V. The Dichotomy of O VI Associated with Low- and High-metallicity Cool Gas at z < 1
Authors: Sameer, Nicolas Lehner, J. Christopher Howk, Andrew J. Fox, John M. O'Meara, Benjamin D. Oppenheimer
Figure caption: The O VI column density as a function of the metallicity of the cool gas for absorbers with detected O VI in our sample colored by their kinematic properties. Left: colored by absolute velocity centroid offset, ∣v O VI  − v H I ∣; right: velocity width, Δv 90(O VI).
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: Integrated spectra for the total matter and neutrino memory signal (top left), the full strain due to matter motions (top right), the strain due to the low-frequency matter memory (bottom left), and the strain due to neutrino memory (bottom right). All plots are calculated from the strain viewed from the x-direction (θ = π/2, ϕ = 0) and assume a distance of 10 kpc. We also plot the noise curves of the aLIGO, ET, CE, and DECIGO detectors obtained from E. Matthew et al. (2020) and M. Arca Sedda et al. (2020). Here, we use the characteristic strain ﹩{h}_{c}(f)=\sqrt{0.5(| {\tilde{h}}_{+}(f){| }^{2}+| {\tilde{h}}_{\times }(f){| }^{2})}﹩ as defined in K. Takami et al. (2015). Note that the spectra for the neutrino component of the signal are not reliable in the >500 Hz range due to the Nyquist sampling of the neutrino data. Overall, the heavier progenitor models have stronger matter and neutrino memory signals. The neutrino signal dominates the lower-frequency (<10 Hz) component of the spectrum, reaching even higher signals than the higher-frequency matter component. The ratio of the matter signal component to the noise curve for all models is greatest at around 1000 Hz. While at 10 kpc the aLIGO detector (solid black line) will be able to capture higher-frequency features of the matter component of the signal for higher-mass progenitor models, future detectors such as ET and CE will capture almost the full matter and neutrino memory signal for all progenitor models, and DECIGO will be able to capture the full, matter memory, and neutrino memory component of the signal for the majority of progenitor models.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: The h + (solid lines) and h × (dashed lines) GW memory strain due to neutrino emission viewed from the x-direction for all three neutrino species combined. Unlike the matter strains in Figure 1, this strain has only a low-frequency component and does not have the high-frequency burst associated with matter strains. The magnitude of the strain increases roughly with progenitor mass. Most notably, the magnitude of the strain is almost 2 orders of magnitude larger than the strain due to matter motions, reaching almost 2000 cm (divided by distance) for the 40 progenitor model. The neutrino memory strain also continues to grow for all progenitor models until the end of the simulation, indicating the need to carry out CCSN simulations to late times.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: Distribution of possible SNR values calculated from the matter strain obtained by a single aLIGO detector over all viewing angles for all models (top left), lower-mass models (top right), intermediate-mass models (bottom left), and higher-mass models (bottom right), all at a distance 10 kpc from Earth. The area of all histograms has been normalized to one. Unlike the inferred energy histograms, these histograms do not show a defined peak or a “most likely” SNR value. We find that larger progenitor masses not only have larger average SNRs, but also a wider spread in possible SNR values obtained depending on the viewing angle, differing by up to Δρ = 3. There is substantial overlap in the angle-depending SNR values between progenitor models. For the 16 model, some viewing angles result in an SNR less than 8, which is not considered a detection, whereas other viewing angles result in SNR values close to 10 for the same progenitor. Different detectors or network configurations will result in different SNR values on the horizontal axis, but the overall trends and shapes of the histograms will remain the same.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: The h +,× GW strains times distance due to matter motions for all progenitor models viewed from the x-direction. The exploding models have a high-frequency burst within the first ≈0.5–1.5 s. In general, the higher progenitor masses achieve greater strain values than lower-mass progenitors, but this relationship is not strictly monotonic. The 12.24 and 14 progenitor models do not explode and are steadily accreting matter at the end of the simulation. Hence, they will eventually collapse into BHs, but at a time beyond the duration of the simulations. Thus, unlike the other models, their GW signals do not taper after a few seconds after bounce. We note that the strains of almost all of the exploding models do not return to zero by the end of the simulation, indicating the permanent GW memory effect.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: (Left) The total energy radiated in GWs due to matter motion for each progenitor model as a function of time after bounce. (Right) The total energy radiated in GWs due to neutrino emission for each progenitor model as a function of time after bounce. The BH-forming models are plotted with dotted lines. For the radiated GW energy due to matter motions, the total GW energy differs by around 3 orders of magnitude from the least massive 9a and 9b models to the BH-forming 40 and 19.56 progenitor models. Additionally, the total energy radiated from GWs due to matter motions is around 2 orders of magnitude greater than the energy radiated in GWs due to neutrino memory. For the matter plot, all models’ radiated energy grows several orders of magnitude within the first second after bounce and then asymptotes to a constant value, whereas the energy radiated due to GWs from neutrino memory continues to grow until the end of the simulation for all progenitor models, demonstrating the importance of carrying out CCSN simulations to late times.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: Detection range due to the matter strain in kiloparsecs for the 9b, 9.25, 9.5, 14, 15.01, and 40 progenitor models as a function of viewing angle for an aLIGO detector. The dark blue regions indicate shorter detection ranges, whereas the yellow regions indicate longer ranges. As with the inferred energy distributions, the regions of greater and lower detection ranges vary between each progenitor model, with the greatest detection range concentrated toward the equator for the 9b and 14 plots, but are more irregularly distributed for the other models. The spread of attainable detection ranges as a function of angle also increases with progenitor mass, as the possible detection ranges differ by around 0.7 kpc for the 9b model, but around 4 kpc for the 40 model. Therefore, the ability to detect GWs from CCSNe depends on the right combination of progenitor mass, distance, and viewing angle. Using a different detector or a network of detectors will result in different detection ranges on the horizontal axis, but the histogram shapes will remain the same.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: Detection range distributions due to matter motion across all viewing angles for all progenitor mass models (top left), low-mass models 9−12.25 M ⊙(top right), intermediate-mass models 14−19 M ⊙(bottom left), and high-mass models 19.56−60 M ⊙ (bottom right) for an aLIGO detector. The area under each histogram has been normalized to one. Note that these histograms take the same shape as those in Figure 7 since the SNR and detection range expressions in Equations (4) and (13), respectively, contain the same integrand. We observe that higher-mass progenitor models have both greater average detection ranges and also a wider spread in possible detection ranges depending on the angle of observation compared to less-massive progenitors.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: Inferred total radiated energy distributions due to matter motion across all viewing angles for all progenitor mass models in log scale (top left), linear scale (top right), focusing on intermediate-mass models 11–19 M ⊙ (bottom left), and on high-mass models 19.56–60 M ⊙ (bottom right). The area under each histogram has been normalized to one. Looking at the linearly scaled histograms, we observe that the greater progenitor masses have a larger spread of possible inferred energy values and thus have the potential for larger energy calculation biases compared to lower-mass progenitors. Almost all histograms demonstrate a defined peak, indicating a preferred inferred energy value. We also note an overlap in inferred energy values between different progenitor models (e.g., 12.25 and 16) depending on the viewing angle.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: Total inferred energy radiated in GWs due to matter motion as a function of viewing angle (θ, ϕ) for the 9b, 9.5, 12.25, 15.01, 17, 19, 24, and 40 progenitor models. Observing the CCSN event from an angle in the dark blue region results in a lower estimate of the event’s total radiated energy compared to an observation in the yellow regions. Between these eight examples, there are no consistent regions of higher and lower energy estimates, as the direction of greatest inferred energy differs significantly between each model. In addition to having larger total radiated energy values, the higher-mass progenitor models also have a larger spread in possible inferred energy values.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: Polarization angle ﹩\theta =\arctan ({h}_{+}/{h}_{\times })﹩ as a function of time calculated from the high-frequency (blue) and memory component (orange) for the 19 progenitor model viewed from the x-, y-, and z-directions. Here, θ ∈ [ −π, π]. The polarization angle oscillates wildly for the high-frequency component of the signal and effectively averages itself out. On the other hand, the polarization angle calculated from the matter memory and neutrino memory components oscillate on much longer timescales within the first 0.7 s and then settle down to a more constant value at later times. This stability and lack of averaging out of the polarization angle in the low-frequency regime thus serve as a unique signature of the memory effect.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: Polarization angle ﹩\theta =\arctan ({h}_{+}/{h}_{\times })﹩ as a function of time calculated from the high-frequency (blue) and low-frequency matter memory component (orange) for the 14 progenitor model viewed from the x-, y-, and z-directions where θ ∈ [ −π, π]. Once again, the matter memory component of the polarization angle oscillates on a much longer timescale compared to the high-frequency component of the polarization angle, which oscillates at a frequency of around 103 Hz and effectively averages itself out. Therefore, the polarization angle once again serves as a detectable distinguishing factor of matter memory. However, it is important to note that unlike the exploding 19 model in Figure 12, the magnitude of the memory strain from this nonexploding model is very low.
Paper: Gravitational-wave and Gravitational-wave Memory Signatures of Core-collapse Supernovae
Authors: Lyla Choi, Adam Burrows, David Vartanyan
Figure caption: (Left) Detection range in kiloparsecs due to GWs from neutrino emission as a function of viewing angle (θ, ϕ) for the 23 M ⊙ progenitor model using an aLIGO detector. (Right) Distribution of detection ranges across all viewing angles. While the histogram demonstrates a preference for detection ranges around 30–40 kpc, it has not peaked at one particular value. Here, we observe a large variation in possible detection ranges from just over 10–80 kpc, a range larger than the diameter of the Milky Way. Therefore, for the neutrino memory contribution to the GW signal, the angle at which the event can be observed from Earth has a significant impact on detectability—several degrees may determine whether or not we observe the GW strain from neutrino emission.