adsabs/AstroCLIMB huggingface dataset:
Paper: ALMA High-frequency Long-baseline Campaign in 2017: An Investigation of Phase-referencing Cycle Times and Effective Baseline Lengths Using Band-to-band and In-band Phase Calibration Techniques
Authors: Luke T. Maud, Yoshiharu Asaki, William R. F. Dent, Akihiko Hirota, Edward B. Fomalont, Satoko Takahashi, Satoki Matsushita, Neil M. Phillips, Tsuyoshi Sawada, Stuartt Corder, and John Carpenter
Figure caption: Image coherence as a function of phase-referencing cycle time (24, 48, 72, 96, 120  s) at bands 7, 8, and 9 (top to bottom), for both in-band (left) and B2B (right) images. The in-band observations used more distant calibrators (between ∼2° and 11°) whereas the B2B images, like those in Figure 4, use close calibrators (<1.°67). The colors (bands) and symbols (baseline length) follow those in Figure 2 except that the symbol size now indicates the calibrator separation angle as shown in the legend in the bottom-right panel. The upper and lower black lines are related to σϕ = 30° and 90° at the 120  s cycle time and assume a ﹩\sqrt{{\rm{time}}}﹩ scaling to shorter times. The left panel shows that for distant calibrators the coherence changes very little with cycle time. The right panel shows no difference from Figure 4 where coherence follows the expected reduction with increasing cycle time. Note that although all band 9 observations were taken in stable conditions, the targets could not be self-calibrated, and estimated fluxes were used in the calculation of coherence, leading to large uncertainties and likely underestimates (see the text). Comparing left and right panels, the coherence values are always lower for in-band distant calibrators versus the respective B2B calibration using a close calibrator. Uncertainties are not included for plotting clarity but are of the order  ± 5% for bands 7 and 8, and typically  ± 20% for band 9.
Paper: ALMA High-frequency Long-baseline Campaign in 2017: An Investigation of Phase-referencing Cycle Times and Effective Baseline Lengths Using Band-to-band and In-band Phase Calibration Techniques
Authors: Luke T. Maud, Yoshiharu Asaki, William R. F. Dent, Akihiko Hirota, Edward B. Fomalont, Satoko Takahashi, Satoki Matsushita, Neil M. Phillips, Tsuyoshi Sawada, Stuartt Corder, and John Carpenter
Figure caption: Image coherence comparing in-band and B2B images for various cycle times (24, 48, 72, 96, 120  s). The upper panels show the 16 data sets where in-band and B2B used the same close calibrator, while the bottom show the 28 data sets where in-band use distant calibrators. As previously shown in Maud et al. (2020) when using close calibrators, in-band and B2B are have almost coincident image coherence values. When in-band calibrators are more distant, the B2B images are far superior, especially for short cycle times, where the phase rms due to atmospheric fluctuation can be minimized using close calibrators. These in-band images have effectively constant image coherence values regardless of cycle time and are only most coincident with B2B observations for the longest cycle time, where the phase rms of the B2B data with close calibrators worsens. This supports that using distant calibrators, regardless of cycle time, is the same as using close calibrators with long cycle times—the phase error budgets become similar (see Section 4.2). The colors (bands) and symbols (baseline length) follow those in Figure 2 while errors are propagated from an uncertainty in the peak flux density of three times the image noise level.
Paper: ALMA High-frequency Long-baseline Campaign in 2017: An Investigation of Phase-referencing Cycle Times and Effective Baseline Lengths Using Band-to-band and In-band Phase Calibration Techniques
Authors: Luke T. Maud, Yoshiharu Asaki, William R. F. Dent, Akihiko Hirota, Edward B. Fomalont, Satoko Takahashi, Satoki Matsushita, Neil M. Phillips, Tsuyoshi Sawada, Stuartt Corder, and John Carpenter
Figure caption: SSF plot extracted from Figure 1 of Holdaway & Owen (1995) for the source 2131−021. The uncalibrated data are shown by black crosses, whereas the calibrated data, using solutions from a nearby source 2134+004 and an 80  s cycle time, are shown as blue squares. These data were originally used to indicate that the phase rms saturated after the effective baseline, Beff,calc = vwindtcyc/2 ∼ 400  m, when comparing the uncalibrated SSF with the mean phase rms after calibration (∼12°), and considering a the typical site wind speed of ∼10 m s−1. Our fitting method suggests that Beff,fit ﹩{412}_{-57}^{+69}﹩ m, as indicated by the star symbol. The black dashed line is the linear regression to the uncalibrated SSF in log–log space, while the dotted lines indicate the error ranges in the gradient. The solid purple line represents the mean phase rms measured for baselines >1500  m, while the dashed purple line is the continuation to shorter baselines until the intercept with the slope of the uncalibrated SSF.
Paper: ALMA High-frequency Long-baseline Campaign in 2017: An Investigation of Phase-referencing Cycle Times and Effective Baseline Lengths Using Band-to-band and In-band Phase Calibration Techniques
Authors: Luke T. Maud, Yoshiharu Asaki, William R. F. Dent, Akihiko Hirota, Edward B. Fomalont, Satoko Takahashi, Satoki Matsushita, Neil M. Phillips, Tsuyoshi Sawada, Stuartt Corder, and John Carpenter
Figure caption: SSF plots from the DGC source as part of data set J2228-170829-B73-6deg before and after calibration. The five panels left to right show data calibrated with cycle times of 24, 48, 72, 96, and 120  s. The uncalibrated SSF is extracted from the DGC source phases with long-term slopes removed and are shown as black cross symbols. This is unchanged in each panel. The expected phase rms use the values measured on the DGC source at 33  s scaled to 24 and 48 s cycle times, 81 s scaled to 72 and 96 s cycle times, and 129 s scaled to the 120 s cycle time and are shown as blue squares. For shorter to longer cycle times it is clear to see the increasing expected phase rms. The star symbol indicates Beff,fit, which also increases with cycle time. The lines are as described in the text and the caption of Figure 8.
Paper: ALMA High-frequency Long-baseline Campaign in 2017: An Investigation of Phase-referencing Cycle Times and Effective Baseline Lengths Using Band-to-band and In-band Phase Calibration Techniques
Authors: Luke T. Maud, Yoshiharu Asaki, William R. F. Dent, Akihiko Hirota, Edward B. Fomalont, Satoko Takahashi, Satoki Matsushita, Neil M. Phillips, Tsuyoshi Sawada, Stuartt Corder, and John Carpenter
Figure caption: Plot of Beff,fit/Beff,calc as a function of cycle time, wind speed, and observing time referenced to Chilean local midnight (CLT=UTC—4 h), in the left, middle and right panels respectively. Error bars are not included in the left panel but can account for a ratio change of ∼0.5 given the spread in Beff,fit. Typically there is little change in ratio with cycle time and the majority of observations fall within a ratio between one and two. The plot in the middle panel clearly indicates that low wind speed data sets do not match the theoretical prediction and that Beff,fit is always higher than predicted. These data sets have a much larger phase rms than estimated. Tentatively there is a trend of decreasing ratio with wind speed, although only 5 of 44 data sets have a ratio >2.5. The right panel indicates some separation of predominantly low and high ratio observations into day (0900–1800) and night (1800–0900) time respectively. The uncertainties plotted in the middle and right panels are the standard deviation from averaging the five cycle time values. The colors (bands) and symbols (baseline length) follow those in Figure 2.
Paper: ALMA High-frequency Long-baseline Campaign in 2017: An Investigation of Phase-referencing Cycle Times and Effective Baseline Lengths Using Band-to-band and In-band Phase Calibration Techniques
Authors: Luke T. Maud, Yoshiharu Asaki, William R. F. Dent, Akihiko Hirota, Edward B. Fomalont, Satoko Takahashi, Satoki Matsushita, Neil M. Phillips, Tsuyoshi Sawada, Stuartt Corder, and John Carpenter
Figure caption: Plot of d/Beff,fit against in-band calibrator to target separation angles for all cycle times 24, 48, 72, 96, and 120  s. Beff,fit is representative of the true value of vwindtcyc/2 whereas d parameterizes the effective baseline-length component due to a distant calibrator following Holdaway & Owen (1995). The colors (bands) and symbols (baseline length) follow those in Figure 2 although B2B close calibrator observations are plotted in a lighter shade. For short cycle times, it is evident for separation angles >4°–5° that d is dominant and therefore leads to larger effective baseline lengths and thus a larger phase rms. Fast phase referencing with distant calibrators would therefore not yield an optimal phase calibration as d is unchanged. For the longest cycle time, 120  s, d/Beff,fit is of a similar level to B2B observations, indicating a dominance due to atmospheric variability, hence in-band observations with distant calibrators are most similar to B2B observations with close calibrators only in this case. The uncertainties propagate from the calculation of d using a 1000  ±  500  m altitude atmospheric layer. For 24  s cycle times there are five data sets where d/Beff,fit > 2.5 and exceed the plot range.
Paper: ALMA High-frequency Long-baseline Campaign in 2017: An Investigation of Phase-referencing Cycle Times and Effective Baseline Lengths Using Band-to-band and In-band Phase Calibration Techniques
Authors: Luke T. Maud, Yoshiharu Asaki, William R. F. Dent, Akihiko Hirota, Edward B. Fomalont, Satoko Takahashi, Satoki Matsushita, Neil M. Phillips, Tsuyoshi Sawada, Stuartt Corder, and John Carpenter
Figure caption: Plots of the measured in-band image coherence for targets calibrated using distant phase calibrators against the estimated coherence, as calculated from the scaled expected phase rms for all cycle times (24, 48, 72, 96, and 120  s from left to right) to account for d via the theoretical parameterization. We exclude band 9 observations as the image coherence is uncertain. For the band 7 long-baseline data, we correct the image coherence due to the so-called antenna position uncertainties. The colors (bands) and symbols (baseline length) follow those in Figure 2 while errors in image coherence are propagated from an uncertainty in the peak flux density of three times the image noise level, and those in expected coherence propagate from using lower and upper limits of 500 and 1500  m for the screen height in the calculation of d. The main result is that for the shortest cycle times, 24–72 s, the expected coherence is considerably overestimated, i.e., the effective baseline lengths are underestimated. The reason is that the addition of d does not sufficiently increase the effective baseline length and thus the expected phase rms is underestimated. For the longest cycle time, 120  s, the effect of d is almost insignificant (see Figure 11), and the estimated and measured image coherences values are coincident.
Paper: ALMA High-frequency Long-baseline Campaign in 2017: An Investigation of Phase-referencing Cycle Times and Effective Baseline Lengths Using Band-to-band and In-band Phase Calibration Techniques
Authors: Luke T. Maud, Yoshiharu Asaki, William R. F. Dent, Akihiko Hirota, Edward B. Fomalont, Satoko Takahashi, Satoki Matsushita, Neil M. Phillips, Tsuyoshi Sawada, Stuartt Corder, and John Carpenter
Figure caption: Plots of the measured in-band image coherence for targets calibrated using both close and distant phase calibrators against the empirically estimated coherence calculated using the estimated phase rms (over all cycle times, 24, 48, 72, 96, and 120  s from left to right) and while also accounting for any further coherence degradation due to calibrator to target separation angles as parameterized in Maud et al. (2020). Compared to Figure 12, here the empirical estimated coherence is more consistent with the image coherence for all cycle times, and thus the empirical method is better to use as a proxy for estimating what the image would likely achieve. Some estimated coherence values can be unphysical, <0, as the reduction in coherence when accounting for the target to calibrator separation angles is a simple subtraction—this happens only for very high phase rms, poor conditions. The colors (bands) and symbols (baseline length) follow those in Figure 2 while errors in image coherence are propagated from an uncertainty in the peak flux density of three times the image noise level and those in empirical expected coherence are propagated using the lower and upper limits of the degradation of coherence as a function of separation angle.
Paper: PATOKA: Simulating Electromagnetic Observables of Black Hole Accretion
Authors: George N. Wong, Ben S. Prather, Vedant Dhruv, Benjamin R. Ryan, Monika Mościbrodzka, Chi-kwan Chan, Abhishek V. Joshi, Ricardo Yarza, Angelo Ricarte, Hotaka Shiokawa, Joshua C. Dolence, Scott C. Noble, Jonathan C. McKinney, Charles F. Gammie
Figure caption: Location of emission around high-spin black holes with a * = 0.94 on a single KS time slice. In this figure, the emissivity is embedded in and represented with respect to a Cartesian coordinate system from simulation coordinates, and all densities are with respect to the Cartesian volume element. Top: 3D rendering of emission source with color and transparency determined by the total emission produced within that region of space. Bottom: the same data as above after summing across the azimuthal dimension ϕ. The left two columns are a representation of emission from a single snapshot from a typical MAD simulation; emission tracks the fragmentary plasma and is relatively insensitive to the electron thermodynamics, parameterized here by r high. The right two columns represent emission from the same SANE simulation snapshot but with the different electron temperature prescriptions. Larger values of r high shift the emission from the turbulent but steady disk into the funnel region. All simulations have r low = 1 and are imaged at i = 17°. The total flux density produced by each simulation is the same, so the color scales show the relative concentration of emission in the azimuthal sum.
Paper: PATOKA: Simulating Electromagnetic Observables of Black Hole Accretion
Authors: George N. Wong, Ben S. Prather, Vedant Dhruv, Benjamin R. Ryan, Monika Mościbrodzka, Chi-kwan Chan, Abhishek V. Joshi, Ricardo Yarza, Angelo Ricarte, Hotaka Shiokawa, Joshua C. Dolence, Scott C. Noble, Jonathan C. McKinney, Charles F. Gammie
Figure caption: Comparison of images produced from the same fluid snapshot across a range of mass density scales ﹩\widetilde{{ \mathcal M }}\propto { \mathcal M }﹩, where ﹩\widetilde{{ \mathcal M }}=1﹩ is normalized to produce an M87-like flux density F  ν  = 0.8 Jy. Larger values of ﹩\widetilde{{ \mathcal M }}﹩ have higher optical depths; this effect becomes increasingly obvious at ﹩\widetilde{{ \mathcal M }}=40﹩, where the flow begins to occlude the central flux depression. The high density scales chosen for the right two panels light up the funnel wall enough to substantially alter the image morphology, so the field of view for those two panels has been increased. Note that the models with large ﹩\widetilde{{ \mathcal M }}﹩ may not be physical since they are produced from fluid simulations that do not include all dynamically important effects at that mass accretion rate, e.g., cooling.
Paper: PATOKA: Simulating Electromagnetic Observables of Black Hole Accretion
Authors: George N. Wong, Ben S. Prather, Vedant Dhruv, Benjamin R. Ryan, Monika Mościbrodzka, Chi-kwan Chan, Abhishek V. Joshi, Ricardo Yarza, Angelo Ricarte, Hotaka Shiokawa, Joshua C. Dolence, Scott C. Noble, Jonathan C. McKinney, Charles F. Gammie
Figure caption: Paths of five neighboring geodesics (white lines) plotted over bilinearly interpolated magnetization σ (color, rapidly varying rainbow color scale). The σ = 1 surface is denoted with a solid black line, and zone boundaries are denoted by the dotted black lines. The magnetization decreases steadily from left to right but is defined at zone centers. The value of σ at all other points is reconstructed using bilinear interpolation. The interpolation scheme produces a jagged transition at zone centers, and the slight deviations in path length produce ridges even when the value of σ is determined from an interpolated value.
Paper: PATOKA: Simulating Electromagnetic Observables of Black Hole Accretion
Authors: George N. Wong, Ben S. Prather, Vedant Dhruv, Benjamin R. Ryan, Monika Mościbrodzka, Chi-kwan Chan, Abhishek V. Joshi, Ricardo Yarza, Angelo Ricarte, Hotaka Shiokawa, Joshua C. Dolence, Scott C. Noble, Jonathan C. McKinney, Charles F. Gammie
Figure caption: Comparison of log-scaled total intensity image using zone-based vs. interpolated-σ cutoffs. In both panels, the cutoff is implemented by zeroing the electron number density. The sharp edges are from interpolation artifacts due to how linear interpolation deals with rapidly varying quantities on a nearly-regular grid. Notice that ridges are still present in the interpolated-σ cutoff image; they are particularly visible near the bottom of the image. The ridge features may be accentuated when using an integrator that takes large steps.
Paper: PATOKA: Simulating Electromagnetic Observables of Black Hole Accretion
Authors: George N. Wong, Ben S. Prather, Vedant Dhruv, Benjamin R. Ryan, Monika Mościbrodzka, Chi-kwan Chan, Abhishek V. Joshi, Ricardo Yarza, Angelo Ricarte, Hotaka Shiokawa, Joshua C. Dolence, Scott C. Noble, Jonathan C. McKinney, Charles F. Gammie
Figure caption: Quantitative comparison between fiducial reference image with ﹩{ \mathcal M }﹩ chosen for F  ν  = 0.8 Jy and images with other ﹩{ \mathcal M }﹩. All comparison images are drawn from a MAD a * = 0.94 model with r high = 40, as in Figure 13. As before, images produced with large ﹩{ \mathcal M }﹩ may not be representative of physically meaningful scenarios.
Paper: PATOKA: Simulating Electromagnetic Observables of Black Hole Accretion
Authors: George N. Wong, Ben S. Prather, Vedant Dhruv, Benjamin R. Ryan, Monika Mościbrodzka, Chi-kwan Chan, Abhishek V. Joshi, Ricardo Yarza, Angelo Ricarte, Hotaka Shiokawa, Joshua C. Dolence, Scott C. Noble, Jonathan C. McKinney, Charles F. Gammie
Figure caption: Example spectra for MAD (left) and SANE (right) snapshots at low inclination in an r low = 1 model. The spectra for r high = 1, 10, 40, 160 are shown in the top row, and the components of the r high = 10 model are shown in the bottom row. All data points are taken from the simultaneous multiwavelength measurement campaign performed coincident with the 2017 EHT observations of M87 and reported in The EHT MWL Science Working Group et al. (2021).
Paper: PATOKA: Simulating Electromagnetic Observables of Black Hole Accretion
Authors: George N. Wong, Ben S. Prather, Vedant Dhruv, Benjamin R. Ryan, Monika Mościbrodzka, Chi-kwan Chan, Abhishek V. Joshi, Ricardo Yarza, Angelo Ricarte, Hotaka Shiokawa, Joshua C. Dolence, Scott C. Noble, Jonathan C. McKinney, Charles F. Gammie
Figure caption: Total flux density F  ν  at ν = 230  GHz vs. ﹩{ \mathcal M }﹩ for the same snapshot as Figure 13 but for the four different values of r high. As before, note that the values of F  ν  at large ﹩{ \mathcal M }﹩ may not be representative of physically meaningful scenarios, since cooling was not included in the fluid calculation.
Paper: PATOKA: Simulating Electromagnetic Observables of Black Hole Accretion
Authors: George N. Wong, Ben S. Prather, Vedant Dhruv, Benjamin R. Ryan, Monika Mościbrodzka, Chi-kwan Chan, Abhishek V. Joshi, Ricardo Yarza, Angelo Ricarte, Hotaka Shiokawa, Joshua C. Dolence, Scott C. Noble, Jonathan C. McKinney, Charles F. Gammie
Figure caption: Azimuthal slices of initial condition for fluid density (normalized and shown as logarithm) and magnetic field in MAD and SANE flows with different disk sizes (indicated by r in/﹩{r}_{\max }﹩) and adiabatic indices ﹩\hat{\gamma }﹩ drawn from Table 1. The black hole corotates with the flow and has dimensionless spin parameter a * = 0.94. The black hole event horizon is plotted as a filled gray circle at x = z = 0.
Paper: PATOKA: Simulating Electromagnetic Observables of Black Hole Accretion
Authors: George N. Wong, Ben S. Prather, Vedant Dhruv, Benjamin R. Ryan, Monika Mościbrodzka, Chi-kwan Chan, Abhishek V. Joshi, Ricardo Yarza, Angelo Ricarte, Hotaka Shiokawa, Joshua C. Dolence, Scott C. Noble, Jonathan C. McKinney, Charles F. Gammie
Figure caption: Comparison of three radiative transport schemes for the one-zone model. Top panel shows flux densities at the edge-on 90° inclination. Bottom panel shows flux densities for ν = 230 GHz. The approximate unpolarized transport scheme disagrees with the polarized one in the transition region as the ball becomes optically thick.
Paper: Ninety-seven Eclipsing Quadruple Star Candidates Discovered in TESS Full-frame Images
Authors: Veselin B. Kostov, Brian P. Powell, Saul A. Rappaport, Tamás Borkovits, Robert Gagliano, Thomas L. Jacobs, Martti H. Kristiansen, Daryll M. LaCourse, Mark Omohundro, Jerome Orosz, Allan R. Schmitt, Hans M. Schwengeler, Ivan A. Terentev, Guillermo Torres, Thomas Barclay, Adam H. Friedman, Ethan Kruse, Greg Olmschenk, Andrew Vanderburg, William Welsh
Figure caption: Upper panel: same as Figure 15 but for quadruple candidate TIC 256158466. The system consists of two EBs with P  A   = 5.7745 and P  B   = 7.4544 days, highlighted in the figure, both on target. Lower left panel: Skyview image of the target for sector 39, showing all Gaia sources down to G = 21 mag. We note that compared with Skyview images shown above, here the image is zoomed in to a size of 5 × 5 pixels in order to highlight the position of the nearby field star TIC 1508756606 (5.″65 separation, ΔT ≈ 4.8 mag fainter, marked with dashed red arrow). TIC 1508756606 has parallax and proper motion within 1σ of those of the target star and thus might be in a comoving quintuple system with the quadruple TIC 256158466. Third panel: measured eclipse-time variations for P  A  using two lightcurve sections centered on each eclipse—narrow and wide—show potential sine-like variations only in the former case.
Paper: Ninety-seven Eclipsing Quadruple Star Candidates Discovered in TESS Full-frame Images
Authors: Veselin B. Kostov, Brian P. Powell, Saul A. Rappaport, Tamás Borkovits, Robert Gagliano, Thomas L. Jacobs, Martti H. Kristiansen, Daryll M. LaCourse, Mark Omohundro, Jerome Orosz, Allan R. Schmitt, Hans M. Schwengeler, Ivan A. Terentev, Guillermo Torres, Thomas Barclay, Adam H. Friedman, Ethan Kruse, Greg Olmschenk, Andrew Vanderburg, William Welsh
Figure caption: Upper panel: lightcurve for quadruple candidate TIC 392229331 exhibiting four sets of eclipses with two different periods. Second panel from top: trapezoid (magenta), Gaussian (cyan), generalized Gaussian (red), and generalized hyperbolic secant (blue) fits to the phase-folded, primary eclipses of binary A (zoomed in on phase between −0.1 and 0.1). The blended eclipses have been removed, and the lightcurve has been detrended. The magenta, blue, and cyan curves are vertically offset for clarity. Third panel from top: residuals, in terms of (observed-calculated), for the four respective functions. Fourth panel from top: the undetrended, phase-folded lightcurve of binary A using the ephemeris determined by the fits to the individual eclipses. The secondary eclipse is clearly distinguished. The eclipses of binary B have been removed for clarity. Last panel: measured eclipse timing variations for the primary eclipses of binary A, showing no significant trends.