adsabs/AstroCLIMB huggingface dataset:
Paper: Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. XIII. Ultraviolet Time Lag of Hβ Emission in Mrk 142
Authors: Viraja C. Khatu, Sarah C. Gallagher, Keith Horne, Edward M. Cackett, Chen Hu, Sofia Pasquini, Patrick Hall, Jian-Min Wang, Wei-Hao Bian, Yan-Rong Li, Jin-Ming Bai, Yong-Jie Chen, Pu Du, Michael Goad, Bo-Wei Jiang, Sha-Sha Li, Yu-Yang Songsheng, Chan Wang, Ming Xiao, Zhe Yu
Figure caption: Radius–luminosity (R–L) scaling relations for Hβ in the optical including results from this work (red star). Panel (a): R Hβ –L 5100 relation showing Mrk 142 from this work close to the Du et al. (2015) measurement (blue square). Panel (b): R Hβ –L Hβ  relation showing Mrk 142 from this work overlapping with the measured value in Du et al. (2015). The SEAMBH objects (black squares; Du et al. 2014, 2015; Wang et al. 2014a; Hu et al. 2015; Li et al. 2018, 2021; Zhang et al. 2019) appear on the lower right of the gray solid lines, which represent the best-fit R–L relations from Bentz et al. (2013) with L 5100 (panel (a)) and Kaspi et al. (2005) with L Hβ  (panel (b)), indicating a smaller size for the BLR in highly accreting AGNs compared to the more typical, sub-Eddington AGNs mapped in previous studies (green circles, panel (a) and gray circles, panel (b); Stirpe et al. 1994; Santos-Lleó et al. 1997; Collier et al. 1998; Dietrich et al. 1998, 2012; Peterson et al. 1998, 2002, 2014; Kaspi et al. 2000, 2005; Santos-Lleó et al. 2001; Bentz et al. 2006, 2007, 2009a, 2009b, 2013, 2014; Collin et al. 2006; Denney et al. 2006, 2010; Grier et al. 2012; Barth et al. 2013; Pei et al. 2014, and references therein) at the same luminosities. In particular, the departure of Mrk 142 from the Bentz et al. (2013) best-fit relation in panel (a) is more apparent than the deviation from the Kaspi et al. (2005) relation in panel (b) (see the text for additional details).
Paper: Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. XIII. Ultraviolet Time Lag of Hβ Emission in Mrk 142
Authors: Viraja C. Khatu, Sarah C. Gallagher, Keith Horne, Edward M. Cackett, Chen Hu, Sofia Pasquini, Patrick Hall, Jian-Min Wang, Wei-Hao Bian, Yan-Rong Li, Jin-Ming Bai, Yong-Jie Chen, Pu Du, Michael Goad, Bo-Wei Jiang, Sha-Sha Li, Yu-Yang Songsheng, Chan Wang, Ming Xiao, Zhe Yu
Figure caption: Multiepoch NGC 5548 radius–luminosity (R–L) scaling relations for Hβ in the optical and UV including luminosities from Eser et al. (2015) and results from this work (red star). Panel (a): NGC 5548 R Hβ –L 5100 relation from (Eser et al. 2015; green circles) on which Mrk 142 from this work is located to the right at higher luminosities. This follows the similar deviation from the entire RM sample that is observed in Figure 10, panel (a). RM measurements for NGC 5548 post-2015 (Lu et al. 2016, 2022; Pei et al. 2017; De Rosa et al. 2018; black circles) are also included for completeness. Panel (b): R Hβ –L 1350 relation for NGC 5548, including Mrk 142 from this work. From its position to the right of the Eser et al. (2015) best-fit R–L relation (gray solid line) with L 5100 in panel (a), Mrk 142 has moved closer to the Eser et al. (2015) best-fit R–L relation with L 1350 in panel (b), suggesting the UV as a better proxy for the driving continuum than the optical. In general, the offset of the SEAMBHs may reflect a different spectral energy distribution (SED).
Paper: Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. XIII. Ultraviolet Time Lag of Hβ Emission in Mrk 142
Authors: Viraja C. Khatu, Sarah C. Gallagher, Keith Horne, Edward M. Cackett, Chen Hu, Sofia Pasquini, Patrick Hall, Jian-Min Wang, Wei-Hao Bian, Yan-Rong Li, Jin-Ming Bai, Yong-Jie Chen, Pu Du, Michael Goad, Bo-Wei Jiang, Sha-Sha Li, Yu-Yang Songsheng, Chan Wang, Ming Xiao, Zhe Yu
Figure caption: SEDs of Mrk 142 from the National Aeronautics and Space Administration/Infrared Processing and Analysis Center (NASA/IPAC) Extragalactic Database (NED, blue squares), and from Cackett et al. (2020, mean flux density in each band represented with purple triangles); and NGC 5548 from NED (orange circles) showing similar SED shapes for the two objects. We derived the optical-to-X-ray slope (α ox) for the Mrk 142 data from Cackett et al. (2020) regarding a power-law behavior (fit indicated in black solid line) for the SED around the 2500 Å data point (purple open triangle). Results indicate a softer ionizing SED for Mrk 142 (α ox = − 1.35) than NGC 5548 (α ox = − 1.30;  Merritt 2022); see the text for a detailed discussion. The fit to the Cackett et al. (2020) SED is extended to indicate the estimated 1350 Å data point (black open triangle) used in Figure 11.
Paper: Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. XIII. Ultraviolet Time Lag of Hβ Emission in Mrk 142
Authors: Viraja C. Khatu, Sarah C. Gallagher, Keith Horne, Edward M. Cackett, Chen Hu, Sofia Pasquini, Patrick Hall, Jian-Min Wang, Wei-Hao Bian, Yan-Rong Li, Jin-Ming Bai, Yong-Jie Chen, Pu Du, Michael Goad, Bo-Wei Jiang, Sha-Sha Li, Yu-Yang Songsheng, Chan Wang, Ming Xiao, Zhe Yu
Figure caption: He I light curves highlighting the contributions from the individual broad components (referred to as broader and narrower) to the total broad components from Gemini (open circles) and LJT (solid squares) data. The smaller (larger) size of the markers represents narrower (broader) components. It is interesting to note that the broader He I light curve from the LJT spectra is brighter than the broader He I component from the Gemini spectra likely due to the blue wing of He I (traced primarily by the broader component) in the LJT data affected by instrumental broadening.
Paper: CLASS Observations of Atmospheric Cloud Polarization at millimeter Wavelengths
Authors: Yunyang Li, 云炀 李, John W. Appel, Charles L. Bennett, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Rolando Dünner, Joseph R. Eimer, Thomas Essinger-Hileman, Kathleen Harrington, Jeffrey Iuliano, Tobias A. Marriage, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Oliver F. Wolff, Edward J. Wollack, Zhilei Xu, 智磊 徐
Figure caption: Example of multifrequency polarized cloud observation starting on 2022 January 25. Top: sky images recorded by camera 3, shown in 3 hr intervals. As noted in Figure 5, the highest elevation of camera 3 is below the lowest elevation of the telescope; therefore, clouds in the camera image do not directly correspond to the az-map detections. Rows 1–8: Stokes Q (top) and U (bottom) az-maps for four CLASS frequency bands. The x-axis represents time, and the y-axis, from the bottom to the top of each panel, is the −180°–180° azimuth angle. The azimuthal coverage of camera 3 roughly corresponds to the lower quarter of the az-maps. The gray area shows missing data due to Sun avoidance or excessive detector flux jumps. Row 9: az-map of the detector frame Stokes u map for the 90 GHz band. Row 10: the cloud candidates identified from the 90 GHz u az-map. Red patches show the identified cloud candidates and the gray patches show the extended background region. All az-maps and camera images have the temporal axes aligned.
Paper: CLASS Observations of Atmospheric Cloud Polarization at millimeter Wavelengths
Authors: Yunyang Li, 云炀 李, John W. Appel, Charles L. Bennett, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Rolando Dünner, Joseph R. Eimer, Thomas Essinger-Hileman, Kathleen Harrington, Jeffrey Iuliano, Tobias A. Marriage, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Oliver F. Wolff, Edward J. Wollack, Zhilei Xu, 智磊 徐
Figure caption: Cloud cover fraction above elevation 10° (CM10) measured by the CLASS site cameras from 2016 August to 2022 May. The nighttime data prior to 2017 January are discarded due to the low exposure time set for the cameras. Main: histogram of CM10 in local time and date. Top: the annual variation. The PWV measurements during the same time period are shown as shaded curves, with arbitrary scaling. The gray shades break when the APEX weather data are not available. Right: the season-averaged diurnal variation during Austral summer (December, January, February, in green) and Austral winter (June, July, August, in purple).
Paper: CLASS Observations of Atmospheric Cloud Polarization at millimeter Wavelengths
Authors: Yunyang Li, 云炀 李, John W. Appel, Charles L. Bennett, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Rolando Dünner, Joseph R. Eimer, Thomas Essinger-Hileman, Kathleen Harrington, Jeffrey Iuliano, Tobias A. Marriage, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Oliver F. Wolff, Edward J. Wollack, Zhilei Xu, 智磊 徐
Figure caption: Polarization amplitude ratio between the 220 and 150 GHz scaled by the Rayleigh scattering spectrum. The histograms on the right panel are weighted by the observation time of each cloud event. The black points correspond to the Rayleigh model, i.e., the 220/150 GHz ratios are divided by (220/148.2)4. The blue points additionally account for the liquid water absorption, where the absorption term is fit jointly with 90, 150, and 220 GHz data for each sample assuming the R-abs model at cloud temperature −15 °C.
Paper: CLASS Observations of Atmospheric Cloud Polarization at millimeter Wavelengths
Authors: Yunyang Li, 云炀 李, John W. Appel, Charles L. Bennett, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Rolando Dünner, Joseph R. Eimer, Thomas Essinger-Hileman, Kathleen Harrington, Jeffrey Iuliano, Tobias A. Marriage, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Oliver F. Wolff, Edward J. Wollack, Zhilei Xu, 智磊 徐
Figure caption: Detected circular polarization as a function of the Stokes u amplitude. The colored line and the shaded region indicate the best linear fit between the two variables and should be interpreted as the measured transfer function bias term ﹩{\hat{B}}_{{vu}}﹩ and its uncertainty. Separate fits are performed for the 40 GHz data before and after 2021 February 11 due to the replacement of the VPM grid (Li et al. 2023). The 90 GHz panel also suggests a separate fit for data before and after 2019 May 20 due to a bimodal transition in the VPM system.
Paper: CLASS Observations of Atmospheric Cloud Polarization at millimeter Wavelengths
Authors: Yunyang Li, 云炀 李, John W. Appel, Charles L. Bennett, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Rolando Dünner, Joseph R. Eimer, Thomas Essinger-Hileman, Kathleen Harrington, Jeffrey Iuliano, Tobias A. Marriage, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Oliver F. Wolff, Edward J. Wollack, Zhilei Xu, 智磊 徐
Figure caption: Polarization angle distribution of the cloud candidates from each of the four CLASS frequency bands. The histograms are weighted by the observation time of each cloud event. The boundaries of the histograms are periodically extended by 30° to better show the features around 0° (180°).
Paper: CLASS Observations of Atmospheric Cloud Polarization at millimeter Wavelengths
Authors: Yunyang Li, 云炀 李, John W. Appel, Charles L. Bennett, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Rolando Dünner, Joseph R. Eimer, Thomas Essinger-Hileman, Kathleen Harrington, Jeffrey Iuliano, Tobias A. Marriage, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Oliver F. Wolff, Edward J. Wollack, Zhilei Xu, 智磊 徐
Figure caption: Best-fit residual distribution for the spectral models in comparison. The yellow and blue colors represent R-α and R-abs, respectively. Each component of the graph shows the distribution of the residuals from the best-fit model. The two distributions for each frequency are arbitrarily offset for visualization.
Paper: CLASS Observations of Atmospheric Cloud Polarization at millimeter Wavelengths
Authors: Yunyang Li, 云炀 李, John W. Appel, Charles L. Bennett, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Rolando Dünner, Joseph R. Eimer, Thomas Essinger-Hileman, Kathleen Harrington, Jeffrey Iuliano, Tobias A. Marriage, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Oliver F. Wolff, Edward J. Wollack, Zhilei Xu, 智磊 徐
Figure caption: The distribution of cloud measure above 10° elevation (CM10) associated with each of the CLASS polarization detections. The colored histograms correspond to the polarization detections from each of the four frequencies. The global CM10 distribution from all data is shown in gray.
Paper: CLASS Observations of Atmospheric Cloud Polarization at millimeter Wavelengths
Authors: Yunyang Li, 云炀 李, John W. Appel, Charles L. Bennett, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Rolando Dünner, Joseph R. Eimer, Thomas Essinger-Hileman, Kathleen Harrington, Jeffrey Iuliano, Tobias A. Marriage, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Oliver F. Wolff, Edward J. Wollack, Zhilei Xu, 智磊 徐
Figure caption: Measured cloud polarization signal (−Q) as a function of the effective frequency. The samples include all 90 GHz-selected candidates with an S/N > 4 at each band. The dots mark for each distribution the 16, 50, and 84 percentiles.
Paper: CLASS Observations of Atmospheric Cloud Polarization at millimeter Wavelengths
Authors: Yunyang Li, 云炀 李, John W. Appel, Charles L. Bennett, Ricardo Bustos, David T. Chuss, Joseph Cleary, Jullianna Denes Couto, Sumit Dahal, Rahul Datta, Rolando Dünner, Joseph R. Eimer, Thomas Essinger-Hileman, Kathleen Harrington, Jeffrey Iuliano, Tobias A. Marriage, Matthew A. Petroff, Rodrigo A. Reeves, Karwan Rostem, Rui Shi, 瑞 时, Deniz A. N. Valle, Duncan J. Watts, Oliver F. Wolff, Edward J. Wollack, Zhilei Xu, 智磊 徐
Figure caption: The confusion matrix of the CNN classifier. The numbers in the diagonal (off-diagonal) blocks are the true-positive (true-negative) rate for each column. The two values in each block correspond to the statistics for daytime (upper left) and nighttime (lower right).
Paper: Rapid Dust Growth during Hydrodynamic Clumping due to Streaming Instability
Authors: Ryosuke T. Tominaga, Hidekazu Tanaka
Figure caption: Collision velocities as a function of St and ρ d/ρ g for T = 100 and 200 K, for which c s is ≃0.6 and ≃0.8 km s−1, respectively. The adopted critical fragmentation velocity in each case is v frag = 10 m s−1 and v frag = 3 m s−1, which is based on the fact that silicate grains are often assumed to be more fragile than water ice (but see also Kimura et al. 2015; Steinpilz et al. 2019). The white line marks the critical line (τ SI,sat = τ coag,eff). The arrows show the direction of the dust evolution on the ρ d/ρ g–St plane. Dust grains suffer fragmentation in a wider parameter space than shown in Figure 3 because of higher temperature and smaller v frag. Nevertheless, dust growth time is shorter than the pre-clumpting period space once the local ρ d/ρ g increases beyond 15 in Model 1 for T = 200 K (20 in Model 2 for T = 100 K). In Model 2 with T = 200 K (the bottom right panel), the critical line is close to the fragmentation-limited line (Δv = v frag) for St ≳ 0.2.
Paper: Rapid Dust Growth during Hydrodynamic Clumping due to Streaming Instability
Authors: Ryosuke T. Tominaga, Hidekazu Tanaka
Figure caption: Ratio of the pre-clumping period τ SI,sat and the coagulation timescale τ coag,eff. We assume a gas temperature of 50 K (c s ≃ 0.4 km s−1) as an example. We note that we take into account the effect of the possible imperfect sticking (Equation (6)). The blue (red) region indicates that the growth time is shorter (longer) than the pre-clumping period before the strong clumping. We naively expect that the dust evolves along the white region (τ SI,sat ∼ τ coag,eff) on this ρ d/ρ g−St plane (see also Figure 4).
Paper: Rapid Dust Growth during Hydrodynamic Clumping due to Streaming Instability
Authors: Ryosuke T. Tominaga, Hidekazu Tanaka
Figure caption: Schematic figure showing dust evolution on the ρ d/ρ g–St plane. We assume Model 1 for the collision velocity as an example. The dust moves upward if the coagulation timescale is shorter than t SI,sat (the blue region). For τ SI,sat < τ coag,eff, the clumping increases the dust density, and thus, the dust moves rightward on the ρ d/ρ g–St plane (the red region). Therefore, we naively expect that the dust first moves to the boundary of the blue and red regions and then moves along it, which is independent of the initial values of St and ρ d/ρ g. The gray region indicates the maximum density that can be expected via the strong clumping due to streaming instability.
Paper: Cannonball or Bowling Ball: Proper Motion and Parallax for PSR J0002+6216
Authors: S. Bruzewski, F. K. Schinzel, G. B. Taylor, P. Demorest, D. A. Frail, M. Kerr, P. Kumar
Figure caption: The possible history of the pulsar, accounting for uncertainties in position and proper motion. While PSR J0002+6216 is likely to have originated at or near the geometric center of CTB 1 (G116.9+0.2 in this figure), here we illustrate the possible positions if we assume that it originated somewhere beyond the SNR, out to approximately the characteristic age of the pulsar. The black circles represent the possible 1σ, 3σ, and 5σ positional scatter at each epoch, whereas the shaded regions represent the paths between those epoch contours. Colored circles represent remnants found in Green (2019).
Paper: Rapid Dust Growth during Hydrodynamic Clumping due to Streaming Instability
Authors: Ryosuke T. Tominaga, Hidekazu Tanaka
Figure caption: Isolines of the collision velocity of Model 2 Δv 2 with St = 1 on the r–ρ d plane. We adopt 120 K(r/1 au)−3/7 (Chiang & Youdin 2010) and plot lines of 3, 6, and 10 m s−1. Note that the collision velocity decreases as ρ d/ρ g increases for ρ d/ρ g ≥ 20 (Equation (10)).
Paper: Rapid Dust Growth during Hydrodynamic Clumping due to Streaming Instability
Authors: Ryosuke T. Tominaga, Hidekazu Tanaka
Figure caption: Dependence of the critical line (τ SI,sat = τ coag,eff) on τ SI,sat. The solid and dotted lines represent cases where we adopt Model 1 and Model 2, respectively. As in Figure 3, we assume a gas temperature of 50 K to plot this figure. The critical St for which the solid line becomes horizontal (see the case of τ SI,sat = 30,  50) is given by Equation (12).
Paper: Cannonball or Bowling Ball: Proper Motion and Parallax for PSR J0002+6216
Authors: S. Bruzewski, F. K. Schinzel, G. B. Taylor, P. Demorest, D. A. Frail, M. Kerr, P. Kumar
Figure caption: The data points along with the median fit produced by our program. Here, color corresponds to time, such that purple (the beginning of the viridis scale) represents the beginning of the year. The closer a data point is to a similar color in the fit, the better the fit. Ellipses surrounding each data point represent the 1σ, 2σ, and 3σ positional uncertainties.
Paper: Cannonball or Bowling Ball: Proper Motion and Parallax for PSR J0002+6216
Authors: S. Bruzewski, F. K. Schinzel, G. B. Taylor, P. Demorest, D. A. Frail, M. Kerr, P. Kumar
Figure caption: A 2D histogram of the relevant parameter distributions. Here we show the total proper motion, calculated using its individual components.
Paper: AGN STORM 2. IV. Swift X-Ray and Ultraviolet/Optical Monitoring of Mrk 817
Authors: Edward M. Cackett, Jonathan Gelbord, Aaron J. Barth, Gisella De Rosa, Rick Edelson, Michael R. Goad, Yasaman Homayouni, Keith Horne, Erin A. Kara, Gerard A. Kriss, Kirk T. Korista, Hermine Landt, Rachel Plesha, Nahum Arav, Misty C. Bentz, Benjamin D. Boizelle, Elena Dalla Bontà, Maryam Dehghanian, Fergus Donnan, Pu Du, Gary J. Ferland, Carina Fian, Alexei V. Filippenko, Diego H. González Buitrago, Catherine J. Grier, Patrick B. Hall, Chen Hu, Dragana Ilić, Jelle Kaastra, Shai Kaspi, Christopher S. Kochanek, Andjelka B. Kovačević, Daniel Kynoch, Yan-Rong Li, Jacob N. McLane, Missagh Mehdipour, Jake A. Miller, John Montano, Hagai Netzer, Christos Panagiotou, Ethan Partington, Luka Č. Popović, Daniel Proga, Daniele Rogantini, David Sanmartim, Matthew R. Siebert, Thaisa Storchi-Bergmann, Marianne Vestergaard, Jian-Min Wang, Tim Waters, Fatima Zaidouni
Figure caption: Left: Swift (green) and HST (blue) light curves of Mrk 817 during the AGN STORM 2 campaign. Right: CCFs calculated with respect to the light curve in the Swift/UVW2 band (solid lines). Normalized histograms show the ICCF centroid distribution, with the vertical solid and dashed lines showing the lag centroid and 1σ uncertainty range, respectively.
Paper: AGN STORM 2. IV. Swift X-Ray and Ultraviolet/Optical Monitoring of Mrk 817
Authors: Edward M. Cackett, Jonathan Gelbord, Aaron J. Barth, Gisella De Rosa, Rick Edelson, Michael R. Goad, Yasaman Homayouni, Keith Horne, Erin A. Kara, Gerard A. Kriss, Kirk T. Korista, Hermine Landt, Rachel Plesha, Nahum Arav, Misty C. Bentz, Benjamin D. Boizelle, Elena Dalla Bontà, Maryam Dehghanian, Fergus Donnan, Pu Du, Gary J. Ferland, Carina Fian, Alexei V. Filippenko, Diego H. González Buitrago, Catherine J. Grier, Patrick B. Hall, Chen Hu, Dragana Ilić, Jelle Kaastra, Shai Kaspi, Christopher S. Kochanek, Andjelka B. Kovačević, Daniel Kynoch, Yan-Rong Li, Jacob N. McLane, Missagh Mehdipour, Jake A. Miller, John Montano, Hagai Netzer, Christos Panagiotou, Ethan Partington, Luka Č. Popović, Daniel Proga, Daniele Rogantini, David Sanmartim, Matthew R. Siebert, Thaisa Storchi-Bergmann, Marianne Vestergaard, Jian-Min Wang, Tim Waters, Fatima Zaidouni
Figure caption: X-ray HID for Mrk 817. Black circles show data taken during the AGN STORM 2 campaign, while blue squares show data taken prior to this campaign. The variability patterns are clearly different. Error bars are omitted for clarity, but a representative error bar is shown. Purple triangles show the evolution of a power law that is softer when brighter. Orange stars shown the evolution of a partially covered power law with increasing column density from N H = 2.5 × 1021 cm−2 to 2.5 × 1023 cm−2. Arrows indicate the direction in which the parameters increase.
Paper: AGN STORM 2. IV. Swift X-Ray and Ultraviolet/Optical Monitoring of Mrk 817
Authors: Edward M. Cackett, Jonathan Gelbord, Aaron J. Barth, Gisella De Rosa, Rick Edelson, Michael R. Goad, Yasaman Homayouni, Keith Horne, Erin A. Kara, Gerard A. Kriss, Kirk T. Korista, Hermine Landt, Rachel Plesha, Nahum Arav, Misty C. Bentz, Benjamin D. Boizelle, Elena Dalla Bontà, Maryam Dehghanian, Fergus Donnan, Pu Du, Gary J. Ferland, Carina Fian, Alexei V. Filippenko, Diego H. González Buitrago, Catherine J. Grier, Patrick B. Hall, Chen Hu, Dragana Ilić, Jelle Kaastra, Shai Kaspi, Christopher S. Kochanek, Andjelka B. Kovačević, Daniel Kynoch, Yan-Rong Li, Jacob N. McLane, Missagh Mehdipour, Jake A. Miller, John Montano, Hagai Netzer, Christos Panagiotou, Ethan Partington, Luka Č. Popović, Daniel Proga, Daniele Rogantini, David Sanmartim, Matthew R. Siebert, Thaisa Storchi-Bergmann, Marianne Vestergaard, Jian-Min Wang, Tim Waters, Fatima Zaidouni
Figure caption: Swift X-ray light curve of Mrk 817 in the ((a); top) total: 0.3–10 keV, ((b); upper middle) soft: 0.3–1.5 keV, and ((c); lower middle) hard: 1.5–10 keV bands. Panel ((d); bottom) shows the HR. Left-hand panels show the data from Morales et al. (2019), while the right-hand panels show the AGN STORM 2 campaign. There is a gap of ∼830 days between the two campaigns. Blue dashed lines indicate the historical average fluxes calculated from all Swift data taken before this campaign. A dramatic drop in flux is seen for all bands during AGN STORM 2.