adsabs/AstroCLIMB huggingface dataset:
Paper: Recipe for Inferring Subsurface Solar Magnetism via Local Mode Coupling Using Slepian Basis Functions
Authors: Srijan Bharati Das
Figure caption: Components of the distinct Lorentz-stress kernels present in Equation (12), scaled by density ρ(z), plotted from the surface down to a depth of 11 Mm. The x-axis is the kernel magnitude in arbitrary units (a.u.), and the y-axis is the depth z starting at the surface at z = 0 and going deeper for larger values of z. As shown in Section 4.4, the top left panel is also the sound-speed kernel ﹩{{\boldsymbol{ \mathcal K }}}_{{\boldsymbol{k}}{\boldsymbol{k}}^{\prime} }^{c}﹩ plotted in the same arbitrary units for comparison with the Lorentz-stress kernels. Only the self-coupling kernels are plotted where ﹩{\boldsymbol{k}}={\boldsymbol{k}}^{\prime} ﹩. The black, blue, and red lines are in increasing order of wavenumber magnitude.  k  R ⊙ has the value (a) (−471.2, −168.3) for the black line, (b) (−650.7, −258.1) for the blue line, and (c) (−673.2, −594.6) for the red line. The rows are arranged in ascending radial order n = 1, 2, 3 of the eigenfunctions used.
Paper: On the Characterization of GJ 504: A Magnetically Active Planet-host Star Observed by the Transiting Exoplanet Survey Satellite (TESS)
Authors: Maria Pia Di Mauro, Raffaele Reda, Savita Mathur, Rafael A. García, Derek L. Buzasi, Enrico Corsaro, Othman Benomar, Lucía González Cuesta, Keivan G. Stassun, Serena Benatti, Valentina D'Orazi, Luca Giovannelli, Dino Mesa, Nicolas Nardetto
Figure caption: Seismically optimized light curves (left figures) and corresponding PSDs (right figures) from the analysis of the 120 s cadence data as explained in Section 3. The anomalous high scatter observed in this sector is related to the pointing jitter (Fausnaugh et al. 2020). Panels (e) and (e1) depict the results of the 20 s cadence data with the same method applied to get the magenta curves of panels (d) and (d1).
Paper: On the Characterization of GJ 504: A Magnetically Active Planet-host Star Observed by the Transiting Exoplanet Survey Satellite (TESS)
Authors: Maria Pia Di Mauro, Raffaele Reda, Savita Mathur, Rafael A. García, Derek L. Buzasi, Enrico Corsaro, Othman Benomar, Lucía González Cuesta, Keivan G. Stassun, Serena Benatti, Valentina D'Orazi, Luca Giovannelli, Dino Mesa, Nicolas Nardetto
Figure caption: Power spectral density (light gray) with a smoothing overlaid (black curve) of the first seismically optimized light curve (shown in panel (a)) of Figure 3). The background fit is composed of two Harvey-like profiles (blue curves) and a flat noise level (green curve). The sum of the three components is indicated by the thick red line. No evidence of a Gaussian power excess is found.
Paper: On the Characterization of GJ 504: A Magnetically Active Planet-host Star Observed by the Transiting Exoplanet Survey Satellite (TESS)
Authors: Maria Pia Di Mauro, Raffaele Reda, Savita Mathur, Rafael A. García, Derek L. Buzasi, Enrico Corsaro, Othman Benomar, Lucía González Cuesta, Keivan G. Stassun, Serena Benatti, Valentina D'Orazi, Luca Giovannelli, Dino Mesa, Nicolas Nardetto
Figure caption: Top panel: Mount Wilson S-index measurements for the time interval 1966–1995. The dashed black line indicates the mean S-index. The red line shows the sinusoidal fit to the highest peak as provided by the periodogram in the bottom panel. Bottom panel: Lomb–Scargle periodogram of the S-index. The highest peak corresponds to a cycle period of ≃12 a.
Paper: On the Characterization of GJ 504: A Magnetically Active Planet-host Star Observed by the Transiting Exoplanet Survey Satellite (TESS)
Authors: Maria Pia Di Mauro, Raffaele Reda, Savita Mathur, Rafael A. García, Derek L. Buzasi, Enrico Corsaro, Othman Benomar, Lucía González Cuesta, Keivan G. Stassun, Serena Benatti, Valentina D'Orazi, Luca Giovannelli, Dino Mesa, Nicolas Nardetto
Figure caption: (a) TESS light curve for the rotation analysis. (b) Time-period analysis using wavelets. Black corresponds to high power and blue to low power. Black hatched area represents the region that cannot be sampled with the current length of the light curve. (c) Projection of the period-time analysis onto the period axis (black) and corresponding fits with multiple Gaussian functions (green). (d) Autocorrelation function. (e) Composite spectrum (black) and best Gaussian fit (green). (f) PSD in logarithmic scale. The black dotted line indicates the rotation-period estimate.
Paper: On the Characterization of GJ 504: A Magnetically Active Planet-host Star Observed by the Transiting Exoplanet Survey Satellite (TESS)
Authors: Maria Pia Di Mauro, Raffaele Reda, Savita Mathur, Rafael A. García, Derek L. Buzasi, Enrico Corsaro, Othman Benomar, Lucía González Cuesta, Keivan G. Stassun, Serena Benatti, Valentina D'Orazi, Luca Giovannelli, Dino Mesa, Nicolas Nardetto
Figure caption: Enlarged mask used in the fourth calibration method described in Section 3. The selected pixels are depicted in gray.
Paper: X-Ray Sources in the SSA22 Chandra Field
Authors: Brandon T. Radzom, A. J. Taylor, A. J. Barger, L. L. Cowie
Figure caption: Fraction of spectroscopically identified sources as a function of the observed-frame 2–8 keV flux (blue solid line) and the observed-frame 0.5–2 keV flux (red solid line). We note that our sample is moderately complete from f 2−8 keV ≈ 2 × 10−16 to 5 × 10−15 erg s−1 cm−2 and f 0.5−2 keV ≈ 5 × 10−17 to 2 × 10−15 erg s−1 cm−2, with a steady increase to 100% beyond these fluxes.
Paper: X-Ray Sources in the SSA22 Chandra Field
Authors: Brandon T. Radzom, A. J. Taylor, A. J. Barger, L. L. Cowie
Figure caption: Histograms show the distribution of optical spectral classification vs. redshift. The BLAGNs are most prominent at higher redshift (z ≳ 2), while sources undergoing enhanced rates of star formation dominate more recently (0.25 < z ≲ 1). The spike of SFs near z ∼ 3 is due to the SSA22 protocluster on which the X-ray survey was centered.
Paper: Near-infrared Extragalactic Background Light Fluctuations on Nonlinear Scales
Authors: Yun-Ting Cheng, James J. Bock
Figure caption: The IGL power spectrum from MICECAT with no masking (top) and with a “perfect” masking threshold m th = 18 (middle) and 20 (bottom). Gray data points give the power spectrum of the total IGL. Brown data points show the two-halo power spectrum estimator from the central map. Error bars in all cases give the projected uncertainty in a 200 deg2 observation. The green and orange dashed lines give Poisson fluctuations in the full and central maps, respectively, which are fitted at high ℓ. Green and orange solid lines give the clustering power spectrum of the full and central maps, respectively, defined by the difference between the total power spectrum and the Poisson term. The blue line gives the one-halo clustering estimator, which is the difference between the total clustering signal (green line) and the two-halo clustering estimator (orange line). The black dashed line is the angular scale of the average halo profile from stacking, which also corresponds to the turnover ℓ mode for the one-halo power spectrum. Our approximation of one- and two-halo power spectra (﹩{\hat{C}}_{{\ell },1{\rm{h}}}﹩ and ﹩{\hat{C}}_{{\ell },2{\rm{h}}}﹩) is valid for scales larger than this dashed line.
Paper: Near-infrared Extragalactic Background Light Fluctuations on Nonlinear Scales
Authors: Yun-Ting Cheng, James J. Bock
Figure caption: Top: clustering power spectrum of IGL+IHL (one-halo + two-halo) with IHL fractions f IHL = 0 (black), 0.1 (blue), 0.2 (orange), and 0.5 (green). Here, we show the fiducial case where all halos have the same f IHL, and the IHL follows an NFW profile. Error bars denote sample variance for a 200 deg2 survey. Middle: ratio of the IGL+IHL power spectrum to the two-halo IGL power spectrum, normalized by ﹩{\left(\tfrac{1}{1-{f}_{\mathrm{IHL}}}\right)}^{2}﹩. We only show the low-ℓ modes where there is sufficient sensitivity on the two-halo power spectrum. The one-halo power in the IHL model closely follows the one-halo power in the MICECAT simulation with small departures at ℓ ∼ 5000 indicated below. Bottom: ratio of the IGL+IHL and IGL (one-halo + two-halo) power spectra of the same three f IHL cases. The normalization factor ﹩{\left(\tfrac{1}{1-{f}_{\mathrm{IHL}}}\right)}^{2}﹩ accounts for the change in the two-halo amplitude with f IHL.
Paper: Near-infrared Extragalactic Background Light Fluctuations on Nonlinear Scales
Authors: Yun-Ting Cheng, James J. Bock
Figure caption: Top: our model of the f IHL–z relation, adopting the empirical ICL fraction (f ICL) from Contini et al. (2018; black). The IHL model from Cooray et al. (2012) in four near-infrared bands is also shown for comparison (colored lines). The three colored dots correspond to the redshift of the power spectra shown in the bottom panel. Bottom: ratio of galaxy-IGL+IHL and galaxy-IGL cross power spectra at z = 0.2 (blue), 0.5 (green), and 1 (red). Error bars denote sample variance in a 200 deg2 survey. The dashed lines are the ratio of IGL+IHL and IGL flux at each redshift, the expected ratio ﹩\tfrac{1}{1-{f}_{\mathrm{IHL}}}﹩ on large scales.
Paper: Near-infrared Extragalactic Background Light Fluctuations on Nonlinear Scales
Authors: Yun-Ting Cheng, James J. Bock
Figure caption: Ratio of IGL+IHL and IHL power spectra using NFW (blue) and isothermal (orange) IHL profiles with f IHL = 0.1 (top), 0.2 (middle), and 0.5 (bottom). We assume f IHL is independent of redshift and mass as in our fiducial model. Error bars are the sample variance in a 200 deg2 survey. We normalize the ratio by the factor ﹩{\left(\tfrac{1}{1-{f}_{\mathrm{IHL}}}\right)}^{2}﹩ (the ratio of the square of total intensity), which is the expected value on large scales.
Paper: Near-infrared Extragalactic Background Light Fluctuations on Nonlinear Scales
Authors: Yun-Ting Cheng, James J. Bock
Figure caption: Three models of the f IHL–M relations considered in this work. The constant model (blue) is our fiducial case where all the halos have the same IHL fraction. The other two models are a power law with slope α = 2.5 (orange) and 0.1 (green), respectively, for M < 1012.5 M ⊙, and extending the value at 1012.5 M ⊙ (black dashed line) to all M > 1012.5 M ⊙ halos. The three curves are normalized such that the mean IHL fraction ﹩{\bar{f}}_{\mathrm{IHL}}=0.2﹩, where ﹩{\bar{f}}_{\mathrm{IHL}}﹩ is defined as the fraction of total EBL flux from the IHL.
Paper: Near-infrared Extragalactic Background Light Fluctuations on Nonlinear Scales
Authors: Yun-Ting Cheng, James J. Bock
Figure caption: Illustration of the four masking cases in Section 4.4. The top and bottom rows show a source on an image before and after masking, respectively. In cases 1 and 2, there is no PSF so the source is a point source on the image; in cases 3 and 4, we consider a smaller and a larger Gaussian PSF. Case 1 implements perfect masking that completely removes the flux from the source, whereas for the other three cases, we use pixel masking and the white circle denotes the masking region. Cases with a PSF leave low-level residual halos that extend beyond the mask.
Paper: Near-infrared Extragalactic Background Light Fluctuations on Nonlinear Scales
Authors: Yun-Ting Cheng, James J. Bock
Figure caption: Ratio of the galaxy-IGL+IHL and the galaxy-IGL cross power spectrum of the three f IHL(M) models shown in Figure 8 at z = 0.2 (left), 0.5 (middle), and 1 (right). Error bars are the sample variance in a 200 deg2 survey. The values are scaled by the mean ratio of the IGL+IHL and the IGL flux (﹩\tfrac{1}{1-{\bar{f}}_{\mathrm{IHL}}}﹩), which is the expected ratio on large scales. Here, we use ﹩{\bar{f}}_{\mathrm{IHL}}=0.2﹩.
Paper: Near-infrared Extragalactic Background Light Fluctuations on Nonlinear Scales
Authors: Yun-Ting Cheng, James J. Bock
Figure caption: An example of a ﹩5^{\prime} \times 5^{\prime} ﹩ image gridded in our 7″ × 7″-sized pixel. We choose a region centered at a galaxy cluster in MICECAT. Left: IGL intensity field. Middle: IHL+IGL field with fiducial IHL model that follows an NFW profile. Left: IHL+IGL field with fiducial IHL model but using isothermal halo profile. The isothermal model has a more extended halo profile than the NFW model.
Paper: Near-infrared Extragalactic Background Light Fluctuations on Nonlinear Scales
Authors: Yun-Ting Cheng, James J. Bock
Figure caption: The one-(blue) and two-halo (orange) clustering power spectrum from MICECAT with sources at 0.4 < z < 0.6 (top) and 1.2 < z < 1.4 (bottom) with masking threshold m th = 20. The crossover angular scale of the two terms (black dashed lines) is smaller at higher redshifts.
Paper: Testing the Limits of AGN Feedback and the Onset of Thermal Instability in the Most Rapidly Star-forming Brightest Cluster Galaxies
Authors: Michael S. Calzadilla, Michael McDonald, Megan Donahue, Brian R. McNamara, Kevin Fogarty, Massimo Gaspari, Myriam Gitti, Helen R. Russell, Grant R. Tremblay, G. Mark Voit, Francesco Ubertosi
Figure caption: Correlation plots between average extent of multiphase gas (〈R〉Hα,[OII]—based on either Hα (red points) or [OII] emission (blue points), using maps from McDonald et al. 2010 or this work, respectively) vs. different proposed indicators of thermal instability from X-ray observations. In all panels, this average extent was calculated by measuring the maximum extent in 10 equally spaced angular bins, with the y-axis values representing the median over these 10 measurements, and the (colored) uncertainties representing the interquartile range (i.e., the 25th and 75th percentiles). These uncertainties are extended with vertical dotted lines to show the minima and maxima in these measurements. In all panels, a diagonal gray dashed line also represents a one-to-one relationship. Left: 〈R〉Hα,[OII] vs. extent of X-ray cavities (i.e., radio bubbles) from Diehl et al. (2008), using the distance to the cavity center as well as the leading edge distance for the uncertainties. Middle: 〈R〉Hα,[OII] vs. the radius where an inflection in the t cool/t ff profile (modeled as two power laws) occurs. Right: 〈R〉Hα,[OII] vs. radius where t cool = 1 Gyr. In the middle and right panels, the x-axis values for the blue points come from modeling the ICM profiles in our extreme cooling sample, while values calculated using Hogan et al. (2017) profiles are in red. Uncertainties for each are calculated via bootstrap resampling. In each of these panels, we calculate the Spearman-ρ and Pearson-r correlation coefficients between the two axes. The nearly total asymmetry to one side of the one-to-one line in each of these panels demonstrates that all of these predictors of instability establish a volume within which multiphase cooling gas resides.
Paper: Testing the Limits of AGN Feedback and the Onset of Thermal Instability in the Most Rapidly Star-forming Brightest Cluster Galaxies
Authors: Michael S. Calzadilla, Michael McDonald, Megan Donahue, Brian R. McNamara, Kevin Fogarty, Massimo Gaspari, Myriam Gitti, Helen R. Russell, Grant R. Tremblay, G. Mark Voit, Francesco Ubertosi
Figure caption: Left: maximal ICM cooling rates (﹩{\dot{M}}_{\mathrm{cool}}={M}_{\mathrm{gas}}(r\lt {r}_{\mathrm{cool}})/{t}_{\mathrm{cool}}﹩) versus SFR based on new [OII] observations for our sample (starred points; see Table 3), along with homogenized Hα values from the literature (squares; see Table 4). Curves of constant cooling efficiency (﹩{\epsilon }_{\mathrm{cool}}\equiv {SFR}/{\dot{M}}_{\mathrm{cool}}﹩) are plotted as diagonal black lines, from 1% to 100%. Our best-fit relation between SFR and ﹩{\dot{M}}_{\mathrm{cool}}﹩ obtained from a robust Bayesian analysis is quoted at the top and shown as a solid blue line with shaded 1σ uncertainty band, along with the best-fit relations from Fogarty et al. (2015; solid green line) and McDonald et al. (2018; solid red line), and a BCES orthogonal fit (orange line). The steeper-than-unity slope in all of these relations suggests that cooling efficiency increases with ﹩{\dot{M}}_{\mathrm{cool}}﹩, implying a gradual saturation of AGN feedback, likely tied to an increasing fraction of feedback energy output being radiative as opposed to mechanical. Interestingly, the three starred data points with the highest cooling efficiency are also quasar-hosting systems (Phoenix, H1821, and IRAS09104). Points are color coded to correspond to different cooling rate bins, as described in the legend in the top left of this panel. Right: distributions of cooling efficiency after binning the points from the left panel by cooling rate and using the same color coding. Solid curves overlaid on each histogram are smooth probability density functions calculated nonparametrically via kernel density estimation. It is visually straightforward to see that with higher cooling rate bins, the mean cooling efficiency increases significantly. A slight redshift trend may be due to selection bias, but further analysis is left for a future study.
Paper: Updated Low-temperature Gas Opacities with ÆSOPUS 2.0
Authors: Paola Marigo, Bernhard Aringer, Léo Girardi, Alessandro Bressan
Figure caption: Left: weighting function, F R(u), in the integral of Equation (5). Right: dynamic integration limits in wavenumber ﹩\tilde{\nu }﹩, dependent on temperature, used for the integration of the Rosseland mean opacity. Normalized energies ﹩{u}_{\min }=0.1﹩ and ﹩{u}_{\max }=20﹩ correspond to ﹩{\tilde{\nu }}_{\min }﹩ and ﹩{\tilde{\nu }}_{\max }﹩.
Paper: Updated Low-temperature Gas Opacities with ÆSOPUS 2.0
Authors: Paola Marigo, Bernhard Aringer, Léo Girardi, Alessandro Bressan
Figure caption: Absorption cross section of two important molecules in the cool atmospheres of O-rich giants and supergiants, namely, H2O (left panel) and TiO (right panel). We compare the ExoMol recommended line lists (blue) adopted in this work with earlier data (magenta) used in ÆSOPUS 1.0. Line broadening due to the thermal Doppler effect and microturbulent velocity was used for both molecules’ transitions.
Paper: Updated Low-temperature Gas Opacities with ÆSOPUS 2.0
Authors: Paola Marigo, Bernhard Aringer, Léo Girardi, Alessandro Bressan
Figure caption: The CIA of H2/H2 and H2/He pairs. Following standard notation, the absorption coefficient α is normalized by gas density squared. The data adopted in the current version, ÆSOPUS 2.0 (solid line; Abel et al. 2011 for H2/H2 and Abel et al. 2012 for H2/He), are compared to those used in ÆSOPUS 1.0 (dashed–dotted line; Borysow et al. 1997 for H2/H2 and Jørgensen et al. 2000 for H2/He) for three temperature values.
Paper: Testing the Limits of AGN Feedback and the Onset of Thermal Instability in the Most Rapidly Star-forming Brightest Cluster Galaxies
Authors: Michael S. Calzadilla, Michael McDonald, Megan Donahue, Brian R. McNamara, Kevin Fogarty, Massimo Gaspari, Myriam Gitti, Helen R. Russell, Grant R. Tremblay, G. Mark Voit, Francesco Ubertosi
Figure caption: Same as in Figure 4, but focusing on the right panel correlation between 〈R〉Hα,[OII] and a different t cool threshold. Extended y–error bars showing the minimum and maximum extents of filaments have been omitted for clarity. We find that measuring the radius at which t cool reaches 0.5 Gyr (rather than 1 Gyr) has an approximately one-to-one correspondence with the extent of filaments.
Paper: Updated Low-temperature Gas Opacities with ÆSOPUS 2.0
Authors: Paola Marigo, Bernhard Aringer, Léo Girardi, Alessandro Bressan
Figure caption: Photodetachment cross section from McLaughlin et al. (2017; solid line) compared to John's (1988) fit of the cross-section data from Wishart (1979; dashed line). As can be seen, the latter does not account for autodetaching resonances at photon energies above 10 eV. The cross section σ is in units of 106 barn (Mb).
Paper: Deep Learning–based Fast Spectral Inversion of Hα and Ca II 8542 Line Spectra
Authors: Kyoung-Sun Lee, Jongchul Chae, Eunsu Park, Yong-Jae Moon, Hannah Kwak, Kyuhyoun Cho
Figure caption: Three-layer model fitting of the Hα (left) and Ca II 8542 Å (right) line profiles taken from a network feature in the QR, marked by the cross symbol in Figure 4, using the physical parameters derived from the DNN. The parameter values are listed at the bottom of the figure. The thick solid black line indicates the observed line profile, while the solid green, blue, and red lines represent the modeled emergent intensity profiles at different layers—the top of the photosphere (layer 2), the lower chromosphere (layer 1), and the upper chromosphere (layer 0), respectively. The dashed cyan line indicates each reference profile obtained by taking the spatial average of the line spectra over the observed scan raster. The lower panels present the plots of the residuals between the observed and modeled intensity profiles. For comparison, the ϵ  D  calculated from MLSI fitting for the observed spectra is also given.
Paper: Deep Learning–based Fast Spectral Inversion of Hα and Ca II 8542 Line Spectra
Authors: Kyoung-Sun Lee, Jongchul Chae, Eunsu Park, Yong-Jae Moon, Hannah Kwak, Kyuhyoun Cho
Figure caption: 2D density plots for each model parameter calculated from MLSI and our DNN for the Hα line profiles observed in the QR. The red dots denote the parameters with large errors of MLSI model fitting (ϵ  P  > 1 and ϵ  D  > 1). The dashed line indicates the equality of both model parameters, while the solid cyan line shows a linear fit to all the data points.
Paper: Deep Learning–based Fast Spectral Inversion of Hα and Ca II 8542 Line Spectra
Authors: Kyoung-Sun Lee, Jongchul Chae, Eunsu Park, Yong-Jae Moon, Hannah Kwak, Kyuhyoun Cho
Figure caption: Physical parameter maps of Hα using MLSI (upper three rows) and the DNN (lower three rows) for the AR. The cross and square symbols mark the locations of a superpenumbral fibril and a region with a large standard error, respectively.
Paper: Deep Learning–based Fast Spectral Inversion of Hα and Ca II 8542 Line Spectra
Authors: Kyoung-Sun Lee, Jongchul Chae, Eunsu Park, Yong-Jae Moon, Hannah Kwak, Kyuhyoun Cho
Figure caption: The same as Figure 6, but for the Ca II line profiles observed in the AR.
Paper: Deep Learning–based Fast Spectral Inversion of Hα and Ca II 8542 Line Spectra
Authors: Kyoung-Sun Lee, Jongchul Chae, Eunsu Park, Yong-Jae Moon, Hannah Kwak, Kyuhyoun Cho
Figure caption: Three-layer model fitting of the Ca II 8542 Å line profiles taken from the region with a large value of ϵ  D , marked by the square symbol in Figure 4 (QR), using the different models (left: our DNN; right: MLSI).
Paper: Deep Learning–based Fast Spectral Inversion of Hα and Ca II 8542 Line Spectra
Authors: Kyoung-Sun Lee, Jongchul Chae, Eunsu Park, Yong-Jae Moon, Hannah Kwak, Kyuhyoun Cho
Figure caption: Three-layer model fitting of the Hα line profiles taken from the region with a large value of ϵ  D , marked by the square symbol in Figure 5 (AR), using the different models (left: our DNN; right: MLSI).
Paper: Deep Learning–based Fast Spectral Inversion of Hα and Ca II 8542 Line Spectra
Authors: Kyoung-Sun Lee, Jongchul Chae, Eunsu Park, Yong-Jae Moon, Hannah Kwak, Kyuhyoun Cho
Figure caption: The same as Figure 10, but for a superpenumbral fibril in the AR, marked by the cross symbol in Figure 5.
Paper: Deep Learning–based Fast Spectral Inversion of Hα and Ca II 8542 Line Spectra
Authors: Kyoung-Sun Lee, Jongchul Chae, Eunsu Park, Yong-Jae Moon, Hannah Kwak, Kyuhyoun Cho
Figure caption: The same as Figure 6, but for the Ca II line profiles observed in the QR.
Paper: Deep Learning–based Fast Spectral Inversion of Hα and Ca II 8542 Line Spectra
Authors: Kyoung-Sun Lee, Jongchul Chae, Eunsu Park, Yong-Jae Moon, Hannah Kwak, Kyuhyoun Cho
Figure caption: The same as Figure 6, but for the Hα line profiles observed in the AR.