adsabs/AstroCLIMB huggingface dataset:
Paper: Mapping the Filamentary Nebula of NGC 1275 with Multiwavelength SITELLE Observations
Authors: Carter Lee Rhea, Julie Hlavacek-Larrondo, Marie-Lou Gendron-Marsolais, Benjamin Vigneron, Megan Donahue, Auriane Thilloy, Laurie Rousseau-Nepton, Mar Mezcua, Norbert Werner, Jorge Barrera-Ballesteros, Hyunseop Choi, Alastair Edge, Andrew Fabian, G. Mark Voit
Figure caption: Emission-line diagram showing the relationship between log([N II]/Hα) and log(Hα) as a function of the distance from the AGN (designated here at the center) in kiloparsec. The central 0.5 kpc have been excised to remove any issues pertaining to multiple emission lines.
Paper: Mapping the Filamentary Nebula of NGC 1275 with Multiwavelength SITELLE Observations
Authors: Carter Lee Rhea, Julie Hlavacek-Larrondo, Marie-Lou Gendron-Marsolais, Benjamin Vigneron, Megan Donahue, Auriane Thilloy, Laurie Rousseau-Nepton, Mar Mezcua, Norbert Werner, Jorge Barrera-Ballesteros, Hyunseop Choi, Alastair Edge, Andrew Fabian, G. Mark Voit
Figure caption: In the left panel, we have a diagram of the blue loop region. The background image shows the soft X-ray emission captured by Chandra, with white contours following the Hα emission. The loop is segmented into a left and right loop. The WHAN plot is color coded by this distinction.
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: Distribution of the selected raw images. Left: streak lengths. Right: elongation.
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: (a) and (b): basic units of ShuffleNet V1 (X. Zhang et al. 2017). (c) and (d): basic units of ShuffleNet V2 (N. Ma et al. 2018).
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: The survey schedule in azimuth and elevation.
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: Accuracy rate of three datasets with different numbers of streaks on the training and validation sets.
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: Recall for streak (in orange) and nonstreak (in blue) images on the validation set.
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: ShuffleNet V2 network structure.
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: Training (in blue) and validation (in orange) accuracy.
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: Training (in blue) and validation (in orange) loss.
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: Flow chart of our identification architecture. Boxes with bold font exhibit the main process of the framework, and boxes with non-bold font exhibit the output of each process.
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: Recall rate of three datasets with different numbers of streaks for the streak and nonstreak images.
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: Comparison of streak images detected with our framework and previous method based on SExtractor on the test data. The number of observed sky fields each night is also shown.
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: Loss of three datasets with different numbers of streaks on the training and validation sets.
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: Number of sky fields with streak images detected utilizing our framework and previous method.
Paper: Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning
Authors: Liu Liu, Rongyu Sun, He Zhao
Figure caption: Examples of streak images not detected by previous method.
Paper: Precise Parameters from Bayesian Spectral Energy Distribution Fitting Indicate Thermally Driven Mass Loss Likely Driver of Radius Valley
Authors: David Jordan, Inseok Song
Figure caption: The 1434 R* values estimated with SEDmc compared to the values from the NASA Archive data. The legend shows the markers used for SEDmc estimates by model, where the blue circles denote the CK04 model and the orange triangles the NextGen model. Upper: the diagonal line shows where they would be equal, and the dotted lines show the linear fit from low to high R*, where blue is for CK04 and red is for NextGen data. The X markers are for the 39 stars with asteroseismic (AS)-derived values from V. Van Eylen et al. (2018). The median % fractional uncertainty for the SEDmc data is 2.4% compared to that of 9.1% for the NASA data. Model distribution statistics are shown. Lower: fractional residuals for the % difference of SEDmc values from NASA values where the absolute mean value of fractional residuals for all stars is 6.0%. When comparing SEDmc to the AS-derived values, the fractional residuals are reduced to 3.2% for all 39 stars, and 2.2% for 37 stars.
Paper: Precise Parameters from Bayesian Spectral Energy Distribution Fitting Indicate Thermally Driven Mass Loss Likely Driver of Radius Valley
Authors: David Jordan, Inseok Song
Figure caption: Left: plot of Rp vs. period for all 1923 planets, with the equations used for the Rp calculation shown by different colored markers. Right: plot of Rp vs. Sp for all planets, with the equations used for the Sp calculation shown by different colored markers. The size of the markers represents the uncertainty. Distribution statistics for each equation are shown in the inset table.
Paper: Precise Parameters from Bayesian Spectral Energy Distribution Fitting Indicate Thermally Driven Mass Loss Likely Driver of Radius Valley
Authors: David Jordan, Inseok Song
Figure caption: Plot of the relative positions of theoretical model predictions from Table 1 (orange diamonds), observational results from Table 2 (blue circles), and this work (green squares) for the slope of the radius valley when viewed as planet radius vs. orbital period (horizontal axis) and planet radius vs. insolation flux (vertical axis) on a log–log scale. Uncertainties are also shown for all observational results including this work. Planet formation processes and their ranges are given by the shaded regions as predicted by the models for each axis.
Paper: Precise Parameters from Bayesian Spectral Energy Distribution Fitting Indicate Thermally Driven Mass Loss Likely Driver of Radius Valley
Authors: David Jordan, Inseok Song
Figure caption: Plots for the slopes of planet radius vs. orbital period for the full sample of 1923 planets (green), as well as the sample of 1292 planets (blue) filtered by mass along with their shaded uncertainty regions. The KDE plot is for the full sample, with light blue markers showing the planets for the filtered mass. Left: additional plots of the seven different theoretical model predictions. Right: additional plots of the nine different previous observational studies. Four of these had the same slope, which is shown as a black dashed line.
Paper: Precise Parameters from Bayesian Spectral Energy Distribution Fitting Indicate Thermally Driven Mass Loss Likely Driver of Radius Valley
Authors: David Jordan, Inseok Song
Figure caption: The R* values and uncertainties estimated with SEDmc compared to those 50 values from benchmark star data. The red line is where they would be equal, and the dotted green line shows the overall trend from low to high R*. The median % fractional uncertainty of 2.95% is noted as compared to the benchmark value of 1.18%. The lower panel shows fractional residuals for the % difference of SEDmc values from benchmark values where the absolute mean value of fractional residuals for all stars is 2.03%.
Paper: Precise Parameters from Bayesian Spectral Energy Distribution Fitting Indicate Thermally Driven Mass Loss Likely Driver of Radius Valley
Authors: David Jordan, Inseok Song
Figure caption: Distribution of planetary radii of 1923 planets used for the radius valley analysis with 1σ errors and a % median error for Rp of 3.4%. Planet classifications are shown by colored regions, as defined in K. S. Sotzen et al. (2021), highlighting that the radius valley is generally the separation between super-Earths and sub-Neptunes.
Paper: Precise Parameters from Bayesian Spectral Energy Distribution Fitting Indicate Thermally Driven Mass Loss Likely Driver of Radius Valley
Authors: David Jordan, Inseok Song
Figure caption: Plots for the slopes of planet radius vs. stellar mass for the full sample of 1923 planets (green), as well as the sample of 1292 planets (blue) filtered by mass along with their shaded uncertainty regions. The KDE plot is for the full sample, with light blue markers showing the planets for the filtered mass. Left: additional plots of the three different theoretical model predictions for CPML and PE. The slope did not change significantly for the filtered sample. Right: additional plots of the five different previous observational studies.
Paper: Precise Parameters from Bayesian Spectral Energy Distribution Fitting Indicate Thermally Driven Mass Loss Likely Driver of Radius Valley
Authors: David Jordan, Inseok Song
Figure caption: Plots for the slopes of planet radius vs. planet insolation flux for the full sample of 1923 planets (green), as well as the sample of 1292 planets (blue) filtered by mass along with their shaded uncertainty regions. The KDE plot is for the full sample, with light blue markers showing the planets for the filtered mass. Left: additional plots of the three different theoretical model predictions for PE, GP, and IE. Right: additional plots of the five different previous observational studies.
Paper: Precise Parameters from Bayesian Spectral Energy Distribution Fitting Indicate Thermally Driven Mass Loss Likely Driver of Radius Valley
Authors: David Jordan, Inseok Song
Figure caption: This work compared to previous studies. The sample size for this work is shown as a blue bar indicating 1923 planets, with the % fractional uncertainty of Rp as an orange bar indicating 3.4%.
Paper: Giant Outer Transiting Exoplanet Mass (GOT ‘EM) Survey. V. Two Giant Planets in Kepler-511 but Only One Ran Away
Authors: Yayaati Chachan, Paul A. Dalba, Daniel P. Thorngren, Stephen R. Kane, Howard Isaacson, Eve J. Lee, Edward W. Schwieterman, Andrew W. Howard, Matthew J. Payne
Figure caption: The histograms for log-likelihoods of our fits to the RV data and short cadence and long cadence transit data. The preference for a fit without a prior on the stellar density is driven by the RV data, which biases the fitted eccentricity and argument of periastron of Kepler-511 b.
Paper: Giant Outer Transiting Exoplanet Mass (GOT ‘EM) Survey. V. Two Giant Planets in Kepler-511 but Only One Ran Away
Authors: Yayaati Chachan, Paul A. Dalba, Daniel P. Thorngren, Stephen R. Kane, Howard Isaacson, Eve J. Lee, Edward W. Schwieterman, Andrew W. Howard, Matthew J. Payne
Figure caption: The left panel shows the fitted eccentricity and argument of periastron of Kepler-511 b with and without a prior on the stellar density. The right panel compares our stellar density prior with its fitted posterior when no prior is placed.
Paper: OrCAS: Origins, Compositions, and Atmospheres of Sub-Neptunes. I. Survey Definition
Authors: Ian J. M. Crossfield, Alex S. Polanski, Paul Robertson, Joseph Akana Murphy, Emma V. Turtelboom, Rafael Luque, Thomas Beatty, Tansu Daylan, Howard Isaacson, Jonathan Brande, Laura Kreidberg, Natalie M. Batalha, Daniel Huber, Maleah Rhem, Courtney Dressing, Stephen R. Kane, Malik Bossett, Anna Gagnebin, Maxwell A. Kroft, Pranav H. Premnath, Claire J. Rogers, Karen A. Collins, David W. Latham, Cristilyn N. Watkins, David R. Ciardi, Steve B. Howell, Arjun B. Savel, Perry Berlind, Michael L. Calkins, Gilbert A. Esquerdo, Jessica Mink, Catherine A. Clark, Michael B. Lund, Rachel A. Matson, Mark E. Everett, Joshua E. Schlieder, Elisabeth C. Matthews, Steven Giacalone, Thomas Barclay, Roberto Zambelli, Peter Plavchan, Taylor Ellingson, Michael Bowen, Gregor Srdoc, Kim K. McLeod, Richard P. Schwarz, Khalid Barkaoui, Jacob Kamler, Felipe Murgas, Enric Palle, Norio Narita, Akihiko Fukui, Howard M. Relles, Allyson Bieryla, Eric Girardin, Bob Massey, Chris Stockdale, Pablo Lewin, Riccardo Papini, Pere Guerra, Dennis M. Conti, Selçuk Yalçinkaya, Özgür Baştürk, Ghachoui Mourad
Figure caption: Target prioritization. Left: planet occurrence rate (B. J. Fulton & E. A. Petigura 2018), showing that sup-Neptunes and super-Earths are the most common planets on short-period orbits. Center: filled gold hexagons show transiting exoplanets targeted by JWST in Cycles 1 and 2 (our survey began before Cycle 3 results were announced); the shading is a KDE map showing that hot Jupiters and super-Earths were the most commonly targeted planets. Right: difference of the two heat maps, showing that temperate sub-Neptunes (red region) remain underrepresented by JWST relative to the intrinsic underlying population. We identified our targets (gray points) by weighting by this underrepresentation, TSM, and observing efficiency (see the text for details).
Paper: OrCAS: Origins, Compositions, and Atmospheres of Sub-Neptunes. I. Survey Definition
Authors: Ian J. M. Crossfield, Alex S. Polanski, Paul Robertson, Joseph Akana Murphy, Emma V. Turtelboom, Rafael Luque, Thomas Beatty, Tansu Daylan, Howard Isaacson, Jonathan Brande, Laura Kreidberg, Natalie M. Batalha, Daniel Huber, Maleah Rhem, Courtney Dressing, Stephen R. Kane, Malik Bossett, Anna Gagnebin, Maxwell A. Kroft, Pranav H. Premnath, Claire J. Rogers, Karen A. Collins, David W. Latham, Cristilyn N. Watkins, David R. Ciardi, Steve B. Howell, Arjun B. Savel, Perry Berlind, Michael L. Calkins, Gilbert A. Esquerdo, Jessica Mink, Catherine A. Clark, Michael B. Lund, Rachel A. Matson, Mark E. Everett, Joshua E. Schlieder, Elisabeth C. Matthews, Steven Giacalone, Thomas Barclay, Roberto Zambelli, Peter Plavchan, Taylor Ellingson, Michael Bowen, Gregor Srdoc, Kim K. McLeod, Richard P. Schwarz, Khalid Barkaoui, Jacob Kamler, Felipe Murgas, Enric Palle, Norio Narita, Akihiko Fukui, Howard M. Relles, Allyson Bieryla, Eric Girardin, Bob Massey, Chris Stockdale, Pablo Lewin, Riccardo Papini, Pere Guerra, Dennis M. Conti, Selçuk Yalçinkaya, Özgür Baştürk, Ghachoui Mourad
Figure caption: Our target sample, showing the distribution of fit planet size, temperature, and stellar Teff. Red points are our primary sample, white points are known companions to our targets, dashed lines connect planets orbiting the same target star, and light gray points are other known exoplanets. The histogram at right shows how our planet sizes (red) compare to the intrinsic occurrence of sub-Neptunes (gray, B. J. Fulton & E. A. Petigura 2018). Noted that these best-fit radii differ from the TESS-reported radii plotted in Figure 2(c).
Paper: OrCAS: Origins, Compositions, and Atmospheres of Sub-Neptunes. I. Survey Definition
Authors: Ian J. M. Crossfield, Alex S. Polanski, Paul Robertson, Joseph Akana Murphy, Emma V. Turtelboom, Rafael Luque, Thomas Beatty, Tansu Daylan, Howard Isaacson, Jonathan Brande, Laura Kreidberg, Natalie M. Batalha, Daniel Huber, Maleah Rhem, Courtney Dressing, Stephen R. Kane, Malik Bossett, Anna Gagnebin, Maxwell A. Kroft, Pranav H. Premnath, Claire J. Rogers, Karen A. Collins, David W. Latham, Cristilyn N. Watkins, David R. Ciardi, Steve B. Howell, Arjun B. Savel, Perry Berlind, Michael L. Calkins, Gilbert A. Esquerdo, Jessica Mink, Catherine A. Clark, Michael B. Lund, Rachel A. Matson, Mark E. Everett, Joshua E. Schlieder, Elisabeth C. Matthews, Steven Giacalone, Thomas Barclay, Roberto Zambelli, Peter Plavchan, Taylor Ellingson, Michael Bowen, Gregor Srdoc, Kim K. McLeod, Richard P. Schwarz, Khalid Barkaoui, Jacob Kamler, Felipe Murgas, Enric Palle, Norio Narita, Akihiko Fukui, Howard M. Relles, Allyson Bieryla, Eric Girardin, Bob Massey, Chris Stockdale, Pablo Lewin, Riccardo Papini, Pere Guerra, Dennis M. Conti, Selçuk Yalçinkaya, Özgür Baştürk, Ghachoui Mourad
Figure caption: Transit light-curve fits to our target sample, showing TESS photometry (black points, binned to a 10-minute cadence) and the best-fit light curves (red line).
Paper: OrCAS: Origins, Compositions, and Atmospheres of Sub-Neptunes. I. Survey Definition
Authors: Ian J. M. Crossfield, Alex S. Polanski, Paul Robertson, Joseph Akana Murphy, Emma V. Turtelboom, Rafael Luque, Thomas Beatty, Tansu Daylan, Howard Isaacson, Jonathan Brande, Laura Kreidberg, Natalie M. Batalha, Daniel Huber, Maleah Rhem, Courtney Dressing, Stephen R. Kane, Malik Bossett, Anna Gagnebin, Maxwell A. Kroft, Pranav H. Premnath, Claire J. Rogers, Karen A. Collins, David W. Latham, Cristilyn N. Watkins, David R. Ciardi, Steve B. Howell, Arjun B. Savel, Perry Berlind, Michael L. Calkins, Gilbert A. Esquerdo, Jessica Mink, Catherine A. Clark, Michael B. Lund, Rachel A. Matson, Mark E. Everett, Joshua E. Schlieder, Elisabeth C. Matthews, Steven Giacalone, Thomas Barclay, Roberto Zambelli, Peter Plavchan, Taylor Ellingson, Michael Bowen, Gregor Srdoc, Kim K. McLeod, Richard P. Schwarz, Khalid Barkaoui, Jacob Kamler, Felipe Murgas, Enric Palle, Norio Narita, Akihiko Fukui, Howard M. Relles, Allyson Bieryla, Eric Girardin, Bob Massey, Chris Stockdale, Pablo Lewin, Riccardo Papini, Pere Guerra, Dennis M. Conti, Selçuk Yalçinkaya, Özgür Baştürk, Ghachoui Mourad
Figure caption: Number of target systems in each of our science themes: atmospheres (I), interior compositions (II), system architectures (III), and stellar activity (IV). See Section 2 and Table 1 for details.
Paper: Probing Two-dimensional Asymmetries of an Exoplanet Atmosphere from Chromatic Transit Variation
Authors: Shotaro Tada, Hajime Kawahara, Yui Kawashima, Takayuki Kotani, Kento Masuda
Figure caption: Definitions of parameters, coordinate systems, and directional terms used in this paper. (a) The definitions of the parameters in the conventional transit model. k2 represents the depth of the light curve. The four contact times, tI, tII, tIII, and tIV, are defined as the times when the planet is externally and internally tangent to the stellar disk. These contact times can be converted as follows: ti = (tI + tII)/2, τi = tII − tI, te = (tIII + tIV)/2, and τe = tIV − tIII. For a circular orbit, there is a constraint τi = τe, leading to the following useful conversions: Ttot = tIV − tI, Tfull = tIII − tII, and t0 = (tI + tIV)/2 = (tII + tIII)/2. (b) The coordinate systems used in this paper. The X–Y coordinate system is fixed relative to the celestial sphere. The yi-axis and ye-axis are aligned with the tangent lines of the stellar disk at the points where the planetary orbit intersects the edge of the host star. The xi-axis and xe-axis are oriented perpendicularly to the yi-axis and ye-axis, respectively. The xi–yi and xe–ye coordinate systems are fixed relative to the planet’s center of mass and rotate synchronously with the planet’s orbital motion, regardless of whether the planet itself is tidally locked. (c) The definitions of north, south, morning, and evening. (d) The definitions of morning, evening, dayside, and nightside. The planet’s rotation is in the same direction as its orbital motion.
Paper: Probing Two-dimensional Asymmetries of an Exoplanet Atmosphere from Chromatic Transit Variation
Authors: Shotaro Tada, Hajime Kawahara, Yui Kawashima, Takayuki Kotani, Kento Masuda
Figure caption: Patterns of atmospheric asymmetry and their different impacts on light curves. The blue solid lines and gray dashed lines in the figures on the right of each panel correspond to the light curves of the blue and gray disks of the planet’s shadow in the schematics on the left, respectively. (a) A displacement of the planet’s shadow along the X-axis (morning–evening asymmetry) causes a shift of the central transit time t0. (b) A displacement along the Y-axis (north–south asymmetry) causes a change in the transit duration. (c) Opposite displacements along the X-axis between ingress and egress also alter the transit duration. (d) Changes in the apparent radius result in variations in both the transit depth and duration. In panel (d), the radius of the blue disk is exaggerated by a factor of 5 compared to the disk producing the blue light curve, to highlight the differences between the gray and blue disks.
Paper: Probing Two-dimensional Asymmetries of an Exoplanet Atmosphere from Chromatic Transit Variation
Authors: Shotaro Tada, Hajime Kawahara, Yui Kawashima, Takayuki Kotani, Kento Masuda
Figure caption: Inferred values of all the parameters in the light-curve model obtained from the MCMC sampling. The gray dots represent the results using free u1 and u2 for each wavelength, while the blue dots represent the results assuming a linear wavelength dependence for q1 and q2 in the MCMC analysis. For the limb-darkening coefficients inferred using the assumption of a linear wavelength dependence for q1 and q2, the inferred values at the minimum and maximum wavelengths, as well as the wavelength dependence for each sampling, are shown. The dots indicate the median values of the MCMC sampling, and the error bars represent the 68% credible intervals.
Paper: Probing Two-dimensional Asymmetries of an Exoplanet Atmosphere from Chromatic Transit Variation
Authors: Shotaro Tada, Hajime Kawahara, Yui Kawashima, Takayuki Kotani, Kento Masuda
Figure caption: Spectra of ﹩{R}_{{\rm{p}}}^{x{\rm{e}}-}﹩ (top left), ﹩{R}_{{\rm{p}}}^{x{\rm{i}}+}﹩ (top right), ﹩{R}_{{\rm{p}}}^{x{\rm{i}}-}﹩ (bottom left), and ﹩{R}_{{\rm{p}}}^{x{\rm{e}}+}﹩ (bottom right). The dots with error bars represent the median values from the MCMC sampling, with 68% credible intervals. The colored dots indicate the results using free u1 and u2 for each wavelength, while the gray dots indicate the results assuming a linear wavelength dependence for q1 and q2 in the MCMC analysis.
Paper: Landscape of Coronal X-Ray Variability and Cycles
Authors: Thomas Ayres
Figure caption: X-ray light curves for the Cycles sample. The x-axis shows time in years; the y-axis shows bolometrically normalized 0.1–2.4 keV X-ray luminosity. The hatched region at the top of each panel represents the “saturation” zone, where LX/LBOL becomes decoupled from the stellar rotation period. Measurements from different observatories are marked according to legend at the upper left (except α Cen B: diamonds represent mix of HRC-I and scaled EPIC). Individual members are noted by color-coded spectral designations at the right, expanded in the key at the bottom of the panel. Dashed lines connect data points with wide time gaps; otherwise, points are linked by solid lines (histogram style). In panels (b)–(d), obvious cycles are marked by a log-sinusoidal model fit, annotated with cycle period in years. Stars marked “noc” lack obvious cycles; “ind” indicates insufficient temporal coverage to date; and “fa” denotes flat-activity. Panel (a): the whole sample. Panel (b): the subsample consisting of the Sun (small gray points are daily values; dark dots are 81 day averages); Seven Dwarfs; and dM flare stars Proxima and AU Mic. Panel (c): F-dwarf ι Hor, G-subgiant HD 81809, and flare stars CN Leo and DG CVn. Panel (d): various K dwarfs in the middle of the panel; hyperactive AB Dor and AR Lac in the upper portion. For AR Lac, larger orange circles represent HRC-I; smaller dark dots are HRC-S.