adsabs/AstroCLIMB huggingface dataset:
Paper: Binary-star Wave Front Control Based on a Common-path Visible Nulling Coronagraph
Authors: Kenta Yoneta, Naoshi Murakami, Ryuta Koike, Jun Nishikawa
Figure caption: Optical setup for laboratory demonstration of binary-star wave front control for CP-VNC.
Paper: Binary-star Wave Front Control Based on a Common-path Visible Nulling Coronagraph
Authors: Kenta Yoneta, Naoshi Murakami, Ryuta Koike, Jun Nishikawa
Figure caption: Experimental results of wave front control close to model star A: (top) before and (bottom) after wave front control. The leftmost column shows wide-field images and the other columns show close-up images around the target dark-hole region indicated by the red dashed squares in the top row. The ranges of the target region are set to −45 to −5 λ 0/D and 5 to 45 λ 0/D in the x A and y A directions, corresponding to −45 to −5 λ 0/D and −95 to −55 λ 0/D in the x B and y B directions, respectively.
Paper: Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release
Authors: Hyun-Jin Jeong, Yong-Jae Moon, Eunsu Park, Harim Lee, Ji-Hye Baek
Figure caption: Flowchart and structures of the Pix2PixCC model. G, D, and I are the generator, discriminator, and inspector, respectively. The generator produces target-like data from input data. When we train the model, input data are SDO/AIA EUV 304, 193, and 171 Å images and a reference data pair. The reference data pair is composed of the three SDO EUV images and the corresponding SDO/HMI magnetogram, which are the nearest available ones. The discriminator trains for distinguishing between the real pair and generated pair. The real pair consists of an input data and a target data, and the generated pair consists of an input data and a generated data. The inspector computes CCs between the target data and generated data. The generator and discriminator are updated from the losses calculated from the inspector and discriminator.
Paper: Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release
Authors: Hyun-Jin Jeong, Yong-Jae Moon, Eunsu Park, Harim Lee, Ji-Hye Baek
Figure caption: Two objective measures of comparisons between the SDO/HMI magnetograms and AI-generated magnetograms for 1342 full disk, 2926 ARs, and 2684 QRs. Panels (a), (b), and (c) are scatter plots between the TUMFs of the SDO/HMI magnetograms and AI-generated magnetograms for the full disk, ARs, and QRs, respectively. Panels (d), (e), and (f) are scatter plots between the NMFs of the SDO/HMI magnetograms and AI-generated magnetograms for the same data sets, respectively.
Paper: A Swift X-Ray View of the SMS4 Sample—X-Ray Properties of 31 Quasars and Radio Galaxies
Authors: Alessandro Maselli, William R. Forman, Christine Jones, Ralph P. Kraft, Matteo Perri
Figure caption: Infrared and optical maps, at the same scale, of the 20 SMS4 sources detected by Swift-XRT in our sample. Infrared maps (left side of each panel) in the W1 filter (3.4 μm) are from AllWISE, while optical maps (right side; 1 pixel = 1″) in the r filter are from DSS2. Ellipses mark the positional uncertainty of radio sources: blue is used for G4Jy; magenta is used for SUMSS in panels (o), (p), and (t); and red is used for NVSS in panel (s). Black dashed circles mark the positional uncertainty of the X-ray sources. Positional uncertainties are given at 90% confidence level for both the radio and the X-ray sources. Crosses and plus signs mark infrared and optical sources, respectively. Green is used for counterparts associated by W20 and by BH06. For other AllWISE and GSC 2.3.2 sources, black or white is equally used to improve the visibility with respect to the map in the background. In the infrared band: in panels (f), (l), (m), and (r), magenta crosses mark sources from CatWISE2020. In the optical band: in panels (a), (f), (h), and (i), red plus signs mark sources from DES DR2.
Paper: A Swift X-Ray View of the SMS4 Sample—X-Ray Properties of 31 Quasars and Radio Galaxies
Authors: Alessandro Maselli, William R. Forman, Christine Jones, Ralph P. Kraft, Matteo Perri
Figure caption: Swift X-ray maps in the 0.3–10.0 keV band of the 31 sources included in our sample. All maps are centered at the positions of the SMS4 radio sources. Radio flux density contours at 843 MHz from SUMSS overlay the X-ray maps, and they are shown in blue for the 20 sources detected in the X-rays by Swift and in red for the 10 sources that were not detected. Contours have been selected, for each source, to best display the shape of the radio emission. For sources with double or triple morphology, following the classification in W20, distinct SUMSS components, sorted by decreasing flux density, are shown in blue; for MRC B1358−493, distinct TGSS sources are shown in orange; for MRC B1451−364, the core, not resolved by SUMSS but by NVSS, is shown in magenta. Although PKS 2148−555 was not detected as an X-ray point source, blue contours are used also in this case, since we find significant X-ray emission at the source location on arcminute scales. As discussed in the text, PKS 2148−555 lies within the poor cluster A3816 and is associated with the cluster BCG (Lloyd & Jones 2002). The map of PKS 2148−555 (20′ × 20′) is binned by 16 × 16 pixels (1 pixel = 2.″36). The remaining maps are binned by 4 × 4 pixels and are 8′ × 8′ except for MRC B1737−609, shown at smaller scale (4′ × 4′).
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: (a) Histogram of logarithms of temperature values, log(T/[K]), plotted for all of the examined ENA observation years (2009–2016). (b1) 2D histogram of the all-year values of log(n/[cm−3]) vs. log(T/[K]). (c1) 2D distribution from panel (b1) normalized by the histogram of log(T/[K]) values, plotted in (a). (d1) Plot of weighted mean and the standard error for each log(T/[K])—bin. (b2–d2): Same as (b1)–(d1) but with examining variables log(T/[K]) and log(κ-3/2). Note that color bars measure the smoothed 2D occurrence frequency.
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: (a) Secondary polytropic index, ν ≡ 1/(γ − 1), derived macroscopically from the slope of temperature–density diagram (on log-log scales). (b) Histogram of the (secondary) polytropic indices (distribution of fluctuating slopes; see the text). (c) Evolution of the polytropic index γ = 1 + 1/ν.
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: (a) Annual values of entropy associated with kappa distributions, derived from the annual values of the density, temperature, and kappa, which were plotted in Figure 10. (b) Annual values of entropy shown in panel (a), plotted with respect to the annual polytropic indices, which were plotted in Figure 14(c).
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: Annual polytropic indices plotted with respect to the kappa values for the years of ENA observations from 2009–2016. A linear model is fitted to the data.
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: (a) Annual percentage rate of temperature (mean and standard error). (b) Annual temperature values (also shown in Figure 10(a)).
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: The three main statistical analyses of the method.
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: Annual sky maps of the temperature, density, and kappa, plotted on log scales together with their uncertainties for the years 2009–2016.
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: Annual sky maps of the spectral indices (Γ) and kappa (κ), plotted also on log scales together with their uncertainties for the years 2009–2016.
Paper: Binary-star Wave Front Control Based on a Common-path Visible Nulling Coronagraph
Authors: Kenta Yoneta, Naoshi Murakami, Ryuta Koike, Jun Nishikawa
Figure caption: (Left) The coordinate system in the focal plane for representing the position of the target speckle to be rejected. The target speckle positioned at (X, Y) in the coordinate system of the focal plane (x, y) is denoted by (X A, Y A) in the system for star A (x A, y A) and (X B, Y B) in the system for star B (x B, y B). (Right) Electric fields of the residual speckle of stars A and B in the complex plane. The speckle from each of the two stars, which is not coherent with the other, is modulated four times (eight times in total) by the SLM so that their electric fields can be estimated using the SAN algorithm.
Paper: Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release
Authors: Hyun-Jin Jeong, Yong-Jae Moon, Eunsu Park, Harim Lee, Ji-Hye Baek
Figure caption: (a) A temporal variation of the TUMF in NOAA AR 11166 from 2011 February 1 to May 7. The green, red, and blue dots are TUMFs from SDO/HMI, AISFM A, and AISFM B, respectively. (b)–(k) A series of magnetograms tracking the AR over three solar rotations. The magnetograms are converted from full disk data to heliographic coordinated maps. The color map of the magnetic fields is the same as shown in Figures 4(g)–(j).
Paper: Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release
Authors: Hyun-Jin Jeong, Yong-Jae Moon, Eunsu Park, Harim Lee, Ji-Hye Baek
Figure caption: (a)–(f) Solar frontside SDO/HMI magnetogram, the farside STEREO composite EUV images (red: 304 Å; green: 193 Å; and blue: 171 Å), and AISFMs on 2011 March 5. The purple and brown boxes represent NOAA AR 11166 and 11165, respectively. (g)–(j) The two ARs are zoomed and converted from full disk data to heliographic coordinated maps. The color map of zoomed ARs shows the large dynamic range of values in gauss.
Paper: Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release
Authors: Hyun-Jin Jeong, Yong-Jae Moon, Eunsu Park, Harim Lee, Ji-Hye Baek
Figure caption: Comparisons of solar polar field strengths from SDO/HMI and those from AISFM A and B. Yellow (purple) dots represent the mean radial field strength above 60° in the south (north) of SDO/HMI magnetograms; the lines represent the smoothed average. Brown (green) and red (blue) dots represent the mean radial field strength above 60° in the south (north) of AISFMs A and B, respectively.
Paper: Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release
Authors: Hyun-Jin Jeong, Yong-Jae Moon, Eunsu Park, Harim Lee, Ji-Hye Baek
Figure caption: A solar farside STEREO A EUV image and AISFMs on 2021 May 20, and the frontside SDO/HMI magnetogram on 2021 May 24. The yellow dotted circles represent NOAA AR 12824. A result of J20 (AISFM A 2.0) is shown together for comparison with our result (AISFM A 3.0).
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: (a) Temperatures of the 8 pixels surrounding the (central) pixel located at the LOS direction Ω with longitude and latitude indices (long = 13, lat = 12), for the year 2012, presented with respect to the projected angle α (similar to Figure 1(a)). (b) Magnification of (a), showing the small residuals compared to the respective errors of each point. The plotted cumulative percentages of (c) p-values and (d) reduced chi-square χ 2 red show that 41.3% of the GDF pixels have p-value <0.05 (or 58.7% of the GDF pixels with p-value ≥0.05). Note that there is ∼80% of the GDF pixels with p-value ≥0.01 (not shown in the graph); however, there is a larger percentage of 96.8% of GDF pixels with χ 2 red ≤ 3 (and δlogT ≤ 0.5).
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: The linearity of log Π, i.e., the function ﹩\mathrm{log}{j}_{* }-\mathrm{log}{f}_{\kappa }\,={const}.+(\kappa -1/2)\cdot \mathrm{log}({k}_{{\rm{B}}}T)﹩, with kappa κ is a characteristic property of the kappa function. This linearity is shown in (a), plotting the values of ﹩\mathrm{log}{j}_{{* }_{i}}-\mathrm{log}{f}_{{\kappa }_{i}}﹩ against κ  i  for all of the GDF pixels of 2012. The two main linear streams correspond to two average temperatures, ﹩\left\langle \mathrm{log}({k}_{{\rm{B}}}T/[{\rm{K}}])\right\rangle \,=6.049\pm 0.012﹩ (red) and 6.147 ± 0.018 (blue), agreeing with the most frequent temperature value shown in the histogram plotted in (b).
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: (a) Flux spectrum for the pixel at (long = 0, lat = 1) from 2012 observations, plotted with no systematic errors (black) and with the systematic error of Δj/δj = 0.6 (red). (b) Histogram of the temperature systematic error normalized by its (original) statistical error, Δ logT/δlogT 0, for each sky-map pixel during 2012 observations; the estimated HWHM is ∼ 1.18. (c) Plots of HWHM with respect to the maximum error ratio Δj/δj; the linear fit shows that Δ logT/δlogT 0 is about 3 times the value of Δj/δj.
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: Annual values and standard deviation of the modes of the (a) temperature, (b) density, and (c) kappa, plotted for each year of ENA observations.
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: Annual percentage of GDF sky-map pixels, corresponding to: (i) non-kappa behavior, that is, estimated kappa is <1.5 (gray); (ii) all variable, i.e., no statistically constant value of T, n, and κ for neighboring pixels (red); (iii) invariant T, n, and variable κ, i.e., statistical constant value for neighboring pixels (blue); and (iv) invariant κ, i.e., statistically constant value of κ, hence, undetermined values of T and n (green). Pie chart shows the all-years mean percentages.
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: Histograms (unnormalized occurrence) of the temperature logarithm values, taken from all of the GDF sky-map pixels and derived separately for each year of ENA observations.
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: A superposition (red) of two Gaussians (blue) fits the histograms for (a) 2012 and (b) 2015. (c) Derived temperatures for both populations and their weighted mean for all of the observation years.
Paper: Thermodynamics of the Inner Heliosheath
Authors: G. Livadiotis, D. J. McComas, H. O. Funsten, N. A. Schwadron, J. R. Szalay, E. Zirnstein
Figure caption: Overall histogram of the kappa and spectral indices for the examined observation years 2009–2016; the modes are ∼1.8 and ∼2.0, respectively.
Paper: The California Legacy Survey. III. On the Shoulders of (Some) Giants: The Relationship between Inner Small Planets and Outer Massive Planets
Authors: Lee J. Rosenthal, Heather A. Knutson, Yayaati Chachan, Fei Dai, Andrew W. Howard, Benjamin J. Fulton, Ashley Chontos, Justin R. Crepp, Paul A. Dalba, Gregory W. Henry, Stephen R. Kane, Erik A. Petigura, Lauren M. Weiss, Jason T. Wright
Figure caption: Left: occurrence grid for the full CLS sample of 719 stars. Cell shade and number annotation reflect the median expected number of planets per 100 stars in each bin. Empty bins show an expected upper limit on occurrence as the 84.1th percentile on the occurrence rate posterior. Right: same, but only for the 28 hosts of detected small planets with M sin i < 30 M ⊕ and a < 1 au. Note that this sample includes 55 Cnc's four cold giants, whereas our fractional analysis in Figures 3 and 4 excludes 55 Cnc, since its inner ultrashort period planet is undetectable by our automated search due to our period limits. The right-hand panel shows that there is an absence of warm gas giants in our sample of detected small planet hosts.
Paper: The California Legacy Survey. III. On the Shoulders of (Some) Giants: The Relationship between Inner Small Planets and Outer Massive Planets
Authors: Lee J. Rosenthal, Heather A. Knutson, Yayaati Chachan, Fei Dai, Andrew W. Howard, Benjamin J. Fulton, Ashley Chontos, Justin R. Crepp, Paul A. Dalba, Gregory W. Henry, Stephen R. Kane, Erik A. Petigura, Lauren M. Weiss, Jason T. Wright
Figure caption: Left: two measurements of the conditional occurrence of inner small planets given the presence of an outer gas giant. The black distribution is our direct measurement, while the green distribution uses Bayes’ theorem to infer it from other measurements. Right: our sample of planet pairs with small planets within the region of interest, with our inner small planet box outlined in red and our outer giant box outlined in purple. We assign a number to each planetary system and label individual planets accordingly.
Paper: The California Legacy Survey. III. On the Shoulders of (Some) Giants: The Relationship between Inner Small Planets and Outer Massive Planets
Authors: Lee J. Rosenthal, Heather A. Knutson, Yayaati Chachan, Fei Dai, Andrew W. Howard, Benjamin J. Fulton, Ashley Chontos, Justin R. Crepp, Paul A. Dalba, Gregory W. Henry, Stephen R. Kane, Erik A. Petigura, Lauren M. Weiss, Jason T. Wright
Figure caption: Same as Figure 3, but for Jupiter analogs within 3–7 au and 0.3–13 M J instead of the broader giant population.
Paper: The California Legacy Survey. III. On the Shoulders of (Some) Giants: The Relationship between Inner Small Planets and Outer Massive Planets
Authors: Lee J. Rosenthal, Heather A. Knutson, Yayaati Chachan, Fei Dai, Andrew W. Howard, Benjamin J. Fulton, Ashley Chontos, Justin R. Crepp, Paul A. Dalba, Gregory W. Henry, Stephen R. Kane, Erik A. Petigura, Lauren M. Weiss, Jason T. Wright
Figure caption: Top: cold giant planets without detected inner small companions in the CLS sample, with associated completeness contours. The contours show that the data sets associated with these systems are somewhat sensitive to planets within 0.5 au and 30 M ⊕; we have outlined our small planet parameter space in green for context. This means that we can say with some confidence that not all of these systems harbor undetected small planets. Bottom: small planets without detected outer companions in the CLS sample, with associated completeness contours. We have outlined our outer giant parameter space in purple for context.
Paper: The California Legacy Survey. III. On the Shoulders of (Some) Giants: The Relationship between Inner Small Planets and Outer Massive Planets
Authors: Lee J. Rosenthal, Heather A. Knutson, Yayaati Chachan, Fei Dai, Andrew W. Howard, Benjamin J. Fulton, Ashley Chontos, Justin R. Crepp, Paul A. Dalba, Gregory W. Henry, Stephen R. Kane, Erik A. Petigura, Lauren M. Weiss, Jason T. Wright
Figure caption: Analytical mass distributions for small planets used in this work (green) and from Neil & Rogers (2020) (purple). This work assumes a uniform distribution in ln(M). Assuming a uniform distribution in ln(a), this leads to a 25% recovery rate in our survey of small planets within 2–30 M ⊕ and 0.023–1 au, given our search completeness. Neil & Rogers (2020) fit a log-normal mixture model to a sample of Kepler planets and found a distinct small planet component, shown here. This model leads to an 18.3% recovery rate in our survey.
Paper: The California Legacy Survey. III. On the Shoulders of (Some) Giants: The Relationship between Inner Small Planets and Outer Massive Planets
Authors: Lee J. Rosenthal, Heather A. Knutson, Yayaati Chachan, Fei Dai, Andrew W. Howard, Benjamin J. Fulton, Ashley Chontos, Justin R. Crepp, Paul A. Dalba, Gregory W. Henry, Stephen R. Kane, Erik A. Petigura, Lauren M. Weiss, Jason T. Wright
Figure caption: A histogram of observed small planet multiplicity in our sample. This is not corrected for search completeness, so it should only be interpreted as the multiplicity of detected planets, not as the underlying multiplicity distribution. There are 719 total stars in the CLS, in around 29 of which we have detected small planets.
Paper: The California Legacy Survey. III. On the Shoulders of (Some) Giants: The Relationship between Inner Small Planets and Outer Massive Planets
Authors: Lee J. Rosenthal, Heather A. Knutson, Yayaati Chachan, Fei Dai, Andrew W. Howard, Benjamin J. Fulton, Ashley Chontos, Justin R. Crepp, Paul A. Dalba, Gregory W. Henry, Stephen R. Kane, Erik A. Petigura, Lauren M. Weiss, Jason T. Wright
Figure caption: Minimum mass (M sin i) vs. semimajor axis values for small (M sin i < 30 M ⊕) planets, and their giant outer companions, in the CLS catalog. Diamonds are small planets without outer giants, pentagons are small planets with outer giants, and circles are outer giants. Contours are the completeness map for small planet hosts.
Paper: A Blind All-sky Search for Star Clusters in Gaia EDR3: 886 Clusters within 1.2 kpc of the Sun
Authors: Zhihong He, 治宏 何, Kun Wang, 坤 王, Yangping Luo, 杨平 罗, Jing Li, 静 李, Xiaochen Liu, 效臣 刘, Qingquan Jiang, 青权 蒋
Figure caption: A face-on view of star clusters near the solar system; the polar coordinates show the Galactic longitude and the projected distance from the Sun (in parsecs). The filled circles present the locations for the new candidates (+), matched clusters, and unrecovered CG20 OCs (×), the logarithmic ages are shown in different colors, and the size of the circles are proportional to the 1σ dispersion (estimated as ﹩\pi /0.18\cdot {\varpi }^{-1}\cdot \sqrt{{\sigma }_{l}^{2}\cdot {\cos }^{2}(b)+{\sigma }_{b}^{2}}﹩) of the cluster members.
Paper: A Blind All-sky Search for Star Clusters in Gaia EDR3: 886 Clusters within 1.2 kpc of the Sun
Authors: Zhihong He, 治宏 何, Kun Wang, 坤 王, Yangping Luo, 杨平 罗, Jing Li, 静 李, Xiaochen Liu, 效臣 刘, Qingquan Jiang, 青权 蒋
Figure caption: Comparison with cluster ages from this work, from Sim et al. (2019), and from CG20. Upper panels: comparison of the ages for the matched clusters. Lower panels: age differences recorded in different catalogs; the orange curves shown the Gaussian-fitted distributions.
Paper: A Blind All-sky Search for Star Clusters in Gaia EDR3: 886 Clusters within 1.2 kpc of the Sun
Authors: Zhihong He, 治宏 何, Kun Wang, 坤 王, Yangping Luo, 杨平 罗, Jing Li, 静 李, Xiaochen Liu, 效臣 刘, Qingquan Jiang, 青权 蒋
Figure caption: Left: histograms of the clusters’ Galactic altitudes. Right: an edge-on view of the clusters, where the color bar presents the logarithmic age of each cluster. The cross symbols indicate the old clusters with ages ≥109 yr.
Paper: A Blind All-sky Search for Star Clusters in Gaia EDR3: 886 Clusters within 1.2 kpc of the Sun
Authors: Zhihong He, 治宏 何, Kun Wang, 坤 王, Yangping Luo, 杨平 罗, Jing Li, 静 李, Xiaochen Liu, 效臣 刘, Qingquan Jiang, 青权 蒋
Figure caption: Histograms of the cluster ages derived from isochrone fittings in this work, and cluster ages from Sim et al. (2019), the OC ages not recovered in this work are from CG20.
Paper: A Blind All-sky Search for Star Clusters in Gaia EDR3: 886 Clusters within 1.2 kpc of the Sun
Authors: Zhihong He, 治宏 何, Kun Wang, 坤 王, Yangping Luo, 杨平 罗, Jing Li, 静 李, Xiaochen Liu, 效臣 刘, Qingquan Jiang, 青权 蒋
Figure caption: Histograms of the parallax for the new candidates, matched known clusters, and unrecovered CG20 OCs.