adsabs/AstroCLIMB huggingface dataset:
Paper:  KPM: A Flexible and Data-driven K-process Model for Nucleosynthesis
Authors: Emily J. Griffith, David W. Hogg, Julianne J. Dalcanton, Sten Hasselquist, Bridget Ratcliffe, Melissa Ness, David H. Weinberg
Figure caption: Left: [X/Mg] vs. [Mg/H] abundance distributions for the observed sample (first column), K = 2 model (second column), and K = 4 model (third column) for Ca, C+N, Na, Cr, Ni, Mn, Co, and Ce. Observational errors are not added to the model predictions. All density plots are logarithmically scaled. Right: process vector components (fourth column) and median low-Ia fractional contributions from each process (fifth column) for the K = 4 (solid lines) and K = 2 (dashed lines) models to the low-Ia population as a function of [Mg/H]. We plot the median ﹩{q}_{\mathrm{CC},j}^{Z}﹩ and ﹩{f}_{{ij}}^{\mathrm{CC}}﹩ in light purple, ﹩{q}_{\mathrm{Ia},j}^{Z}﹩ and ﹩{f}_{{ij}}^{\mathrm{Ia}}﹩ in dark purple, ﹩{q}_{3,i}^{Z}﹩ and ﹩{f}_{{ij}}^{3}﹩ in light orange, and ﹩{q}_{4,i}^{Z}﹩ and ﹩{f}_{{ij}}^{4}﹩ in dark orange. In the final column, we plot a dotted gray line at 0 for reference.
Paper:  KPM: A Flexible and Data-driven K-process Model for Nucleosynthesis
Authors: Emily J. Griffith, David W. Hogg, Julianne J. Dalcanton, Sten Hasselquist, Bridget Ratcliffe, Melissa Ness, David H. Weinberg
Figure caption: Top: observed [Fe/Mg] vs. [Mg/H] abundance distribution. Bottom: predicted [Fe/Mg] vs. [Mg/H] abundance distributions plus estimated noise for models with ﹩{{dq}}_{\mathrm{CC},\mathrm{Fe}}^{Z}/{dZ}=0.0﹩ (middle row) and ﹩{{dq}}_{\mathrm{CC},\mathrm{Fe}}^{Z}/{dZ}=0.15﹩ (bottom row). The ﹩{q}_{\mathrm{CC},\mathrm{Fe}}^{Z}﹩ value increases from left to right, from 0.35 to 0.5. All density plots are logarithmically scaled. Note that the center panels have different ﹩{q}_{\mathrm{CC},\mathrm{Fe}}^{Z}﹩ values (middle: 0.45; bottom: 0.4) and that the predicted abundance distributions for ﹩{q}_{\mathrm{CC},\mathrm{Fe}}^{Z}=0.4﹩ and ﹩{{dq}}_{\mathrm{CC},\mathrm{Fe}}^{Z}/{dZ}=0.0﹩ can be found in Figure 3. The ﹩{q}_{\mathrm{CC},\mathrm{Fe}}^{Z}﹩ and ﹩{{dq}}_{\mathrm{CC},\mathrm{Fe}}^{Z}/{dZ}﹩ parameters are listed in each panel. By comparing the lower panels to the observed distribution (top), we can evaluate the success or failure of each model; the low-Ia population is most sensitive to the Fe assumptions.
Paper:  KPM: A Flexible and Data-driven K-process Model for Nucleosynthesis
Authors: Emily J. Griffith, David W. Hogg, Julianne J. Dalcanton, Sten Hasselquist, Bridget Ratcliffe, Melissa Ness, David H. Weinberg
Figure caption: Left: distribution of ﹩{A}_{i}^{\mathrm{Ia}}﹩/﹩{A}_{i}^{\mathrm{CC}}﹩ vs. ﹩{A}_{i}^{\mathrm{CC}}﹩ for the fiducial model. This plot is similar to an [Fe/Mg] vs. [Mg/H] distribution, where ﹩{A}_{i}^{\mathrm{Ia}}﹩/﹩{A}_{i}^{\mathrm{CC}}﹩ is a proxy for [Fe/Mg] and ﹩{A}_{i}^{\mathrm{CC}}﹩ is a proxy for [Mg/H]. Note that ﹩{A}_{i}^{\mathrm{CC}}=1﹩ corresponds to [Mg/H] = 0. Center: distribution of ﹩{A}_{i}^{\mathrm{Ia}}﹩/﹩{A}_{i}^{\mathrm{CC}}﹩ vs. [Mg/H]. In the left and center panels, we can clearly see the bimodality to high values of ﹩{A}_{i}^{\mathrm{CC}}﹩ and [Mg/H]. Both density plots are logarithmically scaled. Right: distribution of ﹩{A}_{i}^{\mathrm{Ia}}﹩/﹩{A}_{i}^{\mathrm{CC}}﹩ for ranges of [Mg/H], with metallicity bins increasing from top to bottom. [Mg/H] bins are of width 0.325 dex and span −0.75 to 0.55.
Paper:  KPM: A Flexible and Data-driven K-process Model for Nucleosynthesis
Authors: Emily J. Griffith, David W. Hogg, Julianne J. Dalcanton, Sten Hasselquist, Bridget Ratcliffe, Melissa Ness, David H. Weinberg
Figure caption: Left: distribution of stars in ﹩{A}_{i}^{\mathrm{Ia}}﹩/﹩{A}_{i}^{\mathrm{CC}}﹩ vs. [Mg/H], where the black dashed line is the dividing line between the high-Ia and low-Ia populations (Equation (16)). Stars that are reclassified as high-Ia are shown in purple (647 stars) and stars that are reclassified as low-Ia are shown in orange (224 stars). Right: the same as the left, but in [Mg/Fe] vs. [Fe/H] space. The high-Ia and low-Ia populations have been labeled in both panels for clarity. The symbols emphasize the stars at the edges of the populations, but the reclassified stars make up only ∼2% of the total population.
Paper:  KPM: A Flexible and Data-driven K-process Model for Nucleosynthesis
Authors: Emily J. Griffith, David W. Hogg, Julianne J. Dalcanton, Sten Hasselquist, Bridget Ratcliffe, Melissa Ness, David H. Weinberg
Figure caption: Elemental median values of ﹩{f}_{{ij}}^{\mathrm{CC}}﹩ at solar metallicity for the low-Ia population for KPM with ﹩{q}_{\mathrm{CC},\mathrm{Fe}}^{Z}=0.35﹩ (darkest purple) to 0.5 (lightest purple) and ﹩{{dq}}_{\mathrm{CC},\mathrm{Fe}}^{Z}/{dZ}=0.0﹩. Elements are ordered by atomic number. The median ﹩{f}_{{ij}}^{\mathrm{CC}}﹩ changes most dramatically for elements with strong delayed contributions.
Paper:  KPM: A Flexible and Data-driven K-process Model for Nucleosynthesis
Authors: Emily J. Griffith, David W. Hogg, Julianne J. Dalcanton, Sten Hasselquist, Bridget Ratcliffe, Melissa Ness, David H. Weinberg
Figure caption: Minimum [Fe/Mg] value attainable for ﹩{q}_{\mathrm{CC},\mathrm{Fe}}^{Z}﹩ and ﹩{{dq}}_{\mathrm{CC},\mathrm{Fe}}^{Z}/{dZ}﹩ assumptions. Left: for ﹩{{dq}}_{\mathrm{CC},\mathrm{Fe}}^{Z}/{dZ}=0﹩ with ﹩{q}_{\mathrm{CC},\mathrm{Fe}}^{Z}=0.5﹩, 0.45, 0.4, and 0.35 (light to dark purple). Right: for ﹩{{dq}}_{\mathrm{CC},\mathrm{Fe}}^{Z}/{dZ}=0.15﹩ with ﹩{q}_{\mathrm{CC},\mathrm{Fe}}^{Z}=0.5﹩, 0.4, and 0.35 (light to dark orange).
Paper:  KPM: A Flexible and Data-driven K-process Model for Nucleosynthesis
Authors: Emily J. Griffith, David W. Hogg, Julianne J. Dalcanton, Sten Hasselquist, Bridget Ratcliffe, Melissa Ness, David H. Weinberg
Figure caption: Left: cumulative distribution of ﹩{\mathrm{log}}_{10}({\chi }^{2})﹩ for W22 (dashed orange line) and our fiducial model (G24; solid purple line). Right: χ 2 per element for the same model fits, with elements ordered by atomic number. Overall, our model has a smaller cumulative χ 2 than the previous two-process model and better predicts the abundances of C+N, Na, K, Mn, Co, Ni, and Ce.
Paper:  KPM: A Flexible and Data-driven K-process Model for Nucleosynthesis
Authors: Emily J. Griffith, David W. Hogg, Julianne J. Dalcanton, Sten Hasselquist, Bridget Ratcliffe, Melissa Ness, David H. Weinberg
Figure caption: Left: cumulative distribution of ﹩{\mathrm{log}}_{10}({\chi }^{2})﹩ for the K = 2 model (dotted light purple line) and the K = 4 model (solid dark purple line). Right: χ 2 per element for the same model fits. Elements are ordered by atomic number.
Paper:  KPM: A Flexible and Data-driven K-process Model for Nucleosynthesis
Authors: Emily J. Griffith, David W. Hogg, Julianne J. Dalcanton, Sten Hasselquist, Bridget Ratcliffe, Melissa Ness, David H. Weinberg
Figure caption:                       ﹩{A}_{i}^{k}/{A}_{i}^{\mathrm{CC}}﹩ vs. ﹩{A}_{i}^{\mathrm{CC}}﹩ distribution for ﹩{A}_{i}^{\mathrm{Ia}}﹩ (left), ﹩{A}_{i}^{3}﹩ (center), and ﹩{A}_{i}^{4}﹩ (right). All density plots are logarithmically scaled. The third and fourth processes contribute most to low-metallicity stars.
Paper:  KPM: A Flexible and Data-driven K-process Model for Nucleosynthesis
Authors: Emily J. Griffith, David W. Hogg, Julianne J. Dalcanton, Sten Hasselquist, Bridget Ratcliffe, Melissa Ness, David H. Weinberg
Figure caption:                    ﹩{A}_{i}^{\mathrm{Ia}}﹩/﹩{A}_{i}^{\mathrm{CC}}﹩ vs. ﹩{A}_{i}^{\mathrm{CC}}﹩ distribution for stars in the APOKASC sample (Pinsonneault et al. 2018). Each point is colored by the star’s asteroseismic ﹩{\mathrm{log}}_{10}﹩(age), with younger stars in black and older stars in yellow. A clear gradient with ﹩{\mathrm{log}}_{10}﹩(age) and process parameters exists, with young outliers scattered throughout. This shows that the process amplitudes constitute a good-quality age indicator.
Paper: Resonant Chains and the Convergent Migration of Planets in Protoplanetary Disks
Authors: Ka Ho Wong, Man Hoi Lee
Figure caption: Analytic results on Kepler-223 from Equations (6), (10), and (11), with panels (a), (b), and (c) showing the regions in (α, β) space with convergent migration between planet b and triplet c–d–e, between pair b–c and pair d–e, and between triplet b–c–d and planet e, respectively, and panel (d) showing the combined result from the overlap of panels (a)–(c). Red indicates convergent outward migration, and blue indicates convergent inward migration. Panel (d) shows that the Kepler-223 resonant chain cannot be sustained by inward migration and that the convergent outward migration zone does not include the steady-state, constant α  ν -viscosity disk models (dashed line).
Paper: Resonant Chains and the Convergent Migration of Planets in Protoplanetary Disks
Authors: Ka Ho Wong, Man Hoi Lee
Figure caption: Orbital semimajor axis of innermost planet vs. stellar mass for detected planetary systems with three or more planets, with red and green open circles for the planets discovered by transit and radial velocity, respectively, and blue filled circles for 11 confirmed and suspected resonant chain systems (see text for the list). The green dashed lines are the upper and lower estimates of the magnetic truncated radius ﹩{a}_{\mathrm{tr}}﹩ of the protoplanetary disk (Equation (12)), and the black dashed lines are the upper and lower estimates of ﹩4{a}_{\mathrm{tr}}﹩. The solid red line is ﹩{a}_{\mathrm{tr}}﹩ derived by Batygin et al. (2023). The solid blue line is the best-fit straight line to the blue filled circles. Data obtained from NASA Exoplanet Archive.
Paper: Resonant Chains and the Convergent Migration of Planets in Protoplanetary Disks
Authors: Ka Ho Wong, Man Hoi Lee
Figure caption: Numerical results in (α, β) space for the migration of a pair of 1 M ⊕ planets in 3:2 MMR. Blue and red dots represent simulations where the MMR is maintained as the planets move inward and outward, respectively. Green dots represent the simulations where the MMR is broken. The blue and red solid lines are the analytic estimation of the convergent migration regions from Equations (6), (7), and (8). The black dashed line is α + β = 3/2 for the steady-state, constant α  ν -viscosity disk models, with the star for the MMSN-like model and the other symbols for various regions of the Garaud & Lin (2007) disk model: pentagon for the weakly opaque region, square for the strongly opaque region, and triangle for the viscously heated region.
Paper: Resonant Chains and the Convergent Migration of Planets in Protoplanetary Disks
Authors: Ka Ho Wong, Man Hoi Lee
Figure caption: Analytic convergent migration zones in (α, β) similar to Figure 4(d) but for (a) Kepler-60, (b) Kepler-80, (c) TOI-178, and (d) TRAPPIST-1.
Paper: Resonant Chains and the Convergent Migration of Planets in Protoplanetary Disks
Authors: Ka Ho Wong, Man Hoi Lee
Figure caption: Analytic convergent migration zones in (α, β) similar to Figures 4(d) and 8 but for planetary masses adjusted within 1σ to enhance the inward convergent migration zone.
Paper: Resonant Chains and the Convergent Migration of Planets in Protoplanetary Disks
Authors: Ka Ho Wong, Man Hoi Lee
Figure caption: Time evolution of semimajor axes (upper left panel), eccentricities (lower left panel), and two-body MMR and three-body Laplace angles (right panel) in a disk migration simulation of the Kepler-223 system with α = −2.586 and β = −1.966. This (α, β) combination is just below the analytic convergent outward migration zone, but the resonant chain is maintained to the end of the simulation.
Paper: Resonant Chains and the Convergent Migration of Planets in Protoplanetary Disks
Authors: Ka Ho Wong, Man Hoi Lee
Figure caption: Same as Figure 3 but for the 8:6:4:3 resonant chain of Kepler-223.
Paper: Two Long-period Giant Planets around Two Giant Stars: HD 112570 and HD 154391
Authors: Guang-Yao Xiao, Huan-Yu Teng, Jianzhao Zhou, Bun'ei Sato, Yu-Juan Liu, Shaolan Bi, Takuya Takarada, Masayuki Kuzuhara, Marc Hon, Liang Wang, Masashi Omiya, Hiroki Harakawa, Fei Zhao, Gang Zhao, Eiji Kambe, Hideyuki Izumiura, Hiroyasu Ando, Kunio Noguchi, Wei Wang, Meng Zhai, Nan Song, Chengqun Yang, Tanda Li, Timothy D. Brandt, Michitoshi Yoshida, Yoichi Itoh, Eiichiro Kokubo
Figure caption: Left panel: radial velocity curve of HD 112570. The red points represent the data from HIDES. The blue and green points represent the data from CES-O and HRS, respectively. Middle and right panel: astrometric acceleration in R.A. and decl.. The points near epoch 1991 are measured from Hipparcos, and the points near epoch 2016 are from Gaia EDR3. The black lines represent the best-fit orbit, and the colored lines, color-coded by the companion’s mass, indicate the possible orbital solution randomly drawn from MCMC. All figures are post-processed with orvara.
Paper: Two Long-period Giant Planets around Two Giant Stars: HD 112570 and HD 154391
Authors: Guang-Yao Xiao, Huan-Yu Teng, Jianzhao Zhou, Bun'ei Sato, Yu-Juan Liu, Shaolan Bi, Takuya Takarada, Masayuki Kuzuhara, Marc Hon, Liang Wang, Masashi Omiya, Hiroki Harakawa, Fei Zhao, Gang Zhao, Eiji Kambe, Hideyuki Izumiura, Hiroyasu Ando, Kunio Noguchi, Wei Wang, Meng Zhai, Nan Song, Chengqun Yang, Tanda Li, Timothy D. Brandt, Michitoshi Yoshida, Yoichi Itoh, Eiichiro Kokubo
Figure caption: Corner plot for HD 112570. orvara posteriors for primary mass M pri, secondary mass ﹩{M}_{\sec }﹩, semimajor axis a, eccentricity e, and inclination i.
Paper: Two Long-period Giant Planets around Two Giant Stars: HD 112570 and HD 154391
Authors: Guang-Yao Xiao, Huan-Yu Teng, Jianzhao Zhou, Bun'ei Sato, Yu-Juan Liu, Shaolan Bi, Takuya Takarada, Masayuki Kuzuhara, Marc Hon, Liang Wang, Masashi Omiya, Hiroki Harakawa, Fei Zhao, Gang Zhao, Eiji Kambe, Hideyuki Izumiura, Hiroyasu Ando, Kunio Noguchi, Wei Wang, Meng Zhai, Nan Song, Chengqun Yang, Tanda Li, Timothy D. Brandt, Michitoshi Yoshida, Yoichi Itoh, Eiichiro Kokubo
Figure caption: Corner plot for HD 154391. orvara posteriors for primary mass M pri, secondary mass ﹩{M}_{\sec }﹩, semimajor axis a, eccentricity e, and inclination i.
Paper: Two Long-period Giant Planets around Two Giant Stars: HD 112570 and HD 154391
Authors: Guang-Yao Xiao, Huan-Yu Teng, Jianzhao Zhou, Bun'ei Sato, Yu-Juan Liu, Shaolan Bi, Takuya Takarada, Masayuki Kuzuhara, Marc Hon, Liang Wang, Masashi Omiya, Hiroki Harakawa, Fei Zhao, Gang Zhao, Eiji Kambe, Hideyuki Izumiura, Hiroyasu Ando, Kunio Noguchi, Wei Wang, Meng Zhai, Nan Song, Chengqun Yang, Tanda Li, Timothy D. Brandt, Michitoshi Yoshida, Yoichi Itoh, Eiichiro Kokubo
Figure caption: Radial velocity curve and astrometric acceleration of HD 154391. Same as Figure 15.
Paper: The Weird and the Wonderful in Our Solar System: Searching for Serendipity in the Legacy Survey of Space and Time
Authors: Brian Rogers, Chris J. Lintott, Steve Croft, Megan E. Schwamb, James R. A. Davenport
Figure caption: The generated anomalies visualized using t-SNE. Global anomalies (red) are interwoven between the normal objects (blue). The clustered anomalies (orange) lie in dense groups around the edges of the normal objects in this reduced space. Local anomalies (black) tend to group round the edges of normal clusters of points.
Paper: The Weird and the Wonderful in Our Solar System: Searching for Serendipity in the Legacy Survey of Space and Time
Authors: Brian Rogers, Chris J. Lintott, Steve Croft, Megan E. Schwamb, James R. A. Davenport
Figure caption: Reconstruction of the orbit–color space using the GMM.
Paper: Efficient Search and Detection of Faint Moving Objects in Image Data
Authors: Tam Nguyen, Deborah F. Woods, Jessica Ruprecht, Jonathan Birge
Figure caption: TESS moving-object detection performance with and without multiframe detection. (Top) Histogram of the number of objects as a function of the visual magnitude for (white) all known objects that had passed the instrument’s field of view during the time period of interest, (red) objects detected by the single-frame detection alone, and (blue) objects detected by the single and multiframe faint-object detection. (Bottom) Detection fraction as a function of the visual magnitude with (blue) and without (red) multiframe faint-object detection. Multiframe detection was shown to provide approximately 1.5 visual magnitudes improvement in detection sensitivity, resulting in thousands of detections of objects previously too faint to be detected in the single-frame pipeline.
Paper: Efficient Search and Detection of Faint Moving Objects in Image Data
Authors: Tam Nguyen, Deborah F. Woods, Jessica Ruprecht, Jonathan Birge
Figure caption: Sample cutout of a TESS star-subtracted frame that includes bright objects that were detected in the single-frame pipeline (red squares) and faint objects that were only detected in the multiframe search and detect pipeline (blue circles). The right panel shows the detection of each faint object as detected in the multihypothesis parametrized space along the detected velocity vector after integrating along 128 subsequent frames. All objects are labeled with their corresponding packed MPC designations and estimated MPC visual magnitudes at the time of detection.
Paper: Efficient Search and Detection of Faint Moving Objects in Image Data
Authors: Tam Nguyen, Deborah F. Woods, Jessica Ruprecht, Jonathan Birge
Figure caption: (a) Runtime of FaXT and VMF for different input data volume sizes. (b) Detection probability of FaXT and VMF for different input S/N levels and input data volume sizes. FaXT achieves a similar detection probability to traditional VMF while also providing significant runtime improvement.
Paper: Efficient Search and Detection of Faint Moving Objects in Image Data
Authors: Tam Nguyen, Deborah F. Woods, Jessica Ruprecht, Jonathan Birge
Figure caption: Selected frames from a simulated image stack of size 64 × 64 × 64. The simulated point source is modeled as a 2D Gaussian with σ = 0.5 pixels and a total S/N of 30. The point source moves with linear motion with a starting position (10, 10) in the first frame and an ending position (60, 60) in the last frame. The brightest pixel in each frame is a fraction of the total input S/N based on how the 2D Gaussian PSF is sampled by the pixel grid.
Paper: Efficient Search and Detection of Faint Moving Objects in Image Data
Authors: Tam Nguyen, Deborah F. Woods, Jessica Ruprecht, Jonathan Birge
Figure caption: Proof-of-concept multiframe search and detection pipeline of faint moving objects in TESS data.
Paper: Binary Black Hole Mergers and Intermediate-mass Black Holes in Dense Star Clusters with Collisional Runaways
Authors: Rujuta A. Purohit, Giacomo Fragione, Frederic A. Rasio, Grayson C. Petter, Ryan C. Hickox
Figure caption: The fraction of clusters that form a massive BH as a function of the initial cluster mass. As expected for larger values of f run, this fraction increases, especially for lower cluster mass. We show five different mass thresholds for what we call a massive BH: 100, 500, 103, 5 × 103, and 104 M ⊙.
Paper: Binary Black Hole Mergers and Intermediate-mass Black Holes in Dense Star Clusters with Collisional Runaways
Authors: Rujuta A. Purohit, Giacomo Fragione, Frederic A. Rasio, Grayson C. Petter, Ryan C. Hickox
Figure caption: The binary black hole (BBH) merger rate as a function of redshift for four different fractions of initial cluster mass that undergo the collisional runaway. The total merger rate is shown, along with rates for a primary mass over a given threshold for the primary mass: 100, 200, 500, 1000M ⊙.
Paper: Binary Black Hole Mergers and Intermediate-mass Black Holes in Dense Star Clusters with Collisional Runaways
Authors: Rujuta A. Purohit, Giacomo Fragione, Frederic A. Rasio, Grayson C. Petter, Ryan C. Hickox
Figure caption: Detection fractions for merging IMBH binaries as a function of primary-mass and binary-mass ratio. This figure shows our results for clusters with f run = 0.001 (see Appendix for other values). Top left: LISA; top right: Voyager; bottom left: Einstein Telescope; bottom right: Cosmic Explorer.
Paper: Binary Black Hole Mergers and Intermediate-mass Black Holes in Dense Star Clusters with Collisional Runaways
Authors: Rujuta A. Purohit, Giacomo Fragione, Frederic A. Rasio, Grayson C. Petter, Ryan C. Hickox
Figure caption: Same as Figure 5 but for f run = 0.01.
Paper: Binary Black Hole Mergers and Intermediate-mass Black Holes in Dense Star Clusters with Collisional Runaways
Authors: Rujuta A. Purohit, Giacomo Fragione, Frederic A. Rasio, Grayson C. Petter, Ryan C. Hickox
Figure caption: Same as Figure 5 but for f run = 0.005.
Paper: Binary Black Hole Mergers and Intermediate-mass Black Holes in Dense Star Clusters with Collisional Runaways
Authors: Rujuta A. Purohit, Giacomo Fragione, Frederic A. Rasio, Grayson C. Petter, Ryan C. Hickox
Figure caption: Same as Figure 8 but for f run = 0.
Paper: DIAmante TESS AutoRegressive Planet Search (DTARPS). II. Hundreds of New TESS Candidate Exoplanets
Authors: Elizabeth J. Melton, Eric D. Feigelson, Marco Montalto, Gabriel A. Caceres, Andrew W. Rosenswie, Cullen S. Abelson
Figure caption: Radius–period plots for confirmed planets in the DTARPS-S analysis list (Paper I) that passed DTARPS vetting (left panel) and that were rejected by DTARPS vetting (right panel). All confirmed planets on the NEA, mostly from the Kepler survey, are plotted as small gray dots. The DTARPS-S candidate points are colored by DTARPS-S disposition (left panel). In the right panel, planets rejected by the DTARPS vetting process are plotted as pluses if the planet was rejected by either centroid-crowding analysis or ephemeris matching, or are plotted as circles if the planet was rejected by the visual vetting process.
Paper: DIAmante TESS AutoRegressive Planet Search (DTARPS). II. Hundreds of New TESS Candidate Exoplanets
Authors: Elizabeth J. Melton, Eric D. Feigelson, Marco Montalto, Gabriel A. Caceres, Andrew W. Rosenswie, Cullen S. Abelson
Figure caption: Spatial distribution of DTARPS-S candidates (Tables 1 and 2). Objects are differentiated by DTARPS-S disposition (DTARPS-S Level 1 and 2, DTARPS_GP) and status with respect to previous surveys (previously known confirmed planet, previously known planet candidates, and new DTARPS-S candidates). The black curve indicates the approximate plane of the galaxy.
Paper: Systematic KMTNet Planetary Anomaly Search. X. Complete Sample of 2017 Prime-field Planets
Authors: Yoon-Hyun Ryu, Andrzej Udalski, Jennifer C. Yee, Weicheng Zang, Yossi Shvartzvald, Cheongho Han, Andrew Gould, Michael D. Albrow, Sun-Ju Chung, Kyu-Ha Hwang, Youn Kil Jung, In-Gu Shin, Hongjing Yang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Hanyue Wang, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radek Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof Ulaczyk, Krzysztof A. Rybicki, Patryk Iwanek, Marcin Wrona, Charles Beichman, Geoffry Bryden, Sean Carey, Calen B. Henderson, Sebastiano Calchi Novati, Wei Zhu, Savannah Jacklin, Matthew T. Penny
Figure caption: Data (color-coded by observatory) together with the model predictions for OGLE-2017-BLG-1777. Simple 2L1S models (two bottom panels) leave a P ∼ 13  days wave of residuals. Adding xallarap but without lens-orbital motion (third panel) removes these but cannot explain the last data point. However, 2L1S models that include xallarap, parallax, and lens-orbital motion (top two panels) give a significantly better fit.