adsabs/AstroCLIMB huggingface dataset:
Paper: Identify Light-curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection
Authors: Kaiming Cui, Junjie Liu, Fabo Feng, Jifeng Liu
Figure caption: Scatter plot of the width and height of the detected boxes. The color of the points indicate the confidence level.
Paper: Identify Light-curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection
Authors: Kaiming Cui, Junjie Liu, Fabo Feng, Jifeng Liu
Figure caption: Training steps of transfer learning on TESS data. Same as Figure 3.
Paper: Identify Light-curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection
Authors: Kaiming Cui, Junjie Liu, Fabo Feng, Jifeng Liu
Figure caption: Precision matrix of the best model trained from TESS on the test set.
Paper: Identify Light-curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection
Authors: Kaiming Cui, Junjie Liu, Fabo Feng, Jifeng Liu
Figure caption: Recall matrix of the best model trained from TESS on the test set.
Paper: Visibility Predictions for Near-future Satellite Megaconstellations: Latitudes near 50° Will Experience the Worst Light Pollution
Authors: Samantha M. Lawler, Aaron C. Boley, Hanno Rein
Figure caption: All-sky snapshots of the predicted satellite distribution and brightnesses as viewed from Hawaii (latitude 20°N, will be the same distribution at 20°S, but flipped north–south on the sky). Color shows g-band magnitude. Moving left to right across the grid: winter solstice, equinox, summer solstice. Top to bottom across the grid: nautical dusk, midnight, nautical dawn. For each snapshot, N tot is the total number of sunlit satellites in the sky from this location at this time of day and year, and N vis is the number of satellites that are naked-eye visible (approximately g < 6.5) from this location at this time of day and year. Note that these numbers are not exact and are expected to vary by ﹩\sqrt{N}﹩ due to randomization of satellite distribution within their orbital shells. The satellite distribution is faint and close to the horizon or not illuminated at all for most of the night, except for near dusk and dawn, when the brightest satellites will be close to the zenith, with hundreds being naked-eye visible.
Paper: Visibility Predictions for Near-future Satellite Megaconstellations: Latitudes near 50° Will Experience the Worst Light Pollution
Authors: Samantha M. Lawler, Aaron C. Boley, Hanno Rein
Figure caption: Similar to Figure 5, but viewed from the North Pole (latitude 90°N). Because of extreme seasonal illumination, satellites would not actually be visible close to the summer solstice due to 24 hr daylight, or close to the equinox because of 24 hr near-daylight, but the dozens of satellites brighter than g < 6.5 will be visible 24 hr per day during the winter. For comparison, we show midnight on the winter solstice (24 hr of full darkness), at a solar declination of −18° (24 hr of astronomical twilight), and at a solar declination of −12° (24 hr of nautical twilight). Note that from the North Pole, all directions are south.
Paper: Visibility Predictions for Near-future Satellite Megaconstellations: Latitudes near 50° Will Experience the Worst Light Pollution
Authors: Samantha M. Lawler, Aaron C. Boley, Hanno Rein
Figure caption: Histograms of ﹩g^{\prime} ﹩ magnitudes for observations of Starlink satellites (blue hatched) compared with our satellite model output (gray). The observations (Boley et al. 2021b) were obtained in 2021 July from the Plaskett 1.8 m telescope at the Dominion Astrophysical Observatory (latitude 48.5°N). The satellites were observed between ±1.5 hr of midnight. The Sun’s declination at the time of the observations was about 21◦. Only Starlink satellites near the 550 km orbital shell were targeted. This figure demonstrates that using an effective surface area of ζ = 0.8 m2 is a reasonable choice for our model, with the important caveat that this is applicable to a mixture of Starlink satellites: future satellites may have a very different actual brightness distribution depending on the size, shape, and materials used.
Paper: Visibility Predictions for Near-future Satellite Megaconstellations: Latitudes near 50° Will Experience the Worst Light Pollution
Authors: Samantha M. Lawler, Aaron C. Boley, Hanno Rein
Figure caption: Snapshot of the positions of satellites in our model, over the entire globe (left) using a Mercator projection and the slice over North America (right) using a Lambert conformal map projection. Note that there are obvious bands of higher on-sky density where large numbers of satellites have the same inclination and, as seen in latitude, librate at those parallels. The satellites here use the even-spacing initialization.
Paper: Visibility Predictions for Near-future Satellite Megaconstellations: Latitudes near 50° Will Experience the Worst Light Pollution
Authors: Samantha M. Lawler, Aaron C. Boley, Hanno Rein
Figure caption: Instantaneous number of sunlit satellites above the horizon at different latitudes and different times of night: left panels are heatmaps with daylight (sunrise to sunset) indicated by the gray shaded region; right panels show curves for four different latitudes (shown by lines in the left planels). The four latitudes highlighted are for the approximate latitude of several observatories in Canada (black solid line), the latitude of observatories on the Canary Islands (blue dashed line), the latitude of observatories on Maunakea in Hawaii (green dashed–dotted line), and the approximate latitude of several observatories in Chile (purple dotted line).
Paper: Visibility Predictions for Near-future Satellite Megaconstellations: Latitudes near 50° Will Experience the Worst Light Pollution
Authors: Samantha M. Lawler, Aaron C. Boley, Hanno Rein
Figure caption: Instantaneous number of sunlit satellites more than 30° above the horizon at different latitudes and different times of night: left panels are heatmaps; right panels show curves for four different latitudes (shown by lines in the left plots). Same symbols as Figure 2.
Paper: Visibility Predictions for Near-future Satellite Megaconstellations: Latitudes near 50° Will Experience the Worst Light Pollution
Authors: Samantha M. Lawler, Aaron C. Boley, Hanno Rein
Figure caption: 65,000 satellites on the same orbits as Table 1, but all at an altitude of 1200 km (first and third columns) or 550 km (second and fourth columns), as viewed on the summer solstice. Models are shown at nautical dusk (first and second columns) and at midnight (third and fourth columns). Rows show the models at different latitudes. This plot highlights the fact that there is no “best” altitude for satellites; it is a trade-off of different problems for different locations on Earth.
Paper: Visibility Predictions for Near-future Satellite Megaconstellations: Latitudes near 50° Will Experience the Worst Light Pollution
Authors: Samantha M. Lawler, Aaron C. Boley, Hanno Rein
Figure caption: Same plots as Figure 5, but viewed from a latitude of 50°N (e.g., many Canadian observatories). In the summertime, the number of sunlit and visible satellites (hundreds) only drops by a small fraction over the course of the night.
Paper: Visibility Predictions for Near-future Satellite Megaconstellations: Latitudes near 50° Will Experience the Worst Light Pollution
Authors: Samantha M. Lawler, Aaron C. Boley, Hanno Rein
Figure caption: Same plots as Figure 5, but viewed from the Equator (latitude 0°). There are no sunlit satellites at midnight in any season.
Paper: An Adaptive Optics Census of Companions to Northern Stars Within 25 pc with Robo-AO
Authors: Maïssa Salama, Carl Ziegler, Christoph Baranec, Michael C. Liu, Nicholas M. Law, Reed Riddle, Todd J. Henry, Jennifer G. Winters, Wei-Chun Jao, James Ou, Arcelia Hermosillo Ruiz
Figure caption: (a): ﹩\langle V/{V}_{\max }\rangle ﹩ statistic to assess completeness of our volume-limited sample (top). Percentages are the completeness of the sample out to the corresponding distance. Our sample is 90% complete to 15 pc, and 78% complete to 25 pc. The green line traces the cumulative distance distribution of all observed stars in our sample (bottom). (b): ﹩\langle V/{V}_{\max }\rangle ﹩ statistic to assess completeness of our volume-limited sample, shown for each spectral type subsample.
Paper: An Adaptive Optics Census of Companions to Northern Stars Within 25 pc with Robo-AO
Authors: Maïssa Salama, Carl Ziegler, Christoph Baranec, Michael C. Liu, Nicholas M. Law, Reed Riddle, Todd J. Henry, Jennifer G. Winters, Wei-Chun Jao, James Ou, Arcelia Hermosillo Ruiz
Figure caption: Spectral type distribution of observed stars making up the Robo-AO Solar Neighborhood Survey sample. The dashed lines and corresponding numbers are the stars in our sample with Gaia EDR3 parallaxes. Almost half of our sample are M-type stars.
Paper: An Adaptive Optics Census of Companions to Northern Stars Within 25 pc with Robo-AO
Authors: Maïssa Salama, Carl Ziegler, Christoph Baranec, Michael C. Liu, Nicholas M. Law, Reed Riddle, Todd J. Henry, Jennifer G. Winters, Wei-Chun Jao, James Ou, Arcelia Hermosillo Ruiz
Figure caption: Example of a companion detection in the original reduced image shown in linear-stretch (left) and the PSF-subtracted image (right). The companion is circled in orange.
Paper: PetroFit: A Python Package for Computing Petrosian Radii and Fitting Galaxy Light Profiles
Authors: Robel Geda, Steven M. Crawford, Lucas Hunt, Matthew Bershady, Erik Tollerud, Solohery Randriamampandry
Figure caption: The inputs, steps, and results of fitting a single-component galaxy are shown in this figure. The top row shows, left to right, (a) the background-subtracted input F105W image of the target, (b) the masked image used for photometric measurements (also overplots the apertures as white ellipses), (c) the COG from photometric measurements as a function of aperture radius, and (d) the Petrosian profile derived from the photometric measurements. The bottom row shows, left to right, (e) an overplot of Petrosian radii to show their extent, (f) the fitted Sérsic model sampled onto an image (PSF convolved), (g) the residual produced by subtracting the model image from the original data, and (h) the normalized PSF (cutout of a bright star) that was used for fitting.
Paper: PetroFit: A Python Package for Computing Petrosian Radii and Fitting Galaxy Light Profiles
Authors: Robel Geda, Steven M. Crawford, Lucas Hunt, Matthew Bershady, Erik Tollerud, Solohery Randriamampandry
Figure caption: Examples of azimuthal shape functions implemented as custom 2D Astropy (fittable) models. Inspired by PHIR models (Peng et al. 2010), the left and middle panels show Sérsic models that apply simple rotations to the profile as a function of radius (i.e., θ(r[x, y], m, r in), where m is the number of full rotations, and r in is the rotation cutoff). The right panel shows a barred Sérsic profile that was generated using a generalized ellipse.
Paper: PetroFit: A Python Package for Computing Petrosian Radii and Fitting Galaxy Light Profiles
Authors: Robel Geda, Steven M. Crawford, Lucas Hunt, Matthew Bershady, Erik Tollerud, Solohery Randriamampandry
Figure caption: This figure illustrates PSF effects by plotting ϵ vs. C 2080 (uncorrected) for the same profiles as Figure 4 but with the profiles convolved with an HST WFC3 F105W PSF (sampled at 60 mas). It also shows the same curve as Figure 4 for reference. Notice how the ϵ and C 2080 values of profiles with smaller effective radii are affected (skewed) the most due to the PSF convolution. For this reason, we must make corrections that account for effective radii and the shape of the PSF itself.
Paper: PetroFit: A Python Package for Computing Petrosian Radii and Fitting Galaxy Light Profiles
Authors: Robel Geda, Steven M. Crawford, Lucas Hunt, Matthew Bershady, Erik Tollerud, Solohery Randriamampandry
Figure caption: In this figure, we plot epsilon (ϵ) vs. C 2080 (uncorrected) for simulated 2D Sérsic profiles with varying effective radii (black points). The scatter seen at higher concentrations is due to sampling errors. The red curve is a line of best fit given by Equation (B.9) in Appendix B.2.3. The C 2080 domain corresponds to 0.5 ≤ n ≤ 5 in Sérsic index.
Paper: PetroFit: A Python Package for Computing Petrosian Radii and Fitting Galaxy Light Profiles
Authors: Robel Geda, Steven M. Crawford, Lucas Hunt, Matthew Bershady, Erik Tollerud, Solohery Randriamampandry
Figure caption: This plot shows some of the inputs, steps, and results of fitting a multicomponent model. The top row shows, left to right, (a) the input F105W image of the group of galaxies, (b) a deblended segmentation map with color-coding for pixels that belong to the same source, and (c) an overplot of the Petrosian radii of each source. The bottom row shows, left to right, (d) the fitted compound model with 11 Sérsic components sampled onto an image (PSF convolved), (e) the residual image produced by subtracting the model image from the original data, and (f) a histogram of pixel values in the residual image and a 3σ clipped version of the original image. Note that the residual image histogram takes on a Gaussian form centered closer to zero than the original image.
Paper: PetroFit: A Python Package for Computing Petrosian Radii and Fitting Galaxy Light Profiles
Authors: Robel Geda, Steven M. Crawford, Lucas Hunt, Matthew Bershady, Erik Tollerud, Solohery Randriamampandry
Figure caption: These panels show Lupton-RGB ([i, r, g]-band) images of M91, a corresponding multicomponent Sérsic fit, and the resulting residual. Three components were used to fit this light profile, one each for the disk, bar, and central core. The images were made using a stretch factor of 0.9 and Q = 2. The left panel shows the SDSS M91 cutout that was used for fitting, the middle panel shows the fitted model image, and the right panel shows the residual. The spiral arm, as well as the blue clusters (zoomed-in white box), can also be seen in the residual image.
Paper: PetroFit: A Python Package for Computing Petrosian Radii and Fitting Galaxy Light Profiles
Authors: Robel Geda, Steven M. Crawford, Lucas Hunt, Matthew Bershady, Erik Tollerud, Solohery Randriamampandry
Figure caption: This plot shows three sampling grids. The blue circles represent one-to-one sampling, the orange squares represent an oversampling factor of 2, and the green crosses represent an oversampling factor of 4. The boundaries of a pixel are represented by the solid lines, and the pixel value is calculated by finding the mean of the sampling points. For clarity, only points of the same oversampling factor are used (one-to-one points are not sampled if the oversampling factor is 2, etc.).
Paper: PetroFit: A Python Package for Computing Petrosian Radii and Fitting Galaxy Light Profiles
Authors: Robel Geda, Steven M. Crawford, Lucas Hunt, Matthew Bershady, Erik Tollerud, Solohery Randriamampandry
Figure caption: Left: multifilter absolute AB magnitude comparison between PetroFit aperture photometry and the ASTRODEEP catalog. Right: PetroFit Petrosian apertures plotted over the F105W image (detection image).
Paper: PetroFit: A Python Package for Computing Petrosian Radii and Fitting Galaxy Light Profiles
Authors: Robel Geda, Steven M. Crawford, Lucas Hunt, Matthew Bershady, Erik Tollerud, Solohery Randriamampandry
Figure caption: Corrected C 2080 approximated from the uncorrected C 2080 for 0.5 ≤ n ≤ 15. The black markers represent values obtained from measurements, and the red line is the line of best fit.
Paper: Multiwavelength Mitigation of Stellar Activity in Astrometric Planet Detection
Authors: Avi Kaplan-Lipkin, Bruce Macintosh, Alexander Madurowicz, Krishnamurthy Sowmya, Alexander Shapiro, Natalie Krivova, Sami K. Solanki
Figure caption: The lowest detectable planet mass curves, comparing between the single-band and multiband methods. We plot these curves for semimajor axes between 0.1 and 2.0 au, with 50 points spaced equally on a log scale. For scanning across planet masses until we find a detection, we also search on a log scale. Diagonal plots show results for single-band astrometry, and these curves are shown in each panel of that passband’s column for comparison. The legend in the top right of each panel shows first the passband used as the single-band treatment, and second the subtraction done for noise mitigation according to Equation (11), with the scaling values taken from Figure 6. The sophisticated small-scale features of the curves are highly dependent on the chosen interval of solar time, since they are determined by the locations of the peaks in the solar amplitude spectrum. The general trends we are interested in should nevertheless be robust, regardless of the time series’ specific interval. Noise mitigation scores for each pair of passbands is reported in the bottom left of the panel corresponding to that pair, calculated according to Equation (12). Note that these scores are computed over a relatively narrow range of planet semimajor axes, highlighted in each panel, between 0.7 and 1.5 au.
Paper: Multiwavelength Mitigation of Stellar Activity in Astrometric Planet Detection
Authors: Avi Kaplan-Lipkin, Bruce Macintosh, Alexander Madurowicz, Krishnamurthy Sowmya, Alexander Shapiro, Natalie Krivova, Sami K. Solanki
Figure caption: The noise mitigation scores of each pair of passbands, according to Equation (12). We plot ﹩{\mathrm{log}}_{10}(\mathrm{Score})﹩ vs. the slope’s distance from 1 (﹩| { \mathcal M }-1| ﹩) and the correlation’s distance from 1 (∣r − 1∣). Because we take the log of the slope, colors corresponding to positive values show improvement (blue), and colors corresponding to negative values show that planet detection became more difficult (red). The 10 data points are plotted as circles filled in with the color of their score and outlined in black, labeled by the passband names for the corresponding pair. The contours plotted within the boundaries of the white border are derived from linear interpolation of a nonuniform grid for the 10 scores, implemented with NumPy’s tricontourf function. Outside these bounds, we plot the contours for our functional form from Equation (14) to show how the scores approximately extrapolate, although it is less reliable than the interpolation inside the white border.
Paper: A Systematic Study about Orbit Flips of Test Particles Caused by Eccentric Von Zeipel–Lidov–Kozai Effects
Authors: Hanlun Lei
Figure caption: Poincaré surfaces of section (defined by g = 0 and ﹩\dot{g}\gt 0﹩) at different levels of Hamiltonian shown in the (h, H) plane. In each panel, the green dashed line stands for the location of i = 90° (H = 0) and ΔH represents the size of the flip region. The red dots stand for the boundaries of libration islands arising in the sections.
Paper: A Systematic Study about Orbit Flips of Test Particles Caused by Eccentric Von Zeipel–Lidov–Kozai Effects
Authors: Hanlun Lei
Figure caption: Boundaries of the regions of orbit flips in the (e 0, i 0) space obtained by analyzing invariant manifolds associated with unstable polar orbits. For convenience, the regions of orbit flips are denoted by I, II, and III from left to right. In regions I and III, the longitude of ascending node is taken as h = π; in region II, it is taken as h = 0.
Paper: A Systematic Study about Orbit Flips of Test Particles Caused by Eccentric Von Zeipel–Lidov–Kozai Effects
Authors: Hanlun Lei
Figure caption: Characteristic curves of polar periodic orbits in the plane of eccentricity and period (left panel) and in the plane of Hamiltonian and period (right panel). Blue lines stand for characteristic curves of stable periodic orbits, while red lines stand for characteristic curves of the unstable periodic orbits. SF1 and SF2 represent the polar families, and AF1 and AF2 represent bifurcated families. The dashed lines marked in the right panel stand for the critical lines of Hamiltonian where the stability of periodic orbits changes.
Paper: A Systematic Study about Orbit Flips of Test Particles Caused by Eccentric Von Zeipel–Lidov–Kozai Effects
Authors: Hanlun Lei
Figure caption: Boundaries for the regions of orbit flips in the (e 0, i 0) space obtained by analyzing the Poincaré surfaces of section (defined by g = 0 and ﹩\dot{g}\gt 0﹩). The regions of orbit flips are denoted by I, II, and III from left to right. In particular, the longitude of ascending node is taken as h = π in regions I and III, and it is h = 0 in region II.
Paper: A Systematic Study about Orbit Flips of Test Particles Caused by Eccentric Von Zeipel–Lidov–Kozai Effects
Authors: Hanlun Lei
Figure caption: Poincaré surfaces of section (defined by g = 0 and ﹩\dot{g}\gt 0﹩) for the invariant manifolds associated with unstable polar periodic orbits at different levels of Hamiltonian. The locations of the stable and unstable polar periodic orbits are marked. It is evident that the sections of invariant manifolds provide boundaries of libration islands in the phase space.
Paper: A Systematic Study about Orbit Flips of Test Particles Caused by Eccentric Von Zeipel–Lidov–Kozai Effects
Authors: Hanlun Lei
Figure caption: The solution curve (red line) of ﹩{ \mathcal H }({\sigma }_{1}=\pi ,{{\rm{\Sigma }}}_{1}=0.2,{\sigma }_{2},{{\rm{\Sigma }}}_{2})=-0.2﹩ shown in the (σ 2, Σ2) plane. The path integral of the solution curve Σ2(σ 2) is defined as the adiabatic invariant ﹩{ \mathcal S }﹩, which corresponds to the area of the blue shaded region.
Paper: A Systematic Study about Orbit Flips of Test Particles Caused by Eccentric Von Zeipel–Lidov–Kozai Effects
Authors: Hanlun Lei
Figure caption: Level curves of the adiabatic invariant ﹩{ \mathcal S }﹩ with given Hamiltonian (upper-left panel) and three examples located at the isoline with ﹩{ \mathcal S }=1.359﹩ (the other three panels). The adiabatic invariant ﹩{ \mathcal S }﹩ is defined as the area bounded by the solution curve associated with the “fast” degree of freedom Σ2(σ 2).
Paper: A Systematic Study about Orbit Flips of Test Particles Caused by Eccentric Von Zeipel–Lidov–Kozai Effects
Authors: Hanlun Lei
Figure caption: Polar periodic orbits shown in polar coordinates (﹩G\cos g,G\sin g﹩). The polar periodic orbits correspond to level curves of Hamiltonian under the condition of H = 0 and h = 0 (left panel) and H = 0 and h = π (right panel).
Paper: A Systematic Study about Orbit Flips of Test Particles Caused by Eccentric Von Zeipel–Lidov–Kozai Effects
Authors: Hanlun Lei
Figure caption: Invariant manifolds (a set of asymptotically unstable and/or stable trajectories) stemming from the unstable periodic orbits with ﹩{ \mathcal H }=-0.20﹩ (left panel) and ﹩{ \mathcal H }=-0.80﹩ (right panel). The red lines stand for unstable polar orbits.
Paper: Confirmation of the Long-period Planet Orbiting Gliese 411 and the Detection of a New Planet Candidate
Authors: Spencer A. Hurt, Benjamin Fulton, Howard Isaacson, Lee J. Rosenthal, Andrew W. Howard, Lauren M. Weiss, Erik A. Petigura
Figure caption: Two-dimensional histograms showing the joint planet contrast and projected separation posteriors for GJ 411 c (top row) and the candidate planet (bottom row). Both are approximated as a Lambertian disk with a Bond albedo of 0.5. Panels on the left show the posterior for the entire orbit while panels on the right show the posterior for a single time where the planet would be most easily observed. GJ 411 c is expected to be most easily observed on 2023 December 22 while the candidate would be best detected on 2022 February 15. The predicted postprocessing detectability floor is shown for the Roman Space Telescope CGI in narrow and wide field-of-view modes, along with the HabEx performance requirement. We also show the detection limits for HST ACS in the upper row. Current instrumentation, such as HST ACS, does not have the sensitivity needed to detect reflected light from GJ 411 c and the candidate planet, but future missions such as Roman or HabEx may be able to image the planets in the visual spectrum.