adsabs/AstroCLIMB huggingface dataset:
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 Gaia B simulation of astrometric jitter in the solar photocenter due to faculae and spots. The raw time series of X displacement from the quiet Sun centroid is shown in the left panel. The Hanning window and its application to the time series is shown in the middle panel. Both time series include a solid line at X = 0. The right panel shows the amplitude spectrum derived from applying the discrete Fourier transform to the windowed time series. The vertical dashed line in the amplitude spectrum shows the solar rotation period of 27 days, where a bump in the spectrum is approximately centered.
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: A diagram of the setup for the star-planet orbital system when t = 0 at transit. The telescope is shown on the left, with the host star and its orbiting planet along the line of sight. The star’s equator is at the solar inclination θ = 7.°25, and its axis of rotation is shown with an arrow normal to the equatorial plane. The planet’s and star’s tangential velocities ⇀ v are shown perpendicular to the page, in the ﹩\pm \hat{X}﹩ direction. The distance from the barycenter to the planet is the semimajor axis a, and from it we express the star’s semimajor axis, ﹩\tfrac{m}{m+{M}_{\odot }}a﹩. This result is then used to find the movement of the star’s center of mass (and thus photocenter) due to reflex motion from the orbiting planet, as in Equation (7).
Paper: Shallow Transits—Deep Learning. II. Identify Individual Exoplanetary Transits in Red Noise using Deep Learning
Authors: Elad Dvash, Yam Peleg, Shay Zucker, Raja Giryes
Figure caption: Example A. The upper panel shows the input light curve. The middle panel shows the injected transit, while in the lower panel the light curve is shown again, but the samples that the network tagged as in-transit samples are marked in red. The y-axis scale is identical in all panels to facilitate visual comparison. Four transits can clearly be discerned.
Paper: Shallow Transits—Deep Learning. II. Identify Individual Exoplanetary Transits in Red Noise using Deep Learning
Authors: Elad Dvash, Yam Peleg, Shay Zucker, Raja Giryes
Figure caption: Binned scatterplot of the obtained Dice coefficient as a function of the S/N for the 90,000 light curves containing transits. The greyscale coding of the scatterplot is visualized by the vertical bar on the right. The marginal distributions of the Dice coefficient and the S/N are also shown as normalized complementary cumulative histograms.
Paper: Interplay between Young Stars and Molecular Clouds in the Ophiuchus Star-forming Complex
Authors: Aashish Gupta, Wen-Ping Chen
Figure caption: Tangential speed of YSOs vs. the spectral type. The blue symbol represents the Gaia EDR3 measurements, whereas the orange symbol marks the ground-based data (Monet et al. 2003; Ducourant et al. 2017), along with the error.
Paper: Shallow Transits—Deep Learning. II. Identify Individual Exoplanetary Transits in Red Noise using Deep Learning
Authors: Elad Dvash, Yam Peleg, Shay Zucker, Raja Giryes
Figure caption: The performance of the classifier network presented as a ROC curve. Blue: after training the classifier separately from the segmentation network; red: after training the classifier together with the segmentation network. The dotted-dashed line is the so-called “no-discrimination” line, corresponding to a random guess.
Paper: Shallow Transits—Deep Learning. II. Identify Individual Exoplanetary Transits in Red Noise using Deep Learning
Authors: Elad Dvash, Yam Peleg, Shay Zucker, Raja Giryes
Figure caption: A schematic illustration of the overall network structure. Note the gray arrow between the generator and classifier signifying no gradients flow through those connections.
Paper: Shallow Transits—Deep Learning. II. Identify Individual Exoplanetary Transits in Red Noise using Deep Learning
Authors: Elad Dvash, Yam Peleg, Shay Zucker, Raja Giryes
Figure caption: A schematic illustration of the classifier network, which uses the residual connections from the generator U-Net.
Paper: Shallow Transits—Deep Learning. II. Identify Individual Exoplanetary Transits in Red Noise using Deep Learning
Authors: Elad Dvash, Yam Peleg, Shay Zucker, Raja Giryes
Figure caption: A schematic illustration of the generator U-Net we use in our segmentation network. Note the residual “skip connections” between corresponding layers in the encoder and decoder.
Paper: Shallow Transits—Deep Learning. II. Identify Individual Exoplanetary Transits in Red Noise using Deep Learning
Authors: Elad Dvash, Yam Peleg, Shay Zucker, Raja Giryes
Figure caption: A schematic illustration of the discriminator we use to produce the adversarial loss for training the segmentation network.
Paper: The Transformative Journey of HD 93521
Authors: Douglas R. Gies, Katherine Shepard, Peter Wysocki, Robert Klement
Figure caption: A rotation model image of HD 93521 based upon the parameters listed in Table 2. The model star has a polar radius of 6.1R ⊙ and and an equatorial radius of 7.4R ⊙, and the inclination of the spin axis to the line of sight is i = 90°. The grayscale intensity bar shows the specific intensity for a wavelength of 5450 Å in units of erg cm−2 s−1 Å−1 steradian−1.
Paper: The Transformative Journey of HD 93521
Authors: Douglas R. Gies, Katherine Shepard, Peter Wysocki, Robert Klement
Figure caption: The trajectory of HD 93521 in Galactic cylindrical coordinates (R, z) from an assumed origin in the disk (z = 0) to its current position above the disk (marked by a diamond symbol). Plus signs show time intervals of 1 Myr beginning from the time of disk ejection.
Paper: The Transformative Journey of HD 93521
Authors: Douglas R. Gies, Katherine Shepard, Peter Wysocki, Robert Klement
Figure caption: The H–R diagram for evolutionary tracks from Brott et al. (2011) for solar abundance stars of initial masses of 15M ⊙ and 20M ⊙ with initial equatorial velocities of 431 and 426 km s−1, respectively. Plus signs mark time intervals of 1 Myr starting from the zero-age main sequence. The diamond indicates the derived parameters of HD 93521 ﹩(\langle T\rangle (\mathrm{all}),\mathrm{log}L/{L}_{\odot })﹩ with the diagonal dotted line showing the uncertainty range in surface integrated effective temperature and the vertical dotted line indicating the uncertainty in luminosity related to the uncertainty in stellar radius.
Paper: The Transformative Journey of HD 93521
Authors: Douglas R. Gies, Katherine Shepard, Peter Wysocki, Robert Klement
Figure caption: The spectral energy distribution of HD 93521 (plus signs) compared to a fitted model spectrum (gray line) for a nonrotating star.
Paper: A Census of the Low Accretors. I. The Catalog
Authors: Thanawuth Thanathibodee, Nuria Calvet, Jesús Hernández, Karina Maucó, César Briceño
Figure caption: The distribution of spectral types and masses for objects with different classifications. For all panels, Acc and nAcc are our samples classified using He I profiles. CVSO CTTS/WTTS are from Briceño et al. (2019). The WTTS samples were selected with K s−W3 < 1.0, which could include some debris disks. Only stars with SpT ≤ M3 were included in the analysis.
Paper: A Machine Learning–based Direction-of-origin Filter for the Identification of Radio Frequency Interference in the Search for Technosignatures
Authors: Pavlo Pinchuk, Jean-Luc Margot
Figure caption: Histograms of the (left) drift rates, (middle) S/N, and (right) bandwidths as measured by an FWHM metric of detected signals.
Paper: A Census of the Low Accretors. I. The Catalog
Authors: Thanawuth Thanathibodee, Nuria Calvet, Jesús Hernández, Karina Maucó, César Briceño
Figure caption: The distribution of EW(Hα) and W 10 for objects with different classifications. For both panels, Acc and nAcc are our samples classified using He I profiles.
Paper: A Census of the Low Accretors. I. The Catalog
Authors: Thanawuth Thanathibodee, Nuria Calvet, Jesús Hernández, Karina Maucó, César Briceño
Figure caption: The He I profiles of the targets. The profile classification is marked at the lower-left corner of each panel.
Paper: A Census of the Low Accretors. I. The Catalog
Authors: Thanawuth Thanathibodee, Nuria Calvet, Jesús Hernández, Karina Maucó, César Briceño
Figure caption: Same as Figure B1.
Paper: Terrestrial Planet Optical Phase Curves. I. Direct Measurements of the Earth
Authors: Roderick De Cock, Timothy A. Livengood, Daphne M. Stam, Carey M. Lisse, Tilak Hewagama, L. Drake Deming
Figure caption: The reflected fluxes for the 5 observations (E1, E4, E5, P1, and P2) and the seven spectral filters (350 nm, 450 nm, 550 nm, 650 nm, 750 nm, 850 nm, and 950 nm), as listed in Table 2. The variability in the data is mainly due to the rotation of the Earth and to variations in the weather patterns during the 24 hour period over which the data is acquired. The solid curves represent the reflected fluxes as computed assuming a Lambertian-reflecting planet (without atmosphere) with (for each filter band) a geometric albedo A G derived from the E1 data. Note that while these curves fit not only the E1 data, they also fit the E4 and E5 data, while the P1 and P2 data are clearly higher than the curves.
Paper: Terrestrial Planet Optical Phase Curves. I. Direct Measurements of the Earth
Authors: Roderick De Cock, Timothy A. Livengood, Daphne M. Stam, Carey M. Lisse, Tilak Hewagama, L. Drake Deming
Figure caption: The geometric albedo’s A G as functions of the central filter wavelengths (350, 450, 550, 650, 750, 850, and 950 nm) as derived from the measurements E1, E4, E5, P1, and P2 (see Table 2) assuming a Lambertian-reflecting planet (without atmosphere).
Paper: Orbital Dynamics and the Evolution of Planetary Habitability in the AU Mic System
Authors: Stephen R. Kane, Bradford J. Foley, Michelle L. Hill, Cayman T. Unterborn, Thomas Barclay, Bryson Cale, Emily A. Gilbert, Peter Plavchan, Justin M. Wittrock
Figure caption: A top-down view of the AU Mic system with respect to the HZ. The star is located at the center of the crosshairs and the orbits of the known planets are shown. The CHZ and OHZ are indicated by the light green and dark green regions, respectively.
Paper: Terrestrial Planet Optical Phase Curves. I. Direct Measurements of the Earth
Authors: Roderick De Cock, Timothy A. Livengood, Daphne M. Stam, Carey M. Lisse, Tilak Hewagama, L. Drake Deming
Figure caption: The first Earth image (512 × 512 pixel image size) of four observations at 450 nm as acquired by EPOXI’s HRIV. The legend shows the reflected solar flux in W/(m2 · sr · μm). Top left: “equatorial observation 1”—E1; top right: “equatorial observation 4” - E4; bottom left: “polar observation 1”—P1; bottom right: “polar observation 2”—P2. Not shown: “equatorial observation 5”—E5, which is very similar to E4. The grid lines indicate subimages of 100 × 100 pixels.
Paper: Orbital Dynamics and the Evolution of Planetary Habitability in the AU Mic System
Authors: Stephen R. Kane, Bradford J. Foley, Michelle L. Hill, Cayman T. Unterborn, Thomas Barclay, Bryson Cale, Emily A. Gilbert, Peter Plavchan, Justin M. Wittrock
Figure caption: The evolving HZ for AU Mic, from the present epoch up to an age of 1 Gyr. As in Figure 1, the CHZ and OHZ are represented by the light and dark green regions, respectively. The HZ regions rapidly contract toward the star during the pre-main-sequence phase, then start to gradually move away from the star after ∼200 Myr.
Paper: Terrestrial Planet Optical Phase Curves. I. Direct Measurements of the Earth
Authors: Roderick De Cock, Timothy A. Livengood, Daphne M. Stam, Carey M. Lisse, Tilak Hewagama, L. Drake Deming
Figure caption: Geometric albedo of models (Stam 2008) over wavelength for various phase angles, applying the inverse Lambertian phase function to retrieve geometric albedo. The zero phase angle model is the correct geometric albedo and the difference with the other model lines shows the effect of using a Lambertian phase function to retrieve the geometric albedo. B1 has ocean surface albedo without cloud and B2 with clouds. A vegatation surface without cloud is shown in B3 and with clouds in B4. B5 is an ice surface without clouds.
Paper: Terrestrial Planet Optical Phase Curves. I. Direct Measurements of the Earth
Authors: Roderick De Cock, Timothy A. Livengood, Daphne M. Stam, Carey M. Lisse, Tilak Hewagama, L. Drake Deming
Figure caption: First E1 image (at 450 nm), showing the collection of the signal using aperture photometry. The centroid is found by finding the highest correlation with a half disk. The signal within the aperture (the inner, red, circle) is summed up and subtracted by the local background, which is the signal in the circular band (annulus) from the aperture until the outer, magenta circle.
Paper: A Mid-infrared Study of Directly Imaged Planetary-mass Companions Using Archival Spitzer/IRAC Images
Authors: Raquel A. Martinez, Adam L. Kraus
Figure caption: Stacked images of AB Pic across all four IRAC channels (rows) after it has gone through the PSF-fitting pipeline. All fits were conducted within the CBCD images at the native plate scale, but to convey the full data set, the images here were generated by combining individual frames after they had been rescaled to 0.″24 pixel–1 (∼5× smaller than the original IRAC pixel scale), shifted to a common origin, and rotated so that north is up and east is left. The first and second columns show the original IRAC data of AB Pic and the median two-source PSF model, respectively, displayed with a logarithmic color scale (leftmost color bar). The third column shows the residuals left behind after only the primary PSF model is subtracted from the data. The fourth column shows the residuals left behind after the two-source PSF model is subtracted from the data. Both the third and fourth columns are displayed with a linear color scale (rightmost color bar) and 3σ and 5σ contours overlaid with solid and dotted lines, respectively. The standard deviation of the pixel values is displayed in the lower left corner of the fourth column in units of DN s–1. After subtracting the primary star PSF, a statistically significant positive residual is seen at the expected position of AB Pic b. This residual disappears after subtracting the best-fit system PSFs, indicating that it is a robust detection across all IRAC filters.
Paper: EXPRES. III. Revealing the Stellar Activity Radial Velocity Signature of ϵ Eridani with Photometry and Interferometry
Authors: Rachael M. Roettenbacher, Samuel H. C. Cabot, Debra A. Fischer, John D. Monnier, Gregory W. Henry, Robert O. Harmon, Heidi Korhonen, John M. Brewer, Joe Llama, Ryan R. Petersburg, Lily L. Zhao, Stefan Kraus, Jean-Baptiste Le Bouquin, Narsireddy Anugu, Claire L. Davies, Tyler Gardner, Cyprien Lanthermann, Gail Schaefer, Benjamin Setterholm, Catherine A. Clark, Svetlana G. Jorstad, Kyler Kuehn, Stephen Levine
Figure caption: Left: Reduced χ 2 surface for detecting a starspot in the 2020 November 2 interferometric data. The color gradient was chosen to particularly emphasize the location of the global and local minima. The minimum within the black region is the location of the model starspot shown in Figure 5. Right: Reduced χ 2 surface for detecting a starspot in the 2020 November 5 interferometric data. The black region is the global minimum, which our recovery tests show is likely to be an artifact, but the minimum on the lower right of the plot is aligned with our estimate of the starspot location given the position angles possible for the 2020 November 2 data and the LI reconstructions. The spot at this location and its associated interferometric observations are shown in Figure 5.
Paper: EXPRES. III. Revealing the Stellar Activity Radial Velocity Signature of ϵ Eridani with Photometry and Interferometry
Authors: Rachael M. Roettenbacher, Samuel H. C. Cabot, Debra A. Fischer, John D. Monnier, Gregory W. Henry, Robert O. Harmon, Heidi Korhonen, John M. Brewer, Joe Llama, Ryan R. Petersburg, Lily L. Zhao, Stefan Kraus, Jean-Baptiste Le Bouquin, Narsireddy Anugu, Claire L. Davies, Tyler Gardner, Cyprien Lanthermann, Gail Schaefer, Benjamin Setterholm, Catherine A. Clark, Svetlana G. Jorstad, Kyler Kuehn, Stephen Levine
Figure caption: First and third columns: Best-fit model for the interferometric spot recovery test. The surface temperature ranges from a spot temperature of T spot = 4100 K to a photospheric temperature of T phot = 5100 K. Second and fourth columns: Reduced χ 2 surface for the starspot size indicated by the best-fit model (lower values are white and higher values are dark blue). The red crosses × indicate where the center of the best-fit spot is located. The green crosses × indicate where the center of the spot is positioned. Each row is labeled on the left and corresponds to the appropriately labeled row of test and recovered spot parameters in Table 5.
Paper: EXPRES. III. Revealing the Stellar Activity Radial Velocity Signature of ϵ Eridani with Photometry and Interferometry
Authors: Rachael M. Roettenbacher, Samuel H. C. Cabot, Debra A. Fischer, John D. Monnier, Gregory W. Henry, Robert O. Harmon, Heidi Korhonen, John M. Brewer, Joe Llama, Ryan R. Petersburg, Lily L. Zhao, Stefan Kraus, Jean-Baptiste Le Bouquin, Narsireddy Anugu, Claire L. Davies, Tyler Gardner, Cyprien Lanthermann, Gail Schaefer, Benjamin Setterholm, Catherine A. Clark, Svetlana G. Jorstad, Kyler Kuehn, Stephen Levine
Figure caption: First and third columns: Best-fit model for the interferometric spot recovery test. Second and fourth columns: Reduced χ 2 surface for the starspot size indicated by the best-fit model (lower values are white and higher values are dark blue). The red crosses × indicate where the center of the best-fit spot is located. The green crosses × indicate where the center of the spot is positioned. Each row is labeled on the left and corresponds to the appropriately labeled row of test and recovered spot parameters in Table 5.
Paper: EXPRES. III. Revealing the Stellar Activity Radial Velocity Signature of ϵ Eridani with Photometry and Interferometry
Authors: Rachael M. Roettenbacher, Samuel H. C. Cabot, Debra A. Fischer, John D. Monnier, Gregory W. Henry, Robert O. Harmon, Heidi Korhonen, John M. Brewer, Joe Llama, Ryan R. Petersburg, Lily L. Zhao, Stefan Kraus, Jean-Baptiste Le Bouquin, Narsireddy Anugu, Claire L. Davies, Tyler Gardner, Cyprien Lanthermann, Gail Schaefer, Benjamin Setterholm, Catherine A. Clark, Svetlana G. Jorstad, Kyler Kuehn, Stephen Levine
Figure caption: Outline of the model stellar surface with the starspot location of each bootstrap indicated by a gray circle. Each circle is the size of the starspot used in the fit. The thicker red circle is the location of the best-fit starspot location. The stellar surface is oriented as in Figure 5.
Paper: A Mid-infrared Study of Directly Imaged Planetary-mass Companions Using Archival Spitzer/IRAC Images
Authors: Raquel A. Martinez, Adam L. Kraus
Figure caption: Individual IRAC images of SR 12 before (top row) and after (bottom row) YLW 13B, a nearby young stellar object, is removed. High dynamic range PSFs were used to model the bright PSF wings of YLW 13B, which were then subtracted off to minimize contamination when determining the system parameters of SR 12.
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: Network architecture. Our network structure is based on a standard YOLOv3 architecture with a slightly modified input channel and output dimension. The dashed line region indicates the Darknet-53 backbone. Each convolution block is shown as a CONV, it is combined by a 2D convolution layer, a batch normalization layer, and a Leaky ReLU function (Maas et al. 2013). The numbers split by dashes of a convolution block show the size and stride of the convolution layer. Each residual block is abbreviated as RES. As shown on the right side of the figure, a residual block is combined by two convolution blocks with a shortcut. The scale block is shown as SCALE, and the following number is the size of each scale. Similar to the residual block, each scale block has two convolution blocks but without a shortcut. Combining the results of the three scales to calculate the NMS, the final output is the coordinates and confidence level of N-detected bounding boxes.
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: Schematic diagram of training data set generation steps. The top panel shows a part of a detrended light curve. The color shaded sections indicate 30 day windows, and they have 5 day overlapped areas between adjacent windows. The dashed red lines are 10 day selection ranges, and they have 3 day overlapped areas between adjacent ranges. The bottom two panels are the zoom-ins of two 10 day selection ranges, and the bounding boxes are also plotted with green rectangular boxes. The coordinates of a bounding box (x middle, y middle, w, h) and four corners are indicated in the bottom left panel.
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 Kepler 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 Kepler 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: Training steps on the Kepler light curves. The black line is the loss and the APs are plotted as different colors for different IOU thresholds. The AP values of the best model are marked and written on the figure.
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: The precision-recall curve for IOU > 0.5 of the best model. The blue shaded area is the AP50. The sharp drop at 0.9 is due to our relatively high confidence threshold (0.6), making recall only go up to 0.9.
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: Two examples of TESS training and validation data. The bounding boxes are shown as green boxes. Note that the actual sample does not have a border.
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: Light curve of KOI-1174. (a) is the 4 yr light curve of KOI-1174. (b) is the zoom-in of the detected region. The green box is the detected bounding box and the confidence score is written in the semitransparent blue box.
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: Selected light curves from our detected bounding boxes. (a) is the selection of detected transiting events of Kepler297b. (b) is the selection of detected transiting events of Kepler297c. (c) is the selection of detected transiting events of KOI-1426.03.
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: Light-curve and transit detection results of Kepler-297. The green boxes are the detected bounding boxes, and their top square blue labels indicate the confidence scores.