adsabs/AstroCLIMB huggingface dataset:
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: Completeness maps for the APF (top left), HIRES (top right), SOPHIE (middle left), CARMENES (middle right), and complete data sets (bottom). Signals that were recovered by RVSearch are denoted by blue points while signals that were not detected are given by red points. The contours represent the probability that a planet with the corresponding period and amplitude is detected. The vertical black line marks the period of the candidate signal while the horizontal solid black line gives the amplitude from our adopted model (see Section 4.4). The dashed horizontal black line marks the elevated amplitude from models that ignore the stellar rotation signal. For both amplitudes, HIRES and SOPHIE are unlikely to detect the candidate signal while the APF and combined RVs are more likely to make a detection than CARMENES.
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: Plots of the RVs vs. PSF parameters for the APF data set. Signals corresponding to GJ 411 b, GJ 411 c, and stellar rotation have been subtracted from the RVs using the model presented in Section 4.4. Similar to Figure 3, samples of linear models from the MCMC posterior for each pair of variables are shown as red lines while the 68.3% confidence interval for the slope is given in the top-right corner of each subplot. Most of the PSF parameters show some correlation with the RVs; however, they appear to be weak and are not strictly caused by the candidate signal.
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: GLS periodograms of the APF PSF parameters. As with Figure 2, the three horizontal dashed lines in each panel represent the 1%, 0.05%, and 0.01% FAP levels for the corresponding periodogram. Additionally, the vertical dashed lines represent the periods of interest, with blue marking GJ 411 b, purple giving the rotational modulation, green denoting the candidate signal, and red representing GJ 411 c. No peak near 215 days appears for any of the PSF parameters, suggesting that the candidate signal is not driven by a systematic.
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: GJ 411 RVs with error bars representing observational uncertainties and instrumental jitter values added in quadrature. Panel a: Full time series with the best-fit three-planet model and mean GP shown in blue. Panel b: residuals of the best-fit model and mean GP. Panel c: RVs phase-folded to the ephemeris of GJ 411 b. The Keplerian orbits of all other planets and the rotation signal have been subtracted and the Keplerian fit for the individual planet is shown in blue. Red dots represent the binned velocities. Panels d and e are the same, except they represent the orbits of GJ 411 c and the candidate signal, respectively.
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: Histograms showing the astrometric amplitude posterior for GJ 411 c (blue) and the candidate (green). Assuming Gaia has uncertainties of 0.02 mas, the vertical dashed lines mark 1σ, 5σ, and 10σ detection limits. For an orbit to be detectable, we require the astrometric amplitude to be greater than 10σ. 97% and 2% of simulated orbits for GJ 411 c and the candidate planet, respectively, are detectable with Gaia.
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: Mass or ﹩M\sin i﹩ vs. semimajor axis for planets in the NASA Exoplanet Archive (Retrieved 2021 May 2) and the GJ 411 system. GJ 411 b and c and the candidate have relatively low minimum masses compared to other known planets at similar orbital distances. Additionally, despite GJ 411 c and the candidate possibly being imageable, they have much smaller semimajor axes and masses than planets that have been discovered using direct imaging.
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 detectability of the candidate planet in the thermal infrared, assuming that the planet and star are perfect blackbodies and the orbit is circular. The left plot shows the contrast through the JWST MIRI F1550C filter while the right shows the contrast through the JWST MIRI F2300C filter. The red lines represent the limiting contrast for the coronagraph (Boccaletti et al. 2015). The candidate is likely too dim for MIRI to image.
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: The normalized PDCSAP TESS light curve for GJ 411 during sector 22. The horizontal red dashed line marks a constant normalized flux of 1, from which there is little deviation and no apparent transiting event.
Paper: Interplay between Young Stars and Molecular Clouds in the Ophiuchus Star-forming Complex
Authors: Aashish Gupta, Wen-Ping Chen
Figure caption: Spatial distribution of YSOs and of the 850 μm emission outlined by the contours. The YSOs (class I marked by blue circles, flat-spectrum by purple triangles, class II by green triangles, class III by red crosses, and unclassified sources by yellow symbols) are not randomly distributed. Instead, they appear to follow stream-like patterns. Inner purple dashed line denotes the string of small clumps, as discussed in Section 3 and illustrated in Figure 1. Outer red dashed line denotes string of evolved sources, as discussed in Section 3.1.
Paper: Interplay between Young Stars and Molecular Clouds in the Ophiuchus Star-forming Complex
Authors: Aashish Gupta, Wen-Ping Chen
Figure caption: The JCMT/SCUBA2 850 μm emission map of L1688. The red polygons show the clump boundaries as identified by the FellWalker algorithm. Prominent clouds are labeled. Purple dashed line denotes the string of small clumps, as discussed in Section 3.
Paper: Interplay between Young Stars and Molecular Clouds in the Ophiuchus Star-forming Complex
Authors: Aashish Gupta, Wen-Ping Chen
Figure caption: The peculiar motions of YSOs, i.e., the proper motion relative to the mean motion. Symbols for different stellar classes are the same as in Figure 3. Each arrow marks the direction of the proper motion, with the length proportional to the speed. The blue arrows represent Gaia measurements, whereas the maroon arrows are for values from other data sets (see Section 2.2.1). The scale reference on the top right marks 10 mas yr−1, which, in accord with the coordinate axes, corresponds to a motion of ﹩6^{\prime} ﹩ in 3.5 × 104 yr.
Paper: Interplay between Young Stars and Molecular Clouds in the Ophiuchus Star-forming Complex
Authors: Aashish Gupta, Wen-Ping Chen
Figure caption: Gaia EDR3 data for (a) the parallax distribution, and (b) the vector plot of the proper motions. In each case, the gray symbol represents every Gaia star in the region, whereas the pink symbol marks known YSOs. In (a), the thick and thin dashed lines mark, respectively, the median and interval of five times median-absolute deviation. In (b), the ellipse is the region of five times the median-absolute deviation.
Paper: Interplay between Young Stars and Molecular Clouds in the Ophiuchus Star-forming Complex
Authors: Aashish Gupta, Wen-Ping Chen
Figure caption: Projected distances from the nearest 850 μm emission clumps of YSOs at different evolutionary phases. The solid color bars show the distribution of the YSOs of different classes. The black lines show the distribution for the sample of Dunham et al. (2015).
Paper: Interplay between Young Stars and Molecular Clouds in the Ophiuchus Star-forming Complex
Authors: Aashish Gupta, Wen-Ping Chen
Figure caption: The Ripley’s K function of the class II and III objects in our sample (in red) is clearly different from that of central 95 percentile interval (the shaded gray region) of the K function of a randomly distributed simulated sample.
Paper: Interplay between Young Stars and Molecular Clouds in the Ophiuchus Star-forming Complex
Authors: Aashish Gupta, Wen-Ping Chen
Figure caption: Distributions of relative speeds for YSOs of different classes. The blue bars represent the Gaia EDR3 measurements, whereas the orange bars denote the less-precise ground-based measurements (Monet et al. 2003; Ducourant et al. 2017).
Paper: KELT-9 as an Eclipsing Double-lined Spectroscopic Binary: A Unique and Self-consistent Solution to the System
Authors: Anusha Pai Asnodkar, Ji Wang, 吉 王, B. Scott Gaudi, P. Wilson Cauley, Jason D. Eastman, Ilya Ilyin, Klaus Strassmeier, Thomas Beatty
Figure caption: A 2D map of transmission spectra over the course of KELT-9 b’s transit for the PEPSI 2018 data set; the blue track is formed by the planet’s atmospheric absorption, while the red track is the Doppler shadow from the RME. The top panel displays fully in-transit observations. The middle panel shows the best-fit model from MCMC sampling, with the Doppler shadow and CLV determined from numerical modeling of the planet’s transit using SME stellar models, while the planet absorption track is a uniform Gaussian signal shifted in velocity according to the best-fit orbital motion of the planet, systemic velocity, and best-fit dayside-to-nightside winds. The bottom panel shows the residuals (data–model).
Paper: KELT-9 as an Eclipsing Double-lined Spectroscopic Binary: A Unique and Self-consistent Solution to the System
Authors: Anusha Pai Asnodkar, Ji Wang, 吉 王, B. Scott Gaudi, P. Wilson Cauley, Jason D. Eastman, Ilya Ilyin, Klaus Strassmeier, Thomas Beatty
Figure caption: (a) Example of an LSD of a stellar line profile with an analytic rotational kernel fit to a centroid stellar RV. (b) Stellar RV curve of the KELT-9 system derived from fitting a circular orbital solution to the out-of-transit stellar RVs. The data points are shifted by the best-fit systemic velocities of their corresponding data sets. The inset in the top right panel zooms in on the range of velocities along the y-axis spanned by the best-fit orbital solution.(The data used to create this figure are available.)
Paper: KELT-9 as an Eclipsing Double-lined Spectroscopic Binary: A Unique and Self-consistent Solution to the System
Authors: Anusha Pai Asnodkar, Ji Wang, 吉 王, B. Scott Gaudi, P. Wilson Cauley, Jason D. Eastman, Ilya Ilyin, Klaus Strassmeier, Thomas Beatty
Figure caption: The K p measurements from Fe II line-by-line and cross-correlation analysis with MCMC sampling errors.
Paper: KELT-9 as an Eclipsing Double-lined Spectroscopic Binary: A Unique and Self-consistent Solution to the System
Authors: Anusha Pai Asnodkar, Ji Wang, 吉 王, B. Scott Gaudi, P. Wilson Cauley, Jason D. Eastman, Ilya Ilyin, Klaus Strassmeier, Thomas Beatty
Figure caption: Same as Figure 3 except displaying CCF maps for each data set.
Paper: KELT-9 as an Eclipsing Double-lined Spectroscopic Binary: A Unique and Self-consistent Solution to the System
Authors: Anusha Pai Asnodkar, Ji Wang, 吉 王, B. Scott Gaudi, P. Wilson Cauley, Jason D. Eastman, Ilya Ilyin, Klaus Strassmeier, Thomas Beatty
Figure caption: Cross-correlated flux maps before artifact correction in the top panel and after artifact correction in the bottom panel for the (a) PEPSI 2018 and (b) HARPS-N 2017 data sets.
Paper: KELT-9 as an Eclipsing Double-lined Spectroscopic Binary: A Unique and Self-consistent Solution to the System
Authors: Anusha Pai Asnodkar, Ji Wang, 吉 王, B. Scott Gaudi, P. Wilson Cauley, Jason D. Eastman, Ilya Ilyin, Klaus Strassmeier, Thomas Beatty
Figure caption: Expanded version of Figure 2 displaying the six Fe II absorption lines chosen for fitting K p (PEPSI 2018 data set).
Paper: KELT-9 as an Eclipsing Double-lined Spectroscopic Binary: A Unique and Self-consistent Solution to the System
Authors: Anusha Pai Asnodkar, Ji Wang, 吉 王, B. Scott Gaudi, P. Wilson Cauley, Jason D. Eastman, Ilya Ilyin, Klaus Strassmeier, Thomas Beatty
Figure caption: Example of a corner plot from MCMC fitting of the Fe II λ4923.9 flux map.
Paper: Revisiting KELT-19Ab, WASP-156b, and WASP-121b in the TESS Era
Authors: Fan Yang, Ranga-Ram Chary, Ji-Feng Liu
Figure caption:  R  p /R * of KELT-19Ab in different bandpasses based on our re-analysis of those light curves. The red point is R  p /R * derived from TESS. The black points are R  p /R * from former work. In the top panel, the lowest χ 2 model (haze-dominant) is shown as a blue solid line. The green line indicates a flat, opaque, featureless model that is consistent with the data. Blue diamonds are the best-fit model predictions at the bands. The yellow diamond is the best-fit model prediction at the SDSS z-band; the green diamond is the best-fit model prediction at the Kepler band. In the bottom panel, a clear model with strong water (green) is shown with a faint-water model (orange) as a comparison. Blue diamonds are strong-water model predictions in different wavelength bands while the green diamond is for the Kepler band. Dark green and brown diamonds are the strong-water and faint-water model prediction in the SDSS z-band. Clearly, 500 ppm photometry in the z-band can distinguish between these scenarios.
Paper: Revisiting KELT-19Ab, WASP-156b, and WASP-121b in the TESS Era
Authors: Fan Yang, Ranga-Ram Chary, Ji-Feng Liu
Figure caption: The transmission spectrum for WASP-121b. The solid yellow point is the best-fit value of R  p /R * from the TESS light curve. However, if the inclination is fixed to previous work for a consistent comparison, the solid red point is R  p /R * derived from TESS data. The black points are R  p /R * from former measurements. The gray solid line is the best fit from Evans et al. (2018). The blue line shows the haze model. The brown line shows the opaque featureless model. The black line indicates a clear atmosphere at a cool temperature (shifted for clarity).
Paper: Revisiting KELT-19Ab, WASP-156b, and WASP-121b in the TESS Era
Authors: Fan Yang, Ranga-Ram Chary, Ji-Feng Liu
Figure caption:  R  p /R * of WASP-156b in different bandpasses. The top panel shows the clear model with strong water (blue solid line), the haze-dominant model (green solid line), and the opaque featureless model (brown solid line). The red point is R  p /R * derived from TESS. The black points are R  p /R * from former work with the blue diamonds the “clear model” predictions at those bands. The yellow diamond is the model prediction at the SDSS z-band, the green diamond is the model prediction at the Kepler band. In the bottom panel, a clear model with strong water (green solid line) is shown along with a faint-water model (orange solid line) for comparison. The empty green diamond is a strong-water model prediction at the Kepler band. The dark green diamond is the strong-water model prediction at the SDSS z-band. The brown diamond is the faint-water model prediction at the z-band.
Paper: Revisiting KELT-19Ab, WASP-156b, and WASP-121b in the TESS Era
Authors: Fan Yang, Ranga-Ram Chary, Ji-Feng Liu
Figure caption: Results from fitting inclination (green squares) and semimajor axis (red diamonds) to simulated light curves of different sampling intervals when not correcting for the undersampling. The vertical red dotted line gives the sampling interval limit below which the parameters derived are not biased and corresponds to 5 minutes. The horizontal black dotted line is the input value of the semimajor axis; the dotted blue line is the input value of inclination. Clearly, TESS FFI 30 minute cadence data would result in significantly biased parameters for planets with short-duration transits such as those considered here.
Paper: Revisiting KELT-19Ab, WASP-156b, and WASP-121b in the TESS Era
Authors: Fan Yang, Ranga-Ram Chary, Ji-Feng Liu
Figure caption: Top: result from fitting with a fixed inclination and semimajor axis for WASP-121b (TESS 30 minute data). The red dashed line represents the best-fitting model. The black line indicates the fit from previous work. Bottom: residuals after fitting for the exoplanet transit around the transit midpoint for WASP-121b based on 30 minute data. The upper panel is residuals from the best fit. The lower panel is residuals assuming a fixed inclination and semimajor axis adopted from previous work. The 30 minute data are clearly inconsistent with the previously derived inclination value.
Paper: Revisiting KELT-19Ab, WASP-156b, and WASP-121b in the TESS Era
Authors: Fan Yang, Ranga-Ram Chary, Ji-Feng Liu
Figure caption: The probability distribution of the fit parameters for KELT-19Ab (top), WASP-156b (middle), WASP-121b (bottom). The grayscale map indicates the density of samples derived from MCMC analysis for different pairs of parameters. The histograms along the diagonal are the marginalized density distribution for individual parameters. The dashed lines denote 68% of samples. The plot applies the corner routine (Foreman-Mackey 2016).
Paper: Revisiting KELT-19Ab, WASP-156b, and WASP-121b in the TESS Era
Authors: Fan Yang, Ranga-Ram Chary, Ji-Feng Liu
Figure caption: Results from fitting with fixed inclination and semimajor axis for KELT-19Ab (top), WASP-156b (middle), WASP-121b (bottom). The blue points are deconfused photometry. The green points indicate the 30 minute cadence binned result. The red dashed line represents the best-fitting model. The black line indicates the result of previous work (referenced in the text). The yellow region shows the 1σ confidence region of the fitting. The transit event is shifted at the center of x-axis which is half of the period.
Paper: Revisiting KELT-19Ab, WASP-156b, and WASP-121b in the TESS Era
Authors: Fan Yang, Ranga-Ram Chary, Ji-Feng Liu
Figure caption: Top: the derived inclination distribution from fitting 1000 simulated light curves sampled at 30 minutes for the parameters of WASP-121b. Bottom: the planet-to-star radius ratio distribution when fixing the inclination and semimajor axis. The vertical lines for both panels give the input values to the simulations. Clearly, for WASP-121b, a light-curve cadence of 30 minutes would result in a large bias in derived parameters.
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: Distribution of the ratio of integrated powers, or the P bottom/P top ratio. The dashed orange vertical lines delimit the lower and upper bounds that we used to select 500,000 positive samples (0.75 and 1.25, respectively). The solid blue vertical line is plotted at a ratio of 0.2. We selected 50,000 signals below this value to represent a portion of our negative samples.
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: (Left) Example image pair where the signal in the second image is not centered. This occurs when the properties of the signal in the top image are inaccurately determined. (Middle) Example of a signal that does not appear until the second half of the scan in the second image. The standard application of the network mislabels this signal because the CNN looks at the top half of each scan only. (Right) Example signal that was incorrectly hand-labeled as “1,” seemingly indicating that it contains a signal in the second image.
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: Architecture of the final model presented in this work. This figure is adapted from Figure 5 of Chollet (2017). Batch normalization and activation (ReLu) layers that follow each convolution and separable convolution layer are omitted from the diagram. Data flow follows the arrows. The middle portion of the network is repeated to create eight identical sections. For each layer, we list the name, kernel size, and number of output channels. Layers with a stride length of 2 instead of 1 are distinguished by s = 2. The reduction ratio (14) of the SE layer is presented as 728:52, which denotes the number of input and output channels as a ratio of the number of hidden layer channels (Hu et al. 2018).
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: The ROC curve and AUC scores for the baseline 2D correlation model and existing DoO filter, as well as the CNN evaluated on both the validation and the test set. The DoO curve is linear because the filter only outputs binary scores of 0 or 1, unlike the other models, which output a score in the range from 0 to 1 for each sample. The dashed line shows the ROC curve for a purely random classifier with an AUC score of 0.5.
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: (Left) Drift rate distribution of the 1.1 million signals selected to be part of our training set. Note a significant bias toward signals with a negative drift rate value. (Right) Drift rate distribution of the same set of signals after applying a horizontal flip to 364,184 negative drift rate signals. The bias that affects ∼685,000 signals between −0.5 and 0 Hz s−1 is almost entirely removed at the expense of a slight bias introduced between −2 and −1 Hz s−1 that affects ∼2500 signals.
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: Sample signals used in the ML labeled training set. (a) Signal from the positive category, shifted 3 pixels (∼9 Hz) to the right. (b) Sample signal from the negative category with a P bottom/P top ratio of <0.2. (c) Sample signal from the negative category, shifted 8 pixels (∼24 Hz) to the right. (d) Sample signal from the negative category with an unrelated signal in the bottom image. (e) Sample signal from the negative category with a bottom image consisting completely of simulated noise. (f) Sample signal from the negative category with the primary (center) signal replaced by noise in the bottom image.
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: Precision and recall curves for a baseline 2D correlation model, which does not rely on ML techniques and is used solely to serve as a benchmark to evaluate the performance improvement of our ML application (Section 5.2). With the chosen threshold, the baseline model detects approximately one-third of the RFI in the data with 95% precision.