adsabs/AstroCLIMB huggingface dataset:
Paper: Measuring Interstellar Carbon Abundance via 158 μm [C II] Absorption with SOFIA—A Potential Detection and Proof-of-concept for Depletion Studies with Future Far-IR Facilities
Authors: Christopher J. R. Clark, Julia. C. Roman-Duval, Suzanne C. Madden, Marc Mertens, Claire E. Murray, Jürgen Stutzki, Elizabeth Tarantino, Kirill Tchernyshyov
Figure caption: Corner plot of the probability distribution of our full model (continuum + [C II] emission + [C II] absorption) of the IC 342 spectrum, from MCMC sampling. The marginalized posterior of each parameter is shown, along with its covariance with the other parameters. For each parameter, the medians of the marginal posterior, along with the 68th percentile credible intervals, are indicated with dashed lines and labeled. The corresponding model spectrum is plotted in Figure 7.
Paper: Measuring Interstellar Carbon Abundance via 158 μm [C II] Absorption with SOFIA—A Potential Detection and Proof-of-concept for Depletion Studies with Future Far-IR Facilities
Authors: Christopher J. R. Clark, Julia. C. Roman-Duval, Suzanne C. Madden, Marc Mertens, Claire E. Murray, Jürgen Stutzki, Elizabeth Tarantino, Kirill Tchernyshyov
Figure caption: SOFIA-upGREAT [C II] spectrum (blue), HI4PI 21 cm H I spectrum (black), and Milky Way velocity range (green shading), plotted same as per Figure 3. Also plotted, in pink, is the MAP fit of our full model, which combines dust continuum emission from IC 342, Gaussian [C II] emission from IC 342, and Gaussian [C II] absorption from the foreground Milky Way ISM, fitted over the full velocity range. Model residuals are shown in purple. Also shown, in teal, is the absorption component on its own.
Paper: Measuring Interstellar Carbon Abundance via 158 μm [C II] Absorption with SOFIA—A Potential Detection and Proof-of-concept for Depletion Studies with Future Far-IR Facilities
Authors: Christopher J. R. Clark, Julia. C. Roman-Duval, Suzanne C. Madden, Marc Mertens, Claire E. Murray, Jürgen Stutzki, Elizabeth Tarantino, Kirill Tchernyshyov
Figure caption: Corner plot of the probability distribution of our full model (continuum + [C II] emission + [C II] absorption) of the IC 342 spectrum, as described in Equation (B1), computed via brute-force evaluation of the parameter grid given in Table 2. The marginalized posterior of each parameter is shown, along with its covariance with every other parameter. The maximum a-posteriori (MAP) model parameters are indicated with the dashed lines. There is clearly a single, constrained mode in the probability distribution.
Paper: Measuring Interstellar Carbon Abundance via 158 μm [C II] Absorption with SOFIA—A Potential Detection and Proof-of-concept for Depletion Studies with Future Far-IR Facilities
Authors: Christopher J. R. Clark, Julia. C. Roman-Duval, Suzanne C. Madden, Marc Mertens, Claire E. Murray, Jürgen Stutzki, Elizabeth Tarantino, Kirill Tchernyshyov
Figure caption: Spectrum of CO(1–0) emission from IC 342 (using data from M. Querejeta et al. 2023), within the same aperture as our SOFIA-upGREAT 157.7 μm spectrum, at the same angular resolution. Also plotted is our best-fit Gaussian model to the CO data (produced by fitting only to data outside the Milky Way velocity range), along with its residuals. Unlike with our [C II] data, the CO emission is well fit by a Gaussian model, with no significant residuals or evidence of asymmetry.
Paper: Variable Polarization of WR 31a: Binary Companion or Corotating Interaction Region?
Authors: Christiana Erba, Richard Ignace, Faith Simmons, Ben Davies
Figure caption: Comparison of the observations with an elliptical model that has e = 0.5, i = 75°, and ϕp = 140°. (a) The bold purple curve is for ﹩{\rm{\Delta }}\bar{p}﹩. Panels (b), (c), and (d) are results shown in ﹩{\rm{\Delta }}\bar{q}﹩ vs. ﹩{\rm{\Delta }}\bar{u}﹩ for ψ = 0°, 30°, and 225°, respectively. The data points are weighted averages for all four passbands taken in a given night.
Paper: Measuring Interstellar Carbon Abundance via 158 μm [C II] Absorption with SOFIA—A Potential Detection and Proof-of-concept for Depletion Studies with Future Far-IR Facilities
Authors: Christopher J. R. Clark, Julia. C. Roman-Duval, Suzanne C. Madden, Marc Mertens, Claire E. Murray, Jürgen Stutzki, Elizabeth Tarantino, Kirill Tchernyshyov
Figure caption: Black line: plot of how the fraction of C+ ions in the fine-structure ground state evolves with excitation temperature. Red line: plot of how the column density of C+ determined from a given level of [C II] integrated line opacity, ∫τ[C II] dv, evolves with excitation temperature.
Paper: A Search for Exoplanet Candidates in TESS 2 minute Light Curves Using Joint Bayesian Detection
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: A schematic of the detector data processing pipeline. The input data set obtained from the SPOC pipeline output is shown in pink. Processing steps are shown in rounded boxes; major processing steps are shown with a solid outline with constituent component processing steps below with dashed outlines. Boxes indicating data output products are shaded in gray.
Paper: A Search for Exoplanet Candidates in TESS 2 minute Light Curves Using Joint Bayesian Detection
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: An example light curve (TID: 349518145, sector 1) with a transit signal is shown (top) with the associated fitted autocovariance ﹩{\mathrm{Cov}}_{{\boldsymbol{s}},i}[s-t]=f(| s-t| )=f(k)={\hat{r}}_{ss}(k)﹩ (below). In the top panel, the unprocessed light curve is shown in dark blue (background); this light curve is detrended and thresholded, shown in light blue, and gap-filled with random samples, shown in gray. The resulting light curve of light blue and gray points (foreground) is used to estimate the stochastic sample autocovariance (below, dark purple). The smoothed periodogram is shown in light purple. The smoothing reduces estimator variance and suppresses unfiltered transit variability, however it may also suppress stochastic variability.
Paper: A Search for Exoplanet Candidates in TESS 2 minute Light Curves Using Joint Bayesian Detection
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: The frequency of quality-flagged cadences as they occur in year 1 light curves. The most commonly occurring quality flags have the following keys and descriptors: (8) spacecraft is in Earth point; (128) manual exclude due to an anomaly; (2048) stray light from Earth to Moon in camera FOV; (136) unspecified; and (other) all other quality flags. Broadly, the majority of flagged data is due to the spacecraft being in Earth point, and uniformly occurs over different sectors. We justify masking all flagged data as the three most commonly occurring flags are dominant and indicate a high likelihood of photometric error.
Paper: A Search for Exoplanet Candidates in TESS 2 minute Light Curves Using Joint Bayesian Detection
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: The fraction of missing data in the year 1 light curves, averaged per day and across all target light curves. Each row represents a sector of data. Sectors are variable length; the total number of days in a sector is displayed on the right axis. Each block represents the average fraction of data excluded (due to a quality flag or missing cadence) among the selected light curves, averaged over a day (approximately 720 cadences). Over each sector, most missing data occur at the midpoint due to a data downlink. However, missing data also occur throughout the remaining observational window.
Paper: A Search for Exoplanet Candidates in TESS 2 minute Light Curves Using Joint Bayesian Detection
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: An example of 8/16 TESS cotrending basis vectors (CBVs) for sector 2, camera 4, and CCD 3 are shown at a 2 minutes cadence. The CBVs capture systematics induced by the TESS sensor, in particular the 2–6 days visible periodic pointing jitter. Ramps at the beginning of each 13.7 days orbit are likely due to thermal effects following the pointing slew during sector downlink. The sector downlink at ∼13.7 days appears in the middle of the plot. The data in this gap are masked in the current analysis.
Paper: A Search for Exoplanet Candidates in TESS 2 minute Light Curves Using Joint Bayesian Detection
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: The density of empirical least-squares coefficients ﹩\hat{{\boldsymbol{c}}}﹩ for the four leading basis vectors in sector 33, camera 4 and CCD 4, chosen as a random representative example. A fitted Gaussian prior is overlaid. The samples are heavy tailed in some cases, however they are reasonably close to the fitted Gaussian.
Paper: A Search for Exoplanet Candidates in TESS 2 minute Light Curves Using Joint Bayesian Detection
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: In the upper panel, the count of transit events binned in 0.5 days time-offset intervals between the sector downlink event and each transit event, for year 2 of detected transits and including all sectors. The error bar represents the standard deviation of the transit count over the set of light curves. In the lower panel, the median light-curve flux density in the same time-offset bins is shown at a bin width of 4 hr. Increased negative scatter occurs close to the sector downlink times and is correlated with an increased transit event count, suggesting the many detections with periods between 10 and 15 days are likely FAs.
Paper: Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations
Authors: Snir Pardo, Dovi Poznanski, Steve Croft, Andrew P. V. Siemion, Matthew Lebofsky
Figure caption: Mapping of simulated signals onto an abstract UMAP space. Every point is a simulated signal from our validation data set in any or all of the six windows of a cadence. In green we mark the signals that are only on-target, while in red we show those that exclusively off target for comparison. All the other clusters in black are various on-off combinations. In light green (red) we delineate the probability derived from the exclusively on(off)-target categories of the training set. In the margins we show the marginal distribution of the validation set. In addition to the well-defined clusters, one can see a large group near the left of the plane. Visual inspection shows that this is where undetected weak signals, i.e., background noise, cluster.
Paper: A Search for Exoplanet Candidates in TESS 2 minute Light Curves Using Joint Bayesian Detection
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: The distribution of the median planet signal-to-noise ratio (SNR) of missed TOIs (green line) and detected TOIs (dashed blue line) over period, as obtained using the joint detector. The planet SNRs are those provided by ExoFOP. Although the detection thresholds cannot be compared directly, our detection threshold (τ = 10) exceeds the MES detection threshold of 7.1, and this may reasonably contribute to differences in candidate detections. In general, the missed TOIs have a lower planetary SNR of 11.3, compared to a value of 23.0 for the detected TOIs.
Paper: A Search for Exoplanet Candidates in TESS 2 minute Light Curves Using Joint Bayesian Detection
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: The distribution of known TOIs (light blue), known TOIs recovered as the top detection by the joint detector (darker blue), and known TOIs recovered as any detection above threshold by the joint detector (dashed line) over period. The TOI recovery rate increases slightly in the period range 10–100 days. We note that the bin size on the x-axis is not uniform.
Paper: Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations
Authors: Snir Pardo, Dovi Poznanski, Steve Croft, Andrew P. V. Siemion, Matthew Lebofsky
Figure caption: Three examples of observed cadences of the star HIP 66193. The spectrograms on the left show loud RFI throughout the cadence, the middle example shows essentially background noise, and on the right we show what could be a viable candidate based on this view, having a detectable signal only on target.
Paper: Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations
Authors: Snir Pardo, Dovi Poznanski, Steve Croft, Andrew P. V. Siemion, Matthew Lebofsky
Figure caption: Histogram of all the candidates in each band, after the cross-correlation filter. They seem to prefer some frequencies to others, in a manner that is somewhat correlated with pink bands that are known to be heavily contaminated by RFI, from D. C. Price et al. (2019). The gray bands were completely excluded from our analysis.
Paper: Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations
Authors: Snir Pardo, Dovi Poznanski, Steve Croft, Andrew P. V. Siemion, Matthew Lebofsky
Figure caption: Schematic flow chart describing our first filter. We use dimensionality reduction and simulations to identify sources that have a detectable signal on target—in the “A” parts—but not in the alternates, which allows us to reduce the number of candidates by 5 orders of magnitude.
Paper: Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations
Authors: Snir Pardo, Dovi Poznanski, Steve Croft, Andrew P. V. Siemion, Matthew Lebofsky
Figure caption: Histogram of the probability that a candidate is in the “only on-target” category, using 20 stars. There is an obvious cutoff near 0.0618 which we use as a lower bound to select candidates for further analysis.
Paper: Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations
Authors: Snir Pardo, Dovi Poznanski, Steve Croft, Andrew P. V. Siemion, Matthew Lebofsky
Figure caption: Mapping of individual observations of the entire candidates list onto an abstract UMAP space. In green we show a promising candidate, where all the “A” observations seem similar to each other, and different from the off-target “B” observations, which is reflected in a high similarity score, while the candidate in red shows no such clustering.
Paper: Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations
Authors: Snir Pardo, Dovi Poznanski, Steve Croft, Andrew P. V. Siemion, Matthew Lebofsky
Figure caption: Histogram of the frequency score of all candidates in each band, after the cross-correlation filter. We use these scores to rank candidates for human vetting. We examined and ruled out all the candidates in the top one percent of each band.
Paper: Additive Attention for Vetting Transiting Exoplanet Candidates
Authors: Àlvar Hernàndez-Carnerero, Miquel Sànchez-Marrè, Juan Carlos Morales
Figure caption: LSTM cell diagram.
Paper: Additive Attention for Vetting Transiting Exoplanet Candidates
Authors: Àlvar Hernàndez-Carnerero, Miquel Sànchez-Marrè, Juan Carlos Morales
Figure caption: Visualization of attention weights learned by the Optimized with Attention model for each data point in the global view from the Kepler data set. Attention weights are depicted using a blue line. The average of light-curve views from the test set is shown with a green line, with standard deviation represented by shaded areas around the mean. This visualization is separately displayed for (a) true positive, (b) false negative, (c) true negative, and (d) false positive groups of test instances.
Paper: Additive Attention for Vetting Transiting Exoplanet Candidates
Authors: Àlvar Hernàndez-Carnerero, Miquel Sànchez-Marrè, Juan Carlos Morales
Figure caption: Visualization of attention weights learned by the Optimized with Attention model for each data point in the local view from the Kepler data set. Attention weights are depicted using a blue line. The average of light-curve views from the test set is shown with a green line, with standard deviation represented by shaded areas around the mean. This visualization is separately displayed for (a) true positive, (b) false negative, (c) true negative, and (d) false positive groups of test instances.
Paper: Additive Attention for Vetting Transiting Exoplanet Candidates
Authors: Àlvar Hernàndez-Carnerero, Miquel Sànchez-Marrè, Juan Carlos Morales
Figure caption: Schematic representation of the light-curve processing procedure that prepares them for classification tasks within our models. We use the TCE 757450-1 to exemplify the procedure. (a) Basis spline curve (in red) fitted to the light-curve points. Transit data points are shown in green, and the rest in blue. (b) Flattened light curve after dividing the original flux data points by the fitted basis spline. The rest of the plots (from (c) to (f)) also depict flattened flux values. Panels (a) and (b) represent Quarter 2 of the light curve, and the rest of the plots show the whole light curve. (c) Concatenation of every quarter’s flattened light curves. (d) Complete light curve folded according to the transit ephemerides. (e) Global view of the original light curve. (f) Local view of the original light curve.
Paper: Additive Attention for Vetting Transiting Exoplanet Candidates
Authors: Àlvar Hernàndez-Carnerero, Miquel Sànchez-Marrè, Juan Carlos Morales
Figure caption: Architecture of the best performing model for the Kepler data set. Convolutional layers are denoted as Conv-[filter_size]-[number_filters]-[activation], LSTM layers are denoted as LSTM-[number_units]-[activation], and feed-forward layers are denoted as FF-[number_units]-[activation].
Paper: Additive Attention for Vetting Transiting Exoplanet Candidates
Authors: Àlvar Hernàndez-Carnerero, Miquel Sànchez-Marrè, Juan Carlos Morales
Figure caption: Architecture of the best performing model for the TESS data set. Convolutional layers are denoted as Conv-[filter_size]-[number_filters]-[activation], LSTM layers are denoted as LSTM-[number_units]-[activation], and feed-forward layers are denoted as FF-[number_units]-[activation].
Paper: Additive Attention for Vetting Transiting Exoplanet Candidates
Authors: Àlvar Hernàndez-Carnerero, Miquel Sànchez-Marrè, Juan Carlos Morales
Figure caption: Scatter plot of the instances across orbital period and transit duration, categorized into planet candidates and nonplanet candidates for (a) Kepler and (b) TESS data sets. The top and right marginal panels in each scatter plot show normalized histograms (area = 1 for each category) along each axis.
Paper: Additive Attention for Vetting Transiting Exoplanet Candidates
Authors: Àlvar Hernàndez-Carnerero, Miquel Sànchez-Marrè, Juan Carlos Morales
Figure caption: Visualization of attention weights learned by the Baseline with Attention model for each data point in the concatenation of global and local views from the Kepler data set. Attention weights are depicted using a blue line. The average of light-curve views from the test set is shown with a green line, with standard deviation represented by shaded areas around the mean. This visualization is separately displayed for (a) true positive, (b) false negative, (c) true negative, and (d) false positive groups of test instances.
Paper: Additive Attention for Vetting Transiting Exoplanet Candidates
Authors: Àlvar Hernàndez-Carnerero, Miquel Sànchez-Marrè, Juan Carlos Morales
Figure caption: Average values of (a) global and (b) local views of Kepler light curves. The average of planet candidate light curves is represented by a solid orange line, with the standard deviation depicted as a shaded area around the average line in light orange. Similarly, the average of nonplanet candidate light curves is shown with a solid blue line, accompanied by the standard deviation represented as a shaded area around the average line in light blue.
Paper: Additive Attention for Vetting Transiting Exoplanet Candidates
Authors: Àlvar Hernàndez-Carnerero, Miquel Sànchez-Marrè, Juan Carlos Morales
Figure caption: Average values of (a) global, (b) local, and (c) secondary views of TESS light curves. The average of planet candidate light curves is represented by a solid orange line, with the standard deviation depicted as a shaded area around the average line in light orange. Similarly, the average of nonplanet candidate light curves is shown with a solid blue line, accompanied by the standard deviation represented as a shaded area around the average line in light blue.
Paper: Adaptive Weighted Multi-image Shape from Shading for Precise Topographic Reconstruction at the Lunar South Pole
Authors: Zhen Ye, Jia Qian, Yusheng Xu, Hong Zhang, Tao Tao, Rong Huang, Shijie Liu, Huan Xie, Xiaohua Tong
Figure caption: Technical workflow for generation of high-resolution DEM using the proposed method.
Paper: Adaptive Weighted Multi-image Shape from Shading for Precise Topographic Reconstruction at the Lunar South Pole
Authors: Zhen Ye, Jia Qian, Yusheng Xu, Hong Zhang, Tao Tao, Rong Huang, Shijie Liu, Huan Xie, Xiaohua Tong
Figure caption: A conceptual diagram of (a) SI-SFS and (b) MI-SFS.
Paper: Adaptive Weighted Multi-image Shape from Shading for Precise Topographic Reconstruction at the Lunar South Pole
Authors: Zhen Ye, Jia Qian, Yusheng Xu, Hong Zhang, Tao Tao, Rong Huang, Shijie Liu, Huan Xie, Xiaohua Tong
Figure caption: Technical workflow of smoothness constraint weighting.
Paper: Adaptive Weighted Multi-image Shape from Shading for Precise Topographic Reconstruction at the Lunar South Pole
Authors: Zhen Ye, Jia Qian, Yusheng Xu, Hong Zhang, Tao Tao, Rong Huang, Shijie Liu, Huan Xie, Xiaohua Tong
Figure caption: Comparison of hillshade images generated from different DEMs. Panels (a), (b), and (c) show hillshade images from the MI-AW-SFS, SI-SFS, and MI-SFS DEMs using the Sun illumination direction, respectively. Panels (d), (e), and (f) show hillshade images from the MI-AW-SFS, SI-SFS, and MI-SFS DEMs using the cross-Sun direction, respectively.
Paper: Adaptive Weighted Multi-image Shape from Shading for Precise Topographic Reconstruction at the Lunar South Pole
Authors: Zhen Ye, Jia Qian, Yusheng Xu, Hong Zhang, Tao Tao, Rong Huang, Shijie Liu, Huan Xie, Xiaohua Tong
Figure caption: Height profile comparison of a small crater among different DEMs. Panels (a) and (b) show the profiles along the Sun illumination direction and the cross-Sun direction, respectively.
Paper: Adaptive Weighted Multi-image Shape from Shading for Precise Topographic Reconstruction at the Lunar South Pole
Authors: Zhen Ye, Jia Qian, Yusheng Xu, Hong Zhang, Tao Tao, Rong Huang, Shijie Liu, Huan Xie, Xiaohua Tong
Figure caption: Mismatch map. Panels (a) and (b) represent the LOLA DEM and MI-AW-SFS DEM for experimental data set 1, respectively, while panels (c) and (d) illustrate the LOLA DEM and MI-AW-SFS DEM for experimental data set 2, respectively. The red circles mark the corresponding locations across different DEMs.
Paper: Adaptive Weighted Multi-image Shape from Shading for Precise Topographic Reconstruction at the Lunar South Pole
Authors: Zhen Ye, Jia Qian, Yusheng Xu, Hong Zhang, Tao Tao, Rong Huang, Shijie Liu, Huan Xie, Xiaohua Tong
Figure caption: Laser points superimposed on the hillshade image, with color indicating the absolute error between the DEM and laser points. Panels (a) and (b) represent the LOLA DEM and MI-AW-SFS DEM for experimental data set 1, respectively, while panels (c) and (d) illustrate the LOLA DEM and MI-AW-SFS DEM for experimental data set 2, respectively.
Paper: Adaptive Weighted Multi-image Shape from Shading for Precise Topographic Reconstruction at the Lunar South Pole
Authors: Zhen Ye, Jia Qian, Yusheng Xu, Hong Zhang, Tao Tao, Rong Huang, Shijie Liu, Huan Xie, Xiaohua Tong
Figure caption: (a) Experimental data set 1. Outlines of the rectangular intersection of the five image footprints in red and LROC NAC images. (b) Experimental data set 2. Outlines of the rectangular intersection of the four image footprints in red and LROC NAC images.