adsabs/AstroCLIMB huggingface dataset:
Paper: Toward the Automated Detection of Light Echoes in Synoptic Surveys: Considerations on the Application of Deep Convolutional Neural Networks
Authors: Xiaolong Li, Federica B. Bianco, Gregory Dobler, Roee Partoush, Armin Rest, Tatiana Acero-Cuellar, Riley Clarke, Willow Fox Fortino, Somayeh Khakpash, Ming Lian
Figure caption: Predictions from YOLOv3 for an example image from our test set. The left panel shows a scatter plot of classification score vs. IoU, the traditional metric used in YOLO for training and evaluation, for all predicted bounding boxes. The vertical and horizontal lines indicate the score threshold and IoU threshold (IoP in the middle panel), set in this example to 0.1 and 0.2, respectively. Four boxes pass the score threshold; those are indicated by red circles in the left and center panels of this figure. However, the IoU value is IoU < 0.2 for three out of the four boxes. Based on the traditional IoU definition, three of these detections would be considered false positives (FPs). Due to the complexity of the LE morphology, the label boxes are somewhat arbitrary in their size and position and LEs could be split in different ways. In the rightmost figure we mark the labels that we use in the training phase (solid white boxes, typically smaller) and the labels that we use for model evaluation, which are designed to contain an LE group in its entirety (white dashed box). While three of the four detection boxes do not pass the IoU threshold, two of them are in fact contained entirely inside of an evaluation label box. We calculate the IoP as the intersection of prediction and evaluation label over the area of the predicted box and replace the IoU with the IoP for the evaluation of the LE detection performance. In this example, the model successfully detected the LE group with three detections, all above the IoP threshold and this image contains only one FP, for which IoP = 0.
Paper: Toward the Automated Detection of Light Echoes in Synoptic Surveys: Considerations on the Application of Deep Convolutional Neural Networks
Authors: Xiaolong Li, Federica B. Bianco, Gregory Dobler, Roee Partoush, Armin Rest, Tatiana Acero-Cuellar, Riley Clarke, Willow Fox Fortino, Somayeh Khakpash, Ming Lian
Figure caption: Results from YOLOv3 for LEs (a) and stars (b) for a chosen cross-validation set. In both (a) and (b), the left panel shows a scatter plot of classification score vs. IoP (top, for LEs) or IoU (bottom, for stars) for all predicted bounding boxes. The middle panels show ROC curves and the right panels show the PR curves. The gray regions in the left plots delimit the region of rejected predictions (below a score threshold of 0.2) and the orange regions represent the location where FPs are found (IoP or IoU smaller than 0.2). The ROC (middle) and PR curves (right) are plotted for different score thresholds (points along each curve) and different IoP or IoU (as labeled). In the ROC curve plots, the three black lines indicate a ratio of TP to FPs of 1/1, 1/5 and 1/10. If we choose an IoP > 0.2 and a classification score threshold of 0.4, larger than 70% recall is achieved at a precision of about 95%. This means that we have to tolerate a 5% FPs to detect 70% of LEs.
Paper: Toward the Automated Detection of Light Echoes in Synoptic Surveys: Considerations on the Application of Deep Convolutional Neural Networks
Authors: Xiaolong Li, Federica B. Bianco, Gregory Dobler, Roee Partoush, Armin Rest, Tatiana Acero-Cuellar, Riley Clarke, Willow Fox Fortino, Somayeh Khakpash, Ming Lian
Figure caption: (a) Three types of common FPs from our implementation of YOLOv3 for LE searches in ATLAS images for the same cross-validation set used in Figure 9. FPs are primarily associated with two kinds of features in the images. “Dipole” structures, i.e., features in the difference image that show one bright and one dark side corresponding to moving objects, including LEs, but also, for example, asteroids. This kind of FP is shown in the leftmost image in panel (a). The second kind of FPs are generated by saturated star streaks—middle and right images in panel (a). The class “other” is designed to train the model to recognize non-LE artifacts, including these features. While this class suppresses the number of FPs compared to models trained on two classes only (LE and star), some of these artifacts are still incorrectly classified as LEs. Panel (b) shows that the rate of growth of FPs as a function of the number of images without LEs for which the model is asked to make prediction grows sublinearly for score thresholds between 0.1 and 0.5. This figure is discussed in Section 5.
Paper: Toward the Automated Detection of Light Echoes in Synoptic Surveys: Considerations on the Application of Deep Convolutional Neural Networks
Authors: Xiaolong Li, Federica B. Bianco, Gregory Dobler, Roee Partoush, Armin Rest, Tatiana Acero-Cuellar, Riley Clarke, Willow Fox Fortino, Somayeh Khakpash, Ming Lian
Figure caption: Training of YOLOv3, discussed in Section 4.3. All curves in this figure represent averages across the six cross-validation data sets. The top panel shows the total loss for the training and test set, as labeled. The total loss is defined for YOLOv3 as the sum of classification and regression losses (see Section 4.1). The test loss shows little improvement after about 50 epochs. The bottom plot shows the precision and recall for LEs as a function of the training epoch, as well as the F1 score, as labeled. Precision and recall are calculated with a classification score threshold of 0.2 and IoP threshold of 0.2. The F1 score is the harmonic mean of precision and recall, demonstrating how a balance between the two is achieved near 50 epochs.
Paper: Bridging the Gap—The Disappearance of the Intermediate Period Gap for Fully Convective Stars, Uncovered by New ZTF Rotation Periods
Authors: Yuxi(Lucy) Lu, Jason L. Curtis, Ruth Angus, Trevor J. David, Soichiro Hattori
Figure caption: Three examples of the processed ZTF light curves (left column), processed ZTF light curves folded onto the detected periods (middle column), and the LS periodograms run on the entire light curve (right column) in different period ranges. The top row shows an example of P rot < 10 days; the middle row shows that of 10 days < P rot < 100 days; and the bottom row shows that of P rot > 100 days.
Paper: Bridging the Gap—The Disappearance of the Intermediate Period Gap for Fully Convective Stars, Uncovered by New ZTF Rotation Periods
Authors: Yuxi(Lucy) Lu, Jason L. Curtis, Ruth Angus, Trevor J. David, Soichiro Hattori
Figure caption: Color–magnitude diagram for the ∼7 million selected stars of interest from Gaia eDR3. The top plot shows the raw Gaia absolute magnitude and color, and the bottom plot shows those after accounting for reddening and extinction with the Bayestar19 3D dust map (Green 2018; Green et al. 2019).
Paper: TESS Observations of Kepler Systems with Transit Timing Variations
Authors: Daniel Jontof-Hutter, Paul A. Dalba, John H. Livingston
Figure caption: TTV models (O−C, in minutes) for planet candidates identified within each panel, with the combined Kepler/TESS transit times in black and 68% confidence intervals with equal weight in the tails on simulated transit times in green. (Part 1 of 3).
Paper: TESS Observations of Kepler Systems with Transit Timing Variations
Authors: Daniel Jontof-Hutter, Paul A. Dalba, John H. Livingston
Figure caption: TTV models (O−C, in minutes) for planet candidates identified within each panel, with the combined Kepler/TESS transit times in black and 68% confidence intervals with equal weight in the tails on simulated transit times in green. (Part 2 of 3.)
Paper: TESS Observations of Kepler Systems with Transit Timing Variations
Authors: Daniel Jontof-Hutter, Paul A. Dalba, John H. Livingston
Figure caption: TTV models (O−C, in minutes) for planet candidates identified within each panel, with the combined Kepler/TESS transit times in black and 68% confidence intervals with equal weight in the tails on simulated transit times in green. (Part 3 of 3.)
Paper: Environmental Effects in Herschel Observations of the Ionized Carbon Content of Star-forming Dwarf Galaxies in the Virgo Cluster 
Authors: Robert Minchin, Dario Fadda, Rhys Taylor, Boris Deshev, Jonathan Davies
Figure caption: VCC 841 optical color image from the SDSS (left) with north up and east to the left; and SDSS g-band image overlaid with [C II] (R), WISE band 3 (G), and GALEX NUV (B) contours (right) at levels of 2.5, 5, 10, and 20 × 10−19 W m−2 spaxel−1 (4.5, 9, 18 and 36σ) for the [C II]; 2.5, 5, and 10 DN pixel−1 (6.0, 12 and 23σ) above the sky value of 604 DN pixel−1 for WISE band 3; and 0.0125, 0.025, 0.05, and 0.1 counts s−1 pixel−1 (5.6, 11, 22 and 44σ) for the GALEX NUV. The location of the SDSS spectrum (Figure 12) is marked with a red box on the SDSS color image.
Paper: Environmental Effects in Herschel Observations of the Ionized Carbon Content of Star-forming Dwarf Galaxies in the Virgo Cluster 
Authors: Robert Minchin, Dario Fadda, Rhys Taylor, Boris Deshev, Jonathan Davies
Figure caption: VCC 1686 optical color image from the SDSS (left) with north up and east to the left; and SDSS g-band image overlaid with [C II] (R), WISE band 3 (G), and GALEX NUV (B) contours (right) at levels of 1, 2, and 4 × 10−18 W m−2 spaxel−1 (9.8, 19.5 and 39.0σ) for the [C II]; 2.5, 5, 10, and 20 DN pixel−1 (6.3, 13, 25, and 51σ) above the sky value of 647 DN pixel−1 for WISE band 3; and 0.025, 0.05, 0.1, and 0.2 counts s−1 pixel−1 (7.6 15, 30 and 61σ) for the GALEX NUV.
Paper: Environmental Effects in Herschel Observations of the Ionized Carbon Content of Star-forming Dwarf Galaxies in the Virgo Cluster 
Authors: Robert Minchin, Dario Fadda, Rhys Taylor, Boris Deshev, Jonathan Davies
Figure caption: SDSS preview spectra of VCC 737 central region (left) and western region (right). Red and blue labels indicate automatically identified absorption and emission features; gray band indicates uncertainty in the flux.
Paper: Environmental Effects in Herschel Observations of the Ionized Carbon Content of Star-forming Dwarf Galaxies in the Virgo Cluster 
Authors: Robert Minchin, Dario Fadda, Rhys Taylor, Boris Deshev, Jonathan Davies
Figure caption: SDSS spectrum of VCC 841. Red and blue labels indicate automatically identified absorption and emission features; gray band indicates uncertainty in the flux.
Paper: SPORK That Spectrum: Increasing Detection Significances from High-resolution Exoplanet Spectroscopy with Novel Smoothing Algorithms
Authors: Kaitlin C. Rasmussen, Matteo Brogi, Fahin Rahman, Hayley Beltz, Miles Currie, Emily Rauscher, Alexander P. Ji
Figure caption: SPORK applied to prenormalized CRIRES spectra of HD 179949 b from the ESO reduction pipeline after the airmass detrending, described in Section 3.1, has been performed. Top: a telluric-removed spectrum (gray) displays a strong offset from the continuum, and is fit with SPORK (red). Middle: the SPORK fit (knot spacing M/2–1 where M is the length of the wavelength array, σ upper = 3, σ lower = 3) is divided out, leaving behind a flat telluric-removed spectrum. Bottom: the normalized model exoplanet spectrum which corresponds to the telluric-removed spectral region is shown. Systematic offsets in the normalization of the HD 179949 b data cannot be removed by performing SPORK on the individual spectra, as they persist in the median spectrum as well. In this case, it is better perform telluric removal first and then detrend the residual spectra. These spectra will perform better in the cross-correlation routine against the perfectly normalized exoplanet spectrum.
Paper: SPORK That Spectrum: Increasing Detection Significances from High-resolution Exoplanet Spectroscopy with Novel Smoothing Algorithms
Authors: Kaitlin C. Rasmussen, Matteo Brogi, Fahin Rahman, Hayley Beltz, Miles Currie, Emily Rauscher, Alexander P. Ji
Figure caption: Detection significance vs. smoothing factor for HD 209458 b and HD 179949 b. Top: the full smoothing factor range from 0 to 1 is shown, with the region of stability highlighted in red. This region is chosen to avoid over-smoothing of the spectrum (see Figure 4, blue line), which begins to occur at 0.40 and produces high, but unstable detection significances. We select a smoothing factor from the region of stability of 0.12 to use in our analysis. Bottom: the same smoothing factor range is shown for HD 179949 b, with the region of stability highlighted. As with the case of HD 209458 b, the standard deviation from the running mean increases after 0.40. From this region of stability we choose the value of 0.194 for our analysis.
Paper: SPORK That Spectrum: Increasing Detection Significances from High-resolution Exoplanet Spectroscopy with Novel Smoothing Algorithms
Authors: Kaitlin C. Rasmussen, Matteo Brogi, Fahin Rahman, Hayley Beltz, Miles Currie, Emily Rauscher, Alexander P. Ji
Figure caption: A median CRIRES spectrum of HD 209458 b and its optimally smoothed version. The gray region is highlighted to show the resolution of the CRIRES instrument and to highlight the closeness of the fit of the chosen factor. In iterative smoothing, the smoothing factor C in the python function splrep is increased by 0.001 over a range of 0–1, and a value is chosen from the region of stability to be the final smoothing factor. The red line represents the optimal smoothing factor, while the blue line represents an overfit.
Paper: SPORK That Spectrum: Increasing Detection Significances from High-resolution Exoplanet Spectroscopy with Novel Smoothing Algorithms
Authors: Kaitlin C. Rasmussen, Matteo Brogi, Fahin Rahman, Hayley Beltz, Miles Currie, Emily Rauscher, Alexander P. Ji
Figure caption: Results of cross-correlation on telluric-removed spectra for HD 179949 b. Left: the standard telluric routine leads to a detection of σ = 4.38. Middle left: the inclusion of iterative smoothing on its own, in this case, results in an increase of 2.15 σ. Middle right: when SPORK alone is applied after the telluric removal process, the significance of the detection stays largely the same (σ = 5.06). Right: when SPORK is applied, then iterative smoothing is run, this factor increases to σ = 6.88 for a total increase of 2.50 σ.
Paper: SPORK That Spectrum: Increasing Detection Significances from High-resolution Exoplanet Spectroscopy with Novel Smoothing Algorithms
Authors: Kaitlin C. Rasmussen, Matteo Brogi, Fahin Rahman, Hayley Beltz, Miles Currie, Emily Rauscher, Alexander P. Ji
Figure caption: SPORK applied to CRIRES spectra of HD 209458 b from the ESO reduction pipeline prior to telluric removal. Although the blaze function has been removed within the pipeline, residual wiggles in the continuum can be seen, especially between 2.288 and 2.292 μm, where the unnormalized continuum rises ∼3% above the “true” continuum. If left uncorrected, these wiggles will persist throughout the telluric removal process and hinder the cross-correlation of the data against a perfectly flat model spectrum.
Paper: SPORK That Spectrum: Increasing Detection Significances from High-resolution Exoplanet Spectroscopy with Novel Smoothing Algorithms
Authors: Kaitlin C. Rasmussen, Matteo Brogi, Fahin Rahman, Hayley Beltz, Miles Currie, Emily Rauscher, Alexander P. Ji
Figure caption: Results of cross-correlation on telluric-removed spectra for HD 209458 b. Left: the standard telluric routine leads to a detection of σ = 5.78 at RV = 0, K P = 149, as is reported in Beltz et al. (2021). (Middle) When SPORK is applied before the telluric removal process, the significance of the detection increases to σ = 8.21. Right: when SPORK is applied, then iterative smoothing is run, this factor increases to σ = 9.71.
Paper: TESS Observations of Kepler Systems with Transit Timing Variations
Authors: Daniel Jontof-Hutter, Paul A. Dalba, John H. Livingston
Figure caption: TPF for Kepler-396 from Sector 15 generated with tpfplotter (Aller et al. 2020). The numbered red circles are background stars identified by Gaia, with size corresponding to the magnitude difference compared to Kepler-396 (marked with a cross and numbered “1”). The red squares identify the pixels in our custom aperture, which minimizes contamination from a nearby source. These pixels’ light curves—normalized to their sum—formed the set of basis vectors for the PLD analysis of this system.
Paper: TESS Observations of Kepler Systems with Transit Timing Variations
Authors: Daniel Jontof-Hutter, Paul A. Dalba, John H. Livingston
Figure caption: PLD and trapezoidal models fit to the two transits observed for Kepler-396 c. The style of this figure is identical to that of Figure 3. The Sector 15 transit occurs substantially later than expected.
Paper: TESS Observations of Kepler Systems with Transit Timing Variations
Authors: Daniel Jontof-Hutter, Paul A. Dalba, John H. Livingston
Figure caption: PLD and trapezoidal model fits to the three transits observed for Kepler-396 b. The top panel of each column shows normalized but not detrended flux from the default SPOC apertures. In each column, the data are centered on the expected transit timing based on TTV models of Kepler transit times for this system. The shaded region in the bottom panels shows the 68% credible region for the trapezoidal fits, with the median in bold. The credible region for Kepler-396 b is less constrained in Sector 15 owing to correlated noise that is poorly modeled by PLD.
Paper: TESS Observations of Kepler Systems with Transit Timing Variations
Authors: Daniel Jontof-Hutter, Paul A. Dalba, John H. Livingston
Figure caption: Posterior probability distributions for the PLD and trapezoidal transit fit to the Sector 15 Kepler-396 c transit. The first four columns are the trapezoid parameters, while the remaining columns are PLD basis vector coefficients. The red curves denote the normal priors on T 14 and T 23, the latter of which is transformed from the prior on f  T . These posteriors correspond to the fit shown in Figure 4.
Paper: TESS Observations of Kepler Systems with Transit Timing Variations
Authors: Daniel Jontof-Hutter, Paul A. Dalba, John H. Livingston
Figure caption: TTV models (O−C, in minutes) for KOI-94 (Kepler-89), with the combined Kepler/TESS transit times in black and 68% confidence intervals with equal weight in the tails on simulated transit times in green. The top row corresponds to a four-planet model, the middle row includes a nontransiting planet orbiting beyond Kepler-89 e (model A), and the bottom row includes a nontransiting planet orbiting between Kepler-89 d and Kepler-89 e (model B).
Paper: TESS Observations of Kepler Systems with Transit Timing Variations
Authors: Daniel Jontof-Hutter, Paul A. Dalba, John H. Livingston
Figure caption: TTV models (O − C, in minutes) for Kepler-396, with the combined Kepler/TESS transit times in black and 68% confidence intervals with equal weight in the tails on simulated transit times in green. The top row corresponds to a two-planet model, the middle row includes a nontransiting planet orbiting beyond Kepler-396 c (model A), and the bottom row includes a nontransiting planet orbiting between Kepler-396 b and Kepler-396 c (model B).
Paper: Environmental Effects in Herschel Observations of the Ionized Carbon Content of Star-forming Dwarf Galaxies in the Virgo Cluster 
Authors: Robert Minchin, Dario Fadda, Rhys Taylor, Boris Deshev, Jonathan Davies
Figure caption: [C II]/TIR vs. p loc/p def with ram-pressure stripping category (both from Köppen et al. 2018) for our central galaxies (purple lozenges) and southern galaxies (blue circles). Magenta squares with colored error bars indicate the mean of each category (errors on the means, as before, estimated by propagating the errors on the individual measurements in the samples and combining these in quadrature with the uncertainty in the estimates of the means due to the scatter in the samples) and the dashed magenta line indicates where Köppen et al. (2018) split their categories. VCC 699, which has p loc/p def = 0 in Köppen et al. (2018), is artificially placed at p loc/p def = 0.025 with an arrow to the left.
Paper: Environmental Effects in Herschel Observations of the Ionized Carbon Content of Star-forming Dwarf Galaxies in the Virgo Cluster 
Authors: Robert Minchin, Dario Fadda, Rhys Taylor, Boris Deshev, Jonathan Davies
Figure caption: VCC 737 optical color image from the SDSS (left) with north up and east to the left; and SDSS g-band image overlaid with [C II] (R), WISE band 3 (G), and GALEX NUV (B) contours (right) at levels of 2.5, 5, and 10 × 10−19 W m−2 spaxel−1 (4.4, 8.8 and 18σ) for the [C II]; 2, 2.8, and 4 DN pixel−1 (3.5, 4.9 and 7.0σ) above the sky value of 776.5 DN pixel−1 for WISE band 3; and 0.0125, 0.025, 0.05, and 0.1 counts s−1 pixel−1 (6.0, 12, 24 and 48σ) for the GALEX NUV. The locations of the SDSS spectrum (Figure 10) are marked with red boxes on the SDSS color image.
Paper: Environmental Effects in Herschel Observations of the Ionized Carbon Content of Star-forming Dwarf Galaxies in the Virgo Cluster 
Authors: Robert Minchin, Dario Fadda, Rhys Taylor, Boris Deshev, Jonathan Davies
Figure caption: [C II]/TIR vs. projected distance to M87, the central elliptical galaxy in the Virgo cluster. Error bars indicate 1σ errors based on the Herschel [C II] error budget and the 16%–84% range of the marginalized probability distribution of the TIR flux; larger errors are dominated by the uncertainty in the TIR flux. Points indicate the central value of [C II]/TIR, based on the 50% point of the marginalized probability distribution; shape and color indicate the sample to which each galaxy is assigned: central (purple lozenges) or southern (blue circles). The large symbols (shown for illustrative purposes on the right-hand side) indicate the arithmetic mean and error of [C II]/TIR for each sample, with the large green star showing the mean and error for the comparator sample drawn from the Herschel Dwarf Galaxy Survey (see Section 3.1).
Paper: Environmental Effects in Herschel Observations of the Ionized Carbon Content of Star-forming Dwarf Galaxies in the Virgo Cluster 
Authors: Robert Minchin, Dario Fadda, Rhys Taylor, Boris Deshev, Jonathan Davies
Figure caption: [C II]/TIR vs. local ram pressure (p loc) calculated as in Equation (1) for our central galaxies (purple lozenges) and southern galaxies (blue circles) at 17 Mpc distance. Dashed line indicates best fit, the shaded area the 1σ uncertainty, and the dotted line the 3σ uncertainty.
Paper: Environmental Effects in Herschel Observations of the Ionized Carbon Content of Star-forming Dwarf Galaxies in the Virgo Cluster 
Authors: Robert Minchin, Dario Fadda, Rhys Taylor, Boris Deshev, Jonathan Davies
Figure caption: [C II]/TIR vs. L TIR (upper) and metallicity (lower) for our central galaxies (purple lozenges) and southern galaxies (blue circles) along with the comparator subsample from the DGS (green stars). Red squares indicate the other DGS galaxies and the black dashed lines indicate the limits for our selection in L TIR and metallicity.
Paper: Environmental Effects in Herschel Observations of the Ionized Carbon Content of Star-forming Dwarf Galaxies in the Virgo Cluster 
Authors: Robert Minchin, Dario Fadda, Rhys Taylor, Boris Deshev, Jonathan Davies
Figure caption: Difference between SFR estimated from FUV+TIR and estimated from [C II], plotted against [C II]/TIR. The error bar on the right-hand side indicates the scatter in the SFR indicators and the average error on [C II]/TIR; vertical position of the error bar marks the average difference between the SFR indicators.
Paper: Environmental Effects in Herschel Observations of the Ionized Carbon Content of Star-forming Dwarf Galaxies in the Virgo Cluster 
Authors: Robert Minchin, Dario Fadda, Rhys Taylor, Boris Deshev, Jonathan Davies
Figure caption: Positions of sample galaxies in the cluster with the central value for [C II]/TIR indicated by the color. The positions of M87 and M49 are indicated by blue stars. Contours (at 0.1, 0.2, 0.4, and 0.8 counts s−1 pixel−1) indicate the smoothed hard X-ray counts from the ROSAT All Sky Survey (Voges et al. 1999).