adsabs/AstroCLIMB huggingface dataset:
Paper: HybPSF: Hybrid Point-spread Function Reconstruction for the Observed JWST NIRCam Image
Authors: Lin Nie, Huanyuan Shan, Guoliang Li, Lei Wang, Cheng Cheng, Charling Tao, Qifan Cui, Yushan Xie, Dezi Liu, Zekang Zhang
Figure caption: Statistical results of the shape parameter residuals (δ R 2/R 2, δ e 1 and δ e 2) for the JWST NIRCam imaging data used in this work. The blue and red points represent the results obtained from WebbPSF and HybPSF, respectively. The error bars represent the 1σ width of the distributions.
Paper: HybPSF: Hybrid Point-spread Function Reconstruction for the Observed JWST NIRCam Image
Authors: Lin Nie, Huanyuan Shan, Guoliang Li, Lei Wang, Cheng Cheng, Charling Tao, Qifan Cui, Yushan Xie, Dezi Liu, Zekang Zhang
Figure caption: Star spike deduction test. The figure above shows the results of a star spike deduction test. The left panel shows the observed data, while the upper middle and upper right panels show the residuals obtained by subtracting WebbPSF and HybPSF, respectively, from the data. The lower panels show zoomed-in images of the residuals obtained by the two methods. All images are shown in log scale.
Paper: HybPSF: Hybrid Point-spread Function Reconstruction for the Observed JWST NIRCam Image
Authors: Lin Nie, Huanyuan Shan, Guoliang Li, Lei Wang, Cheng Cheng, Charling Tao, Qifan Cui, Yushan Xie, Dezi Liu, Zekang Zhang
Figure caption: Statistical results of the shape parameter residuals estimated based on coadded images (δ R 2/R 2, δ e 1 and δ e 2) of channels F277W, F356W, and F444W, respectively. The error bars represent the 1σ width of the distributions.
Paper: Improved Period Variations of 32 Contact Binaries with Rapidly Decreasing Periods in the Galactic Bulge
Authors: Kyeongsoo Hong, Jae Woo Lee, Dong-Jin Kim, Jang-Ho Park, Hye-Young Kim, Pakakaew Rittipruk, Cheongho Han
Figure caption: Same as Figure 5, for eight more systems.
Paper: Improved Period Variations of 32 Contact Binaries with Rapidly Decreasing Periods in the Galactic Bulge
Authors: Kyeongsoo Hong, Jae Woo Lee, Dong-Jin Kim, Jang-Ho Park, Hye-Young Kim, Pakakaew Rittipruk, Cheongho Han
Figure caption:  I light curves of 16 binary systems from the OGLE-III (OGIII), OGLE-IV (OGIV), and KMTNet surveys. The points and solid lines represent the observations and the theoretical light curves from the WD code, respectively.
Paper: Improved Period Variations of 32 Contact Binaries with Rapidly Decreasing Periods in the Galactic Bulge
Authors: Kyeongsoo Hong, Jae Woo Lee, Dong-Jin Kim, Jang-Ho Park, Hye-Young Kim, Pakakaew Rittipruk, Cheongho Han
Figure caption: Eclipse-timing diagrams of 16 CEBs with a parabolic plus LTT effect.
Paper: Improved Period Variations of 32 Contact Binaries with Rapidly Decreasing Periods in the Galactic Bulge
Authors: Kyeongsoo Hong, Jae Woo Lee, Dong-Jin Kim, Jang-Ho Park, Hye-Young Kim, Pakakaew Rittipruk, Cheongho Han
Figure caption: Same as Figure 1, for 16 more systems.
Paper: Improved Period Variations of 32 Contact Binaries with Rapidly Decreasing Periods in the Galactic Bulge
Authors: Kyeongsoo Hong, Jae Woo Lee, Dong-Jin Kim, Jang-Ho Park, Hye-Young Kim, Pakakaew Rittipruk, Cheongho Han
Figure caption: Eclipse-timing diagrams of eight CEBs with a parabolic variation.
Paper: Variability in Protoplanetary Nebulae. X. Multiyear Periods as an Indicator of Potential Binaries
Authors: Bruce J. Hrivnak, Wenxian Lu, Gary Henson, Todd C. Hillwig
Figure caption: The ASAS-SN V light curves (red) from 2015 to 2018 and g light curves (blue) from 2018 to 2023, with the later offset to the level of the V light curve for the interval of overlap. The amount of the offset in units of mag is listed in parentheses. Note that the light curves of IRAS 17542−0603 are contaminated by ∼0.2 mag by a nearby star in the large ASAS-SN photometric aperture.
Paper: Variability in Protoplanetary Nebulae. X. Multiyear Periods as an Indicator of Potential Binaries
Authors: Bruce J. Hrivnak, Wenxian Lu, Gary Henson, Todd C. Hillwig
Figure caption: The combined V light curve of the program objects, based on ASAS-3 (purple), VUO-old (white), VUO-new (black), SARA (green), and ASAS-SN (red) data. The solid curve represents the fit to the periodogram solution as listed in Table 5. The short period is also included for IRAS 20056+1834 but not for IRAS 07253−2001 (see Figure 5).
Paper: Variability in Protoplanetary Nebulae. X. Multiyear Periods as an Indicator of Potential Binaries
Authors: Bruce J. Hrivnak, Wenxian Lu, Gary Henson, Todd C. Hillwig
Figure caption: Our new V light curves from 2008 to 2018. The VUO-new data are shown in black and the SARA data in green.
Paper: Variability in Protoplanetary Nebulae. X. Multiyear Periods as an Indicator of Potential Binaries
Authors: Bruce J. Hrivnak, Wenxian Lu, Gary Henson, Todd C. Hillwig
Figure caption: IRAS 07253−2001: the fit of the combined solution to the ASAS-SN V data, including the short period of 73.6 days.
Paper: Variability in Protoplanetary Nebulae. X. Multiyear Periods as an Indicator of Potential Binaries
Authors: Bruce J. Hrivnak, Wenxian Lu, Gary Henson, Todd C. Hillwig
Figure caption: The light and color curves of IRAS 17542−0603 from 1994 to 2018, based on VUO-old (open circle) and VUO-new (filled circle) data. (top) The V light curve. (bottom) The color curve.
Paper: Solving the Traveling Telescope Problem with Mixed-integer Linear Programming
Authors: Luke B. Handley, Erik A. Petigura, Velibor V. Mišić
Figure caption: Schematic of suboptimal, optimal, and infeasible tours in the traveling telescope problem. The four polar plots on top show the sky above Keck Observatory (latitude = +19 deg) in alt/az coordinates. During a duration of 4 hr, targets A–E move across the sky as the Earth rotates. The shaded region shows an inaccessible region of alt/az (here, the Keck I Nasmyth platform). We have exaggerated slew times to clearly illustrate the differences between the three tours. In the top suboptimal tour, we include a horizontal timeline for targets A–D. The windows of accessibility are shown as vertical ticks. For example, the earliest the telescope can complete observations of target A is labeled with t A   e , while t A   l , denotes the latest time. Note that t A   e  depends on both the rise time and exposure duration, and is the either the set time or the end of the observing interval, whichever comes first. This scheduler uses a greedy approach, i.e., once the observation of A is complete, the telescope immediately slews to the unobserved target with the shortest slew time. The tour is feasible, but the entire observing duration is required in this example. The middle optimal tour is scheduled with a global optimization. In our example, the slew between A and D is so long that it is advantageous for the telescope to wait until target B rises to observe it, before proceeding to D. This is the optimal tour to observe targets A–D. Finally, the bottom plot shows the inclusion of a fifth target, E, to illustrate an infeasible tour. There is not enough time to observe all targets.
Paper: Solving the Traveling Telescope Problem with Mixed-integer Linear Programming
Authors: Luke B. Handley, Erik A. Petigura, Velibor V. Mišić
Figure caption: Time-dependent slew overheads at Keck Observatory. The left plot shows the motion of two targets in the alt/az frame across the accessible region at Keck Observatory (latitude = 19.8°N). The top target sits at δ = 43.8°, the bottom at δ = 11.7°. As time progresses, the azimuthal separation dominates the slew and steadily increases to a maximum of over 5 minutes. Late in the night, the lower decl. target crosses azimuth = 270°, which corresponds to the assumed telescope cable-wrap limit. At this time, a direct slew becomes available, and the slew time drops significantly. For any two arbitrary targets i and j, ﹩{\tau }_{\mathrm{ijm}}^{\mathrm{slew}}﹩ approximates the pairwise slew time within the bounds of each time slot as dictated by M. The right plot shows the interpolated slew curve (gray) with the travel time tensor ﹩{\tau }_{\mathrm{ijm}}^{\mathrm{slew}}﹩ (black) overplotted for the full night with M = 3. In this extreme case, ﹩{\tau }_{\mathrm{ijm}}^{\mathrm{slew}}﹩ often misrepresents the slew time by several minutes in the second and third time slots.
Paper: Solving the Traveling Telescope Problem with Mixed-integer Linear Programming
Authors: Luke B. Handley, Erik A. Petigura, Velibor V. Mišić
Figure caption: Top row: average value of Runtime for TTP-Global when scheduling each duration type D, with varying values of M. In the quarter-night case, TTP-Global can schedule all 25 observations for M < 10, but struggles to reach optimality at M = 10. In the half-night case, only the M = 1 model achieves optimality for N = 50. For the full-night case, TTP-Global handles N = 100 far better than expected for the static case in comparison to N = 50. Bottom row: average relative improvement RelRed  real  for each value of D across the model types. For all D and the respective maximum values of N, TTP-Global can reliably reduce slews by around a factor of 5. For small N, there is some benefit to using M > 1. For larger models, the risk of long slews in the M = 1 case has little impact on RelRed  real .
Paper: Solving the Traveling Telescope Problem with Mixed-integer Linear Programming
Authors: Luke B. Handley, Erik A. Petigura, Velibor V. Mišić
Figure caption:  Runtime and RelRed  real  for each duration type D, as found in Tables 1, 3, and 4. The first two rows summarize these statistics for the D = Quarter simulations, the next two for D = Half, and the bottom two for D = Full. The top row in each pair shows the average Runtime (and optimal subset, for TTP-Global) across all runs for D with varying M as a function of N. The bottom row in each pair shows the average RelRed real for the same values of D and M.
Paper: Solving the Traveling Telescope Problem with Mixed-integer Linear Programming
Authors: Luke B. Handley, Erik A. Petigura, Velibor V. Mišić
Figure caption: Comparison of a simple heuristic target ordering and a TTP-optimized tour of N = 50 targets during a simulated half night. The former routine orders the targets by their increasing set times. Top row: the azimuthal coordinate of the simulated telescope in each case. Middle row: the elevation angle of the simulated telescope. Bottom row: time spent exposing, slewing, or idle. The targets have randomized time windows, but are all accessible for at least half of the scheduling duration. The worst-case slew estimation from Equation (11) is 3.34 minutes per target. For the left (heuristic) column, the total slew time is 73 minutes, or an average of 91 s per slew, leaving 27 minutes idle. In the optimized (right) tour, the total time is a mere 13.8 minutes, or an average of 17.3 s per slew, resulting in 86 minutes idle (23 additional targets at the same pace).
Paper: Kepler-discovered Multiple-planet Systems near Period Ratios Suggestive of Mean-motion Resonances Are Young
Authors: Jacob H. Hamer, Kevin C. Schlaufman
Figure caption: Distributions of Galactic velocity dispersion based on bootstrap resampling for systems with (blue) or without (orange) a plausibly first-order resonant planet pair. We indicate median values as solid vertical lines, 1σ ranges between vertical dashed lines, and 2σ ranges between vertical dotted–dashed lines. The top panels include systems in which the innermost planet is likely affected by tidal evolution (i.e., ﹩{\tau }_{e}\lt {\overline{\tau }}_{e}=400﹩ Myr), while the bottom panels include systems in which the innermost planet is unlikely to be affected by tidal dissipation (i.e., ﹩{\tau }_{e}\geqslant {\overline{\tau }}_{e}=400﹩ Myr). Left: plausibly resonant/implausibly resonant according to the libration width–based definition. Right: plausibly resonant/implausibly resonant according to the ﹩{\delta }_{\mathrm{res}}﹩-based definition. Regardless of the definition used to identify plausibly first-order resonant systems, systems with innermost planet circularization times less than the median value ﹩{\overline{\tau }}_{e}=400\,\mathrm{Myr}﹩ have a colder Galactic velocity dispersion and are therefore younger than implausibly resonant systems. The probabilities that offsets in the tidally affected samples this large could occur by chance are about 1 in 18 (equivalent to about 1.6σ) for the libration-width definition and 1 in 93 (equivalent to about 2.3σ) for the ﹩{\delta }_{\mathrm{res}}﹩-based definition. Our conclusion that plausibly first-order resonant systems with an innermost planet likely to be affected by tidal dissipation are young does not sensitively depend on the criteria used to identify systems likely affected by tides.
Paper: Kepler-discovered Multiple-planet Systems near Period Ratios Suggestive of Mean-motion Resonances Are Young
Authors: Jacob H. Hamer, Kevin C. Schlaufman
Figure caption: Distributions of Galactic velocity dispersion based on bootstrap resampling for systems with (blue) or without (orange) a plausibly first-order resonant planet pair. We indicate median values as solid vertical lines, 1σ ranges between vertical dashed lines, and 2σ ranges between vertical dotted–dashed lines. The top panels include systems in which the innermost planet is likely affected by tidal evolution (i.e., τ  e  < 20 Myr), while the bottom panels include systems in which the innermost planet is unlikely to be affected by tidal dissipation (i.e., τ  e  ≥ 20 Myr). Left: plausibly resonant/implausibly resonant according to the libration width–based definition. Right: plausibly resonant/implausibly resonant according to the ﹩{\delta }_{\mathrm{res}}﹩-based definition. The Galactic velocity dispersion of the sample of Kepler-discovered multiple small planet systems likely affected by tides and with a plausibly first-order resonant planet pair is colder than the Galactic velocity dispersion of systems that lack such a pair, regardless of the definition of plausibly first-order resonant systems. The probabilities that offsets this large could occur by chance is about 1 in 72 (equivalent to about 2.2σ) for the libration width–based definition and about 1 in 1500 (equivalent to about 3.1σ) for the ﹩{\delta }_{\mathrm{res}}﹩-based definition. The large and statistically significant offsets present in the likely tidally affected samples support the idea that tidal evolution is responsible for moving planetary systems formed in first-order resonance away from those resonances over Gyr timescales.
Paper: Kepler-discovered Multiple-planet Systems near Period Ratios Suggestive of Mean-motion Resonances Are Young
Authors: Jacob H. Hamer, Kevin C. Schlaufman
Figure caption: Distributions of separation from closest mean-motion resonance. In each panel, the blue histogram gives the number of adjacent planet pairs in each histogram bin, while the orange histogram stacked on top of the blue histogram gives the number of nonadjacent planet pairs in each histogram bin. Top: distribution of separation from first-order mean-motion resonances in units of libration widths. We consider planet pairs within five resonance libration widths “plausibly resonant” in our subsequent analyses. Middle: distribution of separation from first-order mean-motion resonances in ﹩{\delta }_{\mathrm{res}}﹩. Bottom: distribution of separation from second-order mean-motion resonances in ﹩{\delta }_{\mathrm{res}}﹩. We consider planet pairs with ﹩{\delta }_{\mathrm{res}}\lt 0.015﹩ plausibly resonant in our subsequent analyses. Since our definition of plausibly resonant refers only to mean-motion resonances, from here, the phrase “plausibly resonant” implies plausibly mean-motion resonant.
Paper: Kepler-discovered Multiple-planet Systems near Period Ratios Suggestive of Mean-motion Resonances Are Young
Authors: Jacob H. Hamer, Kevin C. Schlaufman
Figure caption: Circularization time as a function of orbital period for the innermost planet in each multiple-planet system in our sample. We plot as blue points planets for which we assume the dissipative properties of Mars, Q p = 30 and k 2 = 0.14 (Duxbury & Callahan 1982; Yoder 1995), and as orange points planets for which we assume the dissipative properties of Neptune, Q p = 9000 and k 2 = 0.41 (Bursa 1992; Zhang & Hamilton 2008). A point’s size encodes the mass of the planet it represents calculated using the Lissauer et al. (2011) mass–radius relation. According to Lee et al. (2013), tidal evolution can measurably alter a system’s period ratio distribution when the circularization time of its innermost planet τ  e  ≲ 20 Myr.
Paper: Kepler-discovered Multiple-planet Systems near Period Ratios Suggestive of Mean-motion Resonances Are Young
Authors: Jacob H. Hamer, Kevin C. Schlaufman
Figure caption: Distributions of Galactic velocity dispersion based on bootstrap resampling for systems with (blue) or without (orange) a plausibly first-order resonant planet pair. We indicate median values as solid vertical lines, 1σ ranges between vertical dashed lines, and 2σ ranges between vertical dotted–dashed lines. Top: plausibly resonant/implausibly resonant according to the libration width–based definition. Bottom: plausibly resonant/implausibly resonant according to the ﹩{\delta }_{\mathrm{res}}﹩-based definition. The Galactic velocity dispersion of the sample of Kepler-discovered multiple small planet systems with a plausibly first-order resonant planet pair is consistent with the Galactic velocity dispersion of systems that lack such a pair regardless of the heuristic used to identify systems with a plausibly first-order resonant planet pair.
Paper: Kepler-discovered Multiple-planet Systems near Period Ratios Suggestive of Mean-motion Resonances Are Young
Authors: Jacob H. Hamer, Kevin C. Schlaufman
Figure caption: Distribution of planet-pair period ratios. The orange histogram gives the number of implausibly resonant pairs in each period ratio bin, while the blue histogram stacked on top of the orange histogram gives the number of plausibly resonant pairs in each period ratio bin. Top: plausibly resonant/implausibly resonant according to the libration width–based definition. Plausibly resonant pairs far from period ratios suggestive of resonances are high total mass planet pairs with larger libration widths. Bottom: plausibly resonant/implausibly resonant according to the ﹩{\delta }_{\mathrm{res}}﹩-based definition.
Paper: Direct Exoplanet Detection using Convolutional Image Reconstruction (ConStruct): A New Algorithm for Post-processing High-contrast Images
Authors: Trevor N. Wolf, Brandon A. Jones, Brendan P. Bowler
Figure caption: Comparative analysis between ConStruct and annular PCA. Each point source’s S/N produced by ConStruct is plotted against that obtained with PCA. The left panel shows the maximum obtainable S/N by tuning both ConStruct and PCA on each point source. In the right panel, we fix the design parameters for both ConStruct and PCA to reduce 18 ADI sequences, which together contain 30 unique point sources. For this set of tuning parameters, we find the number of frames in the image sequence is an indication of the relative performance of ConStruct, with more frames (typically above 100) yielding improved results compared to the standard PCA-based approach.
Paper: Direct Exoplanet Detection using Convolutional Image Reconstruction (ConStruct): A New Algorithm for Post-processing High-contrast Images
Authors: Trevor N. Wolf, Brandon A. Jones, Brendan P. Bowler
Figure caption: Two examples of the residual speckle patches in the region of interest ﹩{\mathbb{Y}}﹩ produced (left) with the autoencoder only and (right) with the autoencoder augmented with the regularized least-squares regression step. Both of the samples are extracted from the same spatial location, but at different frames in the ADI sequence. Each of the samples is first linearly scaled between 0 and 1. The autoencoder prediction removes most of the speckle noise in the prediction region. The bias in the autoencoder prediction is removed with the linear regression step.
Paper: Direct Exoplanet Detection using Convolutional Image Reconstruction (ConStruct): A New Algorithm for Post-processing High-contrast Images
Authors: Trevor N. Wolf, Brandon A. Jones, Brendan P. Bowler
Figure caption: Two autoencoder prediction examples for the speckle noise contained in a masked patch extracted from an ADI frame. Each of the patches are linearly scaled between 0 and 1. In each of the examples provided, panels (a) and (b) are the masked autoencoder input and the ground-truth region of interest, respectively. Panel (c) is the ground truth, with an injected Gaussian signal with a peak scaled intensity of 0.2. Panel (d) is the prediction formed by passing the masked input into our network. Panels (e) and (f) are the residual images produced by subtracting the predicted image regions from the ground truth without and with the injected source, respectively.
Paper: Direct Exoplanet Detection using Convolutional Image Reconstruction (ConStruct): A New Algorithm for Post-processing High-contrast Images
Authors: Trevor N. Wolf, Brandon A. Jones, Brendan P. Bowler
Figure caption: Full-frame reductions with ConStruct and PCA of HR 8799 (Marois et al. 2008, 2010) for Sequence 11 in Table 1. The top two panels correspond to (left) the flux map and (right) the S/N map produced by ConStruct. The bottom panels are that produced with PCA. In the side panels, we show a cut-out view of each planetary point source, and the source’s S/N produced by each algorithm.