adsabs/AstroCLIMB huggingface dataset:
Paper: High-resolution Linear Spectropolarimetry of Red Supergiant VY CMa: Identification of Atomic and Molecular Features
Authors: Dinh-V-Trung, N. T. T. Bao, P. M. Tien, B. V. Hai, P. H. Minh, L. H. Khiem
Figure caption: Spectrum of Ti I and Fe I absorption lines in VY CMa and the corresponding Q and U Stokes parameters.
Paper: The Space Density of Intermediate-redshift, Extremely Compact, Massive Starburst Galaxies
Authors: Kelly E. Whalen, Ryan C. Hickox, Alison L. Coil, Aleksandar M. Diamond-Stanic, James E. Geach, John Moustakas, Gregory H. Rudnick, David S. N. Rupke, Paul H. Sell, Christy A. Tremonti, Julie D. Davis, Serena Perrotta, Grayson C. Petter
Figure caption: Comparison of the average timescales (in Gyr) upon which various phases of massive galaxy evolution would be observable. The black star represents the average selectability timescale for the modeled compact starburst galaxies in our sample, its error bar along the redshift axis represents the size of the redshift range of our sources, and the error bar along the t  obs  axis is the statistical uncertainty calculated via bootstrapping as described in Section 5.2 The gray, purple, and blue shaded regions represent the range of observable timescales for galaxy mergers (Lotz et al. 2011), ULIRGs (Farrah et al. 2003), and post-starburst galaxies (PSBs; Wild et al. 2016), respectively. We note that the timescales presented for galaxy mergers and PSBs correspond to the amount of time for which a source would be targeted under a set of selection criteria (similar to the value calculated for our sources), while the timescale for ULIRGs reflects the amount of physical time a source would experience star formation characteristic of the ULIRG phase. We elaborate on how we obtain the timescale estimates for the shaded regions in the text. It is clear that compact starburst galaxies like the ones in our sample occur on relatively short-lived timescales that are comparable to that of ULIRG star formation.
Paper: The Space Density of Intermediate-redshift, Extremely Compact, Massive Starburst Galaxies
Authors: Kelly E. Whalen, Ryan C. Hickox, Alison L. Coil, Aleksandar M. Diamond-Stanic, James E. Geach, John Moustakas, Gregory H. Rudnick, David S. N. Rupke, Paul H. Sell, Christy A. Tremonti, Julie D. Davis, Serena Perrotta, Grayson C. Petter
Figure caption: HST WFC3 cutouts of six representative galaxies in our sample that overlap with those presented in Sell et al. (2014). We note that we omit J0944+0930 and J1104+5946 from Sell et al. (2014), as they do not satisfy all of our selection criteria. All of these galaxies show clear signs of tidal disruptions, consistent with their extreme nuclear starbursts being triggered by major merger events.
Paper: The Space Density of Intermediate-redshift, Extremely Compact, Massive Starburst Galaxies
Authors: Kelly E. Whalen, Ryan C. Hickox, Alison L. Coil, Aleksandar M. Diamond-Stanic, James E. Geach, John Moustakas, Gregory H. Rudnick, David S. N. Rupke, Paul H. Sell, Christy A. Tremonti, Julie D. Davis, Serena Perrotta, Grayson C. Petter
Figure caption: Location of our galaxies (black star) within the 0.5 < z < 1 size–mass plane as presented in Mowla et al. (2019). Blue and red points are van der Wel et al. (2014) star-forming and quiescent galaxies, respectively. The red, blue, and gray lines are the best-fit size–mass relations for the quiescent, star-forming, and total CANDELS/3DHST galaxies in Mowla et al. (2019). Our data point represents the average R eff and M * for a subset of the Mg II galaxies presented in J. D. Davis et al. (2022, in preparation). Our sources are significantly more compact than other galaxies at similar z and M *.
Paper: The Space Density of Intermediate-redshift, Extremely Compact, Massive Starburst Galaxies
Authors: Kelly E. Whalen, Ryan C. Hickox, Alison L. Coil, Aleksandar M. Diamond-Stanic, James E. Geach, John Moustakas, Gregory H. Rudnick, David S. N. Rupke, Paul H. Sell, Christy A. Tremonti, Julie D. Davis, Serena Perrotta, Grayson C. Petter
Figure caption: Panel (a): Best-fit SED for galaxy J0826+4305. The red points and error bars are the observed photometry and ±0.25 mag uncertainty region, respectively. The open black squares are the modeled photometry. The blue, violet, and green curves are the modeled SED for the total galaxy system, nuclear burst, and host galaxy, respectively. Panel (b): Triangle plot of parameter posterior distributions for galaxy J0826+4305. We calculate the mean and covariances of these posterior distributions in order to model them as 4D-Gaussian distributions. We then randomly draw sets of parameter values from the Gaussian-modeled posterior in order to construct a mock population of compact starbursts. Panel (c): Galaxy cutout as seen in Figure 1.
Paper: The Space Density of Intermediate-redshift, Extremely Compact, Massive Starburst Galaxies
Authors: Kelly E. Whalen, Ryan C. Hickox, Alison L. Coil, Aleksandar M. Diamond-Stanic, James E. Geach, John Moustakas, Gregory H. Rudnick, David S. N. Rupke, Paul H. Sell, Christy A. Tremonti, Julie D. Davis, Serena Perrotta, Grayson C. Petter
Figure caption: Shown here are the modeled evolutionary tracks of the apparent i-band and g-band SDSS magnitudes (panels (a) & (b)), [O II] equivalent width (panel (c)), and WISE W1 − W2 color (panel (d)) for a subsample of modeled galaxies. The x-axis is age relative to the burst peak. The gray shaded rectangles represent the regions of parameter space that would not be selected by the criteria placed on that given parameter. This is a schematic representation—the full details of our source selection can be found in Section 2.
Paper: The Space Density of Intermediate-redshift, Extremely Compact, Massive Starburst Galaxies
Authors: Kelly E. Whalen, Ryan C. Hickox, Alison L. Coil, Aleksandar M. Diamond-Stanic, James E. Geach, John Moustakas, Gregory H. Rudnick, David S. N. Rupke, Paul H. Sell, Christy A. Tremonti, Julie D. Davis, Serena Perrotta, Grayson C. Petter
Figure caption: Distribution of average selected lifetimes from the mock sample. We find that extreme nuclear starbursts like the ones observed in our galaxies would be selected for ﹩\sim {148}_{-24}^{+27}﹩ Myr, consistent with the burst ages calculated in J. D. Davis et al. (2022, in preparation).
Paper: The Space Density of Intermediate-redshift, Extremely Compact, Massive Starburst Galaxies
Authors: Kelly E. Whalen, Ryan C. Hickox, Alison L. Coil, Aleksandar M. Diamond-Stanic, James E. Geach, John Moustakas, Gregory H. Rudnick, David S. N. Rupke, Paul H. Sell, Christy A. Tremonti, Julie D. Davis, Serena Perrotta, Grayson C. Petter
Figure caption: Space density distribution calculated from our mock population of galaxies. We estimate that the space density for our population of 0.4 < z < 0.9 compact starburst galaxies is ﹩({1.1}_{-0.3}^{+0.5})\times {10}^{-6}\,{\mathrm{Mpc}}^{-3}﹩.
Paper: The Upper Edge of the Neptune Desert Is Stable Against Photoevaporation
Authors: Shreyas Vissapragada, Heather A. Knutson, Michael Greklek-McKeon, Antonija Oklopčić, Fei Dai, Leonardo A. dos Santos, Nemanja Jovanovic, Dimitri Mawet, Maxwell A. Millar-Blanchaer, Kimberly Paragas, Jessica J. Spake, Samaporn Tinyanont, Gautam Vasisht
Figure caption: Light curves (top) and residuals (bottom) for all planets in our sample, including the narrowband helium light curves (a-f) and the TESS light curve for WASP-177b (g). Gray points are detrended data, which are binned to 10 minute cadence in black. The red curve indicates our best-fit model, with 1﹩\sigma ﹩ uncertainty indicated by the red shading. The blue curve indicates the nominal light-curve model (outside the helium bandpass). The blue shading in (e) indicates the nonnegligible uncertainty on the baseline transit model for this planet.
Paper: The Upper Edge of the Neptune Desert Is Stable Against Photoevaporation
Authors: Shreyas Vissapragada, Heather A. Knutson, Michael Greklek-McKeon, Antonija Oklopčić, Fei Dai, Leonardo A. dos Santos, Nemanja Jovanovic, Dimitri Mawet, Maxwell A. Millar-Blanchaer, Kimberly Paragas, Jessica J. Spake, Samaporn Tinyanont, Gautam Vasisht
Figure caption:  K-corrected mass-loss rate ﹩\dot{M}﹩ as a function of F XUV/ρ p for all of the planets in our sample, following Caldiroli et al. (2022). The shaded violins indicate the marginalized distribution of K-corrected mass-loss rates with the median and 95% confidence interval for the distribution given by the black points and error bars, respectively. The triangle denotes the 95% upper limit for WASP-80. The orange curve indicates the average inferred mass-loss efficiency ε, with the 1σ uncertainty given by the shaded orange region. The blue curve indicates predictions from the ATES code (Caldiroli et al. 2021, 2022).
Paper: The Upper Edge of the Neptune Desert Is Stable Against Photoevaporation
Authors: Shreyas Vissapragada, Heather A. Knutson, Michael Greklek-McKeon, Antonija Oklopčić, Fei Dai, Leonardo A. dos Santos, Nemanja Jovanovic, Dimitri Mawet, Maxwell A. Millar-Blanchaer, Kimberly Paragas, Jessica J. Spake, Samaporn Tinyanont, Gautam Vasisht
Figure caption:  M p − a plane with marginal stability curves. Planets above the red curve cannot have lost more than 50% of their initial mass M 0 to photoevaporation, even assuming an energy-limited outflow for the entire planetary lifetime. The red shaded region corresponds to the uncertainty on our empirical estimate for the mass-loss efficiency ε with the uncertainty in the empirical radius–temperature relation from Owen & Lai (2018) included as well. The orange curve shows the same for 10% mass loss. Points indicate transiting exoplanets with fractional mass uncertainties of less than 30%; those orbiting stars with 4000 K < T eff < 5400 K are colored blue, and the seven targets constituting our sample are labeled and outlined with red stars. The gray shaded region indicates M p < 0.2M J wherein our assumed radii are incorrect.
Paper: The Upper Edge of the Neptune Desert Is Stable Against Photoevaporation
Authors: Shreyas Vissapragada, Heather A. Knutson, Michael Greklek-McKeon, Antonija Oklopčić, Fei Dai, Leonardo A. dos Santos, Nemanja Jovanovic, Dimitri Mawet, Maxwell A. Millar-Blanchaer, Kimberly Paragas, Jessica J. Spake, Samaporn Tinyanont, Gautam Vasisht
Figure caption: Mass-loss models for the seven planets in our sample. The shading gives the σ agreement between the observations and a 1D transonic Parker wind model characterized by the mass-loss rate ﹩\dot{M}﹩ and thermospheric temperature T 0. Models that violate energy balance (Vissapragada et al. 2022) are rejected (blue shaded regions).
Paper: The Upper Edge of the Neptune Desert Is Stable Against Photoevaporation
Authors: Shreyas Vissapragada, Heather A. Knutson, Michael Greklek-McKeon, Antonija Oklopčić, Fei Dai, Leonardo A. dos Santos, Nemanja Jovanovic, Dimitri Mawet, Maxwell A. Millar-Blanchaer, Kimberly Paragas, Jessica J. Spake, Samaporn Tinyanont, Gautam Vasisht
Figure caption: Posteriors on the quadratic limb-darkening coefficients for all stars analyzed in this work. Contours indicate the 1σ, 2σ, and 3σ levels on the posterior mass. The red star on each plot indicates the limb-darkening coefficient in the helium bandpass computed using ldtk (Husser et al. 2013; Parviainen & Aigrain 2015).
Paper: The Upper Edge of the Neptune Desert Is Stable Against Photoevaporation
Authors: Shreyas Vissapragada, Heather A. Knutson, Michael Greklek-McKeon, Antonija Oklopčić, Fei Dai, Leonardo A. dos Santos, Nemanja Jovanovic, Dimitri Mawet, Maxwell A. Millar-Blanchaer, Kimberly Paragas, Jessica J. Spake, Samaporn Tinyanont, Gautam Vasisht
Figure caption: Allan deviation plots for all light curves analyzed in this work. The rms of the binned residuals for each light-curve fit is shown with the black curves, the red noise indicates the expectation from photon noise statistics, and the dashed red line is the red line scaled up to match the first point of the black curve. The photon noise values and scaling factors are given in Table 2.
Paper: Probing Photon Statistics in Adaptive Optics Images with SCExAO/MEC 
Authors: Sarah Steiger, Timothy D. Brandt, Olivier Guyon, Noah Swimmer, Alexander B. Walter, Clinton Bockstiegel, Julien Lozi, Vincent Deo, Sebastien Vievard, Nour Skaf, Kyohoon Ahn, Nemanja Jovanovic, Frantz Martinache, Benjamin A. Mazin
Figure caption: Simulated I  C , I  S , and I  P  images generated by running PCSSD on mock photon lists that sampled a Gamma PDF for the companion intensities. Left: No background included (same as the second column of Figure 8, left). Middle: Adding an additional constant and uncorrelated 50 photons s−1 pixel−1 background. Right: The 50 photons s−1 pixel−1 background is inserted but then removed before running PCSSD. Any photons that were excluded from the analysis due to their proximity to a background photon (i.e., falling within the 10 μs dead time) are not accounted for, which simulates performing a wavelength cut on MEC data in post-processing.
Paper: The Gliese 86 Binary System: A Warm Jupiter Formed in a Disk Truncated at ≈2 au
Authors: Yunlin Zeng, Timothy D. Brandt, Gongjie Li, Trent J. Dupuy, Yiting Li, G. Mirek Brandt, Jay Farihi, Jonathan Horner, Robert A. Wittenmyer, R. Paul. Butler, Christopher G. Tinney, Bradley D. Carter, Duncan J. Wright, Hugh R. A. Jones, Simon J. O'Toole
Figure caption: The blue and yellow shade is the 3D Roche lobe of Gl 86 B when it was in the post-AGB phase, during which Gl 86 B expanded most. The orange sphere represents the size of Gl 86 B at that time. The figure demonstrates that it is safe to assume that there is no mass transfer, as Gl 86 B is far from touching the surface of the Roche lobe.
Paper: The Gliese 86 Binary System: A Warm Jupiter Formed in a Disk Truncated at ≈2 au
Authors: Yunlin Zeng, Timothy D. Brandt, Gongjie Li, Trent J. Dupuy, Yiting Li, G. Mirek Brandt, Jay Farihi, Jonathan Horner, Robert A. Wittenmyer, R. Paul. Butler, Christopher G. Tinney, Bradley D. Carter, Duncan J. Wright, Hugh R. A. Jones, Simon J. O'Toole
Figure caption: MCMC fitting results of the RV orbit (top), separation and position angle (middle), and proper motions (bottom). The black curve indicates the highest likelihood orbit; the 50 colored curves are randomly selected from the posterior and color-coded by eccentricity. For clarity, the influence of the inner planet Gl 86 Ab has been subtracted from all panels. Data points in all figures are shown with filled circles, while the small lower panels show the residuals of the fit.
Paper: The Gliese 86 Binary System: A Warm Jupiter Formed in a Disk Truncated at ≈2 au
Authors: Yunlin Zeng, Timothy D. Brandt, Gongjie Li, Trent J. Dupuy, Yiting Li, G. Mirek Brandt, Jay Farihi, Jonathan Horner, Robert A. Wittenmyer, R. Paul. Butler, Christopher G. Tinney, Bradley D. Carter, Duncan J. Wright, Hugh R. A. Jones, Simon J. O'Toole
Figure caption: Left: the mass of Gl 86 B vs. star age, assuming different metallicity. Right: the mass loss of Gl 86 B was most rapid during the AGB and post-AGB phases, where it lost about 0.57 M ☉ in the last 0.8 Myr of evolution. However, even during the final stages, mass was lost over several × 104 yr, much longer than the ≈100 yr orbital time of Gl 86 B.
Paper: The Gliese 86 Binary System: A Warm Jupiter Formed in a Disk Truncated at ≈2 au
Authors: Yunlin Zeng, Timothy D. Brandt, Gongjie Li, Trent J. Dupuy, Yiting Li, G. Mirek Brandt, Jay Farihi, Jonathan Horner, Robert A. Wittenmyer, R. Paul. Butler, Christopher G. Tinney, Bradley D. Carter, Duncan J. Wright, Hugh R. A. Jones, Simon J. O'Toole
Figure caption: Same as Figure 1, but for the parameters of the planet Gl 86 Ab. The planet’s short orbital period prevents a constraint on either inclination or position angle; we plot only ﹩m\sin i﹩. The near-total correlation between mass and semimajor axis results from Kepler’s third law.
Paper: The Gliese 86 Binary System: A Warm Jupiter Formed in a Disk Truncated at ≈2 au
Authors: Yunlin Zeng, Timothy D. Brandt, Gongjie Li, Trent J. Dupuy, Yiting Li, G. Mirek Brandt, Jay Farihi, Jonathan Horner, Robert A. Wittenmyer, R. Paul. Butler, Christopher G. Tinney, Bradley D. Carter, Duncan J. Wright, Hugh R. A. Jones, Simon J. O'Toole
Figure caption: The corner plot of parameters of the white dwarf Gl 86 B. Along the diagonal are the marginalized distributions of each parameter. Others are 2D joint posterior distributions of each of two parameters. Most parameters are well constrained, though some are strongly covariant.
Paper: The Gliese 86 Binary System: A Warm Jupiter Formed in a Disk Truncated at ≈2 au
Authors: Yunlin Zeng, Timothy D. Brandt, Gongjie Li, Trent J. Dupuy, Yiting Li, G. Mirek Brandt, Jay Farihi, Jonathan Horner, Robert A. Wittenmyer, R. Paul. Butler, Christopher G. Tinney, Bradley D. Carter, Duncan J. Wright, Hugh R. A. Jones, Simon J. O'Toole
Figure caption: Relative astrometric orbit of Gl 86 AB. The solid black curve indicates the most probable orbit. The colored curves show 50 randomly selected orbits from the posterior probability distribution, with color denoting eccentricity. The star symbol at the origin represents the host star, the dotted line connects the host star to the periapsis, and the dashed line is the line of nodes. The orange circles are the relative astrometry data points.
Paper: The Gliese 86 Binary System: A Warm Jupiter Formed in a Disk Truncated at ≈2 au
Authors: Yunlin Zeng, Timothy D. Brandt, Gongjie Li, Trent J. Dupuy, Yiting Li, G. Mirek Brandt, Jay Farihi, Jonathan Horner, Robert A. Wittenmyer, R. Paul. Butler, Christopher G. Tinney, Bradley D. Carter, Duncan J. Wright, Hugh R. A. Jones, Simon J. O'Toole
Figure caption: Mercury simulation of the orbit of Gl 86 B about Gl 86 A during the period of Gl 86 B’s mass loss. The star symbol at the origin stands for Gl 86 A. When both stars were on the AGB phase, where Gl 86 B mainly started to lose mass, it was at the innermost (yellow) orbit. As it shed mass, the semimajor axis increased, and it gradually spiraled out and ended up as a white dwarf at the outermost (purple) orbit.
Paper: MAGIC: Microlensing Analysis Guided by Intelligent Computation
Authors: Haimeng Zhao, 海萌 赵, Wei Zhu, 伟 祝
Figure caption: The schematic view of MAGIC. The binary microlensing parameters are divided into two groups. We use locator, which is a U-Net architecture, to infer the time-dependent parameters, namely the peak time t 0 and the event timescale t E. The output is used to transform the light curve into a function of τ ≡ (t − t 0)/t E, and estimator, which combines neural controlled differential equation (neural CDE) and mixture density network (MDN), is used to infer the other microlensing parameters. We refer to Sections 3, 4, and 5 for a detailed description of our method.
Paper: MAGIC: Microlensing Analysis Guided by Intelligent Computation
Authors: Haimeng Zhao, 海萌 赵, Wei Zhu, 伟 祝
Figure caption: An illustration of the output mask of locator. The two-dimensional histogram shows the distribution of p values inside the chosen τ window for 16,384 light curves with s < 1. The red dashed lines indicate τ = ±k = ±1/3, which are the boundaries of the input mask in this realization.
Paper: MAGIC: Microlensing Analysis Guided by Intelligent Computation
Authors: Haimeng Zhao, 海萌 赵, Wei Zhu, 伟 祝
Figure caption: All except the lower right panel show comparisons between the input and the optimized parameter values. From the means of the 12 multivariate Gaussians, we apply Nelder & Mead (1965) simplex method and obtain optimized model parameters. The set of parameters closest to the ground truth in the parameter space is used in these comparisons. The lower right panel shows the prediction accuracy of microlensing parameters evaluated on events with various numbers of data points and no gap. Here MAD500,gap is the value of MAD evaluated on events with 500 points and data gap, as incidated in the top left corner of the other panels.
Paper: MAGIC: Microlensing Analysis Guided by Intelligent Computation
Authors: Haimeng Zhao, 海萌 赵, Wei Zhu, 伟 祝
Figure caption: An example event in which the binary caustic-crossing feature is partially missing due to the injected data gap, as shown by the light curve in the top right corner. The red color is for the input model, whereas the blue color is for the prediction given by estimator. The corner plot shows the probability distributions of the four MDN Gaussians with the highest weights, with the colors indicating the relative weights. Contours denote the 1σ boundaries of the multivariate Gaussians with diagonal covariance matrices. As we have chosen axis limits to optimize the overall appearance, not all four contours are visible in every panel. Values and positions of the ground truth and the prediction of estimator on the corner plot are also indicated. Even without the final optimization, the Gaussian distribution with the highest weight gives a fairly good approximation of the input parameters.
Paper: MAGIC: Microlensing Analysis Guided by Intelligent Computation
Authors: Haimeng Zhao, 海萌 赵, Wei Zhu, 伟 祝
Figure caption: An example event for which the raw prediction of estimator yields the largest χ 2 value among all test events. The top panel shows the data and model light curves, and the middle and bottom panels are the corresponding microlensing geometry plots. The raw prediction of estimator is shown in red, and the ground truth is shown in black. We then change one by one the values of α, u 0, and s from the raw prediction to the ground truth values and show the corresponding light curves and lensing geometry plots with different colors.
Paper: MAGIC: Microlensing Analysis Guided by Intelligent Computation
Authors: Haimeng Zhao, 海萌 赵, Wei Zhu, 伟 祝
Figure caption: Comparisons between the input parameter values and the direct output of estimator. Out of the 12 Gaussian means of MDN, we identify the one that is closest to the ground truth in the parameter space and take it as the prediction. The median absolute deviation (MAD) is used to evaluate the agreement, and the RMSE is also provided for reference.
Paper: MAGIC: Microlensing Analysis Guided by Intelligent Computation
Authors: Haimeng Zhao, 海萌 赵, Wei Zhu, 伟 祝
Figure caption: The application of MAGIC to KB190371 and the output. The light curve and the MAGIC predictions are shown in the top right panel. Two solutions, labeled “close” and “wide,” have comparable χ 2 values, and the corresponding microlensing geometries (i.e., caustic curve and source trajectory) are shown below the light-curve plot. The corner plot shows the positions of the optimized solutions as well as the top two Gaussians that correspond to the “close” and “wide” solutions.
Paper: MAGIC: Microlensing Analysis Guided by Intelligent Computation
Authors: Haimeng Zhao, 海萌 赵, Wei Zhu, 伟 祝
Figure caption: Similar to Figure 12 except that the Gaussian with the largest weight is taken as the prediction.
Paper: MAGIC: Microlensing Analysis Guided by Intelligent Computation
Authors: Haimeng Zhao, 海萌 赵, Wei Zhu, 伟 祝
Figure caption: Similar to Figure 17 except that observations from the South Africa site of KMTNet are excluded in this analysis. As a result, the exit portion of the caustic-crossing feature is not covered with observations. Even so, MAGIC is able to identify the two degenerate solutions at high accuracy.
Paper: MAGIC: Microlensing Analysis Guided by Intelligent Computation
Authors: Haimeng Zhao, 海萌 赵, Wei Zhu, 伟 祝
Figure caption: Performance of the joint pipeline. Simulated light curves for locator are used in this test. After the application of locator, the light curve is sent to estimator for the inference of the time-independent parameters.