adsabs/AstroCLIMB huggingface dataset:
Paper: Discovery of 34 Low-mass Comoving Systems Using NOIRLab Source Catalog DR2
Authors: Frank Kiwy, Jacqueline K. Faherty, Aaron Meisner, Adam C. Schneider, J. Davy Kirkpatrick, Marc J. Kuchner, Adam J. Burgasser, Sarah Casewell, Rocio Kiman, Emily Calamari, Christian Aganze, Chih-Chun Hsu, Arttu Sainio, Vinod Thakur
Figure caption: Top left panel: Gaia CMD of the primaries. Top right panel: Pan-STARRS CMD. Bottom left panel: NSC CMD. Bottom right panel: VHS CMD. Five-pointed star symbols: the blue stars represent the white dwarf (WD) primaries, the red stars correspond to the main-sequence (MS) primaries, and the black stars refer to the secondaries. We used the parallax of the primaries to calculate the absolute magnitude of the secondaries.
Paper: Discovery of 34 Low-mass Comoving Systems Using NOIRLab Source Catalog DR2
Authors: Frank Kiwy, Jacqueline K. Faherty, Aaron Meisner, Adam C. Schneider, J. Davy Kirkpatrick, Marc J. Kuchner, Adam J. Burgasser, Sarah Casewell, Rocio Kiman, Emily Calamari, Christian Aganze, Chih-Chun Hsu, Arttu Sainio, Vinod Thakur
Figure caption: unWISE NEO7 vs. LS DR9 images (north up, east to the left). We used VISTA VHS DR6 images for systems #27 and #28 since there is no corresponding LS DR9 imagery. The primaries are in the center of the images, the secondaries are marked by a white dash. White lettering indicates the system number and name followed by the size of the field of view (50″ × 50″, 80″ × 80″, or 120″ × 120″, depending on the angular separation of the system components).
Paper: Discovery of 34 Low-mass Comoving Systems Using NOIRLab Source Catalog DR2
Authors: Frank Kiwy, Jacqueline K. Faherty, Aaron Meisner, Adam C. Schneider, J. Davy Kirkpatrick, Marc J. Kuchner, Adam J. Burgasser, Sarah Casewell, Rocio Kiman, Emily Calamari, Christian Aganze, Chih-Chun Hsu, Arttu Sainio, Vinod Thakur
Figure caption: Secondary mean photometric distance vs. primary parallactic distance.
Paper: Discovery of 34 Low-mass Comoving Systems Using NOIRLab Source Catalog DR2
Authors: Frank Kiwy, Jacqueline K. Faherty, Aaron Meisner, Adam C. Schneider, J. Davy Kirkpatrick, Marc J. Kuchner, Adam J. Burgasser, Sarah Casewell, Rocio Kiman, Emily Calamari, Christian Aganze, Chih-Chun Hsu, Arttu Sainio, Vinod Thakur
Figure caption: Distributions of parallactic distance, angular separation, projected physical separation, total proper motion, and ﹩{\upsilon }_{\tan }﹩ from Tables 1 and 2.
Paper: Incorporating Measurement Error in Astronomical Object Classification
Authors: Sarah Shy, Hyungsuk Tak, Eric D. Feigelson, John D. Timlin, G. Jogesh Babu
Figure caption: Left: the true unobserved data. Center: the observed data without considering measurement error, suitable for standard classification methods. Right: the observed data with error bars whose lengths represent two standard deviations of measurement error uncertainties (i.e., measurement ±1σ), which the proposed framework utilizes to simulate perturbed data sets.
Paper: Incorporating Measurement Error in Astronomical Object Classification
Authors: Sarah Shy, Hyungsuk Tak, Eric D. Feigelson, John D. Timlin, G. Jogesh Babu
Figure caption: Left: for a single observation, a hard classifier outputs a single predicted class label, i.e., the object is predicted to be in that class with probability 1. Center: a soft classifier reports the relative probability of falling into each class. Right: a further softened soft classifier assesses the variability in the class probabilities. The proposed method converts a hard classifier into a soft one (left to center) and further softens soft classifiers (center to right).
Paper: Incorporating Measurement Error in Astronomical Object Classification
Authors: Sarah Shy, Hyungsuk Tak, Eric D. Feigelson, John D. Timlin, G. Jogesh Babu
Figure caption: The proposed framework uses the original observed data to simulate multiple perturbed data sets. Each perturbed data set is independently drawn from the posterior predictive distribution of a Gaussian measurement error model. We refit the classifier to each perturbed data set and calculate any metrics of interest, such as prediction accuracy. This will produce a posterior predictive distribution for each metric, from which we can assess uncertainty by the spread of the distribution. Consequently, the measurement error uncertainty in the original data is propagated through each step in the framework.
Paper: Incorporating Measurement Error in Astronomical Object Classification
Authors: Sarah Shy, Hyungsuk Tak, Eric D. Feigelson, John D. Timlin, G. Jogesh Babu
Figure caption: Two observations in the unlabeled set are superimposed on the 200 observations in the labeled set.
Paper: Incorporating Measurement Error in Astronomical Object Classification
Authors: Sarah Shy, Hyungsuk Tak, Eric D. Feigelson, John D. Timlin, G. Jogesh Babu
Figure caption: The perturbed data sets simulated by a posterior predictive distribution of a measurement error model. The information about the measurement error is used only for simulation, and does not appear in the perturbed data sets. Thus, any traditional classification method can be trained on each perturbed data set independently, and the variation of the multiple fits can be ascribed to the measurement error uncertainty.
Paper: Incorporating Measurement Error in Astronomical Object Classification
Authors: Sarah Shy, Hyungsuk Tak, Eric D. Feigelson, John D. Timlin, G. Jogesh Babu
Figure caption: In a typical classification problem, a classifier is trained and validated on the labeled set, and then used to make predictions on the unlabeled set. In astronomy, the information about measurement error uncertainty is additionally given in the data set, as illustrated in the right panel.
Paper: Incorporating Measurement Error in Astronomical Object Classification
Authors: Sarah Shy, Hyungsuk Tak, Eric D. Feigelson, John D. Timlin, G. Jogesh Babu
Figure caption: Top: linear SVM boundary fit to the observed data without accounting for measurement error, colored by the predicted labels. The probability of being in class 1 is 1 for blue dots and 0 for yellow dots. The red line is the fitted decision boundary of the linear SVM. Bottom: 500 SVM decision boundaries obtained by fitting the SVM on each of the 500 perturbed data sets, are colored in gray. The red line is reproduced from the top panel for a comparison. The estimated probability of belonging to class 1, i.e., ﹩{\hat{p}}_{i,1}﹩, for each object i is denoted by a blue–green–yellow color gradient; darker dots are more likely to belong to class 1. Both the training and test sets are plotted, but each decision boundary was derived using the training set only. The length of each error bar is two standard deviations of measurement error (i.e., measurement ±1σ).
Paper: Incorporating Measurement Error in Astronomical Object Classification
Authors: Sarah Shy, Hyungsuk Tak, Eric D. Feigelson, John D. Timlin, G. Jogesh Babu
Figure caption: The probability estimate of being in Class 1 computed from a one-time implementation of random forest without measurement error (﹩{\hat{p}}_{i1}﹩ for each object i, defined in Equation (5)) is visualized via color gradation in the top panel. The probability estimate calculated by the proposed approach that accounts for measurement error (﹩{\hat{p}}_{i1}^{+}﹩ for each object i, defined in Equation (6)) is visualized via the same color gradation in the bottom panel. Darker dots are more likely to belong to class 1.
Paper: Incorporating Measurement Error in Astronomical Object Classification
Authors: Sarah Shy, Hyungsuk Tak, Eric D. Feigelson, John D. Timlin, G. Jogesh Babu
Figure caption: Posterior predictive distributions of completeness (left) and efficiency (right) obtained by Gaussian perturbation with 500 simulations. The mean and standard deviations are superimposed in each panel. The vertical dashed line indicates the value of completeness or that of efficiency obtained without considering measurement error.
Paper: Characterizing and Mitigating the Impact of Telluric Absorption in Precise Radial Velocities
Authors: Sharon Xuesong Wang, 雪凇 王, Natasha Latouf, Peter Plavchan, Bryson Cale, Cullen Blake, Étienne Artigau, Carey M. Lisse, Jonathan Gagné, Jonathan Crass, Angelle Tanner
Figure caption: Comparison of the line profiles for a CO2 line near 1602 nm for two observatories: Kitt Peak (used in simulated spectra) and Maunakea (used as input to fit the simulated spectra). Both spectra are at airmass = 1. The Maunakea scaled spectrum is the best-fit version when fitting the Kitt Peak profile by scaling the Maunakea line by a power law. The rms of the residual of this fit is 1.2%, which is around the typical value for all lines and smaller than the typical residuals (2%–5%) reported by Ulmer-Moll et al. (2019).
Paper: Characterizing and Mitigating the Impact of Telluric Absorption in Precise Radial Velocities
Authors: Sharon Xuesong Wang, 雪凇 王, Natasha Latouf, Peter Plavchan, Bryson Cale, Cullen Blake, Étienne Artigau, Carey M. Lisse, Jonathan Gagné, Jonathan Crass, Angelle Tanner
Figure caption: These histograms show the distribution of the precipitable water vapor (PWV) and airmass used for the 365 nights in our simulation. The PWV values are drawn from an ensemble of real PWV measurements for Kitt Peak but restricted between 2 and 10 mm, and the airmass values are drawn from real Keck observations of RV standard stars.
Paper: Characterizing and Mitigating the Impact of Telluric Absorption in Precise Radial Velocities
Authors: Sharon Xuesong Wang, 雪凇 王, Natasha Latouf, Peter Plavchan, Bryson Cale, Cullen Blake, Étienne Artigau, Carey M. Lisse, Jonathan Gagné, Jonathan Crass, Angelle Tanner
Figure caption: Flowchart illustrating our simulation methodology, including the generation of synthetic spectra and the RV extraction process using the cross-correlation function (CCF) and the forward-modeling (FM) methods with both correct and incorrect line profiles. The process of RV extraction is repeated each night for 365 nights for each method and line profile, and then they are combined to form a single nightly RV.
Paper: Characterizing and Mitigating the Impact of Telluric Absorption in Precise Radial Velocities
Authors: Sharon Xuesong Wang, 雪凇 王, Natasha Latouf, Peter Plavchan, Bryson Cale, Cullen Blake, Étienne Artigau, Carey M. Lisse, Jonathan Gagné, Jonathan Crass, Angelle Tanner
Figure caption: Qualitative illustration of how the telluric lines “pull” the centroid of stellar absorption lines off from their true Doppler-shifted stellar absorption lines and introduce a bias to the final measured RV (Wright et al. 2013).
Paper: Search for an Alien Message to a Nearby Star
Authors: Michaël Gillon, Artem Burdanov, Jason T. Wright
Figure caption: Communication gain relative to a non-GL emission as a function of distance between the emitter and the Sun (663 au = in-focus), as computed from ray-tracing simulations under the assumptions described in the text.
Paper: Search for an Alien Message to a Nearby Star
Authors: Michaël Gillon, Artem Burdanov, Jason T. Wright
Figure caption: Illustration of off-axis FICDs located closer to the Sun than the SGL.
Paper: Search for an Alien Message to a Nearby Star
Authors: Michaël Gillon, Artem Burdanov, Jason T. Wright
Figure caption: Illustration showing the geometry of the GL communication link assuming that both stars S1 and S2 are used as GLs and the transmitter T is placed closer to S1 and off-axis (relative to the SGL). See text for details.
Paper: Search for an Alien Message to a Nearby Star
Authors: Michaël Gillon, Artem Burdanov, Jason T. Wright
Figure caption: Same as Figure 13, but for a slightly different impact parameter of the light ray, so as to illustrate how the width of the ER is computed.
Paper: Search for an Alien Message to a Nearby Star
Authors: Michaël Gillon, Artem Burdanov, Jason T. Wright
Figure caption: Illustration showing the geometry of the GL communication link assuming that both stars S1 and S2 are used as GLs. See text for details.
Paper: Search for an Alien Message to a Nearby Star
Authors: Michaël Gillon, Artem Burdanov, Jason T. Wright
Figure caption: Evolution of the impact parameter of Earth as seen from the hypothetical FICD around the SPECULOOS-South observations of 2018. The red line corresponds to an impact parameter of 1.1. The green zone corresponds to the SPECULOOS-South observations.
Paper: Search for an Alien Message to a Nearby Star
Authors: Michaël Gillon, Artem Burdanov, Jason T. Wright
Figure caption: Illustration showing the geometry of the GL communication link assuming that only star S2 is used as a GL. See text for details.
Paper: Search for an Alien Message to a Nearby Star
Authors: Michaël Gillon, Artem Burdanov, Jason T. Wright
Figure caption: Illustration showing the geometry of the GL communication link assuming that only star S1 is used as a GL. See text for details.
Paper: Constructing the Milky Way Stellar Halo in the Galactic Center by Direct Orbit Integration
Authors: Chengqun Yang, Ling Zhu, Behzad Tahmasebzadeh, Xiang-Xiang Xue, Chao Liu
Figure caption: Left: the density distribution of 38,457 LAMOST K giants in (R, z) space, the density in the unit of log10(N/pixel) is indicated by the color bar. Right: the distribution of LAMOST K giants in v  ϕ  vs. [Fe/H] space, color-coded by the probability of being a halo star (﹩{{ \mathcal P }}_{\mathrm{halo}}﹩). The blue and red histograms in the top (right) subpanel indicate the v  ϕ  ([Fe/H]) distribution of halo stars (﹩{{ \mathcal P }}_{\mathrm{halo}}﹩ > 0.6) and disk stars (﹩{{ \mathcal P }}_{\mathrm{disk}}﹩ > 0.6), respectively.
Paper: Constructing the Milky Way Stellar Halo in the Galactic Center by Direct Orbit Integration
Authors: Chengqun Yang, Ling Zhu, Behzad Tahmasebzadeh, Xiang-Xiang Xue, Chao Liu
Figure caption: Left: the stellar number density distribution of halo K giants in the (R, z) plane, after the correction of the selection effects, the density in the unit of number per kpc3 is indicated by the color bar. The solid black curves with labeled values are the isodensity contours. The dashed curves are the concentric circles for every 5 kpc. Right: similar to the left panel, but the number density distribution constructed by particles sampled from the stellar orbits integrated in MW potential model M3. The stars observed at large radius but on highly-radial orbits pass through the Galactic center, thus fill in the density distribution of the very inner regions.
Paper: Constructing the Milky Way Stellar Halo in the Galactic Center by Direct Orbit Integration
Authors: Chengqun Yang, Ling Zhu, Behzad Tahmasebzadeh, Xiang-Xiang Xue, Chao Liu
Figure caption: The density profiles from halo K giants (black) and the orbital particles (colored) as a function of r. From left to right, we show the averaged density profile (the dashed curve) across the elevation angles of 0° < θ < 30°, 30° < θ < 60°, 60° < θ < 90°, respectively. The shadowed regions mark the minimum and maximum of the curve across the region of θ. The orbital particles are integrated in seven different MW models of Table 1 (shown in different colors), but they are entirely the same and consistent with the stellar density profiles in the overlapping regions.
Paper: Refining the Masses and Radii of the Star Kepler-33 and its Five Transiting Planets
Authors: James Sikora, Jason Rowe, Daniel Jontof-Hutter, Jack J. Lissauer
Figure caption: Simulated JWST observations of Kepler-33 d assuming M p,d = 4 M ⊕ and T eq = 908 K. Model transmission spectra for one clear (black) and two cloudy atmospheres (blue and green) are shown. The simulated NIRISS and NIRSpec observations (yellow circles and red squares, respectively) are calculated for three transits using the clear atmosphere.
Paper: Refining the Masses and Radii of the Star Kepler-33 and its Five Transiting Planets
Authors: James Sikora, Jason Rowe, Daniel Jontof-Hutter, Jack J. Lissauer
Figure caption: Marginalized posterior distributions of R p/R ⋆ and M p/M ⋆ for planets c, d, e, and f derived from the photodynamics modeling (Section 2).
Paper: Refining the Masses and Radii of the Star Kepler-33 and its Five Transiting Planets
Authors: James Sikora, Jason Rowe, Daniel Jontof-Hutter, Jack J. Lissauer
Figure caption: Same as Figure 10 but for impact parameters (b) and eccentricities (e).
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: Top: the period distribution for stars with P rot > 10 days and low reddening (Av < 0.2). The red dashed lines show the edges of the bins in the bottom plot. The blue solid line (G BP − G RP = 2.45) shows the color of the left bin edge for the first bin without the gap based on the number of stars in the gap for each color bin. Middle: period histograms for stars in 0.1 mag G BP − G RP bins (from 1.45 mag to 3.25 mag). The solid lines show the KDE for each subpopulation. The horizontal lines show the local minimum determined using the KDEs. These lines indicate the locations of the intermediate period gap in each color bin (when there is one). The colors of the histograms and lines indicate the bins with the intermediate period gap (black), and those without (red). Bottom: same as the top plot but plotting the number of stars (blue points) in the gap for each color bin. Empty circles indicate the bins without a noticeable gap. The numbers are determined by calculating how many stars lay within 0.05 dex of log10(P rot) around the local minimuma (gray boxes). The trend suggests the number of stars in the gap stays relatively constant from G BP − G RP = 1.5 mag to 1.7 mag and the gap eventually closes up around G BP − G RP of 2.45 mag.
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–P rot diagram for ZTF stars (black), MEarth stars (blue), Kepler stars with R per > 1.2 × 104 (red), Praesepe periods from Rampalli et al. (2021), and other cluster periods from Curtis et al. (2020) in log period space (top) and in linear period space (bottom). The subplot in the bottom panel shows the dereddened color vs. rotation periods in linear space for ZTF and cluster stars around the fully convective limit. The ZTF period distribution is able to bridge the gap between Kepler and MEarth for the first time. Based on the cluster data, we propose that stars beyond the fully convective boundary directly converge onto the slowly rotating sequence because they do not go through stalled spin-down as they do not have radiative cores.
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: Period measured for the same stars but from separate ZTF fields/CCDs. All measurements have P rot > 1.1 days, maximum LS peak power >0.55, and S/N > 10. The yellow line shows the 1 day systematic alias. Stars in blue are excluded from our catalog (either on the systematic line or do not agree within 20%). We take the period with the highest LS peak power to be the measured period for each star.
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: Period distribution of all vetted rotation periods from this work (top) compared to all periods from Kepler (middle) and to periods with light-curve variability (R per) >1.2 × 104 ppm (bottom). The boxes show the corresponding features described in the text. The period distribution of ZTF matches well with Kepler stars with high variability. The underdensity at P rot ∼2 days results from the systematic removal of stars on the 1 day alias line (yellow line in Figure 5). The underdensity of the distribution for stars right above the period gap (log10(P rot) between 1.35 and 1.6 dex and G BP − G RP between 1.5 and 2.2 dex; red box), if real, can provide insights on the formation theories of the gap.
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: Rossby number, Ro, vs. gyro-kinematic ages for Kepler for stars with G BP − G RP between 1.65 and 1.85 mag in Lu et al. (2021) with P rot > 10 days, colored by G BP − G RP. We picked this color range since the gap is most prominent. The green lines show the gap in constant Ro of 0.47 dex and 0.50 dex. The gray lines show the linear fits, assuming Skumanich spin-down, in every 0.02 mag color bin. We are only fitting the intercepts, b, by using log(Ro) = 0.5 log(age)+b. The fits are done separately for each color bin below and above the gap. The black lines show the average relation from the individual fits. The discontinued jump between the black lines below and above the gap suggests stars could have gone through a phase of rapid spin-down after core–envelope recoupling. However, more rigorous testing is needed to confirm this idea.
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: Measurement of period uncertainty, ΔP. This uncertainty is likely a combination of differential rotation and measurement uncertainty. We calculate ΔP by first selecting all stars with at least two good period measurements (LS peak power >0.55 and S/N > 10) from two different seasons. Stars with season 1 period and season 2 period between a small bin of ﹩{\mathrm{log}}_{10}({P}_{{\rm{rot}}})﹩ are then selected to calculate the log average period, ﹩{\mathrm{log}}_{10}(\langle ﹩                      P rot〉), and ΔP. We do this by taking the mean and standard deviation of these periods in linear space, respectively. We then estimated the the relation by fitting an exponential relation between ΔP and ﹩{\mathrm{log}}_{10}(\langle ﹩                      P rot〉).
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: Receiver operating characteristic (ROC) curve for the three period indicators (black: maximum LS peak power; green: minimum ln(FAP); red: maximum S/N). Since our goal was to minimize the FPR while keeping the TPR high, we used the LS peak power to vet our periods. We accepted all periods that have LS peak powers >0.55 (black star), and this gives us a FPR of 19% and a TPR of 55%. We also used a S/N cut of 10 to ensure the LS peak is significant.
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: Performance of the vetting criteria (maximum LS peak power >0.55 and S/N > 10) for MEarth (left) and Kepler (right). The top row shows the period comparison for the unvetted sample and the bottom row shows that of the vetted sample. Stars are colored by their maximum LS peak powers. The black lines show the 1-to-1 line, and the red lines show the half-period harmonics. The gray lines show the 1 day systematic alias calculated by ∣1/P rot ± 1∣−1. The vetting technique is able to eliminate most disagreeing periods outside of the 1 day alias systematic. The resulting TPR and FPR are 0.46 and 0.15 for MEarth, and 0.21 and 0.01 for Kepler.
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: Period comparison for the same stars between Chen et al. (2020) and this work.