adsabs/AstroCLIMB huggingface dataset:
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: RV errors induced by telluric lines for each order in our simulation as a function of wavelength for various RV extraction methods. Each point in the top two plots shows the rms value of the RVs extracted from one spectral order across 365 nights. The top left plot illustrates how using a wrong line profile in the telluric model introduces additional errors. The top right plot illustrates how well the forward-modeling method outperforms the CCF+division method in the NIR. RVs extracted using the forward-modeling method with the correct telluric line profile give the correct answers (0 m s−1) across all orders (thus not plotted here). The bottom plot is a smoothed version of the top plots for clarity (using a moving average with a bin size of five orders), with the normalized solar (orange) and telluric (blue) spectra plotted at the bottom (scaled arbitrarily, just to show where the lines are).
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: Extracted RVs for each night as a function of airmass, PWV, and BC for three different methods of RV extraction (left: no correction; middle: CCF+division with a wrong telluric line profile; right: forward modeling with a wrong telluric line profile). Each RV data point is a weighted average of the RVs reported by all orders in the entire wavelength range used in our simulation (350 nm–2.5 μm). The bottom panels are color coded by the PWV values in millimeters. The weights are a combination of photon noise (assuming SNR = 100 per pixel) and RV rms induced by tellurics, as described in Section 2 and as used for data presented in Table 1. Water absorption clearly plays an important role in the final RVs, as shown by the strong correlation between RVs and PWVs. Similar to the right-side plots in Figure 6, the extracted RVs have strong temporal structures caused by changes in the separation between telluric lines and stellar lines in wavelength space due to the Earth’s barycentric motion.
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: Example spectral orders and their corresponding RVs vs. the barycentric correction (BC; relative velocity to the star due to the Earth’s barycentric motion) when tellurics are ignored in the RV extraction. The plots on the left are the solar spectrum (blue) and the telluric spectrum (orange) for each order, with the telluric absorption on for the top plot (order #28) amplified by 10 times for clarity. The plots on the right illustrate how telluric lines of different depths affect the RVs to a different degree, and the RV biases caused by tellurics are highly structured in the temporal space due to the Earth’s barycentric motion. The RV errors in the bottom right NIR plot are a thousand times larger than the errors in the upper right blue optical plot because the solar lines are much weaker and the telluric lines are much stronger in the NIR.
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: Demonstration of how convolution does not distribute over multiplication (difference between the gray and red lines). The residuals are amplified by a factor of 10 and shifted up arbitrarily for clarity. As shown here, dividing a telluric spectrum (convolved with the spectral PSF, “IP”) from an observed stellar × telluric spectrum will introduce errors. Such a division, commonly used as a treatment for tellurics when performing CCF to extract RVs (e.g., Artigau et al. 2014), will not result in a totally clean stellar spectrum at the percent level vs. convolution with the spectral PSF.
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: RV signals vs. time, as measured by the visible arm (upper panel) and NIR arm (lower panel) of an EarthFinder equivalent (EFE) spectrograph from the ground, extracted with four different telluric mitigation methods, assuming SNR = 400 per pixel for R = 120,000. The RV signal of an Earth analog is plotted as the purple line (semi-amplitude = 9 cm s−1). See Section 3.4 for more detail.
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: Periodograms for the RVs plotted in Figure 10, with the same RV data repeated for three years in the calculations of these periodograms to reduce the effects caused by the finite time baseline. The power in these Lomb–Scargle periodograms is unnormalized with the unit of (m s−1)2. The period of one year and some of its harmonics are labeled on top of the corresponding peaks. The power for the Earth’s signal was computed with 1 cm s−1 white noise added, and in the top plot it is scaled down by a factor of 1000 to show its position, while it is barely visible in the bottom plot, highlighting the challenge in the NIR. See Section 3.4 for more detail.
Paper: Characterizing and Mitigating Telluric Absorption in Precise Radial Velocities. II. A Study of an M2-type Star
Authors: Natasha Latouf, Sharon Xuesong Wang, Bryson Cale, Peter Plavchan
Figure caption: Top: fluxes with arbitrary offsets of the Kurucz solar model, the BT-Settl M-dwarf model (Allard et al. 2012a, 2012b), and the normalized telluric spectra gathered from TAPAS (Bertaux et al. 2014), shown in blue, orange, and green, respectively, plotted as a function of wavelength in nanometers. This plot ranges from 350 to 850 nm. There is more spectral information content in the M-dwarf red-optical spectrum, where there are more telluric lines. By contrast, the solar spectrum has more information content in the optical, with fewer telluric lines. Middle: the same as the top panel, but from 850 to 1650 nm. Bottom: the same as the other two panels, but from 1650 to 2500 nm.
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: Telluric-induced RV errors for each photometric band. The horizontal lines mark the photon-limited precision in each band (for SNR = 100 per pixel) and the wavelength range of each band. Each vertical line marks the standard deviation in the RVs (lower end of line; same as in Table 2) and the range of the RV systematics (95% percentile range; upper end of line) from each of the four simulation methods listed in Table 2 (plotted in groups of four vertical lines within the wavelength range of each band).
Paper: Adaptive Optics Imaging Can Break the Central Caustic Cusp Approach Degeneracy in High-magnification Microlensing Events
Authors: Sean K. Terry, David P. Bennett, Aparna Bhattacharya, Naoki Koshimoto, Jean-Philippe Beaulieu, Joshua W. Blackman, Ian A. Bond, Andrew A. Cole, Jessica R. Lu, Jean Baptiste Marquette, Clément Ranc, Natalia Rektsini, Aikaterini Vandorou
Figure caption: Upper left panel: coadded sum of 24 60 s OSIRIS Kp-band images from 2021. Upper right panel: close-up of blended source and lens stars. Lower left panel: single-star PSF residual clearly showing a signal from the blend. Lower right panel: two-star PSF residual showing a smooth subtraction. The color bar represents the intensity (counts) in the bottom panel residual images.
Paper: Adaptive Optics Imaging Can Break the Central Caustic Cusp Approach Degeneracy in High-magnification Microlensing Events
Authors: Sean K. Terry, David P. Bennett, Aparna Bhattacharya, Naoki Koshimoto, Jean-Philippe Beaulieu, Joshua W. Blackman, Ian A. Bond, Andrew A. Cole, Jessica R. Lu, Jean Baptiste Marquette, Clément Ranc, Natalia Rektsini, Aikaterini Vandorou
Figure caption: The best-fit MCMC contours (68.3%, 99.5%, and 99.7%) for the source and lens positions are shown (in black) overplotted on the 0.″2 × 0.″2 Ks-band image from 2019 (left) and Kp-band image from 2021 (right). The color bar refers to the pixel intensity. The measured separations and uncertainties in both epochs are given in Table 3. These multi-epoch data confirm that the lens and source are separating from each other at a rate of 4.20 ± 0.21 mas yr−1 in the heliocentric reference frame.
Paper: Adaptive Optics Imaging Can Break the Central Caustic Cusp Approach Degeneracy in High-magnification Microlensing Events
Authors: Sean K. Terry, David P. Bennett, Aparna Bhattacharya, Naoki Koshimoto, Jean-Philippe Beaulieu, Joshua W. Blackman, Ian A. Bond, Andrew A. Cole, Jessica R. Lu, Jean Baptiste Marquette, Clément Ranc, Natalia Rektsini, Aikaterini Vandorou
Figure caption: Probability distribution for μ rel derived using a Galactic model from Koshimoto et al. (2021a; blue, where dark shades are the central 68% of the distribution, and light shades are the central 95% of the distribution). The posterior distribution from the direct measurement with Keck is shown in red. The orange hatched region indicates the predicted μ rel from the best-fit planetary solution, and the black solid line with arrow shows the 2σ upper limit on μ rel from the best-fit stellar binary solution.
Paper: Adaptive Optics Imaging Can Break the Central Caustic Cusp Approach Degeneracy in High-magnification Microlensing Events
Authors: Sean K. Terry, David P. Bennett, Aparna Bhattacharya, Naoki Koshimoto, Jean-Philippe Beaulieu, Joshua W. Blackman, Ian A. Bond, Andrew A. Cole, Jessica R. Lu, Jean Baptiste Marquette, Clément Ranc, Natalia Rektsini, Aikaterini Vandorou
Figure caption: Probability distribution for the north and east components of lens–source relative proper motion (μ rel) using the Galactic model from Koshimoto et al. (2021a). The lens positions (NNW and SSE) are plotted in black and given by the relative motion of the two stars detected in the 2021 OSIRIS data. This implies that the NNW star is >3× more likely to be the lens than the SSE star.
Paper: Adaptive Optics Imaging Can Break the Central Caustic Cusp Approach Degeneracy in High-magnification Microlensing Events
Authors: Sean K. Terry, David P. Bennett, Aparna Bhattacharya, Naoki Koshimoto, Jean-Philippe Beaulieu, Joshua W. Blackman, Ian A. Bond, Andrew A. Cole, Jessica R. Lu, Jean Baptiste Marquette, Clément Ranc, Natalia Rektsini, Aikaterini Vandorou
Figure caption: The OGLE-2011-BLG-0950 photometry with the updated best-fit light-curve model (second column of Table 1, the stellar binary with s < 1). The middle panel shows an enlarged view of the peak, and the bottom panel shows the residuals to the best-fit stellar binary solution.
Paper: Adaptive Optics Imaging Can Break the Central Caustic Cusp Approach Degeneracy in High-magnification Microlensing Events
Authors: Sean K. Terry, David P. Bennett, Aparna Bhattacharya, Naoki Koshimoto, Jean-Philippe Beaulieu, Joshua W. Blackman, Ian A. Bond, Andrew A. Cole, Jessica R. Lu, Jean Baptiste Marquette, Clément Ranc, Natalia Rektsini, Aikaterini Vandorou
Figure caption: Left: three-star MCMC distributions for the positions of the source star and luminous lens component (black contours), as well as the dark/faint lens component (blue/white contours). Right: posterior probability distribution for the K-band magnitude of the dark/faint lens component. The central 68.3% of the distribution is shown in dark blue, and the remaining central 95.4% of the distribution is in light blue.
Paper: Adaptive Optics Imaging Can Break the Central Caustic Cusp Approach Degeneracy in High-magnification Microlensing Events
Authors: Sean K. Terry, David P. Bennett, Aparna Bhattacharya, Naoki Koshimoto, Jean-Philippe Beaulieu, Joshua W. Blackman, Ian A. Bond, Andrew A. Cole, Jessica R. Lu, Jean Baptiste Marquette, Clément Ranc, Natalia Rektsini, Aikaterini Vandorou
Figure caption: Central caustic for the best planetary (left) and binary (right) models. The source size and trajectory are denoted by the green circle and red solid line, 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: Kinematics of the MW stellar halo derived from the orbital particles integrated in the seven MW models and shown in different colors. The panels from left to right indicate the kinematics of the stellar halo from different elevation angular θ. Top: the mean Galactocentric azimuthal velocity v  ϕ  varies with r, and the colored dashed lines shown as M1 to M7 correspond to the seven MW potential models, M5 in orange is the one with the lowest pattern speed of −35 km s−1 kpc−1, while M6 in cyan is the one with the highest pattern speed of −45 km s−1 kpc−1. The horizontal dashed line indicates zero velocity. Middle: the velocity dispersions in spherical coordinates (σ  r , σ  θ , σ  ϕ ) as a function of r. The velocity dispersions are indicated by different line styles, as shown in the legend. Bottom: the velocity anisotropy parameter β as a function of r. The velocity dispersion and velocity anisotropy profiles obtained from the orbital particles integrated in the seven different MW models are identical. While the bar pattern speed affects the rotation of the halo stars in the inner ∼5 kpc.
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: Top: the surface brightness of halo interlopers toward the Galactic center derived from the particles integrated from our sample, shown in the Galactic coordinates (l,b). The black circles labeled L1 to L5 indicate the location of five ARGOS target fields in Lucey et al. (2021). Bottom: comparison of star number/luminosity as a function of metallicity between the halo interlopers expected from orbit integration of our sample (blue curves) and the stars directly detected in the Galactic center (Lucey et al. 2021; black curves) in the five fields L1-L5. The first row shows the number of stars directly. The second row shows the stellar luminosity of the general halo populations normalized from the detected RGBs+RCs+HBs, and K-giant halo interlopers, respectively. The luminosity of halo interlopers is almost the same as stars directly detected with [Fe/H] < −1.5 in L1-L5.
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: Top: the density profile of the stellar halo along the semimajor axis r ellip, the black bars indicate the results fitted from single stars, and the red bars are the results fitted from the orbital particles integrated in M3. The errors are derived from the MCMC process but are very small. The red dashed–dotted lines indicate the double-broken power-law fitting to the red bars, with the power-law coefficients α labeled. The colored lines indicate the density profiles from previous results of Xue et al. (2015; yellow), Das et al. (2016; green), and Xu et al. (2018; blue), and the dashed lines are the models’ inward extrapolation. Bottom: the flattening q of the stellar halo along the semimajor axis, symbols are the same as the top panel. The stellar halo becomes flattered from r ≳ 30 kpc to the inner regions, our results indicate that it keeps q ≳ 0.5 in the inner 5 kpc, unlike the extrapolations of the models from previous work (dashed lines).
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 metallicity distribution of stellar halo constructed by the orbital particles integrated in the Milky Way model M3. Left: the metallicity map of stellar halo in (R, z) space. The inner region is slightly more metal rich than the outer region. Right: the metallicity distribution function from different slices of r, which are labeled in the upper right corner. The distributions are similar in all bins peaking at [Fe/H] = −1.3 to −1.4. While there is an excess of relative metal-rich stars in the bin with r < 10 kpc.
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 surface density of halo K giants on bar-resonant orbits shown in the corotating frame of the bar. The left and right panels display the density distributions from the a1−a2 sequences and the other eight sequences b1−b8, respectively. The bar-resonant orbits are calculated from the MW model of M3. The black dashed oval indicates the Galactic bar. There is no clear bar feature constructed by a1−a2 aligned with the Galactic bar, but a bar-like structure perpendicular to the Galactic bar constructed by b1−b8 as shown in the right panel.
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 frequency map of (Ω − Ω b , κ) for ∼100,000 orbits shown as red dots (10 orbits for each star). The orbits in this map are integrated in the MW model M3. There are 10 bar-resonant orbits, shown by blue dots, labeled a1, a2, b1, ..., b8, and their slopes are indicated after the labels.
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 comparison between our distance and StarHorse distance d SH, the dashed line indicates a 1:1 ratio, and the black contour includes 90% of the stars. StarHorse failed to give a proper estimation for a large fraction of stars at d > 20 kpc, for which they just give zero values.
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: absolute magnitude vs. color for stars from Dartmouth isochrones. Giant stars with color range of 1.0 < G BP − G RP < 1.6 are shown in red. Right: the relation between total luminosity and number of RGB stars per 0.1 color bin.
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 surface density of halo K giants on the bar-resonant orbits shown in the corotating frame of the bar. The orbits are calculated in six different MW models as labeled in the upper right corner (M1, M2, M4, M5, M6, M7).
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: Stellar parameter marginalized posterior distributions generated by fitting Kepler-33's ﹩{T}_{\mathrm{eff}}^{\mathrm{CKS}}﹩ and [Fe/H]CKS (Fulton & Petigura 2018) along with ﹩{R}_{\star }^{\mathrm{EDR}3}﹩ and ﹩{\rho }_{\star }^{\mathrm{tr}}﹩ using the MIST grid of evolutionary models (Choi et al. 2016). Blue lines indicate each distribution’s mode corresponding to the adopted value. The Hertzsprung–Russell Diagram (inset, top right) shows Kepler-33's position (blue open circle) along with the interpolated MIST evolutionary track associated with the adopted parameters (solid black). Black circles indicate the zero-age and terminal-age main sequence (ZAMS and TAMS) along with intermediary points labeled according to the age in Gyr. Dashed lines correspond to tracks computed for the 1σ [Fe/H] limits of 0.10 and 0.18.
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: Colored circles show the masses and radii for Kepler-33 e and f derived using the adopted stellar parameters, where the color corresponds to each planet’s insolation flux (S) (bottom panel). The 1σ upper mass limit of Kepler-33 c and d are denoted by the arrows. Gray squares/arrows indicate the masses and radii reported by Hadden & Lithwick (2016). The colored dashed–dotted lines are the best-fitting mass–radius relations for planets e and f derived using the models published by Lopez & Fortney (2014). The solid black and dotted blue lines show the two core mass–radius relations used in this work (Lopez & Fortney 2014; Zeng et al. 2019). The top panel shows the marginalized posterior distributions for the masses of planets c, d, e, and f.
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: Top: O−C diagram showing the predicted TTVs from BJD ≈ 2456400 (April 2013) to BJD ≈ 2462600 (April 2030). Colors correspond to low-mass (<2 M ⊕, yellow) and high-mass (>2 M ⊕, green) solutions for planet d. Bottom: estimated differences between the predicted TTVs for planets c, d, e, and f if the mass of planet d is <2 M ⊕ (TTVlow) or >2 M ⊕ (TTVhigh). Vertical dashed lines indicate transits of planet e that are observable from Palomar Observatory.
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: Stellar bulk density posterior distributions computed from the transit modeling (solid black) and from fitting the observed stellar parameters to the MIST grid of evolution models (Choi et al. 2016): the dotted–dashed red distribution is obtained using the CKS T eff, [Fe/H], and ﹩{R}_{\star }^{\mathrm{CKS}}﹩ values (Fulton & Petigura 2018), while the dashed blue distribution is obtained using the CKS T eff and [Fe/H] values along with the newly derived ﹩{R}_{\star }^{\mathrm{EDR}3}﹩ computed using the corrected Gaia EDR3 parallax.
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: Orbital periods vs. radii of Kepler-33's five planets. The size of each circle increases with decreasing density (i.e., larger circles are puffier planets); open circles correspond to upper ρ limits, blue circles have well-constrained ρ, while no ρ constraints were derived for the black circle (planet b). Kepler-79 is shown for comparison (Jontof-Hutter et al. 2014).
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: Comparison between the best-fitting transit models (solid lines) and the observed Kepler DR25 measurements (black points; blue points are binned values). The panels are ordered from the largest to the smallest planets (from top to bottom: d, f, e, c, b). For each panel, the TTV associated with each transit has been removed along with the contribution from every other planet.