adsabs/AstroCLIMB huggingface dataset:
Paper: Resolved SPLASH Chemodynamics in Andromeda’s PHAT Stellar Halo and Disk: On the Nature of the Inner Halo along the Major Axis
Authors: Ivanna Escala, Amanda C. N. Quirk, Puragra Guhathakurta, Karoline M. Gilbert, J. Leigh Wojno, Lara Cullinane, Benjamin F. Williams, Julianne Dalcanton
Figure caption: Photometric metallicity vs. heliocentric velocity for the RGB stars in SPLASH (Section 5.1). We omit showing the [Fe/H]phot and v helio measurement uncertainties for clarity (with typical values of 0.03 and 5–10 km s−1, respectively). From left to right, the stars are separated into halo, mixed, and disk populations. We show a histogram in the higher-density region of the disk population (bins 25 km s−1 and 0.1). The halo population is concentrated at v helio ≲ −200 km s−1, owing to the high likelihood that stars with −200 km s−1 ≲ v helio ≲ 0 km s−1 belong to the dominant disk component (Table 1). Most disk stars are concentrated around [Fe/H]phot ∼ −0.10 and v helio ∼ −100 km s−1, although a metal-poor tail is also present. The mixed population is dominated by stars with disk-like [Fe/H]phot, but lower v helio ∼ −200 km s−1. The halo shows evidence of a population with disk-like [Fe/H]phot at v helio ∼ −280 km s−1, and another that is kinematically hotter and more metal-poor.
Paper: Resolved SPLASH Chemodynamics in Andromeda’s PHAT Stellar Halo and Disk: On the Nature of the Inner Halo along the Major Axis
Authors: Ivanna Escala, Amanda C. N. Quirk, Puragra Guhathakurta, Karoline M. Gilbert, J. Leigh Wojno, Lara Cullinane, Benjamin F. Williams, Julianne Dalcanton
Figure caption: Top: MDFs (left) and velocity distributions (right) for M31's stellar halo along the minor axis (gray histograms; Gilbert et al. 2012, 2014), the major axis (black outlined histograms; this work; Dorman et al. 2012, 2013, 2015), and for the NE shelf (brown hatched histograms; Escala et al. 2022). Bottom: photometric metallicity vs. heliocentric velocity for the minor-axis halo (left), major-axis halo (middle), and NE shelf (right). See Section 6.2 for the selection of the minor-axis halo and NE shelf samples and Section 5.1 for the major-axis halo. The outlined points (bottom left) are stars in the innermost minor-axis field, f109. The systemic velocity of M31 (−300 km s−1) is shown as a dotted line. We assumed 4 Gyr isochrones to compare the [Fe/H]phot between the three populations, though the mean stellar ages of the minor-axis halo and NE shelf are significantly older (Section 6.2). The black arrows in the bottom panels show the shift in [Fe/H]phot ( −0.26) when assuming 12 Gyr isochrones. Regardless of the assumed stellar age, the [Fe/H]phot–v helio distribution of the major-axis halo is distinct from those of the minor-axis halo and NE shelf. In particular, the pronounced cluster of stars in the major-axis halo at [Fe/H]phot ∼ − 0.10 and v helio ∼ −280 km s−1 is absent from the other populations (top, blue arrows; bottom, blue contours; Section 5.1)
Paper: Resolved SPLASH Chemodynamics in Andromeda’s PHAT Stellar Halo and Disk: On the Nature of the Inner Halo along the Major Axis
Authors: Ivanna Escala, Amanda C. N. Quirk, Puragra Guhathakurta, Karoline M. Gilbert, J. Leigh Wojno, Lara Cullinane, Benjamin F. Williams, Julianne Dalcanton
Figure caption: Heliocentric velocity shifted by M31's systemic velocity vs. projected M31-centric radius for the major-axis halo (black circles; this work) compared against model predictions for the NE shelf tidal debris (red points; Fardal et al. 2007; Escala et al. 2022) at the SPLASH data locations (Figure 1). Note that the model predicts M31 itself, not the NE shelf, as dominating the stars this region (Section 6.2). The metal-rich group of stars concentrated near v helio − v M31 ∼ 20 km s−1 (blue circles; p mr > 0.75; Section 5.2) is inconsistent with the predictions for the NE shelf, which should instead be most visible at v helio − v M31 ≲−200 km s−1 in the SPLASH disk region (Dorman et al. 2012).
Paper: Resolved SPLASH Chemodynamics in Andromeda’s PHAT Stellar Halo and Disk: On the Nature of the Inner Halo along the Major Axis
Authors: Ivanna Escala, Amanda C. N. Quirk, Puragra Guhathakurta, Karoline M. Gilbert, J. Leigh Wojno, Lara Cullinane, Benjamin F. Williams, Julianne Dalcanton
Figure caption: AD with respect to H I (Quirk et al. 2019) vs. heliocentric velocity for SPLASH RGB stars (Section 5.2). We show the stars that are located off (left) and on (right) the major axis, where the AD for the off-axis stars exhibits more scatter, due to geometrical effects. Each star is color-coded by its disk probability. The dotted line is M31's systemic velocity. The orange points are the median ADs, in 50 km s−1 v helio bins. The variation in AD is much larger between different stellar types than within the RGB population (Quirk et al. 2019). Despite this, a clear increase in AD is visible between the disk (p disk > 0.75) and halo (p disk < 0.25) RGB populations. The metal-rich group of halo stars (v helio ∼ −280 km s−1, [Fe/H]phot ∼ −0.10; Figure 10) lags the gaseous disk to a similar extent as the rest of the halo population.
Paper: Resolved SPLASH Chemodynamics in Andromeda’s PHAT Stellar Halo and Disk: On the Nature of the Inner Halo along the Major Axis
Authors: Ivanna Escala, Amanda C. N. Quirk, Puragra Guhathakurta, Karoline M. Gilbert, J. Leigh Wojno, Lara Cullinane, Benjamin F. Williams, Julianne Dalcanton
Figure caption: [Fe/H]phot,init distributions for all the RGB stars (gray filled histogram) and the RGB stars that are located in low-extinction regions of the disk (black open histogram). We measured [Fe/H]phot,init using 4 Gyr PARSEC RGB isochrones on the foreground reddening–corrected optical CMD (Section 4). We defined low-extinction regions using f red × A  V,Dal < 0.25 (Gregersen et al. 2015). The median [Fe/H]phot,init for all (low-extinction) RGB stars is shown as the gray (black) arrow, where [Fe/H]phot,init,med =  −0.18 (−0.21). This implies that most of the RGB stars in SPLASH are located in front of the dust layer and are not significantly reddened.
Paper: Resolved SPLASH Chemodynamics in Andromeda’s PHAT Stellar Halo and Disk: On the Nature of the Inner Halo along the Major Axis
Authors: Ivanna Escala, Amanda C. N. Quirk, Puragra Guhathakurta, Karoline M. Gilbert, J. Leigh Wojno, Lara Cullinane, Benjamin F. Williams, Julianne Dalcanton
Figure caption: MDFs of the RGB stars (black outlined, gray filled histograms; Section 5.1), corrected for all sources of dust extinction ([Fe/H]phot; Section 4.2). We separate the stars into halo (left; p disk < 0.25; Section 3.2.2), mixed (middle; 0.25 < p disk <0.75), and disk (right; p disk > 0.75) populations. The bin size is 0.10, where the median δ[Fe/H]phot is 0.03. The median metallicity for each population is indicated by the arrows. For the halo, mixed, and disk populations, the median [Fe/H]phot are  −0.46,  −0.16, and  −0.19, respectively (Table 2). The dotted histogram shows the resulting halo (disk) MDF in the most extreme case of contamination by metal-rich disk (metal-poor halo) interlopers (Appendix D). The blue shading defines the uncertainty region between the fiducial (solid) and contamination-corrected (dotted) MDFs. The MDFs for the disk and mixed populations are similar. The halo population is more metal-poor, but still has metal-rich stars (Section 5.1).
Paper: Resolved SPLASH Chemodynamics in Andromeda’s PHAT Stellar Halo and Disk: On the Nature of the Inner Halo along the Major Axis
Authors: Ivanna Escala, Amanda C. N. Quirk, Puragra Guhathakurta, Karoline M. Gilbert, J. Leigh Wojno, Lara Cullinane, Benjamin F. Williams, Julianne Dalcanton
Figure caption: Left: the fraction of expected MW contaminants (Equation (6)) in the M31 giant star region of the CMD, defined using the CMD membership criterion from Section 3.1. We show 4 Gyr PARSEC RGB isochrones (gray lines) for reference, as in Figure 2. The most contaminated portion of the CMD corresponds to relatively blue stars above the TRGB. Right: the number density of the M31 giant stars in SPLASH, classified using the CMD criterion (i.e., without EWNa measurements; Section 3.1). There are few SPLASH stars in the contaminated portion of the CMD (see also Figure 2). We expect ∼0.2% of the SPLASH stars to be MW contaminants, based on CMD information alone.
Paper: Resolved SPLASH Chemodynamics in Andromeda’s PHAT Stellar Halo and Disk: On the Nature of the Inner Halo along the Major Axis
Authors: Ivanna Escala, Amanda C. N. Quirk, Puragra Guhathakurta, Karoline M. Gilbert, J. Leigh Wojno, Lara Cullinane, Benjamin F. Williams, Julianne Dalcanton
Figure caption: Foreground reddening–corrected (F475W0, F814W0) CMDs for the SPLASH RGB stars. The CMDs are color-coded by (left) the difference between the photometric metallicity computed with ([Fe/H]phot; Section 4.2) and without ([Fe/H]phot,init; Section 4.1) taking into account the reddening caused by the dust in M31's disk and (right) the spread in [Fe/H]phot, as quantified by the average of the 16th (﹩{\sigma }_{[\mathrm{Fe}/{\rm{H}}]}^{-}﹩) and 84th (﹩{\sigma }_{[\mathrm{Fe}/{\rm{H}}]}^{+}﹩) percentile errors of the [Fe/H]phot distribution. We adopt [Fe/H]phot for stars with (﹩{\sigma }_{[\mathrm{Fe}/{\rm{H}}]}^{-}+{\sigma }_{[\mathrm{Fe}/{\rm{H}}]}^{-})/2\lt 0.03﹩, and we exclude all other stars from the following analysis. We find that the median [Fe/H]phot − [Fe/H]phot,init =  −0.04, when accounting for both their CMD and spatial positions (see Figure 7).
Paper: Resolved SPLASH Chemodynamics in Andromeda’s PHAT Stellar Halo and Disk: On the Nature of the Inner Halo along the Major Axis
Authors: Ivanna Escala, Amanda C. N. Quirk, Puragra Guhathakurta, Karoline M. Gilbert, J. Leigh Wojno, Lara Cullinane, Benjamin F. Williams, Julianne Dalcanton
Figure caption: [Fe/H]phot as a function of the deprojected radius in the disk plane (Rdisk) for the SPLASH RGB stars (Section 5.3). The points are color-coded by disk probability. We determined metallicity gradients by fitting to all the stars in the disk (p disk > 0.75; solid line) and halo (p disk < 0.25; dashed line) populations. We also show the median [Fe/H]phot in radial bins for the disk (outlined circles) and the halo (outlined squares). We find a gradient in the disk (−0.018 dex kpc−1) and a weakly negative gradient in the halo.
Paper: The Roasting Marshmallows Program with IGRINS on Gemini South I: Composition and Climate of the Ultrahot Jupiter WASP-18 b
Authors: Matteo Brogi, Vanessa Emeka-Okafor, Michael R. Line, Siddharth Gandhi, Lorenzo Pino, Eliza M.-R. Kempton, Emily Rauscher, Vivien Parmentier, Jacob L. Bean, Gregory N. Mace, Nicolas B. Cowan, Evgenya Shkolnik, Joost P. Wardenier, Megan Mansfield, Luis Welbanks, Peter Smith, Jonathan J. Fortney, Jayne L. Birkby, Joseph A. Zalesky, Lisa Dang, Jennifer Patience, Jean-Michel Désert
Figure caption: Corner plot for the six parameters of the grid retrieval including a scaling factor. The red dots mark the grid points corresponding to each of the computed 1D-RCTE models. The same grid points are marked for each parameter as solid red lines in the marginalized posteriors. It is apparent that the grid points are sampled finely enough to resolve the posterior shape. See Section 5.2 for details.
Paper: The Roasting Marshmallows Program with IGRINS on Gemini South I: Composition and Climate of the Ultrahot Jupiter WASP-18 b
Authors: Matteo Brogi, Vanessa Emeka-Okafor, Michael R. Line, Siddharth Gandhi, Lorenzo Pino, Eliza M.-R. Kempton, Emily Rauscher, Vivien Parmentier, Jacob L. Bean, Gregory N. Mace, Nicolas B. Cowan, Evgenya Shkolnik, Joost P. Wardenier, Megan Mansfield, Luis Welbanks, Peter Smith, Jonathan J. Fortney, Jayne L. Birkby, Joseph A. Zalesky, Lisa Dang, Jennifer Patience, Jean-Michel Désert
Figure caption: Corner plots for the 15 parameters of the retrieval including only the second half of the spectra (labeled as “2nd half” in Table 2). See Section 4.3 for details.
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption:  Astronet-Triage-v2 neural network architecture.
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Six example visual representations used for human labeling with labels in red. The different figures within each representation were made to mirror the information described in Section 3. Each image was individually labeled by at least three individual vetters. Conflicting labels were discussed and resolved each week.
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Distribution of labels across our data set (see Section 2.3 for descriptions of each type). As described in Section 2.4, some TCEs were assigned fractional B and J labels, so these counts have been rounded to the nearest integer.
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Scatterplot of transit duration vs. orbital period for our data set. TCEs with E labels are shown in blue. Red lines mark 13.7 and 27.4, the orbital period and twice the orbital period of TESS.
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Scatterplot of transit depth vs. orbital period for our data set. TCEs with E labels are shown in blue. Red lines mark 13.7 and 27.4, the orbital period and twice the orbital period of TESS.
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Scatterplot of planet radii vs. orbital period for our data set. TCEs with E labels are shown in blue. Red lines mark 13.7 and 27.4, the orbital period and twice the orbital period of TESS.
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Structure of a convolutional neural network tower. Each convolution tower has one to four blocks. Each block has one to four layers.
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Structure of the fully connected tower.
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Sky map showing the locations of the 24,926 TCEs presented here (black starred data points) compared to the coverage of each TESS Prime Mission sector (colored data points). The black and red labels are the Prime Mission sector numbers in the southern and northern ecliptic hemispheres, respectively. Note that we also include 2588 TCEs from the 1st Extended Mission, for which sector coverage is not shown here. The under- and overdensities of TCEs are due to the selection criteria as described in the text.
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Distribution of T mag across our data set. Both Y1 and Y2 portions of the data set focused on the brightest TCEs, while Y3 added TCEs more uniformly across magnitudes. More details on TCE selection can be found in Section 2.1.
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Precision vs. recall for 1315 TCEs selected from Sector 33 of the 1st Extended Mission. Since Astronet-Triage (Yu et al. 2019) only distinguished between transit-like and nontransit-like, it gives high scores to TCEs we either consider to have E or S labels. For a more direct comparison to Astronet-Triage-v2, we choose to ignore all S-labeled TCEs when calculating precision and recall. We see that across all levels of recall, Astronet-Triage-v2 provides higher precision even when trained only on Primary Mission data taken during Y1 or Y2. Although the Y3 data set bears the most resemblance to the S33 evaluation set here, the size of the Y3 data set is only ∼2500, so the Y3-trained model does not quite reach the performance of the other models.
Paper: TESS Discovery of Twin Planets near 2:1 Resonance around Early M Dwarf TOI 4342
Authors: Evan Tey, Chelsea X. Huang, Michelle Kunimoto, Andrew Vanderburg, Avi Shporer, Samuel N. Quinn, George Zhou, Karen A. Collins, Kevin I. Collins, Eric L. N. Jensen, Richard P. Schwarz, Ramotholo Sefako, Tianjun Gan, Elise Furlan, Crystal L. Gnilka, Steve B. Howell, Kathryn V. Lester, Carl Ziegler, César Briceño, Nicholas Law, Andrew W. Mann, George R. Ricker, Roland K. Vanderspek, David W. Latham, S. Seager, Jon M. Jenkins, Joshua N. Winn, Douglas A. Caldwell, David Charbonneau, Christopher J. Burke, Zahra Essack
Figure caption: Images of the field surrounding TOI 4342 in 1976 (left), 1992 (top right), and 1995 (bottom right). In all images, the orange circle shows the 2021.0 location of TOI 4342 and the blue circle shows the location on J2000. Both circles have radii of 2″. Nearby stars in the TIC are shown in green with marker size corresponding to brightness. In red is a 10″ scale (∼half a TESS pixel).
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Top: recall as a function of cutoff threshold between this work and Astronet-Triage (Yu et al. 2019). For Astronet-Triage-v2, we choose to focus on just E scores even though some TOIs are true S labels. Bottom: Astronet-Triage-v2 recall as a function of cutoff threshold when the data set is separated into planets (P; validated, confirmed, and known planets) and planet candidates (PCs; TOIs that are not validated, confirmed, or known planets, and were also not identified as false positives with follow-up observations).
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Example of incorrect BLS estimation. The detected period is close, but when the light curve contains a large number of folds, the error compounds and leads to a blurring of the transit view. This is due to QLP searching the light curve with an undersampled BLS frequency grid (necessary due to the computational time needed to run BLS on a large number of targets each sector), as discussed in M. Kunimoto et al. (2023, in preparation).
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Top: distribution of E score between this work and Astronet-Triage (Yu et al. 2019) on the whole TOI data set. Bottom: distribution of E scores from this work when the data set is separated into planets (P; validated, confirmed, and known planets) and planet candidates (PCs; TOIs that are not validated, confirmed, or known planets, and were also not identified as false positives with follow-up observations).
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Example of incorrect BLS estimation. Although the phase and period are close, the transit duration is too small, causing the transit to be clipped by the detrending process.
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Example of borderline pattern. The true label for this example is “E,” but the folded light curve appears very similar to a “B.”
Paper: Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations
Authors: Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager
Figure caption: Example of borderline pattern. The very low signal-to-noise ratio of the transit signal is easily mistaken for a “J.”
Paper: TESS Discovery of Twin Planets near 2:1 Resonance around Early M Dwarf TOI 4342
Authors: Evan Tey, Chelsea X. Huang, Michelle Kunimoto, Andrew Vanderburg, Avi Shporer, Samuel N. Quinn, George Zhou, Karen A. Collins, Kevin I. Collins, Eric L. N. Jensen, Richard P. Schwarz, Ramotholo Sefako, Tianjun Gan, Elise Furlan, Crystal L. Gnilka, Steve B. Howell, Kathryn V. Lester, Carl Ziegler, César Briceño, Nicholas Law, Andrew W. Mann, George R. Ricker, Roland K. Vanderspek, David W. Latham, S. Seager, Jon M. Jenkins, Joshua N. Winn, Douglas A. Caldwell, David Charbonneau, Christopher J. Burke, Zahra Essack
Figure caption: Observed minus calculated transit times for all observed transits across TESS and SG1 data. Observed transit times were modeled with exoplanet's TTVOrbit (Section 4.3) and the expected transit times were linearly propagated from the best-fit ephemerides (Section 3.2.1). Based on the current data, we see no evidence for significant deviations from expected transit times. With expected TTV amplitudes of only ∼5 minutes, future observations could help could pinpoint TTV amplitudes.