adsabs/AstroCLIMB huggingface dataset:
Paper: Formation of Complex Organic Molecules in Hot Molecular Cores through Nondiffusive Grain-surface and Ice-mantle Chemistry
Authors: Robin T. Garrod, Miwha Jin, Kayla A. Matis, Dylan Jones, Eric R. Willis, Eric Herbst
Figure caption: Left: Net rate of change (arbitrary units) in the abundances of selected oxygen-bearing molecules, summed over all chemical phases, during stages 1 and 2. Data correspond to the final model, using the medium warm-up timescale in stage 2; the vertical dotted line indicates the start of the warm-up phase. Net gain is shown in green, net loss in blue. Vertical dashed lines indicate the onset and end-point of water desorption. Background colors indicate the dust temperature regime; from left to right these are: >10, <10, 10–50, 50–100, 100–200, 200–400 K. The initial dust temperature is ∼14.7 K. Middle: Net rates of change integrated over each temperature range. Positive (formation) and negative (destruction) rates are integrated independently; both are normalized to the total integrated formation rate. Right: The main formation (green) and destruction mechanisms (blue) at points indicated in the left panels (see text).
Paper: Formation of Complex Organic Molecules in Hot Molecular Cores through Nondiffusive Grain-surface and Ice-mantle Chemistry
Authors: Robin T. Garrod, Miwha Jin, Kayla A. Matis, Dylan Jones, Eric R. Willis, Eric Herbst
Figure caption: Net rate of change in the abundances of selected nitrogen-bearing molecules, summed over all chemical phases; description as per Figure 13. Data correspond to the final model, using the medium warm-up timescale in stage 2.
Paper: Formation of Complex Organic Molecules in Hot Molecular Cores through Nondiffusive Grain-surface and Ice-mantle Chemistry
Authors: Robin T. Garrod, Miwha Jin, Kayla A. Matis, Dylan Jones, Eric R. Willis, Eric Herbst
Figure caption: Left: Net rate of change (arbitrary units) in the abundances of selected oxygen-bearing molecules, summed over all chemical phases, during stages 1 and 2. Data correspond to the final model, using the fast warm-up timescale in stage 2; the vertical dotted line indicates the start of the warm-up phase. Net gain is shown in green, net loss in blue. Background colors indicate the dust temperature regime; from left to right these are: >10, <10, 10–50, 50–100, 100–200, 200–400 K. The initial dust temperature is ∼14.7 K. Right: Net rates of change integrated over each temperature range. Positive (formation) and negative (destruction) rates are integrated independently; both are normalized to the total integrated formation rate.
Paper: Formation of Complex Organic Molecules in Hot Molecular Cores through Nondiffusive Grain-surface and Ice-mantle Chemistry
Authors: Robin T. Garrod, Miwha Jin, Kayla A. Matis, Dylan Jones, Eric R. Willis, Eric Herbst
Figure caption: Comparison between the peak gas-phase molecular abundances achieved in the final models (normalized to the models’ peak methanol abundances), ﹩{R}_{\mathrm{mod}}﹩, and observational values of the same quantities (based on column densities), R obs, using data taken from Table 18. Bars indicate the logarithm of ﹩{R}_{\mathrm{mod}}/{R}_{\mathrm{obs}}﹩. The shaded area represents values where the models and observations vary by 1 order of magnitude or less. Bars ending in an arrow indicate values that exceed the bounds of the plots. Blue bars are based on observational upper limits. Panels on the left correspond to the PILS data set, panels on the right to the EMoCA data set.
Paper: Formation of Complex Organic Molecules in Hot Molecular Cores through Nondiffusive Grain-surface and Ice-mantle Chemistry
Authors: Robin T. Garrod, Miwha Jin, Kayla A. Matis, Dylan Jones, Eric R. Willis, Eric Herbst
Figure caption: Description as per Figure 2. Results correspond to model final stage 2, using the slow warm-up timescale.
Paper: Formation of Complex Organic Molecules in Hot Molecular Cores through Nondiffusive Grain-surface and Ice-mantle Chemistry
Authors: Robin T. Garrod, Miwha Jin, Kayla A. Matis, Dylan Jones, Eric R. Willis, Eric Herbst
Figure caption: Switching function for smooth transition between two rates (each of which is a function of arbitrary variable x), as used in the modified-rate treatment to ensure that the finalized rate, R final, never exceeds the lower of the two rates. Higher values of α produce a tighter switchover between the two values.
Paper: Formation of Complex Organic Molecules in Hot Molecular Cores through Nondiffusive Grain-surface and Ice-mantle Chemistry
Authors: Robin T. Garrod, Miwha Jin, Kayla A. Matis, Dylan Jones, Eric R. Willis, Eric Herbst
Figure caption: Comparison between the peak gas-phase molecular abundances achieved in the basic models (normalized to the models’ peak methanol abundances) and observational values of the same quantities; other details as per Figure 15.
Paper: Formation of Complex Organic Molecules in Hot Molecular Cores through Nondiffusive Grain-surface and Ice-mantle Chemistry
Authors: Robin T. Garrod, Miwha Jin, Kayla A. Matis, Dylan Jones, Eric R. Willis, Eric Herbst
Figure caption: Net production rates as a function of time; description as per Figure 13. Data correspond to selected oxygen-bearing molecules in the final model, using the slow warm-up timescale in stage 2.
Paper: Formation of Complex Organic Molecules in Hot Molecular Cores through Nondiffusive Grain-surface and Ice-mantle Chemistry
Authors: Robin T. Garrod, Miwha Jin, Kayla A. Matis, Dylan Jones, Eric R. Willis, Eric Herbst
Figure caption: Net production rates as a function of time; description as per Figure 18. Data correspond to selected nitrogen-bearing molecules in the final model, using the fast warm-up timescale in stage 2.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: Strong lensing metrics as a function of selected observing strategies. Table 2 contains the exact simulation names corresponding to the short names used here. Metrics are transformed using the equations in Table 5 and are taken relative to their values at baseline in order to be directly comparable, with larger values always being better. Metric values at baseline are indicated in parentheses. Select annotations are added to highlight factors driving metric behavior. We find that a higher cadence (increased number of visits) is preferred for strong lensing in terms of maximizing the number of lens systems. Further, a larger survey area helps, but only if the cadence is not impacted significantly. The lensed quasar case suffers less from a lower cadence in comparison to SNe because SNe will fade away in contrast to quasars and therefore the time of detection is more important. For the case of 99% of visits in WFD, the slightly larger long cumulative season length also helps for the science case of SNe and quasars lensed by galaxies. Furthermore, given the redshift distribution of the expected cluster-lensed SNe Ia, the yields are also sensitive to the choice of filters, so the bluer filter distribution negatively affects the number of expected cluster-lensed SNe Ia.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: WL metrics as a function of selected observing strategies. Table 2 contains the exact simulation names corresponding to the short names used here. Metrics are transformed using the equations in Table 5 and are taken relative to their values at baseline in order to be directly comparable, with larger values always being better. Metric values at baseline are indicated in parentheses. Select annotations are added to highlight factors driving metric behavior. Note that the extinction cut E(B − V) < 0.2 is always applied. As shown, the metrics that describe statistical constraining power (“WL + LSS DETF FoM”) and reduction in systematic uncertainties in WL shear (“WL Average visits”) do not necessarily correlate, as the former tends to prefer an increased area while the latter tends to prefer an increased number of visits at each point in the WFD survey on average. Thus, observing strategy changes such as increasing the fraction of time in the WFD survey can increase both metrics, while observing strategy changes that increase area at the expense of decreasing the average number of visits across the survey area will lead to opposing changes in the metrics.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: Static science metrics as a function of selected observing strategies. Table 2 contains the exact simulation names corresponding to the short names used here. Metrics are transformed using the equations in Table 5 and are taken relative to their values at baseline in order to be directly comparable, with larger values always being better. Metric values at baseline are indicated in parentheses. Select annotations are added to highlight factors driving metric behavior. Larger area simulations clearly indicate the usual area vs. depth trade-off, while increasing extragalactic visits improves all metrics. The efficiency lost with 2 × 15 s exposures is clear, and we note that bluer filter distributions decrease all metrics, which are evaluated on the i and z bands.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: LSS metrics as a function of selected observing strategies. Table 2 contains the exact simulation names corresponding to the short names used here. Metrics are transformed using the equations in Table 5 and are taken relative to their values at baseline in order to be directly comparable, with larger values always being better. Metric values at baseline are indicated in parentheses. Select annotations are added to highlight factors driving metric behavior. These metrics are largely driven by area, and are improved by strategies with a larger footprint. As noted in the text, all of the strategies generate galactic samples for which shot noise is small, making even 10% changes in galaxy number unimportant for cosmological constraints and placing more emphasis on the LSS systematics FoM and the WL+LSS 3 × 2 pt FoM of Figure 3.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: SN metrics as a function of selected observing strategies. Table 2 contains the exact simulation names corresponding to the short names used here. Metrics are transformed using the equations in Table 5 and are taken relative to their values at baseline in order to be directly comparable, with larger values always being better. Metric values at baseline are indicated in parentheses. Select annotations are added to highlight factors driving metric behavior. As described in Section 5.1.1, ﹩{z}_{\mathrm{lim}}﹩ corresponds to the redshift beyond which SNe no longer pass light-curve requirements. “med” and “faint” refer to a sample of typical and faint SNe, respectively. SN metrics in general are decreased by any loss in depth (and hence cadence) in the extragalactic part of the WFD footprint.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: Photometric redshift metrics as a function of selected observing strategies. Table 2 contains the exact simulation names corresponding to the short names used here. Metrics are transformed using the equations in Table 5 and are taken relative to their values at baseline in order to be directly comparable, with larger values always being better. Metric values at baseline are indicated in parentheses. Select annotations are added to highlight factors driving metric behavior. As described in Section 3.2, the low-z bin is (0.6–1.0) and the high-z bin is (1.8–2.2). The most significant degradation to photo-z metrics comes from reducing the depth in the extragalactic part of the footprint, losing efficiency from 2 × 15 s exposures and redistributing visits to bluer bands.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: Layout of the SEGUE-1 and SEGUE-2 spectroscopy in Galactic coordinates in an Aitoff projection. The red circles are at the locations of the 7 deg2 spectroscopic fields that comprise the SEGUE-1 and SEGUE-2 surveys. These are not to scale but show the SEGUE-2 field layout strategy of small overlapping groups of SEGUE-1 and SEGUE-2 fields to allow repeat observations of SEGUE-2 and SEGUE-1 targets. Each field has 640 spectroscopic targets. The orange stripes trace the original (contiguous) SDSS imaging survey area in the north Galactic sky. The blue stripes are the individual 2.°5 wide stripes of SDSS imaging data in the south Galactic cap, through the Galactic plane, and along known stellar streams that were taken in the SDSS and SDSS-II surveys. All the SEGUE-1 and SEGUE-2 spectroscopic targets were selected from the SDSS imaging data. The grayscale background is the Schlegel et al. (1998) dust extinction map.
Paper: The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence
Authors: Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés-Alcázar, Leander Thiele, Romeel Dave, Desika Narayanan, Andrina Nicola, Yin Li, Pablo Villanueva-Domingo, Benjamin Wandelt, David N. Spergel, Rachel S. Somerville, Jose Manuel Zorrilla Matilla, Faizan G. Mohammad, Sultan Hassan, Helen Shao, Digvijay Wadekar, Michael Eickenberg, Kaze W. K. Wong, Gabriella Contardo, Yongseok Jo, Emily Moser, Erwin T. Lau, Luis Fernando Machado Poletti Valle, Lucia A. Perez, Daisuke Nagai, Nicholas Battaglia, Mark Vogelsberger
Figure caption: We show 10 examples of 2D maps for each of the 13 different fields present in the IllustrisTNG simulations. Each image has a different value of the cosmological parameters, Ωm and σ 8, and astrophysical parameters ﹩{A}_{\mathrm{SN}1}﹩, ﹩{A}_{\mathrm{SN}2}﹩, A AGN1, and A AGN2. The different columns represent the same region for the different fields. Each image has a physical size of ﹩25\times 25\,{({h}^{-1}\,\mathrm{Mpc})}^{2}﹩.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: Run of [Fe/H] as a function of (g − r)0 (left panels) and average signal-to-noise ratio 〈S/N〉 per angstrom (right panels) for member stars of open and global clusters. The Gaussian mean (μ) and standard deviation (σ) of the SEGUE SSPP parameter values are listed in the right panels. [Fe/H]Lit indicates the literature value used for our comparison and plotted as a solid line, while our derived value is the dashed line.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: SEGUE vs. APOGEE ﹩\mathrm{log}\,g﹩ comparison. Because ASPCAP DR14 did not provide calibrated values for dwarf stars, there are few stars in the sample with SSPP ﹩\mathrm{log}\,g﹩ > 4, and those must have large differences between ASPCAP and the SSPP to make it into the sample. Therefore, we focus our attention on the comparison with the subgiant and giant ﹩\mathrm{log}\,g﹩ measurements, where the mean offset is small. The SSPP ﹩\mathrm{log}\,g﹩ values are useful for separating giants from dwarfs but not as accurate as the estimates from the higher-resolution APOGEE data. See also the discussion of Figure 9.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: Summary of SEGUE SSPP cluster sample [Fe/H] measurements compared with literature values. The literature values are as given in the left panels of Figure 11. The SSPP-measured mean [Fe/H] values for the SEGUE cluster sample are given in the right panels of Figure 11. The vertical error bars are the size of the standard deviation of the [Fe/H] measurements for the SEGUE sample, also shown in the right panels. There is no systematic trend in the comparison over the entire metallicity range of the cluster sample.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: SEGUE vs. APOGEE [Fe/H] comparison. Dwarfs are shown as small magenta points, giants larger points color-coded by T eff. The comparison sample for giants spans a large range of [Fe/H]] and T eff over which the offset is –0.06 and the scatter 0.15 dex. The only dwarf stars common between the surveys are metal rich and suggest that SSPP underestimates [Fe/H] for solar-metallicity dwarfs; see discussion of Figure 9.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: (g − r)0 vs. (u − g)0 diagram illustrating the effect of ubercalibration on the targeting of rare stars such as M giants in the halo. Black points show the (pre-DR8) photometry used in SEGUE-2 target selection; open blue points show the DR12 (ubercalibrated) photometry. It can be seen that many of the targets had extreme colors due to errors in the pre-DR8 photometry. Spectroscopically confirmed M giants are shown in magenta, and the M-giant selection box is outlined with a magenta line.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Orbital period error based on fitting literature data only vs. orbital period error derived in our work. The gray area encapsulates the region between 0.5 times the identity line and 1.5 times the identity line. The identity line is shown in blue.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Top panel: Timing residuals of WASP-19 b. Blue points are based on previously reported transit times drawn from the literature. Red points are based on TESS data. Bottom panel: close-up view of the TESS data points.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Top panel: Timing residuals of XO-3 b. Blue points are based on previously reported transit times drawn from the literature. Red points are based on TESS data. Bottom panel: close-up view of the TESS data points.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Top panel: Timing residuals of WASP-99 b. The blue point is based on a previously reported transit time drawn from the literature. Red points are based on TESS data. Bottom panel: close-up view of the TESS data points.