adsabs/AstroCLIMB huggingface dataset:
Paper: Distributions of Wide Binary Stars in Theory and in Gaia Data. I. Generalized Ambartsumian (1937) Approach and the Family of Power-law Distributions of Eccentricity
Authors: Valeri V. Makarov
Figure caption: Distribution concordance metric L1F of Monte Carlo random samples of orbital configurations with the empirical sample from Gaia GR3 data in the motion angle parameter ϑ vs. the assumed power-index ﹩\alpha ^{\prime} ﹩ of the intrinsic eccentricity PDF. Small L1F values indicate a nearly perfect match of the distributions.
Paper: TESS Giants Transiting Giants. VII. A Hot Saturn Orbiting an Oscillating Red Giant Star 
Authors: Nicholas Saunders, Samuel K. Grunblatt, Daniel Huber, J. M. Joel Ong, 加冕 王, Kevin C. Schlaufman, Daniel Hey, Yaguang Li, 亚光 李, R. P. Butler, Jeffrey D. Crane, Steve Shectman, Johanna K. Teske, Samuel N. Quinn, Samuel W. Yee, Rafael Brahm, Trifon Trifonov, Andrés Jordán, Thomas Henning, David K. Sing, Meredith MacGregor, Catherine A. Clark, Colin Littlefield, Sarah Deveny, Steve B. Howell, Emma Page, David Rapetti, Ben Falk, Alan M. Levine, Chelsea X. Huang, Michael B. Lund, George R. Ricker, S. Seager, Joshua N. Winn, Jon M. Jenkins
Figure caption: Amplitude spectrum of TOI-7041 centered on the range of frequencies showing stellar oscillations. The y-axis shows the SNR of the oscillation power at each frequency, calculated by dividing the power spectrum by the estimated background. The orange and blue lines show the identified ℓ = 0 and ℓ = 2 modes, respectively.
Paper: Astronomaly Protege: Discovery through Human-machine Collaboration
Authors: Michelle Lochner, Lawrence Rudnick
Figure caption: Number of sources author M.L. scored 4 or 5 in the evaluation subset when ordered by the predicted score from several algorithms: random ordering (Random), ordering by number of components in an island from PYBDSF (Complexity), ordering by ASTRONOMALY using ellipse-based features with an anomaly detection algorithm and active learning (Ellipses), and two cases of ordering using the BYOL features combined with PROTEGE for active learning (BYOL). The numbers in brackets for BYOL refer to the number of sources used to train the algorithm. The 3500 source subset is excluded due to the curve’s similarity to the full dataset curve. The envelope around the lines indicates the standard deviation from 10 repetitions. As it results in the steepest curve, BYOL combined with PROTEGE outperforms all other methods when sufficient training data is used.
Paper: Astronomaly Protege: Discovery through Human-machine Collaboration
Authors: Michelle Lochner, Lawrence Rudnick
Figure caption: UMAP visualization of the BYOL feature space. Each point represents a distinct source in the evaluation subset. Upper panel: points are colored by their underlying human score, showing that the sources with a score of 1 collect toward the edge of feature space while the sources with scores of 4 and 5 form clusters embedded throughout feature space. Lower panel: the same sources colored by the predicted PROTEGE score, after training on 100 labels selected by the acquisition function. It can be seen that, even with only a few labels, the algorithm learns which regions of feature space to focus on, although naturally a few high human-scoring sources are missed.
Paper: Astronomaly Protege: Discovery through Human-machine Collaboration
Authors: Michelle Lochner, Lawrence Rudnick
Figure caption: Demonstration of the interactive training procedure. In a “query,” a set of objects is shown to the “oracle” (the user), who provides an interest level score for each object (“1” for compact sources or pure noise, to “5” for highly unusual morphology). Images are normalized using asinh scaling with the brightness truncated to 90% of maximum. The active learning algorithm described in Section 6 is then applied, the objects are sorted by acquisition score (which prioritizes which objects will provide the algorithm with the most useful information), and the next set of objects are queried. Within just a few iterations, the algorithm hones in on high human-scoring sources.
Paper: Astronomaly Protege: Discovery through Human-machine Collaboration
Authors: Michelle Lochner, Lawrence Rudnick
Figure caption: Four examples of sources given a high score by PROTEGE and a low score by humans. Upon reinspection, we agree that the sources in (a), (b), and (c) are worth further consideration, as described in the text, but not (d). All images are produced with asinh scaling. To reveal faint structure, the maximum brightness is truncated to 10%, 10%, 90%, and 90% of the maximum for panels (a), (b), (c), and (d), respectively.
Paper: Astronomaly Protege: Discovery through Human-machine Collaboration
Authors: Michelle Lochner, Lawrence Rudnick
Figure caption: Similarity search of feature space for a high human-scoring source for which PROTEGE gave a low score. The source of interest is displayed in the center, with its eight nearest neighbors (by Euclidean distance) around it. These images have not been rescaled and are identical to the input to the feature extraction algorithm. Viewing the central source with asinh scaling reveals the faint structure around the central core, so this source was given a low score purely because of the preprocessing scaling. Nearly all high human-scoring sources that were missed by PROTEGE fall into this category.
Paper: Astronomaly Protege: Discovery through Human-machine Collaboration
Authors: Michelle Lochner, Lawrence Rudnick
Figure caption: Similarity search of feature space for a second high human-scoring source (center) for which PROTEGE gave a low score, showing its eight nearest neighbors. This source is similar to other double core sources, so it is difficult to see how the algorithm could have distinguished between them.
Paper: Astronomaly Protege: Discovery through Human-machine Collaboration
Authors: Michelle Lochner, Lawrence Rudnick
Figure caption: Examples of sources author M.L. scored high while L.R. scored low. The images have been normalized with asinh scaling.
Paper: Astronomaly Protege: Discovery through Human-machine Collaboration
Authors: Michelle Lochner, Lawrence Rudnick
Figure caption: Flowchart of PROTEGE.
Paper: JWST COMPASS: NIRSpec/G395H Transmission Observations of the Super-Earth TOI-776 b
Authors: Lili Alderson, Sarah E. Moran, Nicole L. Wallack, Natasha E. Batalha, Nicholas F. Wogan, Anne Dattilo, Hannah R. Wakeford, Jea Adams Redai, Munazza K. Alam, Artyom Aguichine, Natalie M. Batalha, Anna Gagnebin, Peter Gao, James Kirk, Mercedes López-Morales, Annabella Meech, Johanna Teske, Angie Wolfgang
Figure caption: Top: transmission spectra produced by the ExoTiC-JEDI (left) and Eureka! (right) reductions, with the difference between the two visits shown by the solid line in the lower panel. Across both pipelines, there is a noticeable difference in the morphology between the two visits, particularly due to the transmission spectra of NRS1 in Visit 2 being offset relative to NRS2. Bottom: transmission spectra from Visit 1 (upper panels) and Visit 2 (lower panels) comparing the two different pipelines. ExoTiC-JEDI is shown by the pink circles, and Eureka! is shown by the blue triangles. The difference between the two reductions for each visit is shown by the solid line plotted beneath each transmission spectrum. The pipelines have a median difference of 13 ppm for Visit 1 and 12 ppm for Visit 2 compared to a median transit depth uncertainty of 34 ppm for both Visit 1 and Visit 2.
Paper: Astronomaly Protege: Discovery through Human-machine Collaboration
Authors: Michelle Lochner, Lawrence Rudnick
Figure caption: Loss as a function of epoch for various subsets of the data. The solid lines correspond to the subsets used to evaluate performance in Section 7 while the dashed and dotted lines split either the evaluation subset or full dataset into training and validation sets to check for overfitting. The number of sources in each subset is given in brackets in the legend. It can be seen that the validation loss shows a similar trend to that of the training, indicating no evidence of significant overfitting. Note that the validation loss is slightly lower than the training loss because dropout is disabled for evaluation. Introducing more data naturally improves the loss function.
Paper: Astronomaly Protege: Discovery through Human-machine Collaboration
Authors: Michelle Lochner, Lawrence Rudnick
Figure caption: Number of “interesting” sources (human score of 4 or 5) detected in the top 100 sources with the highest PROTEGE score, after training, as a function of different trade-off parameter values. The results are shown for the evaluation subset only. In this optimization exercise, the features are extracted using 75% of the full dataset for training BYOL with 25% kept for validation. The error bars are the standard deviation from 10 different training-validation set splits. While the trade-off parameter does not significantly affect performance on average, the variance is far lower for values above 0.5.
Paper: JWST COMPASS: NIRSpec/G395H Transmission Observations of the Super-Earth TOI-776 b
Authors: Lili Alderson, Sarah E. Moran, Nicole L. Wallack, Natasha E. Batalha, Nicholas F. Wogan, Anne Dattilo, Hannah R. Wakeford, Jea Adams Redai, Munazza K. Alam, Artyom Aguichine, Natalie M. Batalha, Anna Gagnebin, Peter Gao, James Kirk, Mercedes López-Morales, Annabella Meech, Johanna Teske, Angie Wolfgang
Figure caption: Transit depth precisions achieved by the ExoTiC-JEDI (pink) and Eureka! (blue) reductions compared to predicted values from PandExo simulations for the observation setup. As seen in other low group number studies (e.g., J. Lustig-Yaeger et al. 2023; S. E. Moran et al. 2023; L. Alderson et al. 2024; N. L. Wallack et al. 2024), the observations do not match the precision expected by PandExo. They are on average 1.3×  the prediction, with all values within 1.5×  the PandExo value. We obtain a median transit depth precision in wavelength bins 30 pixels wide (∼0.02 μm, R ∼ 200) of 34 ppm for both reductions of both visits.
Paper: Astronomaly Protege: Discovery through Human-machine Collaboration
Authors: Michelle Lochner, Lawrence Rudnick
Figure caption: PROTEGE predictions vs. author M.L.’s scores for the entire evaluation subset, after training on 100 labels. The data are illustrated using a box and whisker plot, where the median is indicated by a horizontal line in the box, and the boundaries of the box are placed at the quartiles of the distribution, with the whiskers showing the maximum and minimum values barring outliers, given by circles. While the PROTEGE scores do not perfectly match the user scores and have high scatter due to a small training set, they nonetheless correlate well enough to quickly locate sources of interest to the user.
Paper: JWST COMPASS: NIRSpec/G395H Transmission Observations of the Super-Earth TOI-776 b
Authors: Lili Alderson, Sarah E. Moran, Nicole L. Wallack, Natasha E. Batalha, Nicholas F. Wogan, Anne Dattilo, Hannah R. Wakeford, Jea Adams Redai, Munazza K. Alam, Artyom Aguichine, Natalie M. Batalha, Anna Gagnebin, Peter Gao, James Kirk, Mercedes López-Morales, Annabella Meech, Johanna Teske, Angie Wolfgang
Figure caption: The ExoTiC-JEDI and Eureka! white light curves for each detector and visit, with best-fit models and the associated residuals. The alternate colors demonstrate binned light curves and residuals, which are plotted for reference. Histograms of the residuals are shown in the rightmost column.
Paper: JWST COMPASS: NIRSpec/G395H Transmission Observations of the Super-Earth TOI-776 b
Authors: Lili Alderson, Sarah E. Moran, Nicole L. Wallack, Natasha E. Batalha, Nicholas F. Wogan, Anne Dattilo, Hannah R. Wakeford, Jea Adams Redai, Munazza K. Alam, Artyom Aguichine, Natalie M. Batalha, Anna Gagnebin, Peter Gao, James Kirk, Mercedes López-Morales, Annabella Meech, Johanna Teske, Angie Wolfgang
Figure caption: Statistical, nonphysical model fits to the data for Visit 1 (top row) and Visit 2 (bottom) for the ExoTiC-JEDI (left column) and Eureka! (right column) data reductions. Here we show the three highest-likelihood models between the two reductions. Data are shown as gray crosses; each colored line shows a different nonphysical model, with the shading indicating the 1σ (dark) and 3σ (light) regions. For Visit 1, a zero-sloped (i.e., flat) line is strongly preferred by both reductions, while an offset between NRS1 and NRS2 is preferred for Visit 2. Neither Gaussian model is preferred in each visit.
Paper: Astronomaly Protege: Discovery through Human-machine Collaboration
Authors: Michelle Lochner, Lawrence Rudnick
Figure caption: The recall100 as a function of loss (in BYOL), for a series of experiments with the evaluation subset. There is a very broad correlation between them, suggesting that better features result in a better recovery of sources that the user finds “interesting.” At the same time, the scatter shows the limitations of our small dataset.
Paper: Ground-based Reconnaissance Observations of 21 Exoplanet Atmospheres with the Exoplanet Transmission Spectroscopy Imager
Authors: Ryan J. Oelkers, Luke M. Schmidt, Erika Cook, Mary Anne Limbach, D. L. DePoy, J. L. Marshall, Jimmy Ardoin, Mitchell Barry, Evan Batteas, Alexandra Boone, Brant Conway, Silvana Delgado Adrande, John D. Dixon, Enrique Gonzalez-Vega, Alexandra Guajardo, Landon Holcomb, Christian Lambert, Shravan Menon, Divya Mishra, Jacob Purcell, Zachary Reed, Nathan Sala, Noah Siebersma, Nhu Ngoc Ton, Raenessa M. L. Walker, Z. Franklin Wang, Kaitlin Webber
Figure caption: A visual comparison of the transmission spectra measured with ETSI (red data points) and other observatories (black points are bins and gray points are raw data). The figures are ordered from top left to bottom right by decreasing p-value. All comparisons between ETSI and the other observatories return p-values which suggest there is not enough evidence to determine the measurements are statistically dissimilar (p > 0.05). We also find no comparison has more than two points across all 15 bandpasses with a discrepancy more than 3σ.
Paper: Ground-based Reconnaissance Observations of 21 Exoplanet Atmospheres with the Exoplanet Transmission Spectroscopy Imager
Authors: Ryan J. Oelkers, Luke M. Schmidt, Erika Cook, Mary Anne Limbach, D. L. DePoy, J. L. Marshall, Jimmy Ardoin, Mitchell Barry, Evan Batteas, Alexandra Boone, Brant Conway, Silvana Delgado Adrande, John D. Dixon, Enrique Gonzalez-Vega, Alexandra Guajardo, Landon Holcomb, Christian Lambert, Shravan Menon, Divya Mishra, Jacob Purcell, Zachary Reed, Nathan Sala, Noah Siebersma, Nhu Ngoc Ton, Raenessa M. L. Walker, Z. Franklin Wang, Kaitlin Webber
Figure caption: The Pearson (r) correlation matrix for the strengths of various molecular features measured by ETSI and a set of host-system parameters. Generally, we consider any correlation coefficient of ∣r∣ > 0.5 to be a strong relationship.
Paper: Ground-based Reconnaissance Observations of 21 Exoplanet Atmospheres with the Exoplanet Transmission Spectroscopy Imager
Authors: Ryan J. Oelkers, Luke M. Schmidt, Erika Cook, Mary Anne Limbach, D. L. DePoy, J. L. Marshall, Jimmy Ardoin, Mitchell Barry, Evan Batteas, Alexandra Boone, Brant Conway, Silvana Delgado Adrande, John D. Dixon, Enrique Gonzalez-Vega, Alexandra Guajardo, Landon Holcomb, Christian Lambert, Shravan Menon, Divya Mishra, Jacob Purcell, Zachary Reed, Nathan Sala, Noah Siebersma, Nhu Ngoc Ton, Raenessa M. L. Walker, Z. Franklin Wang, Kaitlin Webber
Figure caption: The transmission spectra for each of the observed exoplanets in this work, ranked by their modified TSM (TSMe) from lowest (bottom) to highest (top). TrES-3 b has been plotted in a separate window to improve readability.
Paper: Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors
Authors: Alexandre Adam, Connor Stone, Connor Bottrell, Ronan Legin, Yashar Hezaveh, Laurence Perreaul-Levasseur
Figure caption: An example of posterior samples for the COSMOS (N. Scoville et al. 2007) target located at R.A. = 9h57m56﹩\mathop{.}\limits^{{\rm{s}}}﹩5294, ﹩\mathrm{Decl}.\,=\,{2}^{\circ }2{7}^{{\prime} }37.^{\prime\prime} 963﹩. The top row shows the four exposures jointly used in the likelihood. The bottom rows shows two posterior samples (left) and the corresponding residuals (observation minus posterior sample) for this model. The residuals show that most if not all the signal has been extracted in the posterior samples.
Paper: Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors
Authors: Alexandre Adam, Connor Stone, Connor Bottrell, Ronan Legin, Yashar Hezaveh, Laurence Perreaul-Levasseur
Figure caption: An example of a posterior sample for the SMACS 0723 target located at R.A. = 7h23m20﹩\mathop{.}\limits^{{\rm{s}}}﹩7483, ﹩\mathrm{Decl}.\,=\,-7{3}^{\circ }2{6}^{{\prime} }07.^{\prime\prime} 203﹩, and the corresponding reconstruction of 1 out of 4 exposures with the F105W filter for the WFC3IR camera of HST. The VE SDE and the PROBES prior were used to generate this posterior samples.
Paper: Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors
Authors: Alexandre Adam, Connor Stone, Connor Bottrell, Ronan Legin, Yashar Hezaveh, Laurence Perreaul-Levasseur
Figure caption: In the leftmost column, we show one of the HST exposures in the F160W filter for the target located at R.A. = 7h23m20﹩\mathop{.}\limits^{{\rm{s}}}﹩7483; ﹩\mathrm{decl}.\,=\,-7{3}^{\circ }2{6}^{{\prime} }07.^{\prime\prime} 203﹩ in the SMACS 0723 field used to obtain the drizzled image (second column) and the median image composed of 450 posterior samples using our method (third column). In the rightmost column, we show the corresponding JWST image in the closest available filter to HST’s F160W filter as a reference. Note that neither the JWST image nor the drizzled image were used to perform the inference.
Paper: Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors
Authors: Alexandre Adam, Connor Stone, Connor Bottrell, Ronan Legin, Yashar Hezaveh, Laurence Perreaul-Levasseur
Figure caption: In the first row, leftmost column, we show one of the HST exposures in the F105W filter for the target located at R.A. = 7h23m24﹩\mathop{.}\limits^{{\rm{s}}}﹩5462; ﹩\mathrm{decl}.\,=\,-7{3}^{\circ }2{7}^{{\prime} }20.^{\prime\prime} \,172﹩ in the SMACS 0723 field used to obtain the drizzled image (second column) and the median image composed of 450 posterior samples using our method (third column). In the rightmost column, we show the corresponding JWST image in the closest available filter to HST F105W filter as a reference. Note that neither the JWST image nor the drizzled image were used to perform the inference. The second row reports the corresponding results for the target located at R.A. = 7h23m22﹩\mathop{.}\limits^{{\rm{s}}}﹩0805; ﹩\mathrm{decl}.\,=\,-7{3}^{\circ }2{7}^{{\prime} }24.^{\prime\prime} 575﹩ in the SMACS 0723 field.
Paper: Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors
Authors: Alexandre Adam, Connor Stone, Connor Bottrell, Ronan Legin, Yashar Hezaveh, Laurence Perreaul-Levasseur
Figure caption: An example of two posterior samples for a COSMOS (N. Scoville et al. 2007) target located at R.A. = 9h57m46﹩\mathop{.}\limits^{{\rm{s}}}﹩8867, ﹩\mathrm{decl}.\,=\,{2}^{\circ }2{8}^{{\prime} }22.^{\prime\prime} 735﹩. The top row shows the four exposures jointly used in the likelihood. The middle and bottom rows show two posterior samples and their corresponding residuals against the observation.
Paper: Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors
Authors: Alexandre Adam, Connor Stone, Connor Bottrell, Ronan Legin, Yashar Hezaveh, Laurence Perreaul-Levasseur
Figure caption: An example of two posterior samples for a COSMOS (N. Scoville et al. 2007) target located at R.A. = 9h57m49﹩\mathop{.}\limits^{{\rm{s}}}﹩1102, ﹩\mathrm{decl}.\,=\,{2}^{\circ }2{8}^{{\prime} }19.^{\prime\prime} 430﹩. The top row shows the four exposures jointly used in the likelihood. The middle and bottom rows show two posterior samples and their corresponding residuals against the observation.
Paper: Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors
Authors: Alexandre Adam, Connor Stone, Connor Bottrell, Ronan Legin, Yashar Hezaveh, Laurence Perreaul-Levasseur
Figure caption: Comparison of the posterior samples, median and percentile range, defined as the 84%–16% range, for different SDE hyperparameters and prior for the same target as shown in Figure 3. Although the general shape of the image is recovered across the different hyperparameters, our method is able to recover more details using the VE SDE and the PROBES prior.
Paper: Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors
Authors: Alexandre Adam, Connor Stone, Connor Bottrell, Ronan Legin, Yashar Hezaveh, Laurence Perreaul-Levasseur
Figure caption: Extraction of the column of the linear model implemented as a Pytorch function with the JVP.
Paper: Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors
Authors: Alexandre Adam, Connor Stone, Connor Bottrell, Ronan Legin, Yashar Hezaveh, Laurence Perreaul-Levasseur
Figure caption: Sketched implementation of the SLIC likelihood with the VJP operation using PyTorch (A. Paszke et al. 2019).
Paper: Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors
Authors: Alexandre Adam, Connor Stone, Connor Bottrell, Ronan Legin, Yashar Hezaveh, Laurence Perreaul-Levasseur
Figure caption: Score of the perturbation kernel computed in linear time.
Paper: Refractory and Volatile Species in the UV-to-IR Transmission Spectrum of Ultra-hot Jupiter WASP-178b with HST and JWST
Authors: Joshua D. Lothringer, Katherine A. Bennett, David K. Sing, Brian Kehoe-Seamons, Zafar Rustamkulov, Henrique Reggiani, Kevin C. Schlaufman, Patrick McCreery, Seti Norris, Peter Hauschildt, Ceiligh Cacho-Negrete, Amélie Gressier, Néstor Espinoza, Cyril Gapp, Thomas M. Evans-Soma, Kevin B. Stevenson, Hannah Wakeford, Neale Gibson, Jamie Wilson, Nikolay Nikolov
Figure caption: A comparison of different light-curve fitting methods to the NRS1 data: (1) fixing the limb-darkening coefficients (LDCs, blue), (2) fixed LDCs and with a linear slope in time, and (3) fitted LDCs. All three approaches to fitting the light curve result in data points that are very consistent within the measurement uncertainty of each point, with no discernible systematic trends.
Paper: Refractory and Volatile Species in the UV-to-IR Transmission Spectrum of Ultra-hot Jupiter WASP-178b with HST and JWST
Authors: Joshua D. Lothringer, Katherine A. Bennett, David K. Sing, Brian Kehoe-Seamons, Zafar Rustamkulov, Henrique Reggiani, Kevin C. Schlaufman, Patrick McCreery, Seti Norris, Peter Hauschildt, Ceiligh Cacho-Negrete, Amélie Gressier, Néstor Espinoza, Cyril Gapp, Thomas M. Evans-Soma, Kevin B. Stevenson, Hannah Wakeford, Neale Gibson, Jamie Wilson, Nikolay Nikolov
Figure caption: Same as Figure 13, but for NRS2. As with NRS1, all three approaches to fitting the light curve result in data points that are very consistent within the measurement uncertainty of each point, with no discernible systematic trends.
Paper: Refractory and Volatile Species in the UV-to-IR Transmission Spectrum of Ultra-hot Jupiter WASP-178b with HST and JWST
Authors: Joshua D. Lothringer, Katherine A. Bennett, David K. Sing, Brian Kehoe-Seamons, Zafar Rustamkulov, Henrique Reggiani, Kevin C. Schlaufman, Patrick McCreery, Seti Norris, Peter Hauschildt, Ceiligh Cacho-Negrete, Amélie Gressier, Néstor Espinoza, Cyril Gapp, Thomas M. Evans-Soma, Kevin B. Stevenson, Hannah Wakeford, Neale Gibson, Jamie Wilson, Nikolay Nikolov
Figure caption: Two-dimensional spectroscopic light curves from JWST/NIRSpec G395H/NRS1.