adsabs/AstroCLIMB huggingface dataset:
Paper: Photometric Mapping of Carbonaceous/Siliceous Dust and Water Ice in the ISM with JWST: Applications to the Dense Sightlines
Authors: Burcu Günay, Karl D. Gordon, Joshua E. G. Peek, Marjorie Decleir, Dries Van De Putte, Kirill Tchernyshyov, Michael G. Burton
Figure caption: The maps show coordinates for each data distribution of the 3.0 μm water ice, the 3.4 μm aliphatic hydrocarbon, and the 10.0 μm silicate optical depth values in the scene (modeled FOV). The color of the dots (as indicated by the color bar) represents the optical depth values reported in the literature (E. L. Gibb et al. 2004). The subregions (tiles) are also indicated with dotted lines.
Paper: Binary Analysis and Period Study of the Long-period, High-mass-ratio Contact Binary KIC 7766185
Authors: Conor M. Larsen, Andrej Prša
Figure caption: (a) Model light curve plotted on the Kepler observations. (b) Model radial velocity curve along with the radial velocity measurements. (c) Mesh plot graphically displaying the system face on. (d) Model multicolor light curve along with the Gaia observations. The three light curves in (d) are normalized to the median of the G-mag measurements.
Paper: Binary Analysis and Period Study of the Long-period, High-mass-ratio Contact Binary KIC 7766185
Authors: Conor M. Larsen, Andrej Prša
Figure caption: (a) Amplitudes of the primary eclipse for each pixel in the Kepler pipeline aperture for Quarter 8. The black star is the target KIC 7766185. The two red stars on the left are the faint Gaia objects. The white pixels were not included in the Kepler pipeline aperture for Quarter 8. (b) Amplitudes of the primary eclipse for each pixel in the TESS pipeline aperture for Sector 15. The two red stars are the same objects in (a) and the gray stars are additional nearby objects. The white pixels were not included in the TESS pipeline aperture for Sector 15.
Paper: Binary Analysis and Period Study of the Long-period, High-mass-ratio Contact Binary KIC 7766185
Authors: Conor M. Larsen, Andrej Prša
Figure caption: Phase-folded light curve of KIC 7766185 from Kepler (black) and TESS Sector 55 (red). The light curves are normalized to their median values.
Paper: Photometric Mapping of Carbonaceous/Siliceous Dust and Water Ice in the ISM with JWST: Applications to the Dense Sightlines
Authors: Burcu Günay, Karl D. Gordon, Joshua E. G. Peek, Marjorie Decleir, Dries Van De Putte, Kirill Tchernyshyov, Michael G. Burton
Figure caption: The photometric optical depths, spectroscopic, and reference optical depths at 3.0 μm, 3.35 μm, and 10.0 μm are measured using optimal spectral models and compared to investigate methodological biases. The polynomial equations (Table 7) for calibrating methodological biases due to filter use (Pps(x), the correlation between τp and τs) and total biases (Pp0(x), correlation between τp and τ0, including those arising from limited resolution) are obtained based on these comparisons. The gray dotted line represents equal τp and τs values.
Paper: Binary Analysis and Period Study of the Long-period, High-mass-ratio Contact Binary KIC 7766185
Authors: Conor M. Larsen, Andrej Prša
Figure caption: The Hβ line for all seven spectra. The spectra are ordered by phase with an offset in normalized flux. The spectra were smoothed with the Savitzky–Golay filter to make both lines more apparent. We used a third-order filter with a window length of 15.
Paper: Binary Analysis and Period Study of the Long-period, High-mass-ratio Contact Binary KIC 7766185
Authors: Conor M. Larsen, Andrej Prša
Figure caption: Normalized broadening function (black) computed with SAPHIRES along with Gaussian fits.
Paper: easyspec: An Open-source Python Package for Long-slit Spectroscopy
Authors: Raniere de Menezes, Francesco Massaro, Michela Negro, Claudia M. Raiteri, Harold Peña-Herazo, Jose A. Acosta-Pulido
Figure caption: Detection (top) and assignment (bottom) of the lines done by easyspec. In the top panel, all lines with more than 5σ with respect to the local background are detected. In the bottom panel, the line fitting gives us the average redshift zav of the six lines analyzed and its standard deviation. In this panel, we also see the over-plotted Hydrogen, Oxygen, and Neon lines from the easyspec database. It is clear that some weak lines, like [Ne V] and [O I] are lost below the adopted significance threshold.
Paper: easyspec: An Open-source Python Package for Long-slit Spectroscopy
Authors: Raniere de Menezes, Francesco Massaro, Michela Negro, Claudia M. Raiteri, Harold Peña-Herazo, Jose A. Acosta-Pulido
Figure caption: Non-calibrated spectra for G4Jy 1709 (top) and the Ar, Ne+Hg, Kr lamp (bottom). The numbers over the lines represent the pixel in the x-axis where the peak of each line is found. These lines are strategically selected by easyspec, such that they are relatively strong and far apart from each other.
Paper: easyspec: An Open-source Python Package for Long-slit Spectroscopy
Authors: Raniere de Menezes, Francesco Massaro, Michela Negro, Claudia M. Raiteri, Harold Peña-Herazo, Jose A. Acosta-Pulido
Figure caption: Target and standard star calibrated for atmospheric extinction (top and middle panels, respectively), and flux-calibrated target spectrum (bottom panel). The effect of atmospheric extinction is more relevant toward bluer wavelengths. We also see the presence of a relatively strong telluric line at λ ≈ 7600 Å.
Paper: easyspec: An Open-source Python Package for Long-slit Spectroscopy
Authors: Raniere de Menezes, Francesco Massaro, Michela Negro, Claudia M. Raiteri, Harold Peña-Herazo, Jose A. Acosta-Pulido
Figure caption: Lorentzian profile fit to the Hβ line with the MCMC method. The fit is done in the observed frame, although the results are available in both the observed and rest frames.
Paper: easyspec: An Open-source Python Package for Long-slit Spectroscopy
Authors: Raniere de Menezes, Francesco Massaro, Michela Negro, Claudia M. Raiteri, Harold Peña-Herazo, Jose A. Acosta-Pulido
Figure caption: Wavelength solution (top) and residuals (bottom) for the spectrum of G4Jy 1709.
Paper: easyspec: An Open-source Python Package for Long-slit Spectroscopy
Authors: Raniere de Menezes, Francesco Massaro, Michela Negro, Claudia M. Raiteri, Harold Peña-Herazo, Jose A. Acosta-Pulido
Figure caption: Comparison between the spectral data reduction performed with easyspec and IRAF, showing an excellent agreement between the two data reductions.
Paper: The HUSTLE Program: The UV to Near-IR Transmission Spectrum of the Hot Jupiter KELT-7b
Authors: C. Gascón, M. López-Morales, R. J. MacDonald, J. K. Barstow, V. A. Boehm, H. R. Wakeford, M. K. Alam, L. Alderson, N. E. Batalha, C. E. Fairman, D. Grant, N. K. Lewis, M. S. Marley, S. E. Moran, K. Ohno, G. Anglada-Escudé, I. Ribas
Figure caption: KELT-7b atmospheric retrieval posterior distributions of a subset of the parameters retrieved with POSEIDON (purple) and NEMESISPY (blue). We show constraints on the CO2 and H− abundance, the cloud top pressure, the temperature and covering fraction of the stellar heterogeneities, and the temperature of the photosphere. While POSEIDON uses a much tighter prior on T phot than NEMESISPY, resulting in a sharp distribution, the results are nevertheless consistent.
Paper: The HUSTLE Program: The UV to Near-IR Transmission Spectrum of the Hot Jupiter KELT-7b
Authors: C. Gascón, M. López-Morales, R. J. MacDonald, J. K. Barstow, V. A. Boehm, H. R. Wakeford, M. K. Alam, L. Alderson, N. E. Batalha, C. E. Fairman, D. Grant, N. K. Lewis, M. S. Marley, S. E. Moran, K. Ohno, G. Anglada-Escudé, I. Ribas
Figure caption: Top: raw +1 (left) and −1 (right) order white-light curve obtained with Hazelnut. Bottom: Systematics corrected +1 (left) and −1 (right) order white-light curve. The narrow panel below shows the transit fit residuals, with the dashed lines indicating the standard deviation of the residuals.
Paper: The HUSTLE Program: The UV to Near-IR Transmission Spectrum of the Hot Jupiter KELT-7b
Authors: C. Gascón, M. López-Morales, R. J. MacDonald, J. K. Barstow, V. A. Boehm, H. R. Wakeford, M. K. Alam, L. Alderson, N. E. Batalha, C. E. Fairman, D. Grant, N. K. Lewis, M. S. Marley, S. E. Moran, K. Ohno, G. Anglada-Escudé, I. Ribas
Figure caption: Atmospheric retrievals of KELT-7b’s transmission spectrum conducted with NEMESISPY including the TESS data point (gray). For comparison, the results without the TESS data are also plotted (blue). The top panel shows the median retrieved spectrum. The histograms on the lower panels show the posterior probability distributions of a subset of the parameters retrieved with NEMESISPY.
Paper: The HUSTLE Program: The UV to Near-IR Transmission Spectrum of the Hot Jupiter KELT-7b
Authors: C. Gascón, M. López-Morales, R. J. MacDonald, J. K. Barstow, V. A. Boehm, H. R. Wakeford, M. K. Alam, L. Alderson, N. E. Batalha, C. E. Fairman, D. Grant, N. K. Lewis, M. S. Marley, S. E. Moran, K. Ohno, G. Anglada-Escudé, I. Ribas
Figure caption: +1 order spectroscopic light curves from 4005 to 7705 Å obtained with the lluvia pipeline. The left panel shows the raw light curves, the middle panel shows the systematics corrected and model light curves, and the right panel shows the residuals from the corresponding fit. Light curves are offset vertically by an arbitrary constant for clarity.
Paper: The HUSTLE Program: The UV to Near-IR Transmission Spectrum of the Hot Jupiter KELT-7b
Authors: C. Gascón, M. López-Morales, R. J. MacDonald, J. K. Barstow, V. A. Boehm, H. R. Wakeford, M. K. Alam, L. Alderson, N. E. Batalha, C. E. Fairman, D. Grant, N. K. Lewis, M. S. Marley, S. E. Moran, K. Ohno, G. Anglada-Escudé, I. Ribas
Figure caption: +1 order spectroscopic light curves from 2005 to 3905 Å obtained with the lluvia pipeline. The left panel shows the raw light curves, the middle panel shows the systematics corrected and model light curves, and the right panel shows the residuals from the corresponding fit. Light curves are offset vertically by an arbitrary constant for clarity.
Paper: The HUSTLE Program: The UV to Near-IR Transmission Spectrum of the Hot Jupiter KELT-7b
Authors: C. Gascón, M. López-Morales, R. J. MacDonald, J. K. Barstow, V. A. Boehm, H. R. Wakeford, M. K. Alam, L. Alderson, N. E. Batalha, C. E. Fairman, D. Grant, N. K. Lewis, M. S. Marley, S. E. Moran, K. Ohno, G. Anglada-Escudé, I. Ribas
Figure caption: Transmission spectrum of KELT-7b compared to the WFC3/UVIS G280 UV–optical transmission spectra of WASP-178b (J. D. Lothringer et al. 2022) and HAT-P-41b (H. R. Wakeford et al. 2020), and the STIS E230M, G430L, and G750L transmission spectra of WASP-79b (A. D. Rathcke et al. 2021; A. Gressier et al. 2023) and WASP-121b (D. K. Sing et al. 2019).
Paper: The HUSTLE Program: The UV to Near-IR Transmission Spectrum of the Hot Jupiter KELT-7b
Authors: C. Gascón, M. López-Morales, R. J. MacDonald, J. K. Barstow, V. A. Boehm, H. R. Wakeford, M. K. Alam, L. Alderson, N. E. Batalha, C. E. Fairman, D. Grant, N. K. Lewis, M. S. Marley, S. E. Moran, K. Ohno, G. Anglada-Escudé, I. Ribas
Figure caption: +1 order spectroscopic light curves from 2000 to 7800 Å obtained with the Hazelnut pipeline. The first and third columns show the systematics corrected and fitted model light curves, and the second and fourth columns show the corresponding residuals. Light curves are offset vertically by an arbitrary constant for clarity.
Paper: The HUSTLE Program: The UV to Near-IR Transmission Spectrum of the Hot Jupiter KELT-7b
Authors: C. Gascón, M. López-Morales, R. J. MacDonald, J. K. Barstow, V. A. Boehm, H. R. Wakeford, M. K. Alam, L. Alderson, N. E. Batalha, C. E. Fairman, D. Grant, N. K. Lewis, M. S. Marley, S. E. Moran, K. Ohno, G. Anglada-Escudé, I. Ribas
Figure caption: Atmospheric retrievals of KELT-7b’s transmission spectrum conducted with POSEIDON without including the WFC3/UVIS G280 data (gray). For comparison, the results with the UVIS data are also plotted (purple). The top panel shows the median retrieved spectrum. The histograms on the lower panels show the posterior probability distributions of a subset of the parameters retrieved with POSEIDON.
Paper: Detecting Asteroids in TESS Full-frame Images Using a Gradient-based Iterative Algorithm
Authors: Peilin Zhang, Rui Luo, Man Chen
Figure caption: The velocity distribution of the known asteroids (the left panel) and detections generated by our pipeline (the right panel). The x-axis is along the ecliptic plane and the y-axis is along the normal of the ecliptic plane. Notice that the asteroids outside the velocity range are excluded in this diagram.
Paper: Detecting Asteroids in TESS Full-frame Images Using a Gradient-based Iterative Algorithm
Authors: Peilin Zhang, Rui Luo, Man Chen
Figure caption: The velocity hypothesis diagram (the left panel) and the shift-stacked image of the comet 53P/Van Biesbroeck (the right panel). The comet tail is clearly visible in the stacked image.
Paper: Detecting Asteroids in TESS Full-frame Images Using a Gradient-based Iterative Algorithm
Authors: Peilin Zhang, Rui Luo, Man Chen
Figure caption: Velocity hypothesis diagrams and shift-stacked images of three detections that our pipeline returns, but having no matched known asteroids.
Paper: Delayed and Displaced: The Impact of Binary Interactions on Core-collapse SN Feedback
Authors: Tom Wagg, Julianne J. Dalcanton, Mathieu Renzo, Katelyn Breivik, Matthew E. Orr, Adrian M. Price-Whelan, Akaxia Cruz, Alyson Brooks, Ulrich P. Steinwandel, Eric C. Bellm
Figure caption: The fraction of the total population in the tails of the SNe timing and distance distributions for each variation (variations are outlined in Section 4.1). Scatter points indicate the ft > 44 Myr (green), fD > 100 pc (light blue), and fD > 500 pc (slate blue) fractions. Note that 44 Myr is the assumed final core-collapse SN time in FIRE-3. Dotted lines and shaded regions show the fiducial value and a ±15% region around this value. The exact values for each variation are given in Table C1. An interactive version of this data is available in Figure 12.
Paper: Delayed and Displaced: The Impact of Binary Interactions on Core-collapse SN Feedback
Authors: Tom Wagg, Julianne J. Dalcanton, Mathieu Renzo, Katelyn Breivik, Matthew E. Orr, Adrian M. Price-Whelan, Akaxia Cruz, Alyson Brooks, Ulrich P. Steinwandel, Eric C. Bellm
Figure caption: The total number of SNe that occur for each variation, separated by progenitor type (variations are outlined in Section 4.1). Dotted lines and shaded regions show the fiducial value and a ±15% region around this value. The exact values for each variation are given in Table C1. The solid black line shows the total for a population of single stars that is equivalent to our fiducial model, indicating that binary models typically increase the total SN feedback in a population. The line does not continue for all variations, since the later ones would alter a single star population as well. An interactive version of this data is available in Figure 12.
Paper: Delayed and Displaced: The Impact of Binary Interactions on Core-collapse SN Feedback
Authors: Tom Wagg, Julianne J. Dalcanton, Mathieu Renzo, Katelyn Breivik, Matthew E. Orr, Adrian M. Price-Whelan, Akaxia Cruz, Alyson Brooks, Ulrich P. Steinwandel, Eric C. Bellm
Figure caption: A flowchart of the sampling routine for our analytic model of SN feedback, where ﹩{t}_{{\rm{SN}}}﹩ is the time of the SN and ﹩{v}_{{\rm{SN}}}﹩ is the velocity at which its progenitor moves away from its parent cluster. Based on the sampled time, we draw a progenitor velocity, ﹩{v}_{{\rm{SN}}}﹩. The distribution for this velocity changes based on (a) whether the progenitor was ejected from its binary and, if so, (b) whether it experienced mass transfer before doing so, and, if so, (c) what type of mass transfer it experienced. The velocity distributions are sorted in this flowchart from top to bottom, from slowest to fastest (where unejected progenitors proceed slowest on average, while stars ejected after a common envelope are typically fastest).
Paper: Delayed and Displaced: The Impact of Binary Interactions on Core-collapse SN Feedback
Authors: Tom Wagg, Julianne J. Dalcanton, Mathieu Renzo, Katelyn Breivik, Matthew E. Orr, Adrian M. Price-Whelan, Akaxia Cruz, Alyson Brooks, Ulrich P. Steinwandel, Eric C. Bellm
Figure caption: The fraction of SNe that occur after a given time for the difference metallicity variations we consider are shown in colored lines. The dotted lines indicate the fraction of late SNe when using a fixed 44 Myr limit. The dashed lines show the same fraction when using a limit set by the time of the last single star SN. The fixed limit results in a negative correlation of the late SNe fraction with metallicity, while the variable limit gives a positive correlation.
Paper: The True Stellar Obliquity of a Sub-Saturn Planet from the Tierras Observatory and the Keck Planet Finder
Authors: Patrick Tamburo, Samuel W. Yee, Juliana García-Mejía, Gudmundur Stefánsson, David Charbonneau, Allyson Bieryla, Andrew W. Howard, Howard Isaacson, Benjamin J. Fulton, Aaron Householder
Figure caption: Results from joint modeling of photometric and RV observations. In all panels, the red line shows the best-fit model. The light gray dots show unbinned photometric data, while the black points in the top two rows show the same data binned to 1800 s cadence. The TESS data have been phase-folded according to the best-fit transit ephemeris. For the RV data, unbinned data are shown as black points, and an arbitrary offset has been removed. The error bars for the KPF data have been reduced according to the best-fit jitter value. The data behind this figure are available in machine-readable form.(The data used to create this figure are available in the online article.)(The data used to create this figure are available.)
Paper: The True Stellar Obliquity of a Sub-Saturn Planet from the Tierras Observatory and the Keck Planet Finder
Authors: Patrick Tamburo, Samuel W. Yee, Juliana García-Mejía, Gudmundur Stefánsson, David Charbonneau, Allyson Bieryla, Andrew W. Howard, Howard Isaacson, Benjamin J. Fulton, Aaron Householder
Figure caption: Measurements of the sky-projected obliquity λ (left) and the true three-dimensional obliquity ψ (right) for exoplanetary systems as a function of host star effective temperature. Planets with masses less than that of Saturn are colored in blue, while planets more massive than Saturn are shown in gray. Our measurements for TOI-2364are indicated with an orange star. The possible bimodality of the orbits of planets less massive than Saturn around cool stars is more clearly revealed in the true obliquity distribution than in the sky-projected distribution. Measurements were compiled from S. H. Albrecht et al. (2022), E. Knudstrup et al. (2024), and the TEPCat catalog (J. Southworth 2011), as of 2025 February 28.
Paper: The True Stellar Obliquity of a Sub-Saturn Planet from the Tierras Observatory and the Keck Planet Finder
Authors: Patrick Tamburo, Samuel W. Yee, Juliana García-Mejía, Gudmundur Stefánsson, David Charbonneau, Allyson Bieryla, Andrew W. Howard, Howard Isaacson, Benjamin J. Fulton, Aaron Householder
Figure caption: Top: Tierras light curve of TOI-2364. Data at the native 30 s cadence are shown in gray, with their error bars scaled by a factor of 1.66 compared to our calculated photometric uncertainties. White points points with error bars show the median flux measured on each night. The best-fit QP-GP model is shown as a green line, along with its 1 and 2σ uncertainty intervals as shaded regions. Bottom: the residuals from the best-fit QP-GP model.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: Histograms displaying the number of stars at each estimated age for three different training space density thresholds: density > 108, density > 3 × 109, and density > 109. Upper panel: the legend indicates the total number of stars included using each threshold. Lowering the density threshold increases the sample size but also leads to greater model extrapolation, as evidenced by the accumulation of age estimates at the grid edges (0 and 14 Gyr). Lower panel: histogram illustrating the distribution of reported age uncertainties for stars at the same density thresholds. These panels highlight the impact of density-based selection on the reliability and distribution of our age estimates within the MWM sample.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: This annotated hexbin map illustrates the spatial distribution of the median of our stellar ages across the Milky Way, projected onto the R (Galactocentric radius) vs. Z (height above the Galactic plane) plane. We used photogeometric distances from C. A. L. Bailer-Jones et al. (2021). Only stars with a training space density > 3 × 109 are included. The map is annotated with the Galactic center and the location of the Sun. Regions with older median ages are depicted in warmer colors, while younger regions are shown in cooler colors. This visualization highlights the age gradients and structural features of the Galaxy, such as the distribution of younger stars in the thin disk and older stars in the thick disk. This figure is repeated in Appendix B.2 with different density thresholds to illustrate how changes to the threshold affect the age map.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: Upper panel: histogram of the relative 1σ uncertainties for asteroseismic labels (blue), the averaged upper and lower relative 1σ uncertainties derived from the StarFlow model (orange), and the relative differences between the asteroseismic age and the StarFlow maximum-likelihood ages (green). Lower panel: bar plot showing the median relative uncertainty recovered from the StarFlow age posteriors, binned by asteroseismic age.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: Set of histograms showing our age estimations for nine stellar clusters. Each bin is overlaid with error bars representing the mean positive and negative uncertainties for the stars within that bin. The orange line and shaded region indicate, respectively, the cluster age and its uncertainties as reported by T. Cantat-Gaudin et al. (2020). The black curve illustrates the combined age posteriors from our model for the stars in the cluster. Each histogram includes the number of stars matched to our catalog under the conditions of membership probability > 0.85 and training density >3 × 109. Additionally, each plot shows the mean Z-score and its standard deviation σ for the estimated ages within the respective cluster.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: Scatterplot showing the differences between the model-estimated carbon and nitrogen abundances and the measured abundances from APOGEE. Contour lines indicate density levels, representing the percentage of data points enclosed within each contour. A representative error bar in the upper right corner illustrates the typical uncertainties in the measured abundances. Most stars have estimated C and N abundances within 0.1 dex of the measured values, corresponding to an age uncertainty of approximately 2 Gyr. Additional statistics are displayed in the upper left corner.