adsabs/AstroCLIMB huggingface dataset:
Paper: Velocity Shift and Signal-to-Noise Ratio Limits for High-resolution Spectroscopy of Hot Jupiters Using Keck/KPIC
Authors: Kevin S. Hong, Luke Finnerty, Michael P. Fitzgerald
Figure caption: 100 × 100 detection grids for simulated HD 189733 b data, comparing the two methods of determining detection strengths. The upper grid uses the significance at the true velocities of the simulation. The lower grid uses the maximum significance in a simulated Kp−vsys diagram, given velocity bounds for planet velocity shift in a frame. The detection strength contours are consistent between both methods in the high-Δvpl and high-SNR regime, where the maximum significance is near to the true velocity values. However, using the maximum significance produces a lower detection limit at the low-Δvpl and low-SNR regions.
Paper: Velocity Shift and Signal-to-Noise Ratio Limits for High-resolution Spectroscopy of Hot Jupiters Using Keck/KPIC
Authors: Kevin S. Hong, Luke Finnerty, Michael P. Fitzgerald
Figure caption: Kp−vsys diagrams for simulated HD 189733 b detections with no added time-varying telluric spectrum (top), with added time-varying telluric spectrum but no PCA removal (middle), and with added time-varying telluric spectrum and four PCs removed (bottom). Each simulation used Δvpl = 50 km s−1 and SNR = 1000 with the fiducial phase range for the fixed-phase grid. The simulation with no time-varying telluric features presents the highest detection strength, as expected. By adding the time-varying tellurics, the planet becomes undetected until PCA is performed to remove four PCs, presenting a more realistic detection strength of 6.1σ.
Paper: Velocity Shift and Signal-to-Noise Ratio Limits for High-resolution Spectroscopy of Hot Jupiters Using Keck/KPIC
Authors: Kevin S. Hong, Luke Finnerty, Michael P. Fitzgerald
Figure caption: Histogram of off-peak log-likelihood values in the Kp−vsys diagrams for the observed and simulated WASP-33 b data as in Figure 2. The Kp−vsys diagram is first shifted by the mean of off-peak values and scaled by the standard deviation of off-peak values as detailed in the text. A standard Gaussian is shown in red. We can see that the off-peak likelihood values are approximately Gaussian-distributed for both the real and simulated data.
Paper: Updated Masses for the Gas Giants in the Eight-planet Kepler-90 System Via Transit-timing Variation and Radial Velocity Observations
Authors: David E. Shaw, Lauren M. Weiss, Eric Agol, Karen A. Collins, Khalid Barkaoui, Cristilyn N. Watkins, Richard P. Schwarz, Howard M. Relles, Chris Stockdale, John F. Kielkopf, Fabian Rodriguez Frustaglia, Allyson Bieryla, Joao Gregorio, Owen Mitchem, Katherine Linnenkohl, Adam Popowicz, Norio Narita, Akihiko Fukui, Michaël Gillon, Ramotholo Sefako, Avi Shporer, Adam Lark, Amelie Heying, Isa Khan, Beibei Chen, Kylee Carden, Donald M. Terndrup, Robert Taylor, Dasha Crocker, Sarah Ballard, Daniel C. Fabrycky
Figure caption: (a) Observed RVs for K90 from the HIRES spectrometer along with the best-fit model from our joint analysis of the RV and Kepler TTV data (red line) and the 6438 models from the Monte Carlo estimate of the posterior distribution (gray dashed lines). The time of the last observed transit from the Kepler photometry is indicated (dotted black line). Panel (b) shows the RV residuals from the joint model. Panels (c) and (d) show the RV data phased to the periods of K90g and K90h, along with the best-fit model and posteriors as in panel (a). The period, semiamplitude (K), and eccentricities correspond to the best fit to the joint RV and Kepler TTV model.
Paper: WF-SwinUnet: A Window Fusion-based RFI Segmentation Model and Its Application in FAST
Authors: QingYun Li, MingHui Li, Dongjun Yu, Jie He
Figure caption: In this figure, a comparison is made between a deep learning RFI excision method and other traditional RFI excision methods. Each data time-frequency map and mask map in the large figure has 384 × 384 pixels, where each pixel in the wide direction represents a frequency channel and each pixel in the high direction represents a sampling time. The initial row of images illustrates the time-frequency diagram of the subintegration slice, with subsequent rows depicting the mask diagram of the annotated data set and the following rows displaying the recognition mask diagram for each method. In comparison to the traditional method, our approach possesses the capability to classify and recognize RFI, thus necessitating the utilization of a color mask wherein distinct colors are assigned to differentiate between various types of RFI. Conversely, the traditional method is incapable of distinguishing between RFI, necessitating the employment of a black-and-white mask.
Paper: Updated Masses for the Gas Giants in the Eight-planet Kepler-90 System Via Transit-timing Variation and Radial Velocity Observations
Authors: David E. Shaw, Lauren M. Weiss, Eric Agol, Karen A. Collins, Khalid Barkaoui, Cristilyn N. Watkins, Richard P. Schwarz, Howard M. Relles, Chris Stockdale, John F. Kielkopf, Fabian Rodriguez Frustaglia, Allyson Bieryla, Joao Gregorio, Owen Mitchem, Katherine Linnenkohl, Adam Popowicz, Norio Narita, Akihiko Fukui, Michaël Gillon, Ramotholo Sefako, Avi Shporer, Adam Lark, Amelie Heying, Isa Khan, Beibei Chen, Kylee Carden, Donald M. Terndrup, Robert Taylor, Dasha Crocker, Sarah Ballard, Daniel C. Fabrycky
Figure caption: (Tops) Observed TTVs (O − C) of planets g (left) and h (right) from the Kepler photometry along with the best-fit model from our joint analysis of the RV and Kepler TTV data (black line) and the 6438 models from the Monte Carlo estimate of the posterior distribution (gray lines). The linear ephemeris used to determine these O − C values was determined by performing a linear regression on the observed Kepler transit times. (Bottoms) Residuals after subtracting the modeled transit times from the observed transit times.
Paper: Updated Masses for the Gas Giants in the Eight-planet Kepler-90 System Via Transit-timing Variation and Radial Velocity Observations
Authors: David E. Shaw, Lauren M. Weiss, Eric Agol, Karen A. Collins, Khalid Barkaoui, Cristilyn N. Watkins, Richard P. Schwarz, Howard M. Relles, Chris Stockdale, John F. Kielkopf, Fabian Rodriguez Frustaglia, Allyson Bieryla, Joao Gregorio, Owen Mitchem, Katherine Linnenkohl, Adam Popowicz, Norio Narita, Akihiko Fukui, Michaël Gillon, Ramotholo Sefako, Avi Shporer, Adam Lark, Amelie Heying, Isa Khan, Beibei Chen, Kylee Carden, Donald M. Terndrup, Robert Taylor, Dasha Crocker, Sarah Ballard, Daniel C. Fabrycky
Figure caption: Histogram of the number of iterations performed by the optimizer from 10,000 Monte Carlo synthetic TTV and RV data sets (blue). We removed synthetic data sets for which the optimizer stopped in less than 20 iterations (red line), resulting in a final set of 6438 optimizations.
Paper: WF-SwinUnet: A Window Fusion-based RFI Segmentation Model and Its Application in FAST
Authors: QingYun Li, MingHui Li, Dongjun Yu, Jie He
Figure caption: The mean indicator for each model, categorized by type of RFI. It is evident from the graph that an increase in the indicator value is indicative of a corresponding increase in performance.
Paper: WF-SwinUnet: A Window Fusion-based RFI Segmentation Model and Its Application in FAST
Authors: QingYun Li, MingHui Li, Dongjun Yu, Jie He
Figure caption: The mean indicator value for all RFI categories across all models. Evidently, as the indicator value decreases, the performance value increases.
Paper: WF-SwinUnet: A Window Fusion-based RFI Segmentation Model and Its Application in FAST
Authors: QingYun Li, MingHui Li, Dongjun Yu, Jie He
Figure caption: We used WF-SwinUnet to remove RFI from the 50 FITS files of the FAST observation target NGC6517, and then performed a pulsar folding search in the PRESTO pulsar search software.
Paper: WF-SwinUnet: A Window Fusion-based RFI Segmentation Model and Its Application in FAST
Authors: QingYun Li, MingHui Li, Dongjun Yu, Jie He
Figure caption: We performed a direct pulsar folding search using the PRESTO pulsar search software on the 50 FITS files of the FAST observation target NGC6517.
Paper: A Metal-poor Atmosphere with a Hot Interior for a Young Sub-Neptune Progenitor: JWST/NIRSpec Transmission Spectrum of V1298 Tau b
Authors: Saugata Barat, Jean-Michel Désert, Sagnick Mukherjee, Jayesh M. Goyal, Qiao Xue, Yui Kawashima, Allona Vazan, William Misener, Hilke E. Schlichting, Jonathan J. Fortney, Jacob L. Bean, Swaroop Avarsekar, Gregory W. Henry, Robin Baeyens, Michael R. Line, John H. Livingston, Trevor David, Erik A. Petigura, James T. Sikora, Hinna Shivkumar, Adina D. Feinstein, Antonija Oklopčić
Figure caption: Evolutionary track models of the radius (upper left panel) and of the internal temperature (lower left panel) for V1298 Tau b, considering various ranges of mass and metallicity. Models with two masses (10, 15 M⊕) are calculated, and for each mass, we show two models with solar and 10× solar atmospheric metallicity. Black lines represent 10 M⊕ and red lines represent 15 M⊕. Dotted lines correspond to solar metallicity, and continuous lines correspond to 10× solar metallicity. These ranges of mass and atmospheric metallicity values are chosen as representative and consistent within 3σ of the posterior distribution from the atmospheric retrievals (Table 1). The black dashed models show radius and internal temperature evolution for a 10 M⊕, 10× solar metallicity model including silicate rainout. The evolution tracks for V1298 Tau b have been simulated using interior structure and evolutionary models from A. Vazan et al. (2018, 2024). See Section 4.4 for further details on the models. The green continuous line shows the lower limit of internal temperature inferred from the atmospheric retrievals estimated from this work and the radius of V1298 Tau b. The internal temperature we measure (∼500 K) appears inconsistent with these evolution models and requires alternative formalisms. These models require a 20%–50% by mass H/He envelope; they have been simulated using a gradually mixed interior structure assuming a compositional gradient. In the right panel, we show the atmospheric metallicity profiles that have been used for our evolutionary models presented in the left panel. The same line style and color scheme have been used. For comparison, we also show a core-envelope structure (orange continuous) line for a 15 M⊕, 10× solar atmospheric metallicity model. At the current age, both core-envelope and gradually mixed envelope models can explain the observed composition of the upper atmosphere. Two globally different scenario could be considered: the gradually mixed interior scenario, in which the atmospheric metallicity is expected to increase with age due to removal of upper layers of the envelope, whereas for the core-envelope structure scenario, the metallicity of V1298 Tau b is not expected to change over time, regardless of mass-loss rates (see right panel inset).
Paper: A Metal-poor Atmosphere with a Hot Interior for a Young Sub-Neptune Progenitor: JWST/NIRSpec Transmission Spectrum of V1298 Tau b
Authors: Saugata Barat, Jean-Michel Désert, Sagnick Mukherjee, Jayesh M. Goyal, Qiao Xue, Yui Kawashima, Allona Vazan, William Misener, Hilke E. Schlichting, Jonathan J. Fortney, Jacob L. Bean, Swaroop Avarsekar, Gregory W. Henry, Robin Baeyens, Michael R. Line, John H. Livingston, Trevor David, Erik A. Petigura, James T. Sikora, Hinna Shivkumar, Adina D. Feinstein, Antonija Oklopčić
Figure caption: Transmission spectrum of V1298 Tau b observed with JWST/NIRSpec G395H and HST/WFC3 (Section 4.1), with 1σ error bars, along with some models which were nominally expected based on the published RV mass (A. Suárez Mascareño et al. 2021), equilibrium temperature, predicted internal temperature from evolution models (Figure 7), and the mass–metallicity relation for core-accretion formation (D. P. Thorngren et al. 2016). The blue solid line shows the best-fit model from the PICASSO free retrieval (see Section 4.1 and Appendix D), and the shaded region around it shows the 1σ confidence interval. The black solid line shows a model with 12 M⊕, 100× solar (assumed from the mass–metallicity relation) and Tint = 500 K, which can be ruled out at a 23σ confidence level. The model rejection significance is estimated from the difference in χ2 values between the best-fit model (blue) and the model being compared. The black dotted line shows a solar metallicity, 80 M⊕ (2σ mass lower limit from A. Suárez Mascareño et al. 2021) model, which can also be ruled out at a 30σ confidence level. A hazy atmospheric model (tholin haze) with a haze formation efficiency of 0.01% and 12 M⊕ can be ruled out at 38σ confidence level as it produces a strong slope and a feature at 3.3 μm, which is not seen. The red dashed line shows a 12 M⊕, 10x solar metallicity model with an internal temperature of 100 K, and can be ruled out at 18σ confidence level as it produces a strong methane absorption feature at 3.3 μm. The red solid line is the best-fit model from the HST transmission spectrum (S. Barat et al. 2024b), extrapolated to JWST wavelengths. It has 23 M⊕, solar metallicity, Tint = 400 K. This model is simulated using PetitRadtrans. This model matches the water and methane features in NRS1; however, it underpredicts the size of the CO2 feature, most likely due to a higher mass, lower metallicity, and no photochemistry. All the models presented in this figure have a gray cloud deck at 0.01 bar.
Paper: A Metal-poor Atmosphere with a Hot Interior for a Young Sub-Neptune Progenitor: JWST/NIRSpec Transmission Spectrum of V1298 Tau b
Authors: Saugata Barat, Jean-Michel Désert, Sagnick Mukherjee, Jayesh M. Goyal, Qiao Xue, Yui Kawashima, Allona Vazan, William Misener, Hilke E. Schlichting, Jonathan J. Fortney, Jacob L. Bean, Swaroop Avarsekar, Gregory W. Henry, Robin Baeyens, Michael R. Line, John H. Livingston, Trevor David, Erik A. Petigura, James T. Sikora, Hinna Shivkumar, Adina D. Feinstein, Antonija Oklopčić
Figure caption: Stacked detrended spectroscopic light curves (left NRS1, right NRS2) derived using the data-driven detrending approach described in Appendix C. First, the spectroscopic light curves were fitted using an empirical model (Equation (C1)). Following this, the second-order polynomial coefficients derived from the empirical model fit were smoothed in wavelength using a Gaussian kernel (Figure C2), and the smoothed coefficients were used to create a stellar variability model for each spectroscopic bin. The spectroscopic light curves were detrended using this variability model. The flare profile is derived from the best-fit flare parameters from the white light-curve fit, and the oscillation profiles are calculated by removing the stellar baseline model, transit, and flare profile from the white light curve (Figure C3). The detrending flare and oscillation profiles were scaled for each spectroscopic light curve, and the scaling factors were included as fitting parameters in the second iteration of the spectroscopic light-curve fits (Appendix C.2) to account for the wavelength dependence of the flare and oscillation amplitude. We can identify four outliers by eye on this diagram. These bins can be seen in this figure as horizontal stripes. We identify these bins at 3.03, 3.29, 4.04, and 4.65 μm and remove them from the subsequent analysis. From visual inspection of the light curves at these bins, we find that the temporal shapes of the flare/oscillations are different in these bins compared to the median profile used for detrending. Therefore, the data-driven detrending does not work well for these channels. The transit and the ingress/egress are visible by eye on the stacked detrended light curves. In the right panel, a darker patch is visible between 4.2 and 4.4 μm, which is due to CO2 absorption.
Paper: A Metal-poor Atmosphere with a Hot Interior for a Young Sub-Neptune Progenitor: JWST/NIRSpec Transmission Spectrum of V1298 Tau b
Authors: Saugata Barat, Jean-Michel Désert, Sagnick Mukherjee, Jayesh M. Goyal, Qiao Xue, Yui Kawashima, Allona Vazan, William Misener, Hilke E. Schlichting, Jonathan J. Fortney, Jacob L. Bean, Swaroop Avarsekar, Gregory W. Henry, Robin Baeyens, Michael R. Line, John H. Livingston, Trevor David, Erik A. Petigura, James T. Sikora, Hinna Shivkumar, Adina D. Feinstein, Antonija Oklopčić
Figure caption: Transmission spectrum of V1298 Tau b observed using JWST/NIRSpec G395H (yellow points), and using HST/WFC3 (S. Barat et al. 2024b; green points), with 1σ uncertainties, along with best-fit model (blue solid) from the free chemistry PICASSO retrieval (see Section 4.1 and Appendix D). The JWST spectra and their 1σ error bars have been derived from the Eureka! data reduction (Appendix A), using data-driven flare corrected light-curve fitting methods (Appendix C.2). The HST spectra have been offset by a 400 ppm to adjust for the difference in brightness level of the star between the two epochs estimated from ground-based photometric long-term monitoring (Appendix G). The transmission spectrum shows a mostly clear atmosphere with a large-scale height (∼1500 km). The contribution from H2O (30σ), CH4 (6σ), CO2 (35σ), CO (10σ), and the continuum opacity (gray clouds) are apparent from the PICASSO free retrieval and are shown with the colored shaded regions. In the bottom panel, we show the residuals (scaled with the error bar on each bin) between the combined JWST and HST observations with the best-fit PICASSO free chemistry model. In the HST bandpass, NRS1 and NRS2, the rms residual is 47 ppm, 55 ppm, and 78 ppm, respectively. The gray shaded region in the lower panel shows the 1σ confidence region.
Paper: A Metal-poor Atmosphere with a Hot Interior for a Young Sub-Neptune Progenitor: JWST/NIRSpec Transmission Spectrum of V1298 Tau b
Authors: Saugata Barat, Jean-Michel Désert, Sagnick Mukherjee, Jayesh M. Goyal, Qiao Xue, Yui Kawashima, Allona Vazan, William Misener, Hilke E. Schlichting, Jonathan J. Fortney, Jacob L. Bean, Swaroop Avarsekar, Gregory W. Henry, Robin Baeyens, Michael R. Line, John H. Livingston, Trevor David, Erik A. Petigura, James T. Sikora, Hinna Shivkumar, Adina D. Feinstein, Antonija Oklopčić
Figure caption: Extracted white light curve from the two NIRSpec G395H detectors, NRS1 (left) and NRS2 (right), from the Eureka! data reduction (Appendix A). The upper panel shows the observed counts for each integration (gray points) and the best-fit empirical model (red solid line, see Appendix C). The transit light curves show a large flare in transit followed by postflare oscillations. The baseline also appears curved, which is due to the rotational variability of the young T Tauri host star. The empirical model (Equation (C1)) used to fit the light curves consists of a rotational variability term (quadratic polynomial in time), a double exponential flare model, a sinusoid for the postflare oscillations, and the planet transit modeled using batman (L. Kreidberg 2015). We use a Markov Chain Monte Carlo framework to fit the empirical model to the observed light curve and calculate the best-fit model using median values from the posterior distributions of the fitted parameters. The lower panel shows the residuals obtained by subtracting the best-fit model from the observed white light curve. The residuals show significant structure, indicating that the empirical model fit does not sufficiently account for all the astrophysical systematics.
Paper: A Metal-poor Atmosphere with a Hot Interior for a Young Sub-Neptune Progenitor: JWST/NIRSpec Transmission Spectrum of V1298 Tau b
Authors: Saugata Barat, Jean-Michel Désert, Sagnick Mukherjee, Jayesh M. Goyal, Qiao Xue, Yui Kawashima, Allona Vazan, William Misener, Hilke E. Schlichting, Jonathan J. Fortney, Jacob L. Bean, Swaroop Avarsekar, Gregory W. Henry, Robin Baeyens, Michael R. Line, John H. Livingston, Trevor David, Erik A. Petigura, James T. Sikora, Hinna Shivkumar, Adina D. Feinstein, Antonija Oklopčić
Figure caption: Comparison between the transmission spectrum of V1298 Tau b derived from Eureka! (Appendix A) and SPARTA (Appendix B) data reduction. The transmission spectra shown here have been derived using the data-driven light-curve fitting method as discussed in Appendix C.2 applied independently to the Eureka! and SPARTA data reductions. The transmission spectra from both reductions are consistent with each other and show similar molecular absorption features. In NRS1, both data reductions show a similar level of scatter; however, the SPARTA transmission spectrum shows higher scatter in NRS2 compared to Eureka! This is likely due to the slightly higher scatter we found in the NRS2 1D stellar spectra from SPARTA compared to Eureka! and this difference in scatter for the SPARTA reduction is also seen in the white light-curve comparison (Figure C1).
Paper: A Metal-poor Atmosphere with a Hot Interior for a Young Sub-Neptune Progenitor: JWST/NIRSpec Transmission Spectrum of V1298 Tau b
Authors: Saugata Barat, Jean-Michel Désert, Sagnick Mukherjee, Jayesh M. Goyal, Qiao Xue, Yui Kawashima, Allona Vazan, William Misener, Hilke E. Schlichting, Jonathan J. Fortney, Jacob L. Bean, Swaroop Avarsekar, Gregory W. Henry, Robin Baeyens, Michael R. Line, John H. Livingston, Trevor David, Erik A. Petigura, James T. Sikora, Hinna Shivkumar, Adina D. Feinstein, Antonija Oklopčić
Figure caption: The observed JWST/NIRSpec transmission spectrum of V1298 Tau b (yellow points with 1σ error bars) overplotted with best-fit models from ATMO grids with photochemistry (blue continuous curve) and without photochemistry (black continuous curve), as described in Section 4.2.2. Vertical mixing is included in both grids. The right panel shows their corresponding CO2 VMR profiles. Both models have the same metallicity, C/O ratio, Kzz, and planet mass. The model, including photochemistry, provides a better fit of the CO2 feature. The photochemistry model does not significantly affect the size of the absorption features for other molecules.
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 measurements obtained using the spectral data set from the literature (E. L. Gibb et al. 2004) are compared with their corresponding reference values to examine correlations (R is the correlation coefficient). The dotted lines indicate the equal τp and reference values. First row: linear fits describing the correlation between photometric and spectroscopic fluxes at absorptions derived from simulations. Second row: linear fits describing the correlation between τp and τs values. Third row: linear fits describing the correlation between τp and τr ([G04]) values. Fourth row: linear fits describing the correlation between τp and total absorption (﹩{ \mathcal A }﹩) 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: An example spectral model for 1–30 μm wavelength range, including the water ice (3.0 μm), carbonaceous dust (3.4 μm), and silicate dust (10.0 μm, 18.0 μm) absorption features, is presented on a flat continuum. The normalized spectrum of a Galactic Center source (Sgr A*) is also shown for comparison. The photometric fluxes are shown on the model spectrum, with colored dots indicating the employed JWST filters. The half-power passband wavelengths of the filters are also illustrated in the background using filled areas with matching colors.