adsabs/AstroCLIMB huggingface dataset:
Paper: The TESS-Keck Survey. XX. 15 New TESS Planets and a Uniform RV Analysis of All Survey Targets
Authors: Alex S. Polanski, Jack Lubin, Corey Beard, Joseph M. Akana Murphy, Ryan Rubenzahl, Michelle L. Hill, Ian J. M. Crossfield, Ashley Chontos, Paul Robertson, Howard Isaacson, Stephen R. Kane, David R. Ciardi, Natalie M. Batalha, Courtney Dressing, Benjamin Fulton, Andrew W. Howard, Daniel Huber, Erik A. Petigura, Lauren M. Weiss, Isabel Angelo, Aida Behmard, Sarah Blunt, Casey L. Brinkman, Fei Dai, Paul A. Dalba, Tara Fetherolf, Steven Giacalone, Lea A. Hirsch, Rae Holcomb, Molly R. Kosiarek, Andrew W. Mayo, Mason G. MacDougall, Teo Močnik, Daria Pidhorodetska, Malena Rice, Lee J. Rosenthal, Nicholas Scarsdale, Emma V. Turtelboom, Dakotah Tyler, Judah Van Zandt, Samuel W. Yee, David R. Coria, Shannon D. Dulz, Joel D. Hartman, Aaron Householder, Sarah Lange, Andrew Langford, Emma M. Louden, Jared C. Siegel, Emily A. Gilbert, Erica J. Gonzales, Joshua E. Schlieder, Andrew W. Boyle, Jessie L. Christiansen, Catherine A. Clark, Rachel B. Fernandes, Michael B. Lund, Arjun B. Savel, Holden Gill, Charles Beichman, Rachel Matson, Elisabeth C. Matthews, E. Furlan, Steve B. Howell, Nicholas J. Scott, Mark E. Everett, John H. Livingston, Irina O. Ershova, Dmitry V. Cheryasov, Boris Safonov, Jorge Lillo-Box, David Barrado, María Morales-Calderón
Figure caption: Top: periodogram analysis of all RVs for TOI–1823 b in our data set, first subtracting any offsets, then removing the best-fit planet signal (first two panels). We also show a periodogram of the S-indices and the RV window function (last two panels). The planet period is given is shown with the blue vertical line, while the 1% false-alarm probability (FAP) is the horizontal blue dashed line. Middle: the best-fit Keplerian orbital model (blue line). We add in quadrature the jitter terms in Table 6 with the measurement errors for each RV. Residuals to the one-planet fit are shown in the bottom panel. Bottom: RVs phase-folded to the orbital ephemeris of planet b.
Paper: The TESS-Keck Survey. XX. 15 New TESS Planets and a Uniform RV Analysis of All Survey Targets
Authors: Alex S. Polanski, Jack Lubin, Corey Beard, Joseph M. Akana Murphy, Ryan Rubenzahl, Michelle L. Hill, Ian J. M. Crossfield, Ashley Chontos, Paul Robertson, Howard Isaacson, Stephen R. Kane, David R. Ciardi, Natalie M. Batalha, Courtney Dressing, Benjamin Fulton, Andrew W. Howard, Daniel Huber, Erik A. Petigura, Lauren M. Weiss, Isabel Angelo, Aida Behmard, Sarah Blunt, Casey L. Brinkman, Fei Dai, Paul A. Dalba, Tara Fetherolf, Steven Giacalone, Lea A. Hirsch, Rae Holcomb, Molly R. Kosiarek, Andrew W. Mayo, Mason G. MacDougall, Teo Močnik, Daria Pidhorodetska, Malena Rice, Lee J. Rosenthal, Nicholas Scarsdale, Emma V. Turtelboom, Dakotah Tyler, Judah Van Zandt, Samuel W. Yee, David R. Coria, Shannon D. Dulz, Joel D. Hartman, Aaron Householder, Sarah Lange, Andrew Langford, Emma M. Louden, Jared C. Siegel, Emily A. Gilbert, Erica J. Gonzales, Joshua E. Schlieder, Andrew W. Boyle, Jessie L. Christiansen, Catherine A. Clark, Rachel B. Fernandes, Michael B. Lund, Arjun B. Savel, Holden Gill, Charles Beichman, Rachel Matson, Elisabeth C. Matthews, E. Furlan, Steve B. Howell, Nicholas J. Scott, Mark E. Everett, John H. Livingston, Irina O. Ershova, Dmitry V. Cheryasov, Boris Safonov, Jorge Lillo-Box, David Barrado, María Morales-Calderón
Figure caption: Top: periodogram analysis of all RVs for HD 77946 b in our data set, first subtracting any offsets, then removing the best-fit planet signal (first two panels). We also show a periodogram of the S-indices and the RV window function (last two panels). The planet period is given is shown with the blue vertical line, while the 1% false-alarm probability (FAP) is the horizontal blue dashed line. Middle: the best-fit Keplerian orbital model (blue line). We add in quadrature the jitter terms in Table 6 with the measurement errors for each RV. Residuals to the one-planet fit are shown in the bottom panel. Bottom: RVs phase-folded to the orbital ephemeris of planet b.
Paper: The TESS-Keck Survey. XX. 15 New TESS Planets and a Uniform RV Analysis of All Survey Targets
Authors: Alex S. Polanski, Jack Lubin, Corey Beard, Joseph M. Akana Murphy, Ryan Rubenzahl, Michelle L. Hill, Ian J. M. Crossfield, Ashley Chontos, Paul Robertson, Howard Isaacson, Stephen R. Kane, David R. Ciardi, Natalie M. Batalha, Courtney Dressing, Benjamin Fulton, Andrew W. Howard, Daniel Huber, Erik A. Petigura, Lauren M. Weiss, Isabel Angelo, Aida Behmard, Sarah Blunt, Casey L. Brinkman, Fei Dai, Paul A. Dalba, Tara Fetherolf, Steven Giacalone, Lea A. Hirsch, Rae Holcomb, Molly R. Kosiarek, Andrew W. Mayo, Mason G. MacDougall, Teo Močnik, Daria Pidhorodetska, Malena Rice, Lee J. Rosenthal, Nicholas Scarsdale, Emma V. Turtelboom, Dakotah Tyler, Judah Van Zandt, Samuel W. Yee, David R. Coria, Shannon D. Dulz, Joel D. Hartman, Aaron Householder, Sarah Lange, Andrew Langford, Emma M. Louden, Jared C. Siegel, Emily A. Gilbert, Erica J. Gonzales, Joshua E. Schlieder, Andrew W. Boyle, Jessie L. Christiansen, Catherine A. Clark, Rachel B. Fernandes, Michael B. Lund, Arjun B. Savel, Holden Gill, Charles Beichman, Rachel Matson, Elisabeth C. Matthews, E. Furlan, Steve B. Howell, Nicholas J. Scott, Mark E. Everett, John H. Livingston, Irina O. Ershova, Dmitry V. Cheryasov, Boris Safonov, Jorge Lillo-Box, David Barrado, María Morales-Calderón
Figure caption: Top: periodogram analysis of all RVs for TOI–1798 b and c in our data set, first subtracting any offsets, then removing the best-fit planet signals (first three panels). We also show a periodogram of the S-indices and the RV window function (last two panels). The periods of planet b and c are shown with the blue and orange vertical lines respectively. The 1% false-alarm probability (FAP) is the horizontal blue dashed line. Middle: the best-fit Keplerian orbital model (blue line). We add in quadrature the jitter terms in Table 6 with the measurement errors for each RV. Residuals to the two-planet fit are shown in the bottom panel. Bottom: RVs phase-folded to the orbital ephemeris of planet b and planet c.
Paper: Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Authors: Yong Zhao, Zibo Liu, Dongdong Ni, Zhiyuan Chen
Figure caption: Schematic diagram showing framework architectures for the MDN and MCMC inference methods used in predicting the interior structure of rocky exoplanets. The inputs to the MDN are observed properties of an exoplanet including mass, radius, and bulk molar ratios of Fe/Mg and Si/Mg. The outputs contain the radial fractions of each layer, the pressure and temperature of the core–mantle boundary (CMB), and the tidal Love number k 2. The data set used to train the MDN model is generated using a three-layer interior structure model, with the layers representing the water-ice shell, silicate mantle, and iron-rich core respectively. Within the MDN architecture, hidden layers are tasked with deciphering the complex relationships between the observations and the interior parameters, which are construed as a Gaussian mixture distribution in the resultant outputs. In contrast, Bayesian inference employing the MCMC technique systematically contrasts the observed data, denoted as D, with interior models derived from prior distributions, p(Θ). This process applies MCMC sampling to determine the posterior distributions p(Θ∣D) of the interior parameters.
Paper: Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Authors: Yong Zhao, Zibo Liu, Dongdong Ni, Zhiyuan Chen
Figure caption: MDN predicted distributions of eight output variables: the radial fractions of water-ice shell, mantle, and core, the mass fraction of water-ice shell and core, the core properties P CMB and T CMB, and the tidal Love number k 2 vs. the actual values obtained from the interior structure model for the test data set. Red dashed diagonal lines indicate the best performance of the MDN. For a given sample, the maximum of the predicted probability density is mapped to yellow, while the minimum is mapped to green.
Paper: Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Authors: Yong Zhao, Zibo Liu, Dongdong Ni, Zhiyuan Chen
Figure caption: Learning curves of the MDN model trained on inputs of mass, radius, and bulk molar ratios of Fe/Mg and Si/Mg. (a) Training loss as a function of the training size. We generate different training samples per training size by modifying the random seed. (b) Negative log-likelihood loss over the epochs for both training and validation data.
Paper: Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Authors: Yong Zhao, Zibo Liu, Dongdong Ni, Zhiyuan Chen
Figure caption: Histograms displaying the distributions of the MDN’s input and output parameters within the training set. The inputs, consisting of mass, radius, and bulk molar ratios of Fe/Mg and Si/Mg, are represented in blue. Outputs of WRF, MRF, CRF, WMF, CMF, P CMB, T CMB, and tidal Love number k 2 are depicted in red.
Paper: Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Authors: Yong Zhao, Zibo Liu, Dongdong Ni, Zhiyuan Chen
Figure caption: Comparison of Earth’s interior structure distributions inferred by the well-trained MDN model and MCMC inference. The dashed lines indicate the median values of the posterior distributions for the interior parameters, providing a benchmark for comparing the central tendencies inferred by the two methodologies.
Paper: Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Authors: Yong Zhao, Zibo Liu, Dongdong Ni, Zhiyuan Chen
Figure caption: Same as Figure 4, but for Kepler-78 b.
Paper: Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Authors: Yong Zhao, Zibo Liu, Dongdong Ni, Zhiyuan Chen
Figure caption: Same as Figure 4, but for Kepler-10 b.
Paper: Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Authors: Yong Zhao, Zibo Liu, Dongdong Ni, Zhiyuan Chen
Figure caption: Same as Figure 4, but for EPIC 249893012 b.
Paper: Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Authors: Yong Zhao, Zibo Liu, Dongdong Ni, Zhiyuan Chen
Figure caption: Same as Figure 4, but for HD 219134 b.
Paper: Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Authors: Yong Zhao, Zibo Liu, Dongdong Ni, Zhiyuan Chen
Figure caption: Same as Figure 4, but for HD 3167 b.
Paper: Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Authors: Yong Zhao, Zibo Liu, Dongdong Ni, Zhiyuan Chen
Figure caption: Sampled 1D marginal posterior distributions for Kepler-78 b’s inferred parameters, juxtaposed with their respective priors and observations. The histograms display a strong consistency between the priors (blue) and posteriors (red) for the planet’s mass, bulk molar ratios of Fe/Mg and Si/Mg, mantle FeO content, and WMF, as specified in Table 1. For the planetary radius, the posterior distribution closely aligns with the observations (green).
Paper: Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Authors: Yong Zhao, Zibo Liu, Dongdong Ni, Zhiyuan Chen
Figure caption: 1D and 2D posterior distributions of the WRF, MRF, CRF, WMF, CMF, P CMB, T CMB, and tidal Love number k 2 for Kepler-78 b obtained from the MDN and MCMC inferences. Median values of the parameters are represented by dashed lines, with the red lines indicating medians obtained from the MDN prediction and the blue lines representing the medians derived from the MCMC method.
Paper: Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Authors: Yong Zhao, Zibo Liu, Dongdong Ni, Zhiyuan Chen
Figure caption: Negative log-likelihood loss over the epochs for both training and validation data for the MDN model trained on inputs of mass, radius, and Fe/(Mg + Si).
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: Comparison of photometric redshifts z p with spectroscopic redshifts z s for the faint galaxies of z m > 22 in the deeper region (the left panel) and galaxies in the shallower region (the middle panel). The right panel is plotted for the galaxies in the shallower region of decl. ≥ 32° without i-band data, and the values of z p are directly taken from Zhou et al. (2021). The uncertainty and systematic bias are marked in each panel.
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: Comparison between the newly estimated photometric redshifts z p and spectroscopic redshifts z s for galaxies in the region of middle survey depth and in four z-band magnitude ranges, with different uncertainties and systematic bias, as marked in each panel, where only 10,000 randomly selected galaxies are taken for plotting.
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: Comparison between our estimated stellar masses of galaxies with the values in the COSMOS2015 catalog.
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: Mass–redshift diagram for clusters in the RASS-MCMF (Klein et al. 2023) and eROSITA catalogs (Bulbul et al. 2022). The matched RASS-MCMF clusters are shown by black plus symbols and the unmatched ones by red crosses. The matched eROSITA clusters are shown by blue dots and the unmatched ones by red circles.
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: Verification of cluster members as a function of the stellar mass of galaxies by using the spectroscopic survey data of GAMA (Driver et al. 2022). Upper panel: the fraction of verified member galaxies (N veri./N all). Lower panel: the fraction of missed true member galaxies (N missing/N real). Here, N veri., N missing, and N real are the numbers of galaxies defined within a small spectroscopic redshift offset from the BCGs.
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: Magnitudes at the z and W1 bands and color (r − z m; bottom) against redshift for 10,000 randomly selected known BCGs, as indicated by the small (red) dots. The distribution of all massive galaxies of M ⋆ ≥ 1011 M ⊙ in the DESI Legacy Surveys is shown with the number density in gray. We set criteria for the BCG-like galaxies as shown by the solid line.
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: The left panel shows the comparison between photometric redshifts and spectroscopic redshifts for clusters identified with i-band data. In every redshift interval of 0.1, 1000 clusters are randomly selected. The solid line is for equal values, while the dashed lines represent the deviation of ±1σ. The right panel is the same as the left, but for clusters identified without i-band data in the shallowest region of decl. ≥ 32°.
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: The detection fraction of clusters as a function of richness or halo mass for clusters in the redMaPPer (Rykoff et al. 2016), WH22 (Wen & Han 2022), Y21 (Yang et al. 2021), CFSFDP (Zou et al. 2022) and CluMPR (Yantovski-Barth et al. 2024) catalogs.
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: The distribution of the relaxation parameter Γ for 27,685 massive clusters, indicating no significant evolution with redshift (upper panel) and nondependence with cluster mass (lower panel). The open circles and the error bars represent the mean values and scatter of the data set, respectively.
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: Redshift distribution of the identified 1,581,179 clusters, compared to clusters previously identified from the DESI Legacy Surveys DR9, including Y21 (Yang et al. 2021), CFSFDP (Zou et al. 2022), and CLUMPR (Yantovski-Barth et al. 2024).
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: Correlation between cluster richness and the cluster masses compiled in Wen & Han (2015). The solid line is the best fit to the data.
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: The sky distribution of the identified galaxy clusters in the Equatorial coordinate system, expressed by the number of clusters per square degree.
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: To keep a constant comoving number density of BCGs, the stellar mass threshold of BCGs is shown as a function of redshift. The dots and open circles represent the thresholds, starting from reference values of M ⋆,BCG = 1012 M ⊙ and 1011.9 M ⊙ at z = 0.1–0.2, respectively. The solid and dashed lines are the best fits to the data shown by the dots and open circles, respectively.
Paper: A Catalog of 1.58 Million Clusters of Galaxies Identified from the DESI Legacy Imaging Surveys
Authors: Z. L. Wen, J. L. Han
Figure caption: The fraction of BCG stellar mass among the total stellar mass of galaxies in clusters, plotted as a function of cluster mass. The average and data dispersions are given by the circles and error bars, with a fitted line. The dashed line and dotted lines come from the simulations by Kravtsov et al. (2018) for the mean fraction and the dispersion.
Paper: Numerical Modeling of Energetic Charged-particle Transport with SPECTRUM Software: General Approach and Artificial Effects due to Field Discretization
Authors: J. G. Alonso Guzmán, V. Florinski, G. Tóth, S. Sharma, B. van der Holst, M. Opher
Figure caption: Normalized particle spectrum for the modulation simulation described in the text. The black dashed line shows the unmodulated spectra at 80 au, the red line shows the theoretical approximation for the modulated spectrum at 1 au according to Fisk & Axford (1969), and the markers show the modulated spectrum at 1 au according to the simulations. The green squares correspond to a simulation using an analytic expression for the background, while the blue crosses are the result of using a discretized version of the same background with a resolution of 1 au and trilinear interpolation.
Paper: Numerical Modeling of Energetic Charged-particle Transport with SPECTRUM Software: General Approach and Artificial Effects due to Field Discretization
Authors: J. G. Alonso Guzmán, V. Florinski, G. Tóth, S. Sharma, B. van der Holst, M. Opher
Figure caption: Schematic of SPECTRUM architecture, showing different independent modules (classes) interacting at an MPI and data transfer level. See text for details.
Paper: Numerical Modeling of Energetic Charged-particle Transport with SPECTRUM Software: General Approach and Artificial Effects due to Field Discretization
Authors: J. G. Alonso Guzmán, V. Florinski, G. Tóth, S. Sharma, B. van der Holst, M. Opher
Figure caption: Running diffusion coefficients according to Equation (15) for the guiding center (panel (b)), Newton−Lorentz (panel (c)), and focused (panel (d)) transport and various uniform grid resolutions. Panel (a) shows the same running diffusion coefficient calculations but with  r   d  corresponding to stochastic trajectories traced in an analytic background using a range of perpendicular diffusion coefficients. In all panels, the black dashed line represents the steady-state subdiffusive trend of an already bounce-cycle-randomized distribution, given by Equation (16).
Paper: Numerical Modeling of Energetic Charged-particle Transport with SPECTRUM Software: General Approach and Artificial Effects due to Field Discretization
Authors: J. G. Alonso Guzmán, V. Florinski, G. Tóth, S. Sharma, B. van der Holst, M. Opher
Figure caption: Relative error at y = 0 in the magnitude of a discretized dipole field modeled after Earth’s magnetic field. The scales saturate beyond the bounds shown on the color bars. The left column was obtained with a relatively course uniform discretization (10 blocks per dimension), the middle column uses a much finer uniform resolution (80 blocks per dimension), and the right column displays the field from an adaptive grid with two levels of refinement (40 root blocks per dimension). All blocks contain 4 × 4 × 4 cells. The left and middle panels use trilinear interpolation, and the right panel uses its BATL generalization (ghost cell variation) discussed in the text. The black/white dashed line denotes Earth’s surface (radius R E).
Paper: Numerical Modeling of Energetic Charged-particle Transport with SPECTRUM Software: General Approach and Artificial Effects due to Field Discretization
Authors: J. G. Alonso Guzmán, V. Florinski, G. Tóth, S. Sharma, B. van der Holst, M. Opher
Figure caption: Running diffusion coefficients according to Equation (15) for the guiding center transport model (panel (b)) using test particles of different energies on a discretized background with 80 × 80 × 80 blocks. Panel (a) shows the same running diffusion coefficient calculations but with r   d  corresponding to stochastic trajectories traced in an analytic background using D ⊥ = 108 cm2 s−1. The vertical dashed lines indicate the drift period of particles with a specific energy by color.
Paper: Numerical Modeling of Energetic Charged-particle Transport with SPECTRUM Software: General Approach and Artificial Effects due to Field Discretization
Authors: J. G. Alonso Guzmán, V. Florinski, G. Tóth, S. Sharma, B. van der Holst, M. Opher
Figure caption: Particle spectrum for the modulation simulation described in the text. The black dashed line shows the unmodulated spectra at 80 au, the red line shows the modulated spectrum at 1 au using an analytic background, and the markers show the modulated spectrum at 1 au according to the discretized backgrounds with different resolutions, all using trilinear interpolation and 4 × 4 × 4 cells per block.
Paper: Numerical Modeling of Energetic Charged-particle Transport with SPECTRUM Software: General Approach and Artificial Effects due to Field Discretization
Authors: J. G. Alonso Guzmán, V. Florinski, G. Tóth, S. Sharma, B. van der Holst, M. Opher
Figure caption: Running diffusion coefficients according to Equation (15) for the guiding center transport model and different kinds of interpolation across resolution changes. The results of the 40 × 40 × 40 and 80 × 80 × 80 block uniform discretization trials are also shown for reference.