adsabs/AstroCLIMB huggingface dataset:
Paper: Radio Astrometry at Different Frequencies
Authors: Leonid Petrov
Figure caption: The normalized distribution of the position angles of the differences in source coordinates derived from VLBA SX and IVS R1R4 SX data counted from the jet directions (green circles). The solid blue line shows its fit with two Gaussians and a constant. The yellow vertical lines show the maxima. The thin dashed red line shows the uniform distribution.
Paper: Radio Astrometry at Different Frequencies
Authors: Leonid Petrov
Figure caption: The differences in the positions from SX-band VLBA data with respect to the positions from K-band data. The vertical axis is aligned with the jet directions. The ragged gray line shows the average position offset in a sector of 90°. The thin smooth red line shows its approximation with an ellipse.
Paper: Toward Robust Corrections for Stellar Contamination in JWST Exoplanet Transmission Spectra
Authors: Benjamin V. Rackham, Julien de Wit
Figure caption: Change in bias of transmission spectra after applying corrections implied by the direct retrievals. The top and bottom panels show results for the K0 and M6 stars, respectively. From left to right, the columns correspond to cases 1, 2l, 2h, 3l, and 3h. In each panel the black lines give the true transmission spectra of the planet (warm Jupiter in the top row, super-Earth in the bottom), and the blue and orange points show the observed (contaminated) and corrected transmission spectrum, respectively. The reduced χ 2 values (﹩{\chi }_{r}^{2}﹩) of the residuals with respect to the true transmission spectrum for the observed and corrected spectra are shown in blue and orange, respectively. Uniform offsets (not shown) are applied to minimize ﹩{\chi }_{r}^{2}﹩ in all cases. The corrections reduce the bias for the cases with two or three types of photospheric heterogeneities, either by bringing the transmission spectra more in line with the true values (e.g., cases 2l and 2h) or by increasing the uncertainties to reflect our imperfect understanding of the stellar photospheric properties (e.g., cases 3l and 3h).
Paper: Toward Robust Corrections for Stellar Contamination in JWST Exoplanet Transmission Spectra
Authors: Benjamin V. Rackham, Julien de Wit
Figure caption: Akaike weights for model fits with different levels of complexity in the case of the direct retrievals. Here the same model grid was used to simulate and retrieve on the data. The top and bottom rows give results for the K0 and M6 stars, respectively. From left to right, the panels give results for the 1, 2l, 2h, 3l, and 3h cases, respectively. In each panel, the Akaike weights are shown for the models with N spectral components, and the correct model complexity is indicated by a star. The colors distinguish results for the five noise models. For the direct retrievals, the Akaike weights generally favor the correct model complexity, though the noise instance can change this, and other models sometimes receive substantial weight.
Paper: Toward Robust Corrections for Stellar Contamination in JWST Exoplanet Transmission Spectra
Authors: Benjamin V. Rackham, Julien de Wit
Figure caption: Best fits to the K0 case 1 spectrum for MPS-ATLAS models with one to four components. The top panel shows the simulated NIRSpec/PRISM spectrum in black, along with the four fits. Uncertainties for the data and posterior models are smaller than the line widths. Likewise, differences between the data, simulated with the PHOENIX grid, and the models, interpolated from the MPS-ATLAS grid, are too small to be apparent at this scale. The bottom panel shows the residuals for the fits, normalized to the data. In this view, residuals on the order of 10 ppt (1%) are evident. For comparison, the gray line shows the residuals for a direct retrieval with the best-fit one-component PHOENIX model. The relative scales of the residuals for the cross- and direct retrievals gives a sense of the impact of uncertainties due to stellar models vs. photon noise.
Paper: Toward Robust Corrections for Stellar Contamination in JWST Exoplanet Transmission Spectra
Authors: Benjamin V. Rackham, Julien de Wit
Figure caption: State-of-the-art model spectra of quiescent K0 and M6 dwarfs at the wavelengths and resolution of NIRSpec/PRISM. The left column shows spectra of a K0 dwarf drawn from the PHOENIX and MPS-ATLAS grids. The top panel shows the spectra, and the bottom panel shows the flux difference between them, normalized to the flux of the PHOENIX model. The right column shows the same for the PHOENIX and SPHINX spectra of the M6 dwarf used as an example in this work. See also Iyer et al. (2023, Figure 1) for a comparison of M dwarf spectra across many stellar models.
Paper: Toward Robust Corrections for Stellar Contamination in JWST Exoplanet Transmission Spectra
Authors: Benjamin V. Rackham, Julien de Wit
Figure caption: Akaike weights for model fits with different levels of complexity in the case of the cross-retrievals. The figure elements are the same as those in Figure 2. The top row gives results for the K0 star, simulated with PHOENIX and retrieved with MPS-ATLAS. The bottom row gives results for the M6 star, simulated with PHOENIX and retrieved with SPHINX. In most cases, the cross-retrievals (i.e., retrievals with lower model fidelity) fail to weight models with the correct complexity.
Paper: Toward Robust Corrections for Stellar Contamination in JWST Exoplanet Transmission Spectra
Authors: Benjamin V. Rackham, Julien de Wit
Figure caption: Median fractional uncertainties of the transmission spectrum, med(σ(D)/D), and the stellar contamination signal, med(σ(ϵ)/ϵ), for the K0 case 2h data set. The left (right) column shows BMA results for the direct retrieval (cross-retrieval). In the top row, the black points show the median fractional uncertainty on the transit depth as a function of the out-of-transit baseline, and the green points show the same for the stellar contamination signal. For the direct retrieval, med(σ(ϵ)/ϵ) is consistently smaller than med(σ(D)/D), and it tends to decrease with increasing out-of-transit baseline, as expected. For the cross-retrieval, med(σ(ϵ)/ϵ) is comparable to med(σ(D)/D) and does not decrease with increasing baseline because it is limited by the model fidelity, not the photon noise. The bottom row shows the true (black), observed (blue), and corrected (orange) transmission spectra resulting from these retrievals. The direct-retrieval results show that when the model fidelity is sufficient, an accurate stellar contamination correction is possible and its contribution to the noise budget of the planetary spectrum is negligible. By contrast, the cross-retrieval results show that with limited model fidelity, med(σ(ϵ)/ϵ) is on par with med(σ(D)/D), yielding uncertainties on the corrected transmission spectrum that are roughly twice as large and a stellar contamination correction that is not guaranteed to be accurate.
Paper: Toward Robust Corrections for Stellar Contamination in JWST Exoplanet Transmission Spectra
Authors: Benjamin V. Rackham, Julien de Wit
Figure caption: Flux changes due to temperature variations for the range of stellar models we consider. The left column shows PHOENIX models relevant to our K0 case, and the right column shows PHOENIX models relevant to our M6 case. The top panels show the spectra in absolute flux units, while the bottom panels show each set normalized to the middle-temperature model in each set. The wavelengths and resolution of the spectra are relevant to NIRSpec/PRISM. Larger flux differences are evident for the set of models relevant to the M6 case, which lead to more successful inferences from the retrievals.
Paper: Toward Robust Corrections for Stellar Contamination in JWST Exoplanet Transmission Spectra
Authors: Benjamin V. Rackham, Julien de Wit
Figure caption: The sensitivity of PHOENIX stellar models, sampled at the wavelength range and resolution of NIRSpec/PRISM, to changes in stellar temperature. The relative change in stellar flux is shown as a function of the model effective temperature, normalized to the value for a model of 2500 K. The sensitivity to temperature decreases with increasing temperature, with models around 6000 K displaying roughly 20% of the sensitivity of the 2500 K model.
Paper: Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Authors: John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt
Figure caption: Comparison of the r − i vs. redshift distribution for galaxy samples from CosmoDC2 without photometric noise (left), CosmoDC2 with photometric noise (middle), and from the normalizing flow (right). High-redshift galaxies in CosmoDC2 lie along discrete tracks in redshift–color space. Adding photometric noise somewhat smooths but does not totally remove these tracks. PZFlow produces a catalog with a smooth redshift–color distribution. ⏬                      ✎
Paper: Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Authors: John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt
Figure caption: Distribution of true redshifts and noisy photometry from the CosmoDC2 test set, compared to a sample drawn from the distribution learned by PZFlow. The close overlap of every pair-wise distribution demonstrates that PZFlow has learned the distribution in CosmoDC2 with high fidelity. ⏬                      ✎
Paper: Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Authors: John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt
Figure caption: Example of redshift posteriors for a galaxy simulated with PZFlow. The true redshift of the galaxy is marked by the vertical dashed red line, and the true redshift posterior for the galaxy is drawn in black. Calculating the posterior while marginalizing over the u-band magnitude yields the posterior in blue. Note that the u-band marginalization is only approximate, but increasing the resolution of the grid of u-band values causes the resultant posterior to converge. ⏬                      ✎
Paper: Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Authors: John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt
Figure caption: A normalizing flow demonstrated on the two-moons data set from scikit-learn. The two-moons data on the left is mapped onto a two-dimensional uniform distribution by the bijection f. The data are colored by quadrant to visualize their image in the latent space. You can sample the data distribution by sampling from the uniform distribution, and using f −1 to map the samples back to the data space. ⏬                   ✎
Paper: Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Authors: John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt
Figure caption: Diagram of a conditional coupling layer. The first partition, ﹩{x}_{1:d}^{}﹩, is passed through the layer unchanged. The second partition, ﹩{x}_{d+1:D}^{}﹩, is transformed by the coupling law g, which is parameterized by the coupling function m applied to the first partition and the conditional variables ﹩{z}_{1:L}^{}﹩. The conditional variables are never altered by the flow.
Paper: Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Authors: John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt
Figure caption: Conditional distributions of the ellipticity and size of the galaxies in the CosmoDC2 test set compared to the distribution learned by PZFlow. The close overlap of every pair-wise distribution demonstrates that PZFlow has learned the distribution in CosmoDC2 with high fidelity. ⏬                      ✎
Paper: Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Authors: John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt
Figure caption: Diagram of a coupling layer. The first partition, ﹩{x}_{1:d}^{}﹩, is passed through the layer unchanged. The second partition, ﹩{x}_{d+1:D}^{}﹩, is transformed by the coupling law g, which is parameterized by the coupling function m applied to the first partition.
Paper: Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Authors: John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt
Figure caption: The bias, scatter, and outlier fraction of the photo-z point estimates as a function of true galaxy redshift. The dashed black lines represent the limits for LSST cosmology as stated in the LSST DESC SRD (LSST Dark Energy Science Collaboration et al. 2018). The bias must be between these dashed lines, while the scatter and outlier fraction must be below the dashed lines. You can see that PZFlow meets the bias and scatter requirements up to redshift ∼1.5 (the scatter is high at low redshifts due to the relatively small number of low-redshift galaxies in our training set). PZFlow meets the outlier requirements for all redshifts. We note that individual redshifts do not actually need to meet the bias requirement as long as the bias can be well calibrated via some other source, e.g., galaxy clustering. ⏬                      ✎
Paper: Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Authors: John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt
Figure caption: The ensemble of posteriors for three example galaxies. Flows 1–4 label the individual posteriors produced by each of the flows that make up the ensemble. The dashed black line is the mean of these individual posteriors and is the value used by the ensemble. The vertical gray line labeled “Truth” denotes the true redshift of the galaxy. Averaging the posteriors from each flow in the ensemble approximately marginalizes over the neural network parameters, and smooths over the small-scale variations found in the posterior from any individual flow. The first panel is a galaxy with a narrow and unimodal redshift posterior, while the next two panels demonstrate broad, multimodal posteriors, which is typical of galaxies in the range 1.5 < z < 2.6. ⏬                      ✎
Paper: The Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER). VI. The High-mass Stellar Initial Mass Function of M33
Authors: Tobin M. Wainer, Benjamin F. Williams, L. Clifton Johnson, Daniel R. Weisz, Julianne J. Dalcanton, Anil C. Seth, Andrew Dolphin, Meredith J. Durbin, Eric F. Bell, Zhuo Chen, Puragra Guhathakurta, Eric W. Koch, Christina W. Lindberg, Erik Rosolowsky, Karin M. Sandstrom, Evan D. Skillman, Adam Smercina, Estephani E. TorresVillanueva
Figure caption: The marginalized PDF for M33 cluster 280 showing the the posterior PDFs for Γ, age, and extinction. We also include the F475W and F814W combined color image with the 2.″33 radius, visualized by the green circle. We also show the CMD and best-fit age and A  V  estimates, along with the uncertainties, from Wainer et al. (2022) in the text, which are interesting to compare to our full PDFs. We also show in the text the measured Γ. In the upper left, middle, and lower right panels, the blue line represents the median of the marginalized PDF, and the red lines correspond to the 16th and 84th percentiles. The bin sizes in the PDFs correspond to the grid with which each parameter was fit over, as described in the text.
Paper: Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Authors: John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt
Figure caption: Probability integral transform (PIT) histogram for PZFlow photo-z posteriors. The PIT characterizes the calibration of the estimated posteriors, with the horizontal black line indicating perfect calibration. ⏬                      ✎
Paper: The Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER). VI. The High-mass Stellar Initial Mass Function of M33
Authors: Tobin M. Wainer, Benjamin F. Williams, L. Clifton Johnson, Daniel R. Weisz, Julianne J. Dalcanton, Anil C. Seth, Andrew Dolphin, Meredith J. Durbin, Eric F. Bell, Zhuo Chen, Puragra Guhathakurta, Eric W. Koch, Christina W. Lindberg, Erik Rosolowsky, Karin M. Sandstrom, Evan D. Skillman, Adam Smercina, Estephani E. TorresVillanueva
Figure caption: On the left is a F475W, F814W combined color image for M33 cluster 163 identified by Johnson et al. (2022) clearly showing resolved stars. On the right is the best-fit isochrone to stellar photometry from Wainer et al. (2022), highlighting the level of photometric depth available in the CMD. The blue dots are stars that fall within the photometric aperture, while the open red circles are stars within the background annulus (see text for details), illustrating the input data to MATCH.
Paper: Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Authors: John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt
Figure caption: Training losses for the normalizing flows used to simulate the galaxy catalog. Left: losses for the regular flow. Right: losses for the conditional flow. ⏬                      ✎
Paper: Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Authors: John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt
Figure caption: Training losses for the four flows in the flow ensemble. We have zoomed in to the bottom of the loss curve so you can see that each of the flows converges to a slightly different minimum loss. ⏬                      ✎
Paper: Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Authors: John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt
Figure caption: Photo-z point estimates (maximum a posteriori) vs. true redshift for galaxies in the test set. ⏬                      ✎
Paper: The Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER). VI. The High-mass Stellar Initial Mass Function of M33
Authors: Tobin M. Wainer, Benjamin F. Williams, L. Clifton Johnson, Daniel R. Weisz, Julianne J. Dalcanton, Anil C. Seth, Andrew Dolphin, Meredith J. Durbin, Eric F. Bell, Zhuo Chen, Puragra Guhathakurta, Eric W. Koch, Christina W. Lindberg, Erik Rosolowsky, Karin M. Sandstrom, Evan D. Skillman, Adam Smercina, Estephani E. TorresVillanueva
Figure caption: The joint and marginalized distributions for the distribution of the cluster MF, described by Equations (1) and (2). The dashed lines represent the 16th, 50th, and 84th percentiles of each marginalized distribution. Parameters are the mean slope of the IMF, ﹩\overline{{\rm{\Gamma }}}﹩, and the intrinsic scatter in that slope, σ Γ, from the ensemble of cluster MFs, as well as parameters testing for dependencies on cluster properties: cluster age a  t , mass a  m , cluster radius a  r , local SFR a sfr, and galactocentric radius a gr, as described in the text.
Paper: The Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER). VI. The High-mass Stellar Initial Mass Function of M33
Authors: Tobin M. Wainer, Benjamin F. Williams, L. Clifton Johnson, Daniel R. Weisz, Julianne J. Dalcanton, Anil C. Seth, Andrew Dolphin, Meredith J. Durbin, Eric F. Bell, Zhuo Chen, Puragra Guhathakurta, Eric W. Koch, Christina W. Lindberg, Erik Rosolowsky, Karin M. Sandstrom, Evan D. Skillman, Adam Smercina, Estephani E. TorresVillanueva
Figure caption: The MF slope for each cluster compared to the best-fit cluster age (left) and mass (right) from the Wainer et al. (2022) fits. Each point size is scaled by the cluster radius and colored by the probability of being a cluster based on the weighting system of Johnson et al. (2022). The black dashed line is at the Kroupa IMF of Γ = 1.3, and the red line shows the median IMF result from this work of Γ = 1.49. The gray region represents the region of mass space where our synthetic clusters suggest a potential for bias. The left panel has points that are lower than 3.6 log(Mass/M ⊙) (gray points).
Paper: The Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER). VI. The High-mass Stellar Initial Mass Function of M33
Authors: Tobin M. Wainer, Benjamin F. Williams, L. Clifton Johnson, Daniel R. Weisz, Julianne J. Dalcanton, Anil C. Seth, Andrew Dolphin, Meredith J. Durbin, Eric F. Bell, Zhuo Chen, Puragra Guhathakurta, Eric W. Koch, Christina W. Lindberg, Erik Rosolowsky, Karin M. Sandstrom, Evan D. Skillman, Adam Smercina, Estephani E. TorresVillanueva
Figure caption: Cluster Γ as a function of cluster ΣSFR and galactic radius, which serves as a proxy for metallicity. The ΣSFR is measured for a ∼0.01 kpc2 region containing the cluster, calculated by Lazzarini et al. (2022) from 0 to 100 Myr. Each point is colored by the log(Age/yr) of the cluster and scaled by the radius of the cluster to easily map to Figure 7, and it is grayed out if it is below a log(Mass/M ⊙) of 3.6. These results show no trend in IMF with either ΣSFR or galactic radius. The secondary axis for galactic radius is the change in the 12+log(O/H) abundance gradient with radius according to Rogers et al. (2022).
Paper: The Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER). VI. The High-mass Stellar Initial Mass Function of M33
Authors: Tobin M. Wainer, Benjamin F. Williams, L. Clifton Johnson, Daniel R. Weisz, Julianne J. Dalcanton, Anil C. Seth, Andrew Dolphin, Meredith J. Durbin, Eric F. Bell, Zhuo Chen, Puragra Guhathakurta, Eric W. Koch, Christina W. Lindberg, Erik Rosolowsky, Karin M. Sandstrom, Evan D. Skillman, Adam Smercina, Estephani E. TorresVillanueva
Figure caption: MF recovery for 45 synthetic clusters using the analysis techniques described in Section 3. The x-axis is the input mass of the synthetic cluster, and each point is colored by the input log(Age/yr) of the synthetic cluster. The black line represents the input Kroupa IMF slope. We can see a clear gradient in recovered Γ with mass, with lower-mass clusters recovering higher Γ values. The gray region in the plot signifies the break at log(Mass/M ⊙) of 3.6, where the synthetics recover the input IMF.
Paper: The Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER). VI. The High-mass Stellar Initial Mass Function of M33
Authors: Tobin M. Wainer, Benjamin F. Williams, L. Clifton Johnson, Daniel R. Weisz, Julianne J. Dalcanton, Anil C. Seth, Andrew Dolphin, Meredith J. Durbin, Eric F. Bell, Zhuo Chen, Puragra Guhathakurta, Eric W. Koch, Christina W. Lindberg, Erik Rosolowsky, Karin M. Sandstrom, Evan D. Skillman, Adam Smercina, Estephani E. TorresVillanueva
Figure caption: The 20% F475W photometric completeness limit, as determined by ASTs, as a function of stellar mass. The secondary axis corresponds to the limiting main-sequence stellar mass probed at this magnitude.
Paper: The Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER). VI. The High-mass Stellar Initial Mass Function of M33
Authors: Tobin M. Wainer, Benjamin F. Williams, L. Clifton Johnson, Daniel R. Weisz, Julianne J. Dalcanton, Anil C. Seth, Andrew Dolphin, Meredith J. Durbin, Eric F. Bell, Zhuo Chen, Puragra Guhathakurta, Eric W. Koch, Christina W. Lindberg, Erik Rosolowsky, Karin M. Sandstrom, Evan D. Skillman, Adam Smercina, Estephani E. TorresVillanueva
Figure caption: Shown are cluster CMDs, the Γ marginalized PDFs, and the F745W and F814W color images for two low-mass clusters: clusters 1218 and 1334. The left panels are CMDs as reported in Johnson et al. (2022), where the black points are cluster members. The middle panels are the marginalized Γ PDFs for each cluster, where the blue line is the median and the red lines represent the 16th and 84th percentiles.