adsabs/AstroCLIMB huggingface dataset:
Paper: Forecasts and Statistical Insights for Line Intensity Mapping Cross-correlations: A Case Study with 21 cm × [C II]
Authors: Hannah Fronenberg, Adrian Liu
Figure caption: Mock constraints on the slope m and crossover scale k 0, expressed as marginalized posteriors where all variables except for the parameter in question have been marginalized over. Top: constraints using the true window functions of the power-spectrum estimate, as computed in Figure 12. Bottom: biased inferences made assuming that the window functions are Dirac delta functions. Different solid lines show the effect of different error covariances while the true parameter values are shown with vertical dotted lines. One sees that neglecting window functions in analyses can lead to biased inferences, whereas the mismodeling of error covariances can lead to parameter constraints that are overly pessimistic or overly optimistic.
Paper: Surface Flux Transport Modeling Using Physics-informed Neural Networks
Authors: Jithu J Athalathil, Bhargav Vaidya, Sayan Kundu, Vishal Upendran, Mark C. M. Cheung
Figure caption: Row 1: butterfly diagram obtained by solving the 1D SFT equation including the source term. (a) numerical solution obtained with the RK-IMEX scheme. The solution for the Explicit method is not plotted since it is similar to the RK-IMEX scheme. (b) solution attained through the PINN network. The rectangular box marks the differences near the boundary between the two methods. Row 2: different resolutions of numerical solutions compared with the PINN solution. We can see a decrease in error with increasing resolution. (c) solution for t = 6.384 yr (corresponding to the vertical lines in (a) and (b)). The variation in the boundary can be seen in the inner plot. (d) variation in the loss with an increase in the number of grid points (Ngrid) for both numerical schemes (RK-IMEX and Explicit). The L1 norm is calculated by comparing the numerical solutions with the PINN solution.
Paper: Surface Flux Transport Modeling Using Physics-informed Neural Networks
Authors: Jithu J Athalathil, Bhargav Vaidya, Sayan Kundu, Vishal Upendran, Mark C. M. Cheung
Figure caption: Evolution of BMRs using the RK-IMEX at different initial latitudes (row 1: 0° and row 2: 12°). Column 1: initial BMR planted in the θ–ϕ plane B r(t = 0). Column 2: magnetic field averaged in longitude <B r(t = 0) >. Columns 3 and 4: average magnetic field obtained through the evolution of BMR using 1D SFT equation <B r(t) > with RK-IMEX (column 3) and PINN (column 4) methods.
Paper: Surface Flux Transport Modeling Using Physics-informed Neural Networks
Authors: Jithu J Athalathil, Bhargav Vaidya, Sayan Kundu, Vishal Upendran, Mark C. M. Cheung
Figure caption: Schematic of PINNs to solve SFT equations. The input layer comprises the latitude (λ), longitude (ϕ), and time (t). The output corresponds to the magnetic field at the specified coordinate and time. The loss function incorporates the physical equation (Equation (1)) and initial and boundary conditions. Note that the hidden layers depicted in the image are for representational purposes only, with actual values specified in Table 1.
Paper: Surface Flux Transport Modeling Using Physics-informed Neural Networks
Authors: Jithu J Athalathil, Bhargav Vaidya, Sayan Kundu, Vishal Upendran, Mark C. M. Cheung
Figure caption: Evolved magnetic field for β = 10. (a) L1 norm calculated for the PINNs and RK-IMEX scheme compared to the analytic solution. (b) Solution for the time stamp t = τ d for different numbers of grid points. The PINN solution (blue) and analytical solution (black dashes) are also plotted. (c) Total flux in the southern hemisphere. (d) Percentage error of the flux with respect to the analytical flux.
Paper: Surface Flux Transport Modeling Using Physics-informed Neural Networks
Authors: Jithu J Athalathil, Bhargav Vaidya, Sayan Kundu, Vishal Upendran, Mark C. M. Cheung
Figure caption: Evolution of multiple BMRs using the PINN method. (a) Initial state from which the multiple BMRs (BMR1 and BMR2) evolve. (b) Magnetic field when the BMRs evolved together. (c) Evolved magnetic field of BMR1. (e) Evolution of BMR2. (d) Sum of the individually evolved BMRs. (f) Difference between the sum of BMRs that evolved individually and the BMRs that evolved together.
Paper: Surface Flux Transport Modeling Using Physics-informed Neural Networks
Authors: Jithu J Athalathil, Bhargav Vaidya, Sayan Kundu, Vishal Upendran, Mark C. M. Cheung
Figure caption: Percentage error in (a) magnetic field and (b) magnetic flux due to different sets of hyperparameter perturbation for the analytic validation as given in Table 2. The horizontal dashed lines correspond to the error (magnetic field and flux) for the optimum hyperparameters used as given in Table 1.
Paper: Surface Flux Transport Modeling Using Physics-informed Neural Networks
Authors: Jithu J Athalathil, Bhargav Vaidya, Sayan Kundu, Vishal Upendran, Mark C. M. Cheung
Figure caption: Training and test losses for the PINNs for 1D SFT equation. Left: The weighted loss considers the weights that have been applied to each term during training. Right: The unweighted loss is calculated by dividing out the weight giving the actual weight of the training.
Paper: Surface Flux Transport Modeling Using Physics-informed Neural Networks
Authors: Jithu J Athalathil, Bhargav Vaidya, Sayan Kundu, Vishal Upendran, Mark C. M. Cheung
Figure caption: Solution for the SFT 2D equation. (a) Initial AR at (75°, 270°), (b) solution using the RK-IMEX scheme, (c) difference in the solutions from the PINN and RK-IMEX schemes, and (d) solution using the PINN method.
Paper: Surface Flux Transport Modeling Using Physics-informed Neural Networks
Authors: Jithu J Athalathil, Bhargav Vaidya, Sayan Kundu, Vishal Upendran, Mark C. M. Cheung
Figure caption: Variation in the error (L1 norm) on both the test and training sets on varying the number of training points. The error reduces initially and gets saturated.
Paper: Predicting the Energetic Proton Flux with a Machine Learning Regression Algorithm
Authors: Mirko Stumpo, Monica Laurenza, Simone Benella, Maria Federica Marcucci
Figure caption: (Top panel) A 3 month sample data set was used for testing/visualization purposes. The green line represents the target of the model (true values), while the blue line represents the output of the model (forecasting). Both are representative of the logarithm of the proton flux integrated from 7.8 MeV to 53 MeV energy channels (P8, P25, P41; see Table 1). The proton flux is given in proton flux units, while the logarithm is adimensional. The inset shows a closer look around the ESP event of 2012 March 8, which is not well reproduced by the model. This is due to the fact that the electrons do not exhibit the ESP signature (see the light blue line, which shows the track of the E300 electron differential channel). (Bottom panel) Model predictions during a quiet time period lasting approximately 4 months. Even though the fluctuations of background proton flux are not well reproduced, the model correctly distinguishes quiet from disturbed periods.
Paper: Predicting the Energetic Proton Flux with a Machine Learning Regression Algorithm
Authors: Mirko Stumpo, Monica Laurenza, Simone Benella, Maria Federica Marcucci
Figure caption: (Left panels) Comparison between the predicted time series and the target one for three samples: 23, 18, and 31 (from top to bottom). (Right panel) Correlation plot between the predicted and the target points. The color bar represents the density of the points and the black line represents the bisector (not the linear regression line). Note that the sample 31 is the worst possible example (see Table 3). However, even though the model overestimates the flux, the trend is reproduced quite well during the enhancement of 2016 July 23.
Paper: Predicting the Energetic Proton Flux with a Machine Learning Regression Algorithm
Authors: Mirko Stumpo, Monica Laurenza, Simone Benella, Maria Federica Marcucci
Figure caption: Coefficient of determination R 2 (left panel) and RMSE (right panel) as a function of the target peak.
Paper: Scylla. III. The Outside-in Radial Age Gradient in the Small Magellanic Cloud and the Star Formation Histories of the Main Body, Wing, and Outer Regions
Authors: Roger E. Cohen, Kristen B. W. McQuinn, Claire E. Murray, Benjamin F. Williams, Yumi Choi, Christina W. Lindberg, Clare Burhenne, Karl D. Gordon, Petia Yanchulova Merica-Jones, Caroline Bot, Andrew E. Dolphin, Karoline M. Gilbert, Steven Goldman, Alec S. Hirschauer, Karin M. Sandstrom, O. Grace Telford
Figure caption: Left: cumulative SFHs of fields in the wing (orange solid line), nonwing fields at similarly large radii of ﹩{{R}}_{{\rm{SMC}},{\rm{opt}}}^{{\rm{ell}}}﹩ > 3.5 kpc (purple dashed line), and fields in the main body with ﹩{{R}}_{{\rm{SMC}},{\rm{opt}}}^{{\rm{ell}}}﹩ < 2 kpc (red dashed–dotted line). The wing and nonwing fields assembled a similar fraction of their total mass at early times (look-back time ≥6 Gyr), but in the most recent ∼2 Gyr, the wing fields assembled a much larger fraction of their mass, similar to fields in the main body. Conversely, the outer nonwing fields formed only ∼1% of their mass within the past 1 Gyr. Right: average star formation rate per unit area in several illustrative time intervals. Colors are as in the left panel. Although the wing fields formed a similar mass fraction in the past ∼1−2 Gyr to the main body, their star formation rate per projected unit area ΣSFR has remained an order of magnitude lower. Meanwhile, the wing and nonwing fields, at similarly large distances from the SMC center, have similar star formation rates until ∼2 Gyr ago, when ΣSFR in the nonwing outer fields decreased by an order of magnitude until the most recent LMC–SMC collision.
Paper: Scylla. III. The Outside-in Radial Age Gradient in the Small Magellanic Cloud and the Star Formation Histories of the Main Body, Wing, and Outer Regions
Authors: Roger E. Cohen, Kristen B. W. McQuinn, Claire E. Murray, Benjamin F. Williams, Yumi Choi, Christina W. Lindberg, Clare Burhenne, Karl D. Gordon, Petia Yanchulova Merica-Jones, Caroline Bot, Andrew E. Dolphin, Karoline M. Gilbert, Steven Goldman, Alec S. Hirschauer, Karin M. Sandstrom, O. Grace Telford
Figure caption: Lifetime SFH metrics vs. semimajor-axis equivalent distance from the optical center of the SMC ﹩{{R}}_{{\rm{ell}}}^{{\rm{SMC}}}﹩. Top row: look-back time to form (from left to right) 90%, 75%, and 50% of the cumulative stellar mass as a function of ﹩{{R}}_{{\rm{ell}}}^{{\rm{SMC}}}﹩. Fields are color coded by their position angle east of north, and fields in the wing of the SMC are shown in orange, highlighting their increasing discrepancy with the remainder of fields at similarly large ﹩{{R}}_{{\rm{ell}}}^{{\rm{SMC}}}﹩ at recent look-back times. Linear fits excluding the wing fields are shown in blue, with dark (light) shading representing 1σ (2σ) uncertainties. The linear fit coefficients, Pearson linear correlation coefficient ρ, and Spearman rank correlation coefficient r  s  are given in each panel. Bottom row: fraction of cumulative stellar mass formed by look-back times of (from left to right) 1, 3, and 6 Gyr, again vs. ﹩{{R}}_{{\rm{ell}}}^{{\rm{SMC}}}﹩, with symbols as in the top row. Compared to other fields at similarly large distances from the center of the SMC (﹩{R}_{{\rm{ell}}}^{{\rm{SMC}}}﹩ > 3.5 kpc), fields in the wing assembled a larger fraction of their mass more recently than ∼6 Gyr ago. Results shown assume PARSEC evolutionary models, and in Table 3 we give linear fit coefficients assuming other stellar evolutionary models for SFH fitting and a simple on-sky tangent plane (rather than elliptical) radius to characterize distance from the SMC center.
Paper: Scylla. III. The Outside-in Radial Age Gradient in the Small Magellanic Cloud and the Star Formation Histories of the Main Body, Wing, and Outer Regions
Authors: Roger E. Cohen, Kristen B. W. McQuinn, Claire E. Murray, Benjamin F. Williams, Yumi Choi, Christina W. Lindberg, Clare Burhenne, Karl D. Gordon, Petia Yanchulova Merica-Jones, Caroline Bot, Andrew E. Dolphin, Karoline M. Gilbert, Steven Goldman, Alec S. Hirschauer, Karin M. Sandstrom, O. Grace Telford
Figure caption: Top: map of the fields analyzed here, overplotted on a stellar density map from Gaia DR3 (see text for details). Our fields are shown as filled circles, color coded by the source of the imaging, including new WFC3/UVIS imaging from Scylla (red), archival ACS/WFC and WFC3/UVIS imaging (orange), and archival WFPC2 LGSPA photometry from J. A. Holtzman et al. (2006) (blue; also analyzed by D. R. Weisz et al. 2013). The subset of archival ACS/WFC fields also analyzed by M. Cignoni et al. (2013) are marked by magenta squares, and ground-based fields from N. E. D. Noël et al. (2007) are shown as green squares. Black symbols indicate different locations of the SMC center, including the optical center we adopt (H. H. Crowl et al. 2001; S. Subramanian & A. Subramaniam 2012) and the H I centers from E. M. Di Teodoro et al. (2019) and S. Stanimirović et al. (2004). Bottom: our fields are overplotted on the GASKAP H I column density map (N. M. Pingel et al. 2022), now color coded by location within the SMC, including the main body (red), wing (orange), and outer nonwing fields (purple; see text for details). Open circles indicate fields not falling into any of these three subsets, with 2 kpc < ﹩{R}_{\mathrm{ell}}^{\mathrm{SMC}}﹩ < 3.5 kpc.
Paper: Scylla. III. The Outside-in Radial Age Gradient in the Small Magellanic Cloud and the Star Formation Histories of the Main Body, Wing, and Outer Regions
Authors: Roger E. Cohen, Kristen B. W. McQuinn, Claire E. Murray, Benjamin F. Williams, Yumi Choi, Christina W. Lindberg, Clare Burhenne, Karl D. Gordon, Petia Yanchulova Merica-Jones, Caroline Bot, Andrew E. Dolphin, Karoline M. Gilbert, Steven Goldman, Alec S. Hirschauer, Karin M. Sandstrom, O. Grace Telford
Figure caption: Left columns: completeness vs. magnitude for each individual field, with separate panels for each filter combination present in our imaging sample. In each panel, completeness as a function of magnitude (in the Vegamag photometric system) is shown in purple for the bluer of the two filters and orange for the redder of the two filters. Rightmost column: magnitude difference between the 80% completeness limit, used as the faint limit for SFH fitting, and the MSTO magnitude in each field, assessed using a 13 Gyr PARSEC isochrone with [M/H] = −1.2, (m − M)0,SMC = 18.96, and extinction from D. M. Skowron et al. (2021). The top right and bottom right panels correspond to the bluer and redder filter available in each field, respectively, illustrating that the vast majority (>90%) of our fields (and all of the Scylla fields) are 80% complete to at least 1 mag below the ancient MSTO in both filters.
Paper: Scylla. III. The Outside-in Radial Age Gradient in the Small Magellanic Cloud and the Star Formation Histories of the Main Body, Wing, and Outer Regions
Authors: Roger E. Cohen, Kristen B. W. McQuinn, Claire E. Murray, Benjamin F. Williams, Yumi Choi, Christina W. Lindberg, Clare Burhenne, Karl D. Gordon, Petia Yanchulova Merica-Jones, Caroline Bot, Andrew E. Dolphin, Karoline M. Gilbert, Steven Goldman, Alec S. Hirschauer, Karin M. Sandstrom, O. Grace Telford
Figure caption: Example SFH fits for two fields (top row: SMC_13; bottom row: SMC_40) spanning the range of fit quality seen across our sample to illustrate our typical SFH and AMR precision on a per-field basis. Left: Hess diagrams showing the observed CMD, the best-fit model, and the residuals in the sense (data − model). Middle: best-fit AMR, with uncertainties indicated by gray shading. Despite large per-field uncertainties, both fields are consistent with an AMR that increases monotonically with time. Right: best-fit CSFH, showing the fraction of total mass ever formed as a function of look-back time. We quantify the CSFH using the metrics τ 50, τ 75, and τ 90 to indicate the look-back times by which 50%, 75%, and 90% of the cumulative stellar mass had formed, respectively. These values are overplotted on the CSFHs using blue, orange, and red vertical lines, respectively, with shading corresponding to 1σ uncertainties.
Paper: Scylla. III. The Outside-in Radial Age Gradient in the Small Magellanic Cloud and the Star Formation Histories of the Main Body, Wing, and Outer Regions
Authors: Roger E. Cohen, Kristen B. W. McQuinn, Claire E. Murray, Benjamin F. Williams, Yumi Choi, Christina W. Lindberg, Clare Burhenne, Karl D. Gordon, Petia Yanchulova Merica-Jones, Caroline Bot, Andrew E. Dolphin, Karoline M. Gilbert, Steven Goldman, Alec S. Hirschauer, Karin M. Sandstrom, O. Grace Telford
Figure caption: Stacked Hess diagrams for the various filter combinations present in our imaging. The total number of fields we analyze in a particular filter combination is given in the top left of each panel, along with the total number of stars passing our photometric quality cuts. Median photometric errors are shown along the left side of each CMD. PARSEC isochrones are overplotted to illustrate the range of stellar ages accessible in our photometry in different evolutionary phases, with representative stellar masses indicated along the main sequence. Due to observational restrictions imposed by the ULLYSES primary imaging, the brightest SMC sources (corresponding to main-sequence stellar masses ≳2.5M ⊙) are saturated in many cases.
Paper: Scylla. III. The Outside-in Radial Age Gradient in the Small Magellanic Cloud and the Star Formation Histories of the Main Body, Wing, and Outer Regions
Authors: Roger E. Cohen, Kristen B. W. McQuinn, Claire E. Murray, Benjamin F. Williams, Yumi Choi, Christina W. Lindberg, Clare Burhenne, Karl D. Gordon, Petia Yanchulova Merica-Jones, Caroline Bot, Andrew E. Dolphin, Karoline M. Gilbert, Steven Goldman, Alec S. Hirschauer, Karin M. Sandstrom, O. Grace Telford
Figure caption: Comparison between our combined CSFHs for different spatial regions in the SMC (shown as in Figure 5) and SMC CSFHs from P. Massana et al. (2022; left column), S. Rubele et al. (2018; middle column), and D. R. Weisz et al. (2013; right column). Each row corresponds to a different stellar evolutionary model that we assumed when calculating our SFH results, while each of the literature SFH studies shown used a single, different evolutionary model. Agreement is generally good, especially given differences in spatial sampling of the SMC.
Paper: Scylla. III. The Outside-in Radial Age Gradient in the Small Magellanic Cloud and the Star Formation Histories of the Main Body, Wing, and Outer Regions
Authors: Roger E. Cohen, Kristen B. W. McQuinn, Claire E. Murray, Benjamin F. Williams, Yumi Choi, Christina W. Lindberg, Clare Burhenne, Karl D. Gordon, Petia Yanchulova Merica-Jones, Caroline Bot, Andrew E. Dolphin, Karoline M. Gilbert, Steven Goldman, Alec S. Hirschauer, Karin M. Sandstrom, O. Grace Telford
Figure caption: Relationship between look-back time to form a given mass fraction (from Figure 6) vs. H I column density (N. M. Pingel et al. 2022). The Pearson linear correlation coefficient ρ and the Spearman rank correlation coefficient r  s  are given in the lower left corner of each panel vs. log10 N(H I), along with the coefficients of the best-fit line (shown in blue). The mass assembly histories of our fields correlate loosely with present-day N(H I) over a broad range of look-back times, although this may simply reflect the fairly concentrated present-day H I morphology of the SMC.
Paper: Scylla. III. The Outside-in Radial Age Gradient in the Small Magellanic Cloud and the Star Formation Histories of the Main Body, Wing, and Outer Regions
Authors: Roger E. Cohen, Kristen B. W. McQuinn, Claire E. Murray, Benjamin F. Williams, Yumi Choi, Christina W. Lindberg, Clare Burhenne, Karl D. Gordon, Petia Yanchulova Merica-Jones, Caroline Bot, Andrew E. Dolphin, Karoline M. Gilbert, Steven Goldman, Alec S. Hirschauer, Karin M. Sandstrom, O. Grace Telford
Figure caption: Combined SMC AMR for each of our three assumed evolutionary models. The SMC field AMRs from R. Carrera et al. (2008) and A. E. Piatti & D. Geisler (2013) are overplotted in magenta and orange, respectively, and SMC clusters are overplotted in cyan, with ages and metallicities from F. F. S. Maia et al. (2019), B. Dias et al. (2021, 2022), and R. A. P. Oliveira et al. (2023).
Paper: Evidence for a Fast Soft X-Ray Wind in M82 from XMM-RGS
Authors: Erin Boettcher, Edmund Hodges-Kluck
Figure caption: An archival Chandra-ACIS observation (ObsID: 10542; PI: D. Strickland) shows that the soft X-ray emission from the M82 outflow (E = 0.3−2 keV) is spatially extended on scales of several arcminutes (top left panel). The spatial profile is significantly more extended and displays more substructure in the lowest energy emission lines (i.e., O VIII 0.65 keV) than in the highest energy lines (Mg XII 1.47 keV; right and bottom panels). These differences are real and not attributable to counting statistics. Consequently, to measure the intrinsic emission-line broadening using the slitless RGS, the LSF must be convolved with a narrowband image at each emission line of interest. In the image panels, the white lines show the field of view of the RGS for Obs 1, centered at the blue cross; the yellow lines indicate the ﹩2^{\prime} ﹩ source aperture. The image stretch is adjusted between panels for display purposes.
Paper: Evidence for a Fast Soft X-Ray Wind in M82 from XMM-RGS
Authors: Erin Boettcher, Edmund Hodges-Kluck
Figure caption: Left: The O VIII 0.65 keV line in Obs 3 has a double peak at 18.9 Å and 19.15 Å not seen in other observations. The peak is present in both first-order RGS1 (red) and RGS2 (blue) spectra (extracted from the same aperture as described in Section 2), and there is tentative evidence for it in the second-order RGS1 spectrum (green, which meets the missing chip in the middle of the line; the O VIII line is entirely lost in RGS2). There is no evidence that the line structure results from instrumental artifacts or the surface brightness map that distorts the LSF. Right: Most of the flux in the 19.15 Å peak of the RGS1 spectrum (red) is localized in a 15″-wide strip in cross-dispersion space coincident with the continuum emission from M82 X-2 (blue). There is a corresponding peak in the anomalous O VII 0.57 keV line. The “ULX” spectrum also contains a significant contribution from the galactic wind. Both spectra have been binned to SNR = 3 for visual clarity, and the corresponding RGS2 spectra have been omitted because they do not extend to O VII.
Paper: Evidence for a Fast Soft X-Ray Wind in M82 from XMM-RGS
Authors: Erin Boettcher, Edmund Hodges-Kluck
Figure caption: The O VIII velocity dispersion suggests a fast soft X-ray wind in M82. The expected velocity dispersion of a biconical outflow viewed edge-on (i w = 90°, at left) and at an inclination (i w = 70°, at right) is shown as a function of the radial outflow speed, v out, and opening angle, θ. This is compared to the best-fit value (white solid line) and 90% confidence interval (white dotted lines) measured for the T ≈ 3 × 106 K gas traced by O VIII. The O VIII line width suggests a fast outflow speed (v out ≳ 2000 km s−1) for both inclinations. The 95% upper limit measured from Mg XII, shown by the dashed red line, can accommodate a lower v out or a smaller θ on the more compact spatial scales traced by the T ≈ 107 K gas.