adsabs/AstroCLIMB huggingface dataset:
Paper: Target Selection and Validation of DESI Emission Line Galaxies
Authors: A. Raichoor, J. Moustakas, Jeffrey A. Newman, T. Karim, S. Ahlen, Shadab Alam, S. Bailey, D. Brooks, K. Dawson, A. de la Macorra, A. de Mattia, A. Dey, Biprateep Dey, G. Dhungana, S. Eftekharzadeh, D. J. Eisenstein, K. Fanning, A. Font-Ribera, J. García-Bellido, E. Gaztañaga, S. Gontcho A Gontcho, J. Guy, K. Honscheid, M. Ishak, R. Kehoe, T. Kisner, Anthony Kremin, Ting-Wen Lan, M. Landriau, L. Le Guillou, Michael E. Levi, C. Magneville, M. Manera, P. Martini, Aaron M. Meisner, Adam D. Myers, Jundan Nie, N. Palanque-Delabrouille, W. J. Percival, C. Poppett, F. Prada, A. J. Ross, V. Ruhlmann-Kleider, C. G. Sabiu, E. F. Schlafly, D. Schlegel, Gregory Tarlé, B. A. Weaver, Christophe Yèche, Rongpu Zhou, Zhimin Zhou, H. Zou
Figure caption: The same as Figure 16, but with the data coming from independent repeat observations from the SV, One-Percent, and Main Surveys (see the text for more details).
Paper: Siena Galaxy Atlas 2020
Authors: John Moustakas, Dustin Lang, Arjun Dey, Stéphanie Juneau, Aaron Meisner, Adam D. Myers, Edward F. Schlafly, David J. Schlegel, Francisco Valdes, Benjamin A. Weaver, Rongpu Zhou
Figure caption:  b  t -band magnitude vs. 2 × R 1 for 154,093 objects in the HECATE value-added galaxy catalog (Kovlakas et al. 2021), which lie within the LS/DR9 imaging footprint, where R 1 is the semimajor axis length in HECATE. For reference, the contours enclose 10%, 25%, 50%, 75%, 95%, and 99% of the points. The dashed-red and solid purple histograms (corresponding to the right-hand vertical axis) represent, respectively, the raw and corrected fraction of HECATE galaxies, which match a source in the SGA-2020 galaxy as a function of angular diameter. Finally, the small numbers written above each bin of the histograms report the number of HECATE galaxies in that 0.2 dex wide bin.
Paper: Siena Galaxy Atlas 2020
Authors: John Moustakas, Dustin Lang, Arjun Dey, Stéphanie Juneau, Aaron Meisner, Adam D. Myers, Edward F. Schlafly, David J. Schlegel, Francisco Valdes, Benjamin A. Weaver, Rongpu Zhou
Figure caption: Comparison of the (a) central coordinates; (b) diameters; (c) position angles, ϕ; and (d) ellipticities, ϵ measured in the SGA-2020 against those reported in the WXSC-100 (Jarrett et al. 2019) for an overlapping sample of 59 galaxies. Individual galaxies are color coded by numerical Hubble type, from −5 (E) to 9 (Irr) (see de Vaucouleurs et al. 1991), as indicated by the color bar. See the text in Section 3.3 for the definition of the diameter measured in the WXSC-100 catalog and a detailed discussion of the observed trends.
Paper: Siena Galaxy Atlas 2020
Authors: John Moustakas, Dustin Lang, Arjun Dey, Stéphanie Juneau, Aaron Meisner, Adam D. Myers, Edward F. Schlafly, David J. Schlegel, Francisco Valdes, Benjamin A. Weaver, Rongpu Zhou
Figure caption: Comparison of select observed properties reported in HyperLeda against the newly measured quantities in the SGA-2020. (a) Difference in central coordinates; (b) D L(25) vs. D(25), the major-axis diameter measured at the 25 mag arcsec−2 isophote; (c) galaxy position angle, ϕ; and (d) galaxy ellipticity, ϵ ≡ 1 − b/a, where b/a is the minor-to-major axis ratio. In every panel, the contours enclose 50%, 75%, 95%, and 99.5% of the blue points and have been smoothed by a 0.8 pixel Gaussian kernel. In panels (b), (c), and (d), the solid red line represents the one-to-one relation.
Paper: Siena Galaxy Atlas 2020
Authors: John Moustakas, Dustin Lang, Arjun Dey, Stéphanie Juneau, Aaron Meisner, Adam D. Myers, Edward F. Schlafly, David J. Schlegel, Francisco Valdes, Benjamin A. Weaver, Rongpu Zhou
Figure caption: Multivariate distribution of a subset of measured SGA-2020 galaxy properties. From left to right along the bottom panels, we show r  R(26), the r-band magnitude within R(26), where R(26) is the semimajor axis at the 26 mag arcsec−2 isophote; 〈μ  r,R(26)〉, the mean surface brightness within R(26); ϕ, the galaxy position angle; and ϵ, the galaxy ellipticity. The contours enclose 50%, 75%, 95%, and 99.5% of the blue points and have been smoothed by a 0.8 pixel Gaussian kernel.
Paper: Siena Galaxy Atlas 2020
Authors: John Moustakas, Dustin Lang, Arjun Dey, Stéphanie Juneau, Aaron Meisner, Adam D. Myers, Edward F. Schlafly, David J. Schlegel, Francisco Valdes, Benjamin A. Weaver, Rongpu Zhou
Figure caption: Randomly selected gallery of 20 galaxies where the light-weighted central coordinates measured in the SGA-2020 differ by more than 3″ from the coordinates published in HyperLeda, sorted by increasing D L(25) from the upper left to the lower right. The white bar in the lower left corner of each panel represents 30″, and the blue and red cross-haired ellipses represent the SGA-2020 and HyperLeda positions and mean geometry, respectively. Although the centers of some of these systems are somewhat ambiguous due to dust lanes and other irregular features (e.g., NGC 4607), the SGA-2020 coordinates are generally superior.
Paper: Siena Galaxy Atlas 2020
Authors: John Moustakas, Dustin Lang, Arjun Dey, Stéphanie Juneau, Aaron Meisner, Adam D. Myers, Edward F. Schlafly, David J. Schlegel, Francisco Valdes, Benjamin A. Weaver, Rongpu Zhou
Figure caption: Left: celestial distribution of galaxies in the SGA-2020, which are also DESI targets (color bar) covering ≈14,000 deg2 (Myers et al. 2023) compared to the full ≈20,000 deg2 footprint of the SGA-2020 (dark gray region in the SGC). Right: redshift distribution of galaxies in the SGA-2020 with existing spectroscopic redshifts from HyperLeda (blue histogram) and from the DESI Early Data Release (EDR; orange histogram). The legend in this figure also indicates the approximate final number of SGA-2020 galaxies that DESI will observe by the end of the 5 year Main Survey.
Paper: Siena Galaxy Atlas 2020
Authors: John Moustakas, Dustin Lang, Arjun Dey, Stéphanie Juneau, Aaron Meisner, Adam D. Myers, Edward F. Schlafly, David J. Schlegel, Francisco Valdes, Benjamin A. Weaver, Rongpu Zhou
Figure caption: Distribution of 383,620 galaxies in the final SGA-2020 sample (to be compared with Figure 3, but note the different color-bar scales).
Paper: SPROUT: A Moving-mesh Hydro Code Using a Uniformly Expanding Cartesian Grid
Authors: Soham Mandal, Paul C. Duffell
Figure caption: Convergence of the 3D isentropic pulse test. (Top) Angle-averaged density vs. radial distance at t = 0.1 for both the static and the expanding mesh, compared against a high-resolution solution obtained by the JET code for a 1D isentropic pulse in spherical coordinates (in blue). (Bottom) L 1 error as a function of resolution (Equation (31)), along with the same for the spherical isentropic pulse test done for JET. N stands for number of zones per dimension for both codes. We note that the exact definitions of L 1 for the two codes are not consistent, hence it makes more sense to compare the convergence rate than actual error values.
Paper: SPROUT: A Moving-mesh Hydro Code Using a Uniformly Expanding Cartesian Grid
Authors: Soham Mandal, Paul C. Duffell
Figure caption: Density plotted against distance from the shock scaled with time for the 1D shock tube test, at t = 100. The MM results (dashed lines) and the fixed-mesh result (plus symbols) are compared to the analytical solution (solid line). The moving mesh captures the shocks and contact discontinuities more accurately when compared to the fixed mesh, and in addition preserves self-similarity after a expansion by two orders of magnitude. In both cases, 100 computational zones are used.
Paper: SPROUT: A Moving-mesh Hydro Code Using a Uniformly Expanding Cartesian Grid
Authors: Soham Mandal, Paul C. Duffell
Figure caption: (Left) Two instances of an expanding 2D grid, with the later instance shown in green. The red cross marks the center of expansion. (Right) Zoomed-in view of one zone, which illustrates how the zone faces move in general. This also shows that the zone areas and volumes in Equation (10) have to be treated as functions of time while evaluating the integrals.
Paper: SPROUT: A Moving-mesh Hydro Code Using a Uniformly Expanding Cartesian Grid
Authors: Soham Mandal, Paul C. Duffell
Figure caption: Convergence of the 2D isentropic pulse test. (Top) Angle-averaged density vs. radial distance at t = 0.1 (with static mesh), compared against a high-resolution 1D solution obtained by the DISCO code (in blue) in cylindrical coordinates. (Bottom) L 1 error as a function of resolution (Equation (31)), along with the same for the cylindrical isentropic pulse test done for DISCO. N stands for number of zones per dimension for both codes.
Paper: SPROUT: A Moving-mesh Hydro Code Using a Uniformly Expanding Cartesian Grid
Authors: Soham Mandal, Paul C. Duffell
Figure caption: Convergence of the 1D isentropic pulse test. (Top) Density vs. distance at t = 0.1 obtained using both the static and expanding mesh, compared against a high-resolution calculation (in blue) with mesh motion turned off. (Bottom) L 1 error as a function of resolution (Equation (31)).
Paper: SPROUT: A Moving-mesh Hydro Code Using a Uniformly Expanding Cartesian Grid
Authors: Soham Mandal, Paul C. Duffell
Figure caption: Rayleigh–Taylor Instability generated by an explosion with cylindrical symmetry. The upper and lower panels show the logarithm of density from solutions obtained using a fixed and an expanding mesh (both with a resolution of 1000 × 1000), respectively. Each row shows solutions at three instants of time (t = 0.03, 0.3, 1.0) to showcase the evolution of mixing between two fluid layers. All density maps were plotted by zooming onto the expanding blast wave. The effect of this is displayed most drastically by the leftmost image in the top panel, where the blast wave is still relatively small compared to the grid and is resolved poorly. The poor resolution at early times for the fixed mesh discourages mixing at those times, which may be taken to be a numerical artifact. This can be seen by comparing early instants (t = 0.03, 0.3) for the fixed- and the expanding-mesh configurations.
Paper: SPROUT: A Moving-mesh Hydro Code Using a Uniformly Expanding Cartesian Grid
Authors: Soham Mandal, Paul C. Duffell
Figure caption: Comparison of the quantity 〈C〉 x (y) (as defined in Equation (41)) for Kelvin–Helmholtz instability solutions obtained by Sprout to the converged solutions obtained by Dedalus, a pseudospectral code (Burns et al. 2020). 〈C〉 x (y), or the passive scalar averaged along the flow direction, represents a measure of the amount of mixing for a given solution. The curves from Sprout are seen to follow that due to DEDALUS to a high degree of accuracy.
Paper: SPROUT: A Moving-mesh Hydro Code Using a Uniformly Expanding Cartesian Grid
Authors: Soham Mandal, Paul C. Duffell
Figure caption: Angle-averaged density, pressure, and velocity vs. radial distance for the Sedov–Taylor blast wave at t = 10. Sprout data (markers) is compared to the analytic (solid line) curve. The shock propagates against a ρ ∝ r −1 density profile. This test employed an extremely low resolution of (64 × 64 × 64).
Paper: SPROUT: A Moving-mesh Hydro Code Using a Uniformly Expanding Cartesian Grid
Authors: Soham Mandal, Paul C. Duffell
Figure caption: Slice in the x–z plane of the density map for the Sedov–Taylor blast wave at t = 10. Minor numerical artifacts seen are primarily due to the fact that this test is performed at extremely low resolution (64 × 64 × 64). The hydrodynamic evolution of bulk quantities is captured accurately even at this low resolution, as shown in Figure 8.
Paper: SPROUT: A Moving-mesh Hydro Code Using a Uniformly Expanding Cartesian Grid
Authors: Soham Mandal, Paul C. Duffell
Figure caption: Passive scalar map for the Kelvin–Helmholtz instability on a fixed 2048 × 2048 mesh, plotted at regular intervals between t = 2 and 8.
Paper: SPROUT: A Moving-mesh Hydro Code Using a Uniformly Expanding Cartesian Grid
Authors: Soham Mandal, Paul C. Duffell
Figure caption: Passive scalar evolution for homologous outflow. (Left) Passive scalar map at the initial time (t  =  1). (Middle) Passive scalar map at t = 2, with mesh motion turned off. (Right) Same as middle, but with expanding mesh, following the homologous expansion. The latter shows no measurable numerical diffusion, as opposed to the static-mesh run. The blue line in each panel has a length of unity in code units.
Paper: SPROUT: A Moving-mesh Hydro Code Using a Uniformly Expanding Cartesian Grid
Authors: Soham Mandal, Paul C. Duffell
Figure caption: Density maps for the Rayleigh–Taylor instability on a fixed 48 × 144 mesh, plotted at regular intervals between t = 2.5–20. Qualitative agreement is seen between our results and the corresponding snapshots for a similar test done using the fixed-mesh configuration of the AREPO code (Springel 2010a).
Paper: Nonequilibrium Ionization Effects on Synthetic Spectra in the AWSoM Solar Corona
Authors: J. Szente, E. Landi, B. van der Holst
Figure caption: Synthetic narrowband images of EIT channels 171, 195, and 284 Å (left to right). The two rows correspond to two observational times from Szente et al. (2019).
Paper: Nonequilibrium Ionization Effects on Synthetic Spectra in the AWSoM Solar Corona
Authors: J. Szente, E. Landi, B. van der Holst
Figure caption: Plasma emission ratios of plasma assuming NEI vs. EI for iron ion  XVI.
Paper: Nonequilibrium Ionization Effects on Synthetic Spectra in the AWSoM Solar Corona
Authors: J. Szente, E. Landi, B. van der Holst
Figure caption: Ratio of change in emission due to NEI of synthetic spectral line emission at 171.073 Å vs. synthetic narrowband image at EIT at channel 171 Å (left), line ratios at 195.119 Å vs. EIT channel 195 Å (middle), and line ratios at 284.163 Å vs. EIT channel 284 Å (rightmost).
Paper: Nonequilibrium Ionization Effects on Synthetic Spectra in the AWSoM Solar Corona
Authors: J. Szente, E. Landi, B. van der Holst
Figure caption: NEI effects on synthetic narrowband images of coronal AIA channels 94, 131, 171, 193, 211, and 335 Å for observation time on 2022 February 25 at UT 00:00:00.
Paper: Nonequilibrium Ionization Effects on Synthetic Spectra in the AWSoM Solar Corona
Authors: J. Szente, E. Landi, B. van der Holst
Figure caption: NEI effects on synthetic narrowband images of coronal EUVI channels 171, 195, and 284 Å for observation time on 2022 February 25 at UT 00:00:00.
Paper: Nonequilibrium Ionization Effects on Synthetic Spectra in the AWSoM Solar Corona
Authors: J. Szente, E. Landi, B. van der Holst
Figure caption: Coronal-hole observation comparison to data. Black shows Hinode/EIS observations, green the equilibrium solution, and red the nonequilibrium solution’s emission.
Paper: Nonequilibrium Ionization Effects on Synthetic Spectra in the AWSoM Solar Corona
Authors: J. Szente, E. Landi, B. van der Holst
Figure caption: Plasma emission ratios of plasma assuming NEI vs. EI for iron ions Fe XIII to  XVI. The peak-forming temperatures for the lines presented in this figure can be found in Table 1.
Paper: Nonequilibrium Ionization Effects on Synthetic Spectra in the AWSoM Solar Corona
Authors: J. Szente, E. Landi, B. van der Holst
Figure caption: West limb observation comparison to data. Black shows Hinode/EIS observations, green the equilibrium solution, and red the nonequilibrium solution’s emission.
Paper: HAFFET: Hybrid Analytic Flux FittEr for Transients
Authors: Sheng Yang, Jesper Sollerman
Figure caption: Flowchart of the main steps in the HAFFET process, performed on the stripped envelop SNe in Paper I. As shown, after getting observational data for a single SN, snobject is called to fit on multiband LCs, which could be used to estimate photometric properties, as well as provide interpolated magnitudes for estimating color and bolometric LC. A further step is to fit bolometric LC with, e.g., the Arnett model, and spectral lines can be also fitted for photospheric velocities, which are often used to help break the degeneracy of the Arnett model. Finally, all of the fitted parameters from model fittings are collected by the snelist for a sample study.
Paper: HAFFET: Hybrid Analytic Flux FittEr for Transients
Authors: Sheng Yang, Jesper Sollerman
Figure caption: A view of the HAFFET GUI showing the model fittings on multiband LCs, spectral lines, and the constructed bolometric LC. In the upper panel we show the control window of the GUI.
Paper: HAFFET: Hybrid Analytic Flux FittEr for Transients
Authors: Sheng Yang, Jesper Sollerman
Figure caption: Luminosity comparison between different approaches. In the left panel, the circles represent the luminosity estimated with the analytic bolometric correction from Lyman et al. (2014), while the red circles are bolometric luminosities estimated from blackbody fits on the gri photometry and the orange down-pointing arrows are estimates based on integrating the spectra. In the right panel, we show the peak luminosity comparison between the blackbody fitted and the Lyman-inferred values for 34 SNe Ib and 35 SNe Ic at peak epoch. The blue dashed line is the one-to-one relation. As shown, the luminosities from the two approaches are consistent, although the gri-constructed blackbody provides luminosity estimates with large(r) uncertainties.
Paper: HAFFET: Hybrid Analytic Flux FittEr for Transients
Authors: Sheng Yang, Jesper Sollerman
Figure caption: The image of the field of SN 2020bcq as a finder that was automatically made by HAFFET. The images are queried with the PS1 Image Cutout Service (https://outerspace.stsci.edu/display/PANSTARRS/PS1+Image+Cutout+Service), while the SDSS field stars (the green open circles) are downloaded via astroquery (https://astroquery.readthedocs.io/en/latest/). The position of SN 2020bcq is marked as the red source in the middle of the inset.
Paper: HAFFET: Hybrid Analytic Flux FittEr for Transients
Authors: Sheng Yang, Jesper Sollerman
Figure caption: Baseline correction for ZTF forced-photometry LCs exemplified by SN 2020bcq. This target was observed in three filters (the g, r, and i band), and two fields (field 674 and 171). Observations associated with different fcqf ID (using Equation (1) of Yao et al. 2019) and counts are shown in distinctive colors. The two red vertical dashed lines are set based on the user option for the selection of baseline range. The observations within the range are used to set the baseline flux levels, which are aligned by HAFFET so that observations from different fields for the same filter are mutually consistent.
Paper: HAFFET: Hybrid Analytic Flux FittEr for Transients
Authors: Sheng Yang, Jesper Sollerman
Figure caption: In the left panel, we visualize Equations (11) and (12), showing how both M ej and E kin can be estimated with τ  m  together with a velocity measurement. In the right panel, we instead show T 0 of the bolometric tail in Equation (18). Here we assume constant opacities, i.e., κ = 0.2 cm2 g−1 and κ  γ  = 0.027 cm2 g−1.
Paper: HAFFET: Hybrid Analytic Flux FittEr for Transients
Authors: Sheng Yang, Jesper Sollerman
Figure caption: Flowchart illustrating model-fitting procedures of HAFFET. As shown, HAFFET first obtains model function and priors, as well as data to be fitted that were prepared by the engines. Then a minimizing process is performed to constrain model parameters. The output could be used as a prior for Markov Chain Monte Carlo (MCMC) routines. Finally, the fitted samplings can be called by HAFFET to, e.g., produce parameter contours, and reproduce observational data.
Paper: HAFFET: Hybrid Analytic Flux FittEr for Transients
Authors: Sheng Yang, Jesper Sollerman
Figure caption: An exponential fit of the fitted velocities on the phases for SN 2020bcq. The red dashed lines are the random samplings of the MCMC, and the red open circle is the estimated velocity at the peak phase from the exponential fit.
Paper: Siena Galaxy Atlas 2020
Authors: John Moustakas, Dustin Lang, Arjun Dey, Stéphanie Juneau, Aaron Meisner, Adam D. Myers, Edward F. Schlafly, David J. Schlegel, Francisco Valdes, Benjamin A. Weaver, Rongpu Zhou
Figure caption: Illustration of the key steps and data products of the SGA-2020 pipeline for one example galaxy, NGC 5016. The three panels on the left-hand side show (top left) a color montage of the optical imaging; (middle left) a color montage of the corresponding Tractor model image; and (bottom left) the r-band image with masked pixels zeroed out (white pixel values). The nested black ellipses in this panel correspond to nine surface-brightness levels between μ  r  = 22 and 26 mag arcsec−2 in 0.5 mag arcsec−2 steps, with the solid blue isophote representing the outermost, R(26), size of the galaxy. The right-hand panels show (top right) the azimuthally averaged g- (blue), r- (green), and z-band (red) surface-brightness profiles as a function of the semimajor axis; (middle right) the observed-frame g − r (purple) and r − z (orange) color profiles; and (bottom right) the apparent brightness of NGC 5016 in g (filled blue squares), r (filled green circles), and z (filled red triangles) measured within the same elliptical apertures shown in the lower left panel. The dashed- and solid-gray lines in the top right panel indicate, for reference, the r-band half-light radius from Equation (2) and the R(26) radius, respectively, and the bottom right panel also shows the best-fitting curve-of-growth models (independently fit to the photometric data in each bandpass) given by Equation (1). The legend in the lower right panel also provides the integrated (asymptotic) grz magnitudes for this galaxy.
Paper: Siena Galaxy Atlas 2020
Authors: John Moustakas, Dustin Lang, Arjun Dey, Stéphanie Juneau, Aaron Meisner, Adam D. Myers, Edward F. Schlafly, David J. Schlegel, Francisco Valdes, Benjamin A. Weaver, Rongpu Zhou
Figure caption: Gallery of common types of HyperLeda sources rejected while building the parent sample. In each panel, the horizontal white bar represents 30″. The top row shows one spurious object and three examples of pairs or triples of stars or compact galaxies, which are recorded in HyperLeda as one “large” galaxy (represented by the red ellipse). The middle row shows four examples of how bright stars and galaxies can lead to significantly overestimated galaxy diameters in HyperLeda. For example, PGC 2500856 in the middle right panel is a blue star, which has been miscategorized by HyperLeda as a galaxy with a major-axis diameter of ≈54″ presumably due to the proximity of the nearby bright galaxy PGC 3087062, which appears in the final SGA-2020 catalog (blue ellipse). Finally, the bottom row shows four examples of sources in HyperLeda, which are in fact photometric shreds or misidentified parts of a galaxy. In each case, the blue ellipse shows the correct parent galaxy from the SGA-2020 while the red ellipse is the incorrect source from HyperLeda, which we remove from the parent sample (see Section 2.1). For example, PGC 071502 and PGC 093084 are H II regions in ESO240-004 and NGC 1507, respectively, while SDSSJ211630.38+001817.7 is a foreground star in the body of PGC 188224 with an incorrect angular diameter.
Paper: Siena Galaxy Atlas 2020
Authors: John Moustakas, Dustin Lang, Arjun Dey, Stéphanie Juneau, Aaron Meisner, Adam D. Myers, Edward F. Schlafly, David J. Schlegel, Francisco Valdes, Benjamin A. Weaver, Rongpu Zhou
Figure caption: Isophotal diameter, D L(25), vs. b  t -band magnitude (left) and marginalized distribution of D L(25) (right) for the SGA-2020 parent sample. The black contours, points, and histogram represent galaxies from HyperLeda, while the green points and histogram are galaxies from the supplemental catalogs we use to increase the completeness of the sample (see Section 2.1). For reference, the contours enclose 50%, 75%, 95%, and 99.5% of the blue points. These figures show that, by construction, the SGA-2020 parent sample is largely limited to D L(25) > 20″ (﹩0\buildrel{\,\prime}\over{.} 333﹩) but with a tail of galaxies with diameters as small as ≈10″ (﹩\approx 0\buildrel{\,\prime}\over{.} 167﹩), and it includes galaxies as bright as b  t,Vega ≈ 8.