adsabs/AstroCLIMB huggingface dataset:
Paper: Photometrically Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine-learning-based Classification
Authors: Brian Hsu, Griffin Hosseinzadeh, V. Ashley Villar, Edo Berger
Figure caption: Median values and 1σ uncertainties of the key magnetar model parameters (P, B, M ej, and v ej; solid squares: photometric; open circles: spectroscopic; and the plot symbols for the photometrically classified SLSNe are the same as in Figure 4). The models for PS1-11ap and PS1-12cil are both obtained from Hosseinzadeh et al. (2022). The gray crosses mark the remaining spectroscopically confirmed SLSNe from Hsu et al. (2021). In the top panels, we show the parameter distributions for the PS1-MDS photometric sample (blue), the PS1-MDS spectroscopic sample (red), and the SLSN compilation sample (gray), along with the median p-values associated with both the K–S test and the A–D test statistics, calculated using the PS1-MDS photometric sample and the SLSN compilation. In each panel, we quote the median value and 1σ bound of the Spearman rank correlation coefficient, using the PS1-MDS photometric sample and literature data set. Of all the parameter pairs, P and M ej exhibit the strongest correlation, consistent with the findings in Blanchard et al. (2020) and Hsu et al. (2021).
Paper: Photometrically Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine-learning-based Classification
Authors: Brian Hsu, Griffin Hosseinzadeh, V. Ashley Villar, Edo Berger
Figure caption: Peak absolute rest-frame g-band magnitude vs. redshift for the PS1-MDS SLSNe (solid squares: photometrically classified by Superphot only; solid triangles: photometrically classified by SuperRAENN only; solid stars: photometrically classified by both SuperRAENN and Superphot; open circles: spectroscopically classified). The top panel shows the redshift distribution of the PS1-MDS photometric (blue) and spectroscopic (red) samples compared to the SLSN compilation from Hsu et al. (2021) (82 spectroscopic SLSNe, including the 17 from PS1-MDS), while the right panel shows the distributions of the peak magnitudes for the PS1-MDS samples. Each peak magnitude is corrected for Galactic extinction and includes a cosmological K-correction factor of ﹩2.5\mathrm{log}(1+z)﹩.
Paper: Photometrically Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine-learning-based Classification
Authors: Brian Hsu, Griffin Hosseinzadeh, V. Ashley Villar, Edo Berger
Figure caption: Cumulative distributions of the peak absolute rest-frame g-band magnitude and median magnetar model parameter values for the PS1-MDS spectroscopic (red), photometric (blue), and consensus photometric (purple) samples. The arrows in each panel indicate the median parameter value for the samples. The consensus photometric sample contains only events classified as SLSNe by both classifiers. It exhibits a higher median magnitude of ≈−21.1 (purple arrow) compared to ≈−20.8 for the full photometric sample, but is still ≈1 mag dimmer than the median of ≈−22 for the spectroscopic sample. Even though the full and the consensus photometric samples have comparable median B values (≈1.74 × 1015 G and 1.80 × 1015 G for the full and consensus samples, respectively), the consensus sample spans a much narrower and higher range in B. The shift in M ej is more strongly reflected, with a lower median value (≈3.58 M ⊙, full; ≈2.33 M ⊙, consensus) at a lower range. All of the shifts in the magnetar model parameters are consistent with SLSNe having higher luminosities than in the full photometric sample.
Paper: Photometrically Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine-learning-based Classification
Authors: Brian Hsu, Griffin Hosseinzadeh, V. Ashley Villar, Edo Berger
Figure caption: Multiband extinction-corrected apparent magnitude light curves of the 19 PS1-MDS photometrically classified SLSNe, along with our magnetar model fits using MOSFiT. The name of each transient and its spectroscopic host galaxy redshift are quoted at the top of each panel. The different filters are shifted for clarity, as indicated in the legend. Open triangles indicate 3σ  upper limits, while solid circles indicate detections. The solid lines and shaded regions indicate the median model and 1σ  ranges. Events classified as SLSNe by only one classifier are marked with daggers.
Paper: CLEAR: The Evolution of Spatially Resolved Star Formation in Galaxies between 0.5 ≲ z ≲ 1.7 Using Hα Emission Line Maps
Authors: Jasleen Matharu, Casey Papovich, Raymond C. Simons, Ivelina Momcheva, Gabriel Brammer, Zhiyuan Ji, Bren E. Backhaus, Nikko J. Cleri, Vicente Estrada-Carpenter, Steven L. Finkelstein, Kristian Finlator, Mauro Giavalisco, Intae Jung, Adam Muzzin, Erica J. Nelson, Annalisa Pillepich, Jonathan R. Trump, Benjamin Weiner
Figure caption: Zoomed-in regions (89 × 89 pixels, pixel scale = 0.″1) of individual stellar continuum and Hα thumbnails for select galaxies in the CLEAR sample ordered by descending stellar mass. Original thumbnails are 189 × 189 pixels (see Section 3.1.4 for reasoning). Hα emission line maps are multiplied by 50 for visibility. The 3D-HST source ID for each galaxy is marked in the top-right corner of each Hα thumbnail. GS = GOODS-S, GN = GOODS-N, and see Section 3.2.5 for GS4 explanation. The stellar mass and grism redshift is stated in the bottom-left corner of each F105W thumbnail.
Paper: Photometrically Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine-learning-based Classification
Authors: Brian Hsu, Griffin Hosseinzadeh, V. Ashley Villar, Edo Berger
Figure caption: Normalized joint posterior distributions of the magnetar and ejecta parameters for the PS1-MDS photometric (blue), PS1-MDS spectroscopic (red), and spectroscopic compilation (gray) samples. The distributions are the summed posteriors of the MOSFiT parameters, consisting of 100 randomly sampled MCMC walkers from each SLSN fit. At the top of each panel, we quote the median p-values and their 1σ ranges from both the K–S and the A–D tests, using the bootstrap method, by comparing the PS1-MDS photometric sample and the SLSN compilation.
Paper: Photometrically Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine-learning-based Classification
Authors: Brian Hsu, Griffin Hosseinzadeh, V. Ashley Villar, Edo Berger
Figure caption: Matrix showing the effect of varying the minimum classification confidence and the minimum number of light-curve data points across all four filters. The top number in each cell indicates the total number of events (out of 67) satisfying both thresholds. The numbers in parentheses indicate the final sample size after removing AGN hosts and events with nonconverged models. The region outlined in red marks the boundary for combinations that result in a comparable sample size (≥17) to the PS1-MDS spectroscopic sample.
Paper: Photometrically Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine-learning-based Classification
Authors: Brian Hsu, Griffin Hosseinzadeh, V. Ashley Villar, Edo Berger
Figure caption: The same as Figure 2, but for the PS1-MDS spectroscopically classified SLSNe. The light curves for PS1-11ap and PS1-12cil are from Hosseinzadeh et al. (2022), modeled without the postpeak pumps.
Paper: Chemical Diversity in Protoplanetary Disks and Its Impact on the Formation History of Giant Planets
Authors: Elenia Pacetti, Diego Turrini, Eugenio Schisano, Sergio Molinari, Sergio Fonte, Romolo Politi, Patrick Hennebelle, Ralf Klessen, Leonardo Testi, Ugo Lebreuilly
Figure caption: Radial profiles of molecular abundances of the key volatile C-, O-, and N-bearing species with respect to total H atoms. The profiles are taken from Eistrup et al. (2016) and properly rescaled to account for the different disks and the different compositional models considered in this work. In all panels, solid and dotted lines indicate the solid and gas phases, respectively. The top three panels describe the two inheritance scenarios, while the bottom three ones describe the two reset scenarios. The left-hand and central panels show the abundances of the key volatile molecules carrying C and O, including the contribution of refractory organic carbon (Cref). N-bearing molecules are shown in the two right-hand panels for visual ease. Here, the low (SLRs only) and high (SLRs and CRs) ionization levels are shown in the same plot by means of thick and thin lines, respectively. The gray, dashed, vertical lines in the plots of the inheritance low scenario indicate the position of the snowlines of H2O (2.5 au), Cref (3 au), NH3 (9.4 au), CO2 (10.5 au), and CH4 (105 au). CO and N2 condense beyond 130 au in our model, and their snowlines are therefore not seen. Snowlines are omitted in the other scenarios, as the complexity of the chemistry requires the introduction of multiple snowlines.
Paper: Migration Traps as the Root Cause of the Kepler Dichotomy
Authors: Brianna Zawadzki, Daniel Carrera, Eric B. Ford
Figure caption: Identifying whether clusters are present in models A and B. The scatter plot shows the location of the largest gap in a system (here, defined as the mean period of the two planets with the largest period ratio) with respect to relative gap size. Relative gap size is defined as ﹩{G}_{\max }/{G}_{\mathrm{in},\mathrm{med}}﹩, where ﹩{G}_{\max }﹩ is the largest period ratio in a system, and G in,med is the median period ratio of all planets interior to and including the planets that define ﹩{G}_{\max }﹩. A relative gap size of 1 means that the largest gap in the system occurs between the two innermost planets in the system. We only include bodies more massive than 0.2 M ⊕ (equal to the largest initial embryo mass) so that the analysis of clustering does not include embryos that avoided collision. The top histogram shows where planets are located in each model, and the right histogram shows the distribution of relative gap sizes in each model. The red dashed–dotted line at 200 days roughly matches Kepler’s completeness limit. The black dashed line is placed at a relative gap size of 1.5, dividing the sample into “large gap” and “small gap” systems. Model A resulted in 129/200 systems with a maximum gap ratio greater than 1.5 (large gap), while model B only resulted in 36/200.
Paper: Migration Traps as the Root Cause of the Kepler Dichotomy
Authors: Brianna Zawadzki, Daniel Carrera, Eric B. Ford
Figure caption: Snapshots from a sample run from both our high-mass model A (left) and low-mass model B (right). Planetary embryos are shown as circles with size proportional to their rocky core radius. The vertical line, at 200 days, roughly matches Kepler’s completeness limit. Between 3 and 4 Myr, the high-mass model (left) sees rapid growth that takes the inner embryos above the migration trap (black curve). Freed from the trap, the inner embryos migrate quickly and become dynamically separated from the outer embryos. This helps the inner planets remain dynamically colder after the disk phase. In the low-mass model (right), all embryos remain together and form a single dynamically hot cluster. See Figure 6 for typical outcomes.
Paper: Migration Traps as the Root Cause of the Kepler Dichotomy
Authors: Brianna Zawadzki, Daniel Carrera, Eric B. Ford
Figure caption: Left: the distribution of median inclinations between adjacent planet pairs for each planetary system in models A and B. Only planets within 200 days are included. Simulations that formed only one planet within 200 days are excluded from those plots. Right: a histogram of the probability that a planetary system, if detected, will be identified as a single-transiting system. The bottom histograms, labeled “A+B,” are built by adding the histograms from models A and B. In all cases, the main takeaway is that neither model A or B or their sum shows any clear bimodality in the mutual inclinations, suggesting that a bimodal distribution of mutual inclinations is unlikely to be the driving cause of the dichotomy in single-transiting frequency.
Paper: Migration Traps as the Root Cause of the Kepler Dichotomy
Authors: Brianna Zawadzki, Daniel Carrera, Eric B. Ford
Figure caption: Final configurations for 15 sample simulations from our high-mass model (model A, left) and our low-mass model (model B, right). Each system is shown in a different color. Planets are shown as circles proportional to their rocky radius and they are offset vertically in proportion to their orbital inclination relative to the invariant plane. The horizontal lines range from the planet’s periastron to apastron. Overlapping lines point to orbit crossings and likely future collisions among the outer planets. The vertical line roughly matches Kepler’s completeness limit. The high-mass model (left) often produces migration traps which lead to two distinct planet clusters. The inner cluster typically remains relatively cold, compact, and isolated from a much hotter outer cluster. Figure 5 shows the formation history that leads to separated clusters in one model but not the other.
Paper: Migration Traps as the Root Cause of the Kepler Dichotomy
Authors: Brianna Zawadzki, Daniel Carrera, Eric B. Ford
Figure caption: Evolution of torques exerted on a planet by the disk from t  disk  = 1 Myr to t  disk  = 5 Myr. The color scale shows the torque normalized by the effective adiabatic index γ eff and the scaling factor Γ0 = (q/h)2 Σ r 4Ω2, where q = m pl/M ⋆ and h = H/r. In most cases, the net torque is negative, driving inward migration. Inside the black boundaries, however, the net torque is positive, and planets migrate outward. This occurs when the (positive) corotation torque, which depends strongly on the planet mass, overwhelms the (negative) Lindblad torque. Migration traps occur when a forming planet is at a mass and disk location that prevents inward migration.
Paper: Migration Traps as the Root Cause of the Kepler Dichotomy
Authors: Brianna Zawadzki, Daniel Carrera, Eric B. Ford
Figure caption: Overview of all the planetary systems formed in each of our models (Table 1). Each planet is shown as a circle proportional to the planet’s core radius, and each planetary system is shown in a different row. We only include planets with P < 200 days. For systems with at least two planets within 200 days we draw a horizontal line from the inner to the outer planets. Models B and C produce comparable sets of planetary systems with similar transit multiplicities, while model A differs significantly, highlighting the impact of total solid mass on formation outcomes.
Paper: Migration Traps as the Root Cause of the Kepler Dichotomy
Authors: Brianna Zawadzki, Daniel Carrera, Eric B. Ford
Figure caption: The probability of detecting a transiting planet around two sample G dwarf stars for different planet radii and orbital periods. The detection probability depends on the transit depth, period, and stellar noise. In this example the two stars have comparable stellar noise, but the star in the top panel has a larger radius, which reduces the transit depth.
Paper: Effects of Hydrogen versus Helium on Electromagnetic Black Hole Observables
Authors: George N. Wong, Charles F. Gammie
Figure caption: Comparison of the same fluid snapshot from a Sgr A*–like SANE a * = 0.94 model at 50° inclination with R high = 10 at 230 GHz for hydrogen vs. helium gas composition with and without 15 μas blur (white circle) to simulate the effects of a finite beam size. Columns show total intensity, linear polarization fraction, EVPA, and circular polarization fraction. In the hydrogen model, the EVPA varies rapidly in the lower part of the image owing to Faraday effects, so the blurred linear polarization fraction is significantly decreased compared to the helium case, where the EVPA is coherent over scales ∼the beam size.
Paper: Effects of Hydrogen versus Helium on Electromagnetic Black Hole Observables
Authors: George N. Wong, Charles F. Gammie
Figure caption: Comparison of electron temperature and 230 GHz observed emission for pure-hydrogen vs. pure-helium accretion flow. Data are for a single azimuth ϕ in a snapshot from a SANE a * = −0.5 accretion simulation with R high = 10 imaged with emission computed for an image at 17° inclination. The left panel shows (normalized) rest-mass density of the fluid. Contours of the axisymmetrized density are overplotted on all panels to guide the eye. The second panel shows the temperature increase going from hydrogen to helium composition; regions with higher β show a larger increase in temperature. The right two panels show the location of emission (normalized and computed as seen by the observer and accounting for the effects of optical depth) for the pure-hydrogen case and the pure-helium case. Evidently, the amount of emission along the funnel wall decreases from the hydrogen case to the helium one. All color maps are plotted in linear scale.
Paper: Effects of Hydrogen versus Helium on Electromagnetic Black Hole Observables
Authors: George N. Wong, Charles F. Gammie
Figure caption: Parameters and output of one-zone models that reproduce observed flux density at 230 GHz for both M87 and Sgr A*, plotted vs. helium fraction F (fraction of ions that are helium). Here, t cool is the synchrotron cooling time, and τ  S , τ  Q , and τ  V  are the optical depths to absorption and Faraday conversion and rotation, respectively. Models are initialized for a gas with total specific internal energy such that the electron temperature Θ e  = 10 for a pure hydrogen with uniform T  i /T  e  = 3.
Paper: Effects of Hydrogen versus Helium on Electromagnetic Black Hole Observables
Authors: George N. Wong, Charles F. Gammie
Figure caption: Contours of log10 per-electron synchrotron emissivity at fixed frequency ν = 230 GHz and pitch angle θ = π/3 across electron temperature Θ e  and local magnetic field strength B in ranges relevant to RIAFs near M87 and Sgr A*. Altering the gas composition from hydrogen to helium changes the magnetic field strength by a uniform factor, but the local temperature change can differ for different locations across the domain. Thus, the ratio of local emissivities for hydrogen vs. helium differs across the flow.
Paper: Effects of Hydrogen versus Helium on Electromagnetic Black Hole Observables
Authors: George N. Wong, Charles F. Gammie
Figure caption: Sample rex-fit diameters for individual images from low-inclination models of M87 and Sgr A* shown for pure-hydrogen composition on x-axis vs. pure-helium composition on y-axis. Both MAD and SANE models are considered. The color of each point denotes the model R high, and the marker type indicates the simulation magnetization state. Statistics for each model are computed for five images from the simulation movie time series. Points would lie on the solid black line if the measured diameter were equal for the hydrogen and helium models. Dashed lines show ±10% deviation.
Paper: Effects of Hydrogen versus Helium on Electromagnetic Black Hole Observables
Authors: George N. Wong, Charles F. Gammie
Figure caption: Comparison of polarimetric images at three frequencies for models of M87 (top) and Sgr A* (bottom), with hydrogen-composition images shown above helium-composition ones. Pixel brightness denotes total intensity, the color and length of the tick marks show linear polarization fraction and intensity, and the orientation of the ticks shows EVPA. The M87 model is from a MAD a * = 0.5 simulation with R high = 10. The Sgr A* model is SANE with a * = 0.94, R high = 50, and inclination = 10°.
Paper: Effects of Hydrogen versus Helium on Electromagnetic Black Hole Observables
Authors: George N. Wong, Charles F. Gammie
Figure caption: Modulation index for hydrogen vs. helium gas composition over all GRMHD models considered in this paper. Models of both M87 and Sgr A* are included. Points in the lower right half correspond to the case where a pure-helium gas composition is less variable than a pure-hydrogen one. Typically, MADs and SANEs with low R high are less variable for pure helium.
Paper: Survival of Terrestrial N2–O2 Atmospheres in Violent XUV Environments through Efficient Atomic Line Radiative Cooling
Authors: Akifumi Nakayama, Masahiro Ikoma, Naoki Terada
Figure caption: Profiles of energy budget for the XUV irradiation five times that the present-day Earth’s with (upper panel) and without (lower panel) atomic line cooling. The molecular loss (red) and absorption (yellow) of radiative energy are calculated from the first and second terms, respectively, in Equation (28) for Q rad. The atomic line cooling (brown) is also calculated from Equation (28) for Q rad. The energy budget associated with chemical reactions (blue) and photodissociation (light green) are given by Q chem (see Equation (11)) and by Q uv (see Equation (20)), respectively. The rate of heating caused by photoionization (dark blue), which produces photoelectrons, is given by Q pe (see Section 2.2.2 for the details). The energy budgets associated with conduction (purple) and adiabatic cooling (green) are given by Q diff in Equation (25) and Q hy (green) in Equation (29), respectively.
Paper: Survival of Terrestrial N2–O2 Atmospheres in Violent XUV Environments through Efficient Atomic Line Radiative Cooling
Authors: Akifumi Nakayama, Masahiro Ikoma, Naoki Terada
Figure caption: Exobase temperature as a function of XUV irradiation level, F XUV (in the units of that of the present-day Earth’s, F XUV,⊕). The solid and dashed red lines represent the cases with and without atomic line cooling, respectively. For the stellar XUV spectra, we have simply multiplied the emission intensity for the present-day Sun by F XUV/F XUV,⊕ at each wavelength. The result obtained with inferred spectra for young Sun-like stars at different ages (Claire et al. 2012) is also shown with the blue line (see Section 3.4).
Paper: Survival of Terrestrial N2–O2 Atmospheres in Violent XUV Environments through Efficient Atomic Line Radiative Cooling
Authors: Akifumi Nakayama, Masahiro Ikoma, Naoki Terada
Figure caption: Effects of atomic line cooling on the upper-atmospheric structure. Temperature profiles simulated with (solid lines) and without (dashed lines) atomic line cooling are shown for three different XUV irradiation levels, one times (blue), three times (green), and five times (red) the present-day Earth’s one, F XUV,⊕.
Paper: Survival of Terrestrial N2–O2 Atmospheres in Violent XUV Environments through Efficient Atomic Line Radiative Cooling
Authors: Akifumi Nakayama, Masahiro Ikoma, Naoki Terada
Figure caption: Altitude profiles of absorption and chemical properties for the XUV irradiation 500 times that of the present-day Earth’s (F XUV = 500 F XUV,⊕). Left panel: energy of radiation absorbed per unit mass per unit time in the two wavelength regions of 0.5–91 nm (red) and 91–400 nm (blue). Right panel: fractions of neutral molecules (blue), neutral atoms (green), and ion species (red).
Paper: Survival of Terrestrial N2–O2 Atmospheres in Violent XUV Environments through Efficient Atomic Line Radiative Cooling
Authors: Akifumi Nakayama, Masahiro Ikoma, Naoki Terada
Figure caption: Estimated lifetime of the 1 bar N2–O2 Earth-like atmosphere. We have estimated the lifetime, dividing the mass of present Earth’s atmosphere by the mass-loss rate calculated from our model for each XUV irradiation flux.