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Paper: Resolved Profiles of Stellar Mass, Star Formation Rate, and Predicted CO-to-H2 Conversion Factor Across Thousands of Local Galaxies
Authors: Jiayi Sun, 嘉懿 孙, Yu-Hsuan Teng, I-Da Chiang, 宜達 江, Adam K. Leroy, Karin Sandstrom, Jakob den Brok, Alberto D. Bolatto, Jérémy Chastenet, Ryan Chown, Annie Hughes, Eric W. Koch, Thomas G. Williams
Figure caption: Comparing four versions of CO (1–0)-to-H2 conversion factor predictions (x-axes) with observational estimates from the literature (y-axis). The latter include dust-based estimates across many galaxies presented in J. S. den Brok et al. (2023a, dB23), A. Yasuda et al. (2023, Y23), and I.-D. Chiang et al. (2024, C24), as well as CO multiline modeling (i.e., LVG) by Y.-H. Teng et al. (2023, T23) and carbon budget accounting by F. P. Israel (2020, I20) in galaxy centers. Note that the blue contours and downward triangles separately show measurements in galaxy disks and centers from I.-D. Chiang et al. (2024); the filled and open stars show two versions of results from Y.-H. Teng et al. (2023) assuming CO/H2 abundance ratios of 1.5 × 10−4 and 3 × 10−4, respectively. Top left: The metallicity-dependent CO-dark term f(Z) alone only spans a limited dynamic range, thus failing to match low αCO(1−0) values from observational estimates in galaxy centers. Top right: A combination of the f(Z) term and a Σ⋆-dependent emissivity term g(Σ⋆) matches dust-based and LVG-based estimates well but still disagrees with carbon budget accounting results (F. P. Israel 2020). Bottom left: Adopting an alternative emissivity term ﹩g{({{\rm{\Sigma }}}_{\star })}_{B13}﹩ (A. D. Bolatto et al. 2013) results in lower αCO(1−0) than most estimates for galaxy centers though still moderately higher than those from F. P. Israel (2020). Bottom right: An alternative CO-dark term f(Z)G20 (M. Gong et al. 2020) yields similar results as the fiducial choice (top right), though the weaker metallicity dependence in f(Z)G20 leads to slightly more underestimated αCO at the high end (i.e., in outer galaxy disks).
Paper: Resolved Profiles of Stellar Mass, Star Formation Rate, and Predicted CO-to-H2 Conversion Factor Across Thousands of Local Galaxies
Authors: Jiayi Sun, 嘉懿 孙, Yu-Hsuan Teng, I-Da Chiang, 宜達 江, Adam K. Leroy, Karin Sandstrom, Jakob den Brok, Alberto D. Bolatto, Jérémy Chastenet, Ryan Chown, Annie Hughes, Eric W. Koch, Thomas G. Williams
Figure caption: Median αCO radial profiles for galaxies grouped by stellar mass (M⋆) and offset from the SFMS (ΔMS; Equation (6)). The x-axis represents galactocentric radius in units of r50 in WISE1 band. In lower-mass galaxies (M⋆ < 1010 M⊙, dotted lines), the radial profile of αCO(1−0) (top panel) is effectively set by the CO-dark term f(Z) alone. In higher-mass galaxies (M⋆ > 1010 M⊙, solid lines) the radial profile of αCO(1−0) steepens at r ≲ r50 as the emissivity term g(Σ⋆) plays a significant role. While the αCO(1−0) radial profiles barely vary with ΔMS (color-coded), the αCO(2−1) radial profiles (bottom panel) clearly do depend on this quantity. We predict lower αCO(2−1) in galaxies with higher ΔMS (due to a higher line ratio R21).
Paper: Resolved Profiles of Stellar Mass, Star Formation Rate, and Predicted CO-to-H2 Conversion Factor Across Thousands of Local Galaxies
Authors: Jiayi Sun, 嘉懿 孙, Yu-Hsuan Teng, I-Da Chiang, 宜達 江, Adam K. Leroy, Karin Sandstrom, Jakob den Brok, Alberto D. Bolatto, Jérémy Chastenet, Ryan Chown, Annie Hughes, Eric W. Koch, Thomas G. Williams
Figure caption: The WISE1 band image (left and middle panels) and surface brightness radial profiles (right panel) for PGC 40153 (a.k.a. NGC 4321, i = 27°), as an example of our radial profile construction techniques. A set of elliptical rings (orange dotted lines) in the left panel represent galactocentric radius (rgal) bins, in which we directly compute the mean/median surface brightness. A set of long stripes (red rectangles) in the middle panel represent the regions used for stripe integral (R. H. Warmels 1988), an alternative method for deriving radial profiles that is applicable to even edge-on galaxies (see Section 2.2). For visual clarity, the densities of bins/stripes are reduced by a factor of eight in both panels. Both methods account for masked area due to foreground stars or other galaxies (white ellipses). As shown by the right panel, the radial profiles derived with both methods agree well out to large rgal, even after the stripe integral-based results drop below 3σ significance (red dotted curve).
Paper: Resolved Profiles of Stellar Mass, Star Formation Rate, and Predicted CO-to-H2 Conversion Factor Across Thousands of Local Galaxies
Authors: Jiayi Sun, 嘉懿 孙, Yu-Hsuan Teng, I-Da Chiang, 宜達 江, Adam K. Leroy, Karin Sandstrom, Jakob den Brok, Alberto D. Bolatto, Jérémy Chastenet, Ryan Chown, Annie Hughes, Eric W. Koch, Thomas G. Williams
Figure caption: Comparing three versions of CO (2–1)-to-H2 conversion factor predictions (x-axes) with observational estimates from the literature (y-axis). The latter include a similar set of literature results as Figure 3, except omitting αCO(1−0) estimates by A. Yasuda et al. (2023) and adding αCO(2−1) from K. M. Sandstrom et al. (2013, S13). Left and middle: Assuming a constant line ratio of ﹩{R}_{{\rm{21}},{\rm{const}}}=0.65﹩, the CO-dark and emissivity terms in combination can provide a wider range of αCO(2−1) values and better agreements with observational results than the CO-dark term alone. Right: Including a ΣSFR-dependent line ratio term on top of the CO-dark and emissivity terms further improves the agreement with observations at both low and high αCO(2−1) ends.
Paper: Resolved Profiles of Stellar Mass, Star Formation Rate, and Predicted CO-to-H2 Conversion Factor Across Thousands of Local Galaxies
Authors: Jiayi Sun, 嘉懿 孙, Yu-Hsuan Teng, I-Da Chiang, 宜達 江, Adam K. Leroy, Karin Sandstrom, Jakob den Brok, Alberto D. Bolatto, Jérémy Chastenet, Ryan Chown, Annie Hughes, Eric W. Koch, Thomas G. Williams
Figure caption: Our data processing workflow, from reduced GALEX and WISE images to αCO predictions. Methodological details for the three major steps (image processing and radial profile creation, physical property estimation, and conversion factor prescription) are described in Sections 2.1, 2.2, 2.3, and 2.4, respectively.
Paper: The Role of Magnetospheric Rebound in Breaking Resonant Chains of Super-Earths and Mini-Neptunes
Authors: Mengrui Pan, Andre Izidoro, Sho Shibata
Figure caption: The semimajor axis evolution of planets with orbital period shorter than 100 days in the disk with different dissipation timescales of tdisk = 105 (fiducial case; panel (a)), tdisk = 5 × 104 (panel ((b)), tdisk = 5 × 105 (panel ((c)), and tdisk = 105 (panel (d)). In panels ((a)–(c)), the rebound effect is included, whereas in panel (d), the disk undergoes only exponential dissipation without cavity expansion over time. Solid colored lines show the evolution of the planets’ semimajor axes. Perihelion and aphelion are shown in lighter shades of the same color. The black dashed lines in each panel indicate the disk inner edge. A stronger rebound effect leads to a more widely separated planetary system.
Paper: The Role of Magnetospheric Rebound in Breaking Resonant Chains of Super-Earths and Mini-Neptunes
Authors: Mengrui Pan, Andre Izidoro, Sho Shibata
Figure caption: Cumulative distributions of period ratio (panel (a)), orbital period (panel (b)), radius ratio (panel (c)), planetary mass (panel (d)), eccentricity (panel (e)), and inclination (panel (f)). Colored lines correspond to planet populations formed in different disk models. Dashed lines in panel (a) indicate first-order resonances, while dotted lines represent second-order resonances. The outcomes of the no-rebound scenario are categorized into stable and unstable system groups. The rebound effect efficiently disrupts the original resonance chains established in the gaseous disk without substantially altering the statistical distributions of planet populations.
Paper: The Role of Magnetospheric Rebound in Breaking Resonant Chains of Super-Earths and Mini-Neptunes
Authors: Mengrui Pan, Andre Izidoro, Sho Shibata
Figure caption: Comparison of observationally simulated period ratio (panel (a)), radius ratio (panel (b)), and multiplicity (panel (c)) distributions from different models (colored lines) and the CKS data set (gray lines). Same as in Figure 2(a), the vertical gray dashed and dotted lines indicate the first- and second-order resonances, respectively.
Paper: The Role of Magnetospheric Rebound in Breaking Resonant Chains of Super-Earths and Mini-Neptunes
Authors: Mengrui Pan, Andre Izidoro, Sho Shibata
Figure caption: Architecture of final planetary systems in the fiducial scenario. The size and color of the dots correspond to the radius and water mass fraction of super-Earths/mini-Neptunes, respectively. The solid, dashed, and dotted black lines connecting adjacent planets indicate the near first-, second-, and third-order mean-motion resonances, respectively, whose values are also illustrated above the lines. Only planets with orbital period P < 100 days are shown here.
Paper: The Role of Magnetospheric Rebound in Breaking Resonant Chains of Super-Earths and Mini-Neptunes
Authors: Mengrui Pan, Andre Izidoro, Sho Shibata
Figure caption: Number of planets with orbital period shorter than 100 days in planetary systems formed in different models. Planet multiplicity distributions are consistent across rebound scenarios and unstable nonrebound systems, with each system typically holding two to three planets. By contrast, stable nonrebound systems are characterized by higher multiplicity, usually comprising four to eight planets.
Paper: The Role of Magnetospheric Rebound in Breaking Resonant Chains of Super-Earths and Mini-Neptunes
Authors: Mengrui Pan, Andre Izidoro, Sho Shibata
Figure caption: Radius distributions of super-Earths and mini-Neptunes in different disk models. The results for dynamically unstable systems in each model, along with stable systems formed in disks without the rebound effect, are illustrated. The rebound effect efficiently disrupts the original resonance chains established in the gaseous disk without substantially altering the statistical distributions of planet populations.
Paper: The Role of Magnetospheric Rebound in Breaking Resonant Chains of Super-Earths and Mini-Neptunes
Authors: Mengrui Pan, Andre Izidoro, Sho Shibata
Figure caption: Same as Figure 3 but showing radius distributions after applying synthetic observational filters. The sky-blue curve corresponds to the CKS sample.
Paper: The Role of Magnetospheric Rebound in Breaking Resonant Chains of Super-Earths and Mini-Neptunes
Authors: Mengrui Pan, Andre Izidoro, Sho Shibata
Figure caption: Temporal evolution of resonant chain disruption for P < 100 days under various scenarios.
Paper: CIBER Fourth-flight Fluctuation Analysis: Measurements of Near-infrared Auto and Cross Power Spectra on Arcminute to Subdegree Scales
Authors: Richard M. Feder, James J. Bock, Yun-Ting Cheng, Asantha Cooray, Phillip M. Korngut, Shuji Matsuura, Jordan Mirocha, Chi H. Nguyen, Kohji Takimoto, Kohji Tsumura, Ryan Wills, Michael Zemcov
Figure caption: 1.1 μm × 1.8 μm cross power spectra for a range of source masking cuts indicated in the legend, alongside IGL+ISL (solid curves) and DGL (black) predictions. The large-angle fluctuation power evolves more mildly than that on small scales, with little to no variation beyond J = 17. Note the different normalization of the power spectra Dℓ/ℓ as in Figure 12.
Paper: CIBER Fourth-flight Fluctuation Analysis: Measurements of Near-infrared Auto and Cross Power Spectra on Arcminute to Subdegree Scales
Authors: Richard M. Feder, James J. Bock, Yun-Ting Cheng, Asantha Cooray, Phillip M. Korngut, Shuji Matsuura, Jordan Mirocha, Chi H. Nguyen, Kohji Takimoto, Kohji Tsumura, Ryan Wills, Michael Zemcov
Figure caption: CIBER cross power spectra with SFD maps normalized to 100 μm (top row) and “corrected” SFD maps that are cleaned of large-scale structure (CSFD, bottom row). Individual field measurements are shown by the colored points, while the weighted field averages are shown in black. The best-fit power spectra are calculated assuming a power-law form ﹩{C}_{\ell }=A{(\ell /{\ell }_{\mathrm{pivot}})}^{\gamma }﹩, for which the shaded regions span the ±1σ uncertainties ﹩\sigma (\widehat{A})﹩. We fit the CIBER × DGL cross spectra with free amplitude A but fixed γ. We find nondetections of cross power between 4.5 μm and SFD/CSFD, and so we omit those results here. We note that for some fields and bandpowers, we recover negative cross power, the points of which are omitted in presentation due to our use of logarithmic axes.
Paper: CIBER Fourth-flight Fluctuation Analysis: Measurements of Near-infrared Auto and Cross Power Spectra on Arcminute to Subdegree Scales
Authors: Richard M. Feder, James J. Bock, Yun-Ting Cheng, Asantha Cooray, Phillip M. Korngut, Shuji Matsuura, Jordan Mirocha, Chi H. Nguyen, Kohji Takimoto, Kohji Tsumura, Ryan Wills, Michael Zemcov
Figure caption: CIBER and Spitzer auto and cross power spectra for 1.1, 1.8, 3.6, and 4.5 μm. The CIBER-only power spectra are derived from a weighted average of all five fourth-flight science fields, while all results with Spitzer are derived from the two Boötes fields. Following Section 8.2 we only present measurements for multipoles above ℓmin = 875. The different curves indicate the model predictions for ISL (light blue), IGL (red), and DGL (black, with 1σ/2σ uncertainties), and their sum (dark blue). The astronomical mask removes sources with J < 16, H < 15.5, or L < 15.
Paper: CIBER Fourth-flight Fluctuation Analysis: Measurements of Near-infrared Auto and Cross Power Spectra on Arcminute to Subdegree Scales
Authors: Richard M. Feder, James J. Bock, Yun-Ting Cheng, Asantha Cooray, Phillip M. Korngut, Shuji Matsuura, Jordan Mirocha, Chi H. Nguyen, Kohji Takimoto, Kohji Tsumura, Ryan Wills, Michael Zemcov
Figure caption: Spitzer auto power spectra (left) and CIBER × Spitzer cross power spectra (right). We plot the individual Boötes field measurements in blue and red, while the field averages are plotted in black. In each panel, we plot the noise components that contribute to the derived uncertainties (dashed–dotted curves) and the predicted IGL+ISL contribution (blue curves). The astronomical mask removes sources with J < 16, H < 15.5, or L < 15.
Paper: CIBER Fourth-flight Fluctuation Analysis: Measurements of Near-infrared Auto and Cross Power Spectra on Arcminute to Subdegree Scales
Authors: Richard M. Feder, James J. Bock, Yun-Ting Cheng, Asantha Cooray, Phillip M. Korngut, Shuji Matsuura, Jordan Mirocha, Chi H. Nguyen, Kohji Takimoto, Kohji Tsumura, Ryan Wills, Michael Zemcov
Figure caption: 1.1 × 1.8 μm cross power spectra, for individual fields (colored points) and the weighted field average (black). The blue solid curve shows the IGL+ISL+DGL cross power spectrum predictions described in Section 11.1. The estimated DGL contribution is taken as the geometric mean of the 1.1 and 1.8 μm DGL auto power spectrum predictions in Section 9. The SWIRE field (purple) has ∼50% higher stellar density than the other fields, which explains the slightly higher Poisson level.
Paper: CIBER Fourth-flight Fluctuation Analysis: Measurements of Near-infrared Auto and Cross Power Spectra on Arcminute to Subdegree Scales
Authors: Richard M. Feder, James J. Bock, Yun-Ting Cheng, Asantha Cooray, Phillip M. Korngut, Shuji Matsuura, Jordan Mirocha, Chi H. Nguyen, Kohji Takimoto, Kohji Tsumura, Ryan Wills, Michael Zemcov
Figure caption: Comparison of fourth-flight CIBER auto power spectra (this work) with results from Z14 (light blue). We show our fiducial results in black alongside those using two analysis variations: (1) restricting our masking catalog to 2MASS only (green) and (2) additionally reverting to the masking radius prescription from Z14 (red). These results agree with Z14 with caveats noted in the text. DGL constraints (black shaded region) come from CIBER × CSFD cross correlations in Section 9.
Paper: CIBER Fourth-flight Fluctuation Analysis: Measurements of Near-infrared Auto and Cross Power Spectra on Arcminute to Subdegree Scales
Authors: Richard M. Feder, James J. Bock, Yun-Ting Cheng, Asantha Cooray, Phillip M. Korngut, Shuji Matsuura, Jordan Mirocha, Chi H. Nguyen, Kohji Takimoto, Kohji Tsumura, Ryan Wills, Michael Zemcov
Figure caption: 1.1 μm × 1.8 μm cross correlation coefficient in broad bandpowers as a function of J-band masking depth. We use the masking cuts from Figure 17 to compute the auto and cross power spectra for these results. While we find strong decorrelation on small scales as the masking depth increases, the correlation coefficient on large scales (ℓ < 1000) remains well within uncertainties beyond J = 15.
Paper: CIBER Fourth-flight Fluctuation Analysis: Measurements of Near-infrared Auto and Cross Power Spectra on Arcminute to Subdegree Scales
Authors: Richard M. Feder, James J. Bock, Yun-Ting Cheng, Asantha Cooray, Phillip M. Korngut, Shuji Matsuura, Jordan Mirocha, Chi H. Nguyen, Kohji Takimoto, Kohji Tsumura, Ryan Wills, Michael Zemcov
Figure caption: Comparison of measured DGL emissivity νbν with existing measurements in the literature. Unlike previous measurements that rely on correlations with the DGL mean intensity, the derived values from this work (magenta and red diamonds) come from the cross power spectrum of CIBER and CSFD.
Paper: CIBER Fourth-flight Fluctuation Analysis: Measurements of Near-infrared Auto and Cross Power Spectra on Arcminute to Subdegree Scales
Authors: Richard M. Feder, James J. Bock, Yun-Ting Cheng, Asantha Cooray, Phillip M. Korngut, Shuji Matsuura, Jordan Mirocha, Chi H. Nguyen, Kohji Takimoto, Kohji Tsumura, Ryan Wills, Michael Zemcov
Figure caption: CIBER (left column) and Spitzer (right column) fluctuation maps for the Boötes A field. Each map is low-pass filtered with a Gaussian kernel of width σ = 3′ and high-pass filtered with a Gaussian of width σ = 15′. All maps are regridded to 1.1 μm detector coordinates. Note that the CIBER maps as presented contain errors from the FF correction.
Paper: Probing the Dark Matter Halos of External Galaxies with Stellar Streams
Authors: Madison Walder, Denis Erkal, Michelle Collins, David Martínez-Delgado
Figure caption: A summary figure of our posterior distributions for the enclosed dark matter mass MDM and NFW halo scale radius rs in ΦDM and ΦGal for fitting streams with varying lengths. Top left: the dependence of the enclosed dark matter mass measurement on the length of a stream for ΦDM. A gray dashed line denotes the true value of MDM at 30 kpc, while the colored circles and error bars correspond to the best-fit MDM value and its 1σ (darker error bars) and 2σ (lighter error bars) uncertainties. The blue points correspond to fits made using only stream-track measurements, while the purple ones denote track fits including a RV measurement. Bottom left: rs measurement as a function of stream length. Similar to the panel above, the gray dashed line shows the true value of rs, while the colored points and associated uncertainties correspond to the best-fit rs value. Top right: the relationship between enclosed mass and stream length for streams in ΦGal. The circles correspond to the constraints made with a 1 kpc uncertainty in the stream track, while the star symbols correspond to the constraints made with 0.2 kpc uncertainty in the stream track. Bottom right: the NFW scale radius measurement as a function of stream length for streams in ΦGal. For each panel, the gray shaded region denotes the range of the prior probability for the parameter measured. Key takeaways: with current stream-track uncertainties, streams with multiple wraps yield the best rs measurements when there are no radial velocities available. In ΦDM, rs is recovered with the most accuracy and precision of all the scale radius measurements without including RVs in the fits, while MDM is best recovered for the fiducial stream in ΦGal with a RV.
Paper: Probing the Dark Matter Halos of External Galaxies with Stellar Streams
Authors: Madison Walder, Denis Erkal, Michelle Collins, David Martínez-Delgado
Figure caption: MCMC results for fitting mock stream-track observations of the fiducial stream in ΦDM with σtrack = 1 kpc. The corner plot displays the MCMC samples for the initial stream parameters x, z, vx, vy, vz and ΦDM parameters MNFW (M200) and rs using only the mock track observations. Each 2D histogram displays the concentration of samples in the corresponding parameter space, with solid contours indicating the 0.5σ, 1σ, 1.5σ, and 2σ confidence levels, and the true values indicated by blue dots connected via crosshairs. Within the limit of current observed extragalactic stream-track uncertainties, the posteriors of many of the fitted parameters are dominated by degeneracies due to only having 2D information.
Paper: Probing the Dark Matter Halos of External Galaxies with Stellar Streams
Authors: Madison Walder, Denis Erkal, Michelle Collins, David Martínez-Delgado
Figure caption: Left: a spatial representation of the fiducial stream integrated in ΦPL (center marked by the blue star) as one would imagine observing it along the z-axis. The purple points correspond to the points along the stream at which mock observations are taken. The black cross with the small dashed line passing through indicates the approximate launch point for all of our stream models. Right: the fiducial stream in terms of 2D distance from the host galaxy in kiloparsecs and the position along the stream θ in radians, i.e., the mock observables space.
Paper: Probing the Dark Matter Halos of External Galaxies with Stellar Streams
Authors: Madison Walder, Denis Erkal, Michelle Collins, David Martínez-Delgado
Figure caption: Left: the angle of precession for orbits with the same apocenter and different eccentricities as a function of γ in ΦPL. Each orbit is integrated for various values of γ (i.e., various radial profiles) until it completes multiple wraps around the host galaxy, for which we then take the angular difference between the apocenters in the plane of the orbit. Right: a spatial representation of the same orbit (with e = 0.5) in ΦPL governed by γ = 0 in the top panel and γ = −1 in the bottom panel. This illustrates the relationship between a stream’s properties and the gravitational potential it resides in. Motivated by Figure 9 (right panel) in V. Belokurov et al. (2014).
Paper: Probing the Dark Matter Halos of External Galaxies with Stellar Streams
Authors: Madison Walder, Denis Erkal, Michelle Collins, David Martínez-Delgado
Figure caption: MCMC results for fitting mock stream-track observations with a RV measurement of the fiducial stream in ΦDM with σtrack = 1 kpc. Adding a RV to the fit shown in Figure 3 allows the recovery of the scale radius rs (rightmost panel), while improving upon the precision of the dark matter halo mass enclosed MDM recovered from the measurement of MNFW (top panel).
Paper: Probing the Dark Matter Halos of External Galaxies with Stellar Streams
Authors: Madison Walder, Denis Erkal, Michelle Collins, David Martínez-Delgado
Figure caption: MCMC results for fitting mock stream-track observations with a RV measurement of the fiducial stream in ΦGal with σtrack = 1 kpc. Adding a RV to the fit allows the recovery of rs (rightmost panel) as well as a constraint on the enclosed dark matter mass MDM calculated from the MNFW distribution.
Paper: Probing the Dark Matter Halos of External Galaxies with Stellar Streams
Authors: Madison Walder, Denis Erkal, Michelle Collins, David Martínez-Delgado
Figure caption: MCMC results for fitting mock stream-track observations and a RV measurement of the fiducial stream in ΦGal with σtrack = 0.2 kpc. Adding a RV to the fit allows the recovery of rs (rightmost panel) as well as improves the precision and accuracy of the enclosed dark matter mass MDM measurement calculated from the MNFW distribution.