adsabs/AstroCLIMB huggingface dataset:
Paper: Data-driven Stellar Intrinsic Colors and Dust Reddenings for Spectrophotometric Data: From the Blue-edge Method to a Machine Learning Approach
Authors: He Zhao, 赫 赵, Shu Wang, 舒 王, Biwei Jiang, 碧沩 姜, Jun Li, 军 李, Dongwei Fan, 东卫 樊, Yi Ren, 逸 任, Xiaoxiao Ma, 晓骁 马
Figure caption: Kiel diagram for the training set (left) and the A23-RGB sample selected from R. Andrae et al. (2023b, middle and right). The left panel is colored by the number of stars in each 50 K × 0.1 bin. The contours in each panel from light red to dark red represent 1%, 10%, and 50% of the maximum of the star number (306), respectively. The middle panel shows the mean parameter distance of stars in the A23-RGB sample to the training set (see Section 4.2) in each 20 K × 0.02 bin. The right panel contains 9 million stars in the A23-RGB sample filtered by a parameter distance smaller than 0.03 and is colored by the star number in each 20 K × 0.02 bin.
Paper: Data-driven Stellar Intrinsic Colors and Dust Reddenings for Spectrophotometric Data: From the Blue-edge Method to a Machine Learning Approach
Authors: He Zhao, 赫 赵, Shu Wang, 舒 王, Biwei Jiang, 碧沩 姜, Jun Li, 军 李, Dongwei Fan, 东卫 樊, Yi Ren, 逸 任, Xiaoxiao Ma, 晓骁 马
Figure caption: Differences between observed and XGBoost-predicted colors for the test set, as a function of the GSP-phot stellar parameters (T eff, ﹩{\rm{log}}\,g﹩, and [M/H]) and G magnitude. The color-coded map represents the number density of the sources on a linear scale. The MedAD, RMSD, and AD90% for each color are also marked in the first column of each panel.
Paper: Data-driven Stellar Intrinsic Colors and Dust Reddenings for Spectrophotometric Data: From the Blue-edge Method to a Machine Learning Approach
Authors: He Zhao, 赫 赵, Shu Wang, 舒 王, Biwei Jiang, 碧沩 姜, Jun Li, 军 李, Dongwei Fan, 东卫 樊, Yi Ren, 逸 任, Xiaoxiao Ma, 晓骁 马
Figure caption: Differences of E(J − K S) between this work and literature values from X. Zhang et al. (2023b, top), G. M. Green et al. (2019, middle), and R. Andrae et al. (2023a, bottom) as a function of stellar atmospheric parameters from R. Andrae et al. (2023b) and distance (inverse of parallax) for 5,714,528 sources in the target sample. The color-coded map represents the number density of the sources on a linear scale. The MedAD, RMSD, and AD90% for the E(J − K S) difference are also marked in the first column of each panel.
Paper: Data-driven Stellar Intrinsic Colors and Dust Reddenings for Spectrophotometric Data: From the Blue-edge Method to a Machine Learning Approach
Authors: He Zhao, 赫 赵, Shu Wang, 舒 王, Biwei Jiang, 碧沩 姜, Jun Li, 军 李, Dongwei Fan, 东卫 樊, Yi Ren, 逸 任, Xiaoxiao Ma, 晓骁 马
Figure caption: Validation curves of the optimized hyperparameters for the XGBoost model (see Section 3.1). The blue and orange lines show the mean R 2 score in the 10-fold cross validation for the training and validation sets, respectively, with a span of their standard deviation. The squares indicate the mean R 2 score at specific values of the hyperparameters, and the dashed red lines mark the finally chosen hyperparameters.
Paper: Data-driven Stellar Intrinsic Colors and Dust Reddenings for Spectrophotometric Data: From the Blue-edge Method to a Machine Learning Approach
Authors: He Zhao, 赫 赵, Shu Wang, 舒 王, Biwei Jiang, 碧沩 姜, Jun Li, 军 李, Dongwei Fan, 东卫 樊, Yi Ren, 逸 任, Xiaoxiao Ma, 晓骁 马
Figure caption: Comparison of the stellar atmospheric parameters between the common sources from GSP-phot, APOGEE, and LAMOST. The color in each panel represents the number density of stars calculated in each bin with a step of 25 K for T eff, 0.025 for ﹩{\rm{log}}\,g﹩, and 0.015 dex for [M/H]. The dashed black lines show the one-to-one correspondence. The zoomed-in panels present the distribution of the parameter differences (GSP-phot–APOGEE/LAMOST). The marked statistics are the median absolute difference (MedAD), the rms difference (RMSD), and the absolute difference not exceeded by 90% of sources (AD90%).
Paper: Data-driven Stellar Intrinsic Colors and Dust Reddenings for Spectrophotometric Data: From the Blue-edge Method to a Machine Learning Approach
Authors: He Zhao, 赫 赵, Shu Wang, 舒 王, Biwei Jiang, 碧沩 姜, Jun Li, 军 李, Dongwei Fan, 东卫 樊, Yi Ren, 逸 任, Xiaoxiao Ma, 晓骁 马
Figure caption: Correlation between T eff and observed colors for the 1,040,446 selected stars with low reddenings. The color-coded map presents the average [M/H] in each 25 K × 0.05 mag bin. The zoomed-in panels show the correlation for hotter stars up to 20,000 K, color coded by their [M/H].
Paper: Data-driven Stellar Intrinsic Colors and Dust Reddenings for Spectrophotometric Data: From the Blue-edge Method to a Machine Learning Approach
Authors: He Zhao, 赫 赵, Shu Wang, 舒 王, Biwei Jiang, 碧沩 姜, Jun Li, 军 李, Dongwei Fan, 东卫 樊, Yi Ren, 逸 任, Xiaoxiao Ma, 晓骁 马
Figure caption: Two-dimensional Galactic distribution of the number of stars per pixel (N tar, top), the median E(G BP − G RP) (middle), and the fit E(G BP − K S)/E(G BP − G RP) (bottom) in 18,236 HEALPixels with a pixel resolution of 55′ (see Section 5).
Paper: Data-driven Stellar Intrinsic Colors and Dust Reddenings for Spectrophotometric Data: From the Blue-edge Method to a Machine Learning Approach
Authors: He Zhao, 赫 赵, Shu Wang, 舒 王, Biwei Jiang, 碧沩 姜, Jun Li, 军 李, Dongwei Fan, 东卫 樊, Yi Ren, 逸 任, Xiaoxiao Ma, 晓骁 马
Figure caption: Differences between observed and XGBoost-predicted colors as a function of the parameter differences (GSP-phot–APOGEE/LAMOST) for 51,417 sources selected from the test set. The color-coded map represents the number density of the sources on a linear scale. The MedAD, RMSD, and AD90% for each color are also marked in the first column of each panel.
Paper: Data-driven Stellar Intrinsic Colors and Dust Reddenings for Spectrophotometric Data: From the Blue-edge Method to a Machine Learning Approach
Authors: He Zhao, 赫 赵, Shu Wang, 舒 王, Biwei Jiang, 碧沩 姜, Jun Li, 军 李, Dongwei Fan, 东卫 樊, Yi Ren, 逸 任, Xiaoxiao Ma, 晓骁 马
Figure caption: Distribution of the Gaia G-band magnitude (top panel) and the distance (inverse of parallax, bottom panel) of the 1,040,446 selected stars with low reddenings.
Paper: Multistructured Accretion Flow of Sgr A*. I. Examination of a Radiatively Inefficient Accretion Flow Model
Authors: Mayura Balakrishnan, Lia Corrales, Sera Markoff, Michael Nowak, Daryl Haggard, Q. Daniel Wang, Joey Neilsen, Christopher M. P. Russell, Diego Calderón, Jorge Cuadra, Frederick Baganoff
Figure caption: Diagram illustrating the geometry taken into account when calculating the RIAF model flux for the background region. We include the projected HEG extraction area; this reflects how the spectral extraction pipeline for HEG captures photon events between 1.″5 and 5″ in the cross-dispersion direction and up to 3″ along the dispersion axis. In other words, the source spectrum probes the central 3 × 105 R  g  while the background annulus spans (3–10) × 105 R  g . Only a portion of shells outside 106 R  g  lie within our line-of-sight projection in the background extraction region (green box). We use ﹩{\theta }_{\min }﹩ and ﹩{\theta }_{\max }﹩ to denote the angular boundaries between which a given shell is captured. These quantities are used to calculate the differential volume in each shell, as seen in Equations (3) and (4).
Paper: Multistructured Accretion Flow of Sgr A*. I. Examination of a Radiatively Inefficient Accretion Flow Model
Authors: Mayura Balakrishnan, Lia Corrales, Sera Markoff, Michael Nowak, Daryl Haggard, Q. Daniel Wang, Joey Neilsen, Christopher M. P. Russell, Diego Calderón, Jorge Cuadra, Frederick Baganoff
Figure caption: Left: adaptation of Figure 1 from Corrales et al. (2020) showing the cleaned, quiescent 2–8 keV data for one roll angle. The zeroth-order (nondispersed) image is displayed in HEG coordinates with the dispersion direction on the x-axis and the cross-dispersion direction on the y-axis. Solid black lines bracket the source extraction region out to 1.″5, while the dashed lines denote the background extraction region, 5″ away from Sgr A* in the cross-dispersion direction. The yellow and blue boxes refer to the effective regions of the image that contribute to the source and background spectra, respectively. Right: adapted from Figure 6 of Corrales et al. (2020), showing the combined, unbinned, stacked, and background-subtracted HEG ±1 counts histogram in black. The best-fit RIAF model from W13 is plotted in red, showing that the background-subtracted spectrum has additional structure. The Fe XXV doublet wavelengths are plotted with blue dashed lines, and Fe XX, Fe XXII, Fe XXIII, and Fe XXVI are plotted as brown, teal, blue, and orange lines respectively.
Paper: Multistructured Accretion Flow of Sgr A*. I. Examination of a Radiatively Inefficient Accretion Flow Model
Authors: Mayura Balakrishnan, Lia Corrales, Sera Markoff, Michael Nowak, Daryl Haggard, Q. Daniel Wang, Joey Neilsen, Christopher M. P. Russell, Diego Calderón, Jorge Cuadra, Frederick Baganoff
Figure caption: Final fits to the HEG +1 and −1 source and background regions, with error contours corresponding to RIAFs with parameters from chains within the top 5% likelihood range. The data and best-fit RIAF are plotted in black and blue, respectively, and are binned to have 4 counts/bin for visual clarity. The residual counts (x  i  − m  i ) are plotted as dashed blue lines (note the limits on the y-axis are different for the HEG +1 and HEG −1 residuals). The model captures some of the 6.7 keV emission but does not capture any structure in the background.
Paper: Multistructured Accretion Flow of Sgr A*. I. Examination of a Radiatively Inefficient Accretion Flow Model
Authors: Mayura Balakrishnan, Lia Corrales, Sera Markoff, Michael Nowak, Daryl Haggard, Q. Daniel Wang, Joey Neilsen, Christopher M. P. Russell, Diego Calderón, Jorge Cuadra, Frederick Baganoff
Figure caption: Demonstration of the procedure used to predict the counts histogram for the HEG +1 source region for a given RIAF model. The green curve shows the RIAF emission (in photons s−1) calculated with pyatomdb, integrated over the differential volume corresponding to the physical region probed. We incorporate extinction (orange dashed line) and a power law for contaminating photons (blue dotted–dashed line). Convolving with the HEG ancillary response file and response matrix file leads to the final counts histogram (pink), washing out the fine structure from the original line profiles.
Paper: Multistructured Accretion Flow of Sgr A*. I. Examination of a Radiatively Inefficient Accretion Flow Model
Authors: Mayura Balakrishnan, Lia Corrales, Sera Markoff, Michael Nowak, Daryl Haggard, Q. Daniel Wang, Joey Neilsen, Christopher M. P. Russell, Diego Calderón, Jorge Cuadra, Frederick Baganoff
Figure caption: Left: corner plot from the final fits of our RIAF model with pyatomdb, showing the distributions for RIAF s (material density slope), q (temperature profile slope), and Z Fe (relative to solar; all other elemental abundances were set to 2 Z ⊙). Overall, the data prefer subsolar iron abundances, s-values close to 1, and q ≈ 0.22.
Paper: Reionization Parameter Inference from 3D Minkowski Functionals of the 21 cm Signals
Authors: Kangning Diao, Zhaoting Chen, Xuelei Chen, Yi Mao
Figure caption: Same as Figure 6 using the SKA-Low configuration but the foreground cut is applied herein. We also show the joint constraint (green region).
Paper: Reionization Parameter Inference from 3D Minkowski Functionals of the 21 cm Signals
Authors: Kangning Diao, Zhaoting Chen, Xuelei Chen, Yi Mao
Figure caption: The posterior distribution of EOR parameters using the SKA-Low configuration with 1000 hr observation and no-foreground-cut, for the FAINT GALAXIES model. We show the constraint using only V 1 (red), V 2 (blue), V 3 (green), and using all three MFs jointly (black), respectively.
Paper: Mapping the Longitudinal Magnetic Field in the Atmosphere of an Active Region Plage from the Inversion of the Near-ultraviolet CLASP2.1 Spectropolarimetric Data
Authors: Hao Li, Tanausú del Pino Alemán, Javier Trujillo Bueno, Ryohko Ishikawa, Ernest Alsina Ballester, David E. McKenzie, Luca Belluzzi, Donguk Song, Takenori J. Okamoto, Ken Kobayashi, Laurel A. Rachmeler, Christian Bethge, Frédéric Auchère
Figure caption: Top-left panel: AIA image in the 171 Å band. Bottom-left panel: HMI magnetogram (corresponding color bar at the top of the top-left panel). From the second to the sixth columns: inferred temperature, electron density, longitudinal component of the velocity, microturbulent velocity, and gas pressure at ﹩{\mathrm{log}}_{10}({\tau }_{500})=-6.0﹩ (top panels) and ﹩{\mathrm{log}}_{10}({\tau }_{500})=-4.5﹩ (bottom panels). Purple contours in panels (A) and (K) are drawn according to the intensity of the 171 Å AIA band. Black and blue contours in panels (B), (D), and (F) correspond to the HMI magnetic field values of 70 G and −70 G, respectively. In panel (B), the squares drawn with solid and dashed purple lines delimit the pixels included in the scatterplots in Figure 5 (see the text). The white and green arrows highlight regions of the field of view discussed in the text.
Paper: Mapping the Longitudinal Magnetic Field in the Atmosphere of an Active Region Plage from the Inversion of the Near-ultraviolet CLASP2.1 Spectropolarimetric Data
Authors: Hao Li, Tanausú del Pino Alemán, Javier Trujillo Bueno, Ryohko Ishikawa, Ernest Alsina Ballester, David E. McKenzie, Luca Belluzzi, Donguk Song, Takenori J. Okamoto, Ken Kobayashi, Laurel A. Rachmeler, Christian Bethge, Frédéric Auchère
Figure caption: Scatterplots for selected parameter pairs. In the labels, the number in parenthesis indicates the ﹩{\mathrm{log}}_{10}({\tau }_{500})﹩ where the value of the variable is taken from the inversion result, “(HMI)” indicates that the value is taken from the HMI magnetogram, and the label “171 Intensity [DN]” indicates the intensity in the AIA 171 Å band. The points in panels (A)–(G) correspond to the region delimited by solid purple lines in the bottom-left panel of Figure 4, while the points in panels (H) and (I) correspond to the region delimited by the dashed purple lines in the same panel of Figure 4. The Pearson correlation coefficient is shown in each panel.
Paper: Mapping the Longitudinal Magnetic Field in the Atmosphere of an Active Region Plage from the Inversion of the Near-ultraviolet CLASP2.1 Spectropolarimetric Data
Authors: Hao Li, Tanausú del Pino Alemán, Javier Trujillo Bueno, Ryohko Ishikawa, Ernest Alsina Ballester, David E. McKenzie, Luca Belluzzi, Donguk Song, Takenori J. Okamoto, Ken Kobayashi, Laurel A. Rachmeler, Christian Bethge, Frédéric Auchère
Figure caption: Two left panels: observed (open circles with error bars) and synthesized (blue, orange, and green curves) circular polarization profiles for the pixel in the vicinity of the penumbra considered in Figure 8. Gray curves show 200 Stokes V profiles generated by adding random noise to the observation. Right panel: blue (the magnetic field model from the inversion), orange (with zero magnetic fields in the upper chromosphere), and green (with flipped magnetic fields in the upper chromosphere) curves show the corresponding magnetic fields. Gray curves show the 200 Stokes V profiles mentioned in the text.
Paper: Mapping the Longitudinal Magnetic Field in the Atmosphere of an Active Region Plage from the Inversion of the Near-ultraviolet CLASP2.1 Spectropolarimetric Data
Authors: Hao Li, Tanausú del Pino Alemán, Javier Trujillo Bueno, Ryohko Ishikawa, Ernest Alsina Ballester, David E. McKenzie, Luca Belluzzi, Donguk Song, Takenori J. Okamoto, Ken Kobayashi, Laurel A. Rachmeler, Christian Bethge, Frédéric Auchère
Figure caption: Inferred B ∥ at ﹩{\mathrm{log}}_{10}({\tau }_{500})=-6.0﹩, −5.0, and −4.0 (three left panels, from left to right, respectively). The rightmost panel shows the height where the plasma β = 1 (assuming the magnetic field is parallel to the LOS). Contour curves are the same as in Figure 4.
Paper: Mapping the Longitudinal Magnetic Field in the Atmosphere of an Active Region Plage from the Inversion of the Near-ultraviolet CLASP2.1 Spectropolarimetric Data
Authors: Hao Li, Tanausú del Pino Alemán, Javier Trujillo Bueno, Ryohko Ishikawa, Ernest Alsina Ballester, David E. McKenzie, Luca Belluzzi, Donguk Song, Takenori J. Okamoto, Ken Kobayashi, Laurel A. Rachmeler, Christian Bethge, Frédéric Auchère
Figure caption: Same as Figure 3 but including the inversion accounting for the Mg II UV triplet and the Mn I lines at 279.56, 279.91, and 280.19 nm. The dashed gray curve in the bottom-left panel shows the optical depth (in ﹩{\mathrm{log}}_{10}({\tau }_{500})﹩) corresponding to the atmosphere height for which the optical depth at any given wavelength is equal to 1 (τ  λ  = 1).
Paper: Mapping the Longitudinal Magnetic Field in the Atmosphere of an Active Region Plage from the Inversion of the Near-ultraviolet CLASP2.1 Spectropolarimetric Data
Authors: Hao Li, Tanausú del Pino Alemán, Javier Trujillo Bueno, Ryohko Ishikawa, Ernest Alsina Ballester, David E. McKenzie, Luca Belluzzi, Donguk Song, Takenori J. Okamoto, Ken Kobayashi, Laurel A. Rachmeler, Christian Bethge, Frédéric Auchère
Figure caption: Inferred B ∥ at ﹩{\mathrm{log}}_{10}({\tau }_{500})=-6.0﹩ (blue curves), −5.0 (orange curves), and −4.0 (green curves) for slit locations 9, 11, 15, and 16 (see label on top of the panels). The slit positions on the observation field of view are indicated by the blue-dashed curves in Figure 1.
Paper: Mapping the Longitudinal Magnetic Field in the Atmosphere of an Active Region Plage from the Inversion of the Near-ultraviolet CLASP2.1 Spectropolarimetric Data
Authors: Hao Li, Tanausú del Pino Alemán, Javier Trujillo Bueno, Ryohko Ishikawa, Ernest Alsina Ballester, David E. McKenzie, Luca Belluzzi, Donguk Song, Takenori J. Okamoto, Ken Kobayashi, Laurel A. Rachmeler, Christian Bethge, Frédéric Auchère
Figure caption: Same as Figure 3, but for a pixel in the vicinity of the penumbra (yellow “×” symbol in panel (E) of Figure 1). The vertical orange-dashed lines indicate the location of the k2 and h2 peaks.
Paper: Mapping the Longitudinal Magnetic Field in the Atmosphere of an Active Region Plage from the Inversion of the Near-ultraviolet CLASP2.1 Spectropolarimetric Data
Authors: Hao Li, Tanausú del Pino Alemán, Javier Trujillo Bueno, Ryohko Ishikawa, Ernest Alsina Ballester, David E. McKenzie, Luca Belluzzi, Donguk Song, Takenori J. Okamoto, Ken Kobayashi, Laurel A. Rachmeler, Christian Bethge, Frédéric Auchère
Figure caption: Same as Figure 10, but for a pixel in the penumbra (green “×” symbol in panel (E) of Figure 1).
Paper: Mapping the Longitudinal Magnetic Field in the Atmosphere of an Active Region Plage from the Inversion of the Near-ultraviolet CLASP2.1 Spectropolarimetric Data
Authors: Hao Li, Tanausú del Pino Alemán, Javier Trujillo Bueno, Ryohko Ishikawa, Ernest Alsina Ballester, David E. McKenzie, Luca Belluzzi, Donguk Song, Takenori J. Okamoto, Ken Kobayashi, Laurel A. Rachmeler, Christian Bethge, Frédéric Auchère
Figure caption: Same as Figure 10 but for a pixel in the vicinity of the penumbra (yellow “×” symbol in panel (E) of Figure 1). The red solid curve is the same as in Figure 8.
Paper: High-energy Neutrinos from Late-time Jets of Gamma-Ray Bursts Seeded with Cocoon Photons
Authors: Riki Matsui, Shigeo S. Kimura, Hamid Hamidani
Figure caption: Color maps of the expected number of neutrino detections for 2000 XFs (left) and 1000 EEs (right) with IceCube (top) or IceCube-Gen2 (bottom). Black solid lines are the contours for ﹩{\bar{N}}_{{\nu }_{\mu },\mathrm{stacked}}﹩. The solid red lines represent the condition given by Equation (18), where the pions are sufficiently produced. The dashed (dotted) red lines represent the condition given by Equation (19) (Equation (20)), where pions (muons) are significantly cooled. For XFs, the red dashed lines are outside of the plots. The solid blue lines represent the condition given by Equation (22), where cocoon photons can enter into the dissipation region. The dotted blue lines represent the condition given by Equation (21), where the cocoon can cover the dissipation region.
Paper: High-energy Neutrinos from Late-time Jets of Gamma-Ray Bursts Seeded with Cocoon Photons
Authors: Riki Matsui, Shigeo S. Kimura, Hamid Hamidani
Figure caption: The neutrino fluence for XFs (left) and EEs (right). The blue thick solid is for our fiducial parameters, the blue thin dashed line is the fluence without taking into account cocoon photons, and the orange thin solid line is the fluence for an efficient diffusion case, ﹩{f}_{{\tau }_{\mathrm{coc}},\mathrm{skin}}=1﹩ discussed in the Appendix. The green dotted–dashed line is for the all-sky averaged atmospheric and astronomical background within 3 deg2 and 300 s, as described in Section 3.
Paper: High-energy Neutrinos from Late-time Jets of Gamma-Ray Bursts Seeded with Cocoon Photons
Authors: Riki Matsui, Shigeo S. Kimura, Hamid Hamidani
Figure caption: The cooling and acceleration rates for our fiducial parameters of XFs (in LGRBs; left) and EEs (in SGRBs; right). The red thick dotted line is for the acceleration rate; the blue thick (thin) solid line is for the cooling rate of the p γ process by cocoon (internal) photons, ﹩{t}_{p\gamma ,\mathrm{coc}}^{{\prime} -1}﹩ (﹩{t}_{p\gamma ,\mathrm{int}}^{{\prime} -1}﹩); the green thin dashed (dotted–dashed) line is for the cooling rate of the B–H process by cocoon (internal) photons, ﹩{t}_{{\rm{B}}-{\rm{H}},\ \mathrm{coc}}^{{\prime} -1}﹩ (﹩{t}_{{\rm{B}}-{\rm{H}},\mathrm{int}}^{{\prime} -1}﹩); and the orange thick dotted–dashed line is for the adiabatic cooling rate, ﹩{t}_{\mathrm{ad}}^{{\prime} -1}﹩.
Paper: High-energy Neutrinos from Late-time Jets of Gamma-Ray Bursts Seeded with Cocoon Photons
Authors: Riki Matsui, Shigeo S. Kimura, Hamid Hamidani
Figure caption: Color maps of the expected number of neutrino detections for 2000 XFs (left) and 1000 EEs (right) with IceCube-Gen2 with threshold at ﹩{\varepsilon }_{{\nu }_{\mu }}=10﹩ TeV (top) and at ﹩{\varepsilon }_{{\nu }_{\mu }}=100﹩ TeV (bottom). Lines are the same as in Figure 3.
Paper: High-energy Neutrinos from Late-time Jets of Gamma-Ray Bursts Seeded with Cocoon Photons
Authors: Riki Matsui, Shigeo S. Kimura, Hamid Hamidani
Figure caption: The cooling and acceleration rates for our fiducial parameters of XFs (left) and EEs (right) for ﹩{f}_{{\tau }_{\mathrm{coc}},\mathrm{skin}}=1﹩. Lines are the same as in Figure 1.
Paper: Effects of Bursty Star Formation on [C II] Line Intensity Mapping of High-redshift Galaxies
Authors: Lun-Jun Liu, Guochao Sun, Tzu-Ching Chang, Steven R. Furlanetto, Charles M. Bradford
Figure caption: Comparison of observables predicted by our analytic models with constraints from recent observations. Left: the cosmic star formation rate density (SFRD) in model variations with different values of the cloud-scale SFE (blue solid: ϵ ff = 0.005; black solid: ϵ ff = 0.015; red solid: ϵ ff = 0.045) and constraints from observations of high-z galaxies at UV (circle: P. A. Oesch et al. 2013; R. J. Bouwens et al. 2016; P. A. Oesch et al. 2018; C. T. Donnan et al. 2023) and FIR/mm-wave (square: M. Rowan-Robinson et al. 2016; F. Loiacono et al. 2021; H. S. B. Algera et al. 2023) wavelengths. Middle: comparison of the stellar mass–metallicity relation (MZR) predicted by the model variations and constraints from recent JWST observations (M. Curti et al. 2024; K. Nakajima et al. 2023). The increased scatter in the MZR at the low-mass end is associated with the increased burstiness of star formation. Also shown is the MZR predicted by high-z FIRE-2 simulations (A. Marszewski et al. 2024). Right: comparison between our predicted L [CII]–SFR relation at z ∼ 7 and recent constraints from ALMA REBELS survey (A. Ferrara et al. 2022). Also shown is the best-fit L [CII]–SFR relation for galaxies at 4 < z < 6 from the ALMA ALPINE survey (D. Schaerer et al. 2020), which suggests little evolution from the relation at z ∼ 0. The four sets of data points in blue, black, and red represent our model predictions for halo masses ﹩\mathrm{log}({M}_{{\rm{h}}}/{M}_{\odot })\sim 10.5,11,11.5﹩, and 12, respectively (from left to right).