adsabs/AstroCLIMB huggingface dataset:
Paper: Generation of 30 s Waves Observed Upstream of the Martian Bow Shock: A Case Study
Authors: Taifeng Jin, Binbin Ni, Song Fu, Xing Cao, Shuyue Pang, Xiaotong Yun, Minyi Long, Hengle Du
Figure caption: (a) Correlation analysis of downsampled magnetic field strength ∣ B ∣ (blue line) and proton density N (wine-red line) during 23:01–23:10, with correlation coefficient (ρ = 0.8516) marked at bottom left of the panel; (b) magnetic field strength ∣ B ∣ (blue line) and value of cos〈 B ,  x 〉 (wine-red line) during 23:07–23:08.
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: Same as Figure 1 but for the LGR4-2pc model at t = 298 Myr through y = 0.
Paper: Generation of 30 s Waves Observed Upstream of the Martian Bow Shock: A Case Study
Authors: Taifeng Jin, Binbin Ni, Song Fu, Xing Cao, Shuyue Pang, Xiaotong Yun, Minyi Long, Hengle Du
Figure caption: Wave properties obtained from wavelet analysis. Power spectrum of (a) magnetic field strength ∣B∣; (b) transverse component of  B  in MFA frame; (c) compressional component of  B  in MFA frame; (d) ratio of transverse component over compressional component; (e) ellipticity; (f) wave normal angle; and (g) integrated wave amplitude. The white (black) curves overplotted on panels (a)–(f) represent local proton gyrofrequency.
Paper: Generation of 30 s Waves Observed Upstream of the Martian Bow Shock: A Case Study
Authors: Taifeng Jin, Binbin Ni, Song Fu, Xing Cao, Shuyue Pang, Xiaotong Yun, Minyi Long, Hengle Du
Figure caption: Frequency solution from dispersion relation during (a) 23:07:17–20, when local ∣ B ∣ is minimum, and (b) 23:07:29–33, when local ∣ B ∣ is maximum. Blue and magenta lines refer to imaginary and real parts of normalized frequency (ω) using proton gyrofrequency (ω p), respectively. The normalized wavenumbers (k/k  n ) are 0.53 in (a) and 0.34 in (b).
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: Snapshots of the column density, N H, (first row) and the dust heating parameter ﹩{{\boldsymbol{ \mathcal N }}}_{d}﹩ from Equation (19) renormalized by a constant factor ϕ  s  (second row), and the estimate N H,est from Equation (20) that applies a correction based on the sum of sources radiation map (third row). The final row gives contours of both N H,est and ﹩{{\boldsymbol{ \mathcal N }}}_{d}/{\phi }_{s}﹩ compared to N  H  representing the 50th and 90th percentiles by area. These are shown for the R8-4pc simulation at time t = 430 Myr (left), and for the LGR4-2pc simulation at time t = 298 Myr (right). The gray lines in the final row show where ﹩{{\boldsymbol{ \mathcal N }}}_{d}/{\phi }_{s}﹩ or N H,est are equal to N H.
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: Comparison of the normalized mean FUV radiation intensity ﹩{\boldsymbol{ \mathcal J }}﹩ from the simulation (R8-4pc) with different approximate models, as described in Sections 4.2.1–4.2.3. Top: values are computed at the midplane. Bottom: values represent a volume-weighted average within 300 pc of the midplane. In the upper panel, ﹩{{\boldsymbol{ \mathcal J }}}_{\mathrm{slab}}﹩ uses Equation (11); in the lower panel, ﹩{{\boldsymbol{ \mathcal J }}}_{\mathrm{slab}}﹩ uses Equation (13). The solid red curves use measured midplane or volume-averaged densities to evaluate τ * = l * ρ κ FUV, while the dashed curves use the measured surface density and the mean value of 〈H〉 to estimate τ * = l *Σgas κ FUV/(2〈H〉).
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: Distribution of the renormalized dust heating parameter ﹩{{\boldsymbol{ \mathcal N }}}_{d}/{\phi }_{s}﹩ (left, see Equation (19)) and N H,est (right, see Equation (20)) vs. gas column N H. Both distributions include all snapshots from the R8-4pc simulation between 250 and 450 Myr. The inset axes represent the projections of the two-dimensional histogram into one dimension. A log-normal fit is plotted over each of the one-dimensional distributions. The red points represent the median and 25th–75th percentile values of ﹩{{\boldsymbol{ \mathcal N }}}_{d}/{\phi }_{s}﹩ or N H,est within individual N H bins. The black lines show where ﹩{{\boldsymbol{ \mathcal N }}}_{d}/{\phi }_{s}﹩ or N H,est are equal to N H.
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: Comparison of the temporal history of the mean FUV intensity from ray-tracing to the plane-parallel approximation and to the slab approximation (Equation (11)) adopted for TIGRESS-classic. The comparison is for the volume-weighted averages within ∣z∣ = 300 pc of the midplane and is shown for both the R8-4pc model (top) and LGR4-2pc model (bottom). In the upper panel, the gray band represents the range of observed estimates for the solar neighborhood including E. F. van Dishoeck & J. H. Black (1982), B. T. Draine (1978b), and J. S. Mathis et al. (1983). The insets show the distributions of the ratio between the model and simulation results.
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: From left to right, the midplane FUV intensity J FUV for model R8-4pc at t = 430 Myr in comparison to the model based on the contribution from a sum over attenuated FUV sources (Equation (17)) and the same model with local attenuation (Equation (18)). The far right panel represents the ratio of the attenuated sum of sources model to the true FUV radiation field.
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: Distribution from R8-4pc (left) and LGR4-2pc (right) of volume as functions of U/U crit,pi and ﹩{x}_{{{\rm{H}}}^{+}}{x}_{e}/{x}_{\mathrm{HI}}﹩ (Equation (23)). This includes all warm gas (between 6 × 103 and 3.5 × 104 K) with nonzero ﹩{{\boldsymbol{ \mathcal E }}}_{\mathrm{LyC}}﹩ and within ∣z∣ < 4 pc or ∣z∣ < 2 pc respectively. U crit,pi is defined in Equation (24). The black dashed line represents photoionization equilibrium (see Equation (23)); most locations with ﹩{x}_{{{\rm{H}}}^{+}}{x}_{e}/{x}_{\mathrm{HI}}\gt 10﹩ (x  n  ≲ 0.1) are consistent with this. Top axes indicate the ionization parameter U for T = 8000 K.
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: Distribution of mass as a function of the ratio of the true FUV radiation field to the estimate based on attenuated sum over sources models and the local density. On the left is the model given by Equation (17), and on the right is the model with additional local attenuation given by Equation (18). Results are based on ∼200 snapshots from the R8-4pc model between t = 250 and 450 Myr considering only the midplane (z = 2 pc).
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: Comparison of the vertical profile of the mean FUV intensity to the plane-parallel approximation (see Section 4.2.1) for model R8-4pc. The upper panel shows the average mean intensity as a function of z with individual snapshots shown in gray. The lower panel shows a comparison for a single snapshot at t = 430 Myr.
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: Same as Figure 18 but for the LGR4-2pc simulation between 250 and 350 Myr.
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: The mass weighted distribution of ionization parameter U and gas density n H for all WIM at ∣z∣ < 300 pc. The left panel shows the R8-4pc simulation at t = 430 Myr. The right panel shows the LGR4-2pc at t = 298 Myr. The top and side panels show the projection of this distribution into distributions of U and n H alone. Dotted diagonal lines show the corresponding value of the LyC energy density at each gas density under the assumption of ionization equilibrium (Equation (22)). Below U crit,d, absorption by neutral hydrogen exceeds dust absorption (see Equation (25)). The black and red horizontal lines show the critical ionization parameters for α B = 3 × 10−13 cm3 s−1 (Equations (24) and (25)).
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: Projection on the disk midplane of the EM from the simulation at t = 430 Myr, panel (a), in comparison to model EM determined from the attenuated ionizing multisource model, using different values of x n,eff in Equations (28) and (30). In panel (b), we show the best-fit value based on minimizing the difference in EM as illustrated in Figure 24 (x n,eff = 0.004). In the first two panels, we include blue circles representing the regions around each source, which are masked for the fit. The value based on the peak of the emission pdf in U and n H in Figure 21 (x n,eff = 0.009) is shown in panel (c). A value consistent with a similar modeling approach reported in F. Belfiore et al. (2022) (x n,eff = 0.0001) is shown in panel (d).
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: The same as Figure 21 but for midplane WIM across all snapshots. The left panel shows the R8-4pc simulation for t = 250–450 Myr. The right panel shows the LGR4-2pc simulation for t = 250–350 Myr. The gas is limited to that within ∣z∣ < 4 pc or ∣z∣ < 2 pc respectively.
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: For 200 different snapshots of model R8-8pc, the left panel shows a comparison of x n,eff from minimizing the difference between EM from the simulation and EM calculated via the attenuated ionizing multisource model (x n,eff [fit]; see Section 5.2), and the most likely value of x n,eff based on the ionization parameter and density distribution (x n,eff [U]; see Section 5.1). The right panel shows the corresponding mean free path (MFP) of ionizing photons ﹩{{\ell }}_{\mathrm{LyC}}={(\langle {n}_{{\rm{H}}}\rangle {\sigma }_{\mathrm{pi}}{x}_{{\rm{n}}})}^{-1}﹩ for each value of x n,eff [fit] or x n,eff [U].
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: Comparison of the normalized FUV radiation field using different approximations as a function of τ ⊥. For the ﹩{{\boldsymbol{ \mathcal J }}}_{\mathrm{Bialy}}﹩ model, τ ⊥ is defined using average values from the R8-4pc simulation such that x 0 τ * = 0.26τ ⊥.
Paper: Ultraviolet Radiation Fields in Star-forming Disk Galaxies: Numerical Simulations with TIGRESS-NCR
Authors: Nora B. Linzer, Jeong-Gyu Kim, Chang-Goo Kim, Eve C. Ostriker
Figure caption: Weighted square difference (Equation (31)), summed over the map, between the simulation value of EM and the EM calculated with the attenuated ionizing multisource model, for different values of the parameter x  n  in Equations (28) and (30). The snapshot is at t = 430 Myr. This allows us to identify the best-fit value as x n,eff = 0.004. See text in Section 5.2 for model details.
Paper: Modeling the Energy-dependent Broadband Variability in the Black Hole Transient GX 339–4 Using AstroSat and NICER
Authors: Hitesh Tanenia, Akash Garg, Ranjeev Misra, Somasri Sen
Figure caption: The top panels show the fitted time lag (a) and frms (b) for BF1 at 0.22 Hz using Model1, Model2, and Model3 for NICER (0.5–10.0 keV) and Astrosat/LAXPC (4.0–30.0 keV). The bottom panels show the fitted time lag (c) and frms (d) for BF2 at 2.94 Hz employing Model1, Model2, and Model3 for NICER (0.5–10.0 keV) and Astrosat/LAXPC (4.0–30.0 keV). Note: In the sign convention used here, a positive time lag indicates a hard lag, where the hard energy photons lag behind the soft energy photons at a given Fourier frequency. Conversely, a negative time lag indicates a soft lag, where the soft energy photons lag behind the hard energy photons at a given frequency (D. C. Baughman & P. A. Becker 2022).
Paper: Modeling the Energy-dependent Broadband Variability in the Black Hole Transient GX 339–4 Using AstroSat and NICER
Authors: Hitesh Tanenia, Akash Garg, Ranjeev Misra, Somasri Sen
Figure caption: The left and right first panels show the PDS of GX 339–4 from AstroSat/LAXPC20 (4–10 keV) and NICER (4–10 keV) respectively as fitted with Lmodel1, along with their residuals in the third panels. The left and right second panels show the same PDS but are fitted with Lmodel2, along with their residuals in the fourth panels.
Paper: Modeling the Energy-dependent Broadband Variability in the Black Hole Transient GX 339–4 Using AstroSat and NICER
Authors: Hitesh Tanenia, Akash Garg, Ranjeev Misra, Somasri Sen
Figure caption: Photon spectrum in 0.7–30.0 keV as observed by SXT (red), LAXPC20 (green), and NICER (black). The top-left panel shows the fitting of data with the XSPEC model constant *Tbabs*(ThComp *Diskbb+Gaussian), and the top-right panel shows the fitting with the model constant *Tbabs*(edge *ThComp *Diskbb+Gaussian+Gaussian). The bottom panels show residuals.
Paper: Modeling the Energy-dependent Broadband Variability in the Black Hole Transient GX 339–4 Using AstroSat and NICER
Authors: Hitesh Tanenia, Akash Garg, Ranjeev Misra, Somasri Sen
Figure caption: The top panel shows the 1.0 s binned background-subtracted light curve in the 4.0–30.0 keV energy band for LAXPC20 unit, and the bottom panel shows the simultaneous NICER light curve in the 0.5–10.0 keV energy band with 1.0 s binning.
Paper: Modeling the Energy-dependent Broadband Variability in the Black Hole Transient GX 339–4 Using AstroSat and NICER
Authors: Hitesh Tanenia, Akash Garg, Ranjeev Misra, Somasri Sen
Figure caption: MAXI light curve of GX 339–4 in the 2–20 keV energy band, with the shaded region indicating the simultaneous NICER and AstroSat observations.
Paper: A Systematic Search of Distant Superclusters with the Subaru Hyper Suprime-Cam Survey
Authors: Tsung-Chi Chen, Yen-Ting Lin, Hsi-Yu Schive, Masamune Oguri, Kai-Feng Chen, Nobuhiro Okabe, Sadman Ali, Connor Bottrell, Roohi Dalal, Yusei Koyama, Rogério Monteiro-Oliveira, Rhythm Shimakawa, Tomotsugu Goto, Bau-Ching Hsieh, Tadayuki Kodama, Atsushi J. Nishizawa
Figure caption: Optimized linking length l opt as a function of redshift for different richness cuts N cut. The colors corresponding to different N cut s are indicated in the legend in the upper left corner. The square points and error bars are mean and 1σ standard deviation computed over all 60 realizations. We offset the square points and error bars along the x-axis slightly for clarity. The curves indicate the best fit to the data points (Equation (4)). The background gray points show the optimized results with N cut = 15 distributed in l opt − z space. For the sake of simplicity, we do not show optimized results for other richness cuts.
Paper: A Systematic Search of Distant Superclusters with the Subaru Hyper Suprime-Cam Survey
Authors: Tsung-Chi Chen, Yen-Ting Lin, Hsi-Yu Schive, Masamune Oguri, Kai-Feng Chen, Nobuhiro Okabe, Sadman Ali, Connor Bottrell, Roohi Dalal, Yusei Koyama, Rogério Monteiro-Oliveira, Rhythm Shimakawa, Tomotsugu Goto, Bau-Ching Hsieh, Tadayuki Kodama, Atsushi J. Nishizawa
Figure caption: The multiplicity function at redshift z = [0.55, 0.65] (top left panel), z = [0.65, 0.75] (top right panel), z = [0.75, 0.85] (bottom left panel), and z = [0.85, 0.95] (bottom right panel). The gray histograms are multiplicity functions of ground-truth superclusters. The green histograms and blue histograms are multiplicity functions of superclusters extracted from unperturbed light cones and perturbed light cones, respectively. The error bars here indicate the 1σ standard deviation in six realizations. The red histogram shows the multiplicity function of CAMIRA supercluster candidates. The consistency between the multiplicity function of CAMIRA supercluster candidates and mock perturbed superclusters again suggests the impact of photo-z uncertainty.
Paper: A Systematic Search of Distant Superclusters with the Subaru Hyper Suprime-Cam Survey
Authors: Tsung-Chi Chen, Yen-Ting Lin, Hsi-Yu Schive, Masamune Oguri, Kai-Feng Chen, Nobuhiro Okabe, Sadman Ali, Connor Bottrell, Roohi Dalal, Yusei Koyama, Rogério Monteiro-Oliveira, Rhythm Shimakawa, Tomotsugu Goto, Bau-Ching Hsieh, Tadayuki Kodama, Atsushi J. Nishizawa
Figure caption: Performance as a function of redshift derived from the cross-validation procedure described in Section 3.4. Points and error bars indicate the mean and 1σ standard deviation calculated over all 60 realizations. The blue and red points indicate the supercluster catalog constructed “without merging” and “with merging,” respectively. Using the “with merging” approach, the completeness throughout z = 0.5–1.0 is enhanced by 5% compared to the “without merging” approach.
Paper: A Systematic Search of Distant Superclusters with the Subaru Hyper Suprime-Cam Survey
Authors: Tsung-Chi Chen, Yen-Ting Lin, Hsi-Yu Schive, Masamune Oguri, Kai-Feng Chen, Nobuhiro Okabe, Sadman Ali, Connor Bottrell, Roohi Dalal, Yusei Koyama, Rogério Monteiro-Oliveira, Rhythm Shimakawa, Tomotsugu Goto, Bau-Ching Hsieh, Tadayuki Kodama, Atsushi J. Nishizawa
Figure caption: The number density of the supercluster candidates as a function of redshift. The green curve is calculated from the CAMIRA supercluster catalog. The red points are estimated from the cross validation of  GADGET  snapshots. The blue and orange curves are calculated from the halo light cones with and without redshift perturbation, respectively. Except for the green curve, whose error bars are assumed to be Poisson noise, the error bars of other curves/points are ﹩1\sigma ﹩ standard deviation calculated over six realizations. The consistency between the supercluster candidates' number density of the CAMIRA superclusters and the perturbed mock superclusters suggests that the photo-z uncertainty presents a limiting factor for the supercluster finding.
Paper: A Systematic Search of Distant Superclusters with the Subaru Hyper Suprime-Cam Survey
Authors: Tsung-Chi Chen, Yen-Ting Lin, Hsi-Yu Schive, Masamune Oguri, Kai-Feng Chen, Nobuhiro Okabe, Sadman Ali, Connor Bottrell, Roohi Dalal, Yusei Koyama, Rogério Monteiro-Oliveira, Rhythm Shimakawa, Tomotsugu Goto, Bau-Ching Hsieh, Tadayuki Kodama, Atsushi J. Nishizawa
Figure caption: The optimization of linking length at redshift z  i  = 0.7 with richness cut N cut = 10.0 (upper panel) and N cut = 20.0 (lower panel). The y-axis indicates purity (red circle) and completeness (blue cross) defined by Equations (2) and (3), respectively. The horizontal dashed line indicates the completeness threshold ﹩{C}_{\mathrm{thres}}=0.7﹩, and the vertical dotted line shows the optimization result l opt.
Paper: A Systematic Search of Distant Superclusters with the Subaru Hyper Suprime-Cam Survey
Authors: Tsung-Chi Chen, Yen-Ting Lin, Hsi-Yu Schive, Masamune Oguri, Kai-Feng Chen, Nobuhiro Okabe, Sadman Ali, Connor Bottrell, Roohi Dalal, Yusei Koyama, Rogério Monteiro-Oliveira, Rhythm Shimakawa, Tomotsugu Goto, Bau-Ching Hsieh, Tadayuki Kodama, Atsushi J. Nishizawa
Figure caption: The contamination rate estimation as a function of redshift, which is derived by comparing superclusters extracted from the unperturbed light cones with those extracted from the perturbed light cones. The points and error bars represent the mean and standard deviation of the contamination rate over six N-body simulations.
Paper: A Systematic Search of Distant Superclusters with the Subaru Hyper Suprime-Cam Survey
Authors: Tsung-Chi Chen, Yen-Ting Lin, Hsi-Yu Schive, Masamune Oguri, Kai-Feng Chen, Nobuhiro Okabe, Sadman Ali, Connor Bottrell, Roohi Dalal, Yusei Koyama, Rogério Monteiro-Oliveira, Rhythm Shimakawa, Tomotsugu Goto, Bau-Ching Hsieh, Tadayuki Kodama, Atsushi J. Nishizawa
Figure caption: Left panel: HSC riz color composite image of one of the supercluster candidates with the highest multiplicity (5), at redshift z ≈ 0.87, in our catalog. The field of view is ﹩16^{\prime} \times 36^{\prime} ﹩. White contours represent galaxy number density, stepping three galaxies/pixel from one galaxy/pixel. Right panel: zoom-in HSC riz composite image of each member cluster. The field of view is ﹩3^{\prime} \times 3^{\prime} ﹩.
Paper: A Systematic Search of Distant Superclusters with the Subaru Hyper Suprime-Cam Survey
Authors: Tsung-Chi Chen, Yen-Ting Lin, Hsi-Yu Schive, Masamune Oguri, Kai-Feng Chen, Nobuhiro Okabe, Sadman Ali, Connor Bottrell, Roohi Dalal, Yusei Koyama, Rogério Monteiro-Oliveira, Rhythm Shimakawa, Tomotsugu Goto, Bau-Ching Hsieh, Tadayuki Kodama, Atsushi J. Nishizawa
Figure caption: The cluster photo-z bias (blue curve) and scatter (red curve) as a function of richness. The black curve represents the best linear fit for the scatter as a function of richness.
Paper: A Systematic Search of Distant Superclusters with the Subaru Hyper Suprime-Cam Survey
Authors: Tsung-Chi Chen, Yen-Ting Lin, Hsi-Yu Schive, Masamune Oguri, Kai-Feng Chen, Nobuhiro Okabe, Sadman Ali, Connor Bottrell, Roohi Dalal, Yusei Koyama, Rogério Monteiro-Oliveira, Rhythm Shimakawa, Tomotsugu Goto, Bau-Ching Hsieh, Tadayuki Kodama, Atsushi J. Nishizawa
Figure caption: The richness distributions of the CAMIRA clusters (gray), a mock catalog constructed from our simulations (blue), and a mock catalog extracted from the R. Takahashi et al. (2017) light cone (red) at redshift between 0.65 and 0.75. The consistency of richness distribution between observational data and mock data validates our approach.
Paper: A Systematic Search of Distant Superclusters with the Subaru Hyper Suprime-Cam Survey
Authors: Tsung-Chi Chen, Yen-Ting Lin, Hsi-Yu Schive, Masamune Oguri, Kai-Feng Chen, Nobuhiro Okabe, Sadman Ali, Connor Bottrell, Roohi Dalal, Yusei Koyama, Rogério Monteiro-Oliveira, Rhythm Shimakawa, Tomotsugu Goto, Bau-Ching Hsieh, Tadayuki Kodama, Atsushi J. Nishizawa
Figure caption: Upper left panel: the confirmed fraction of CAMIRA clusters in the DESI SV3 footprint validated by DESI EDR as a function of redshift interval size Δz (see Section 2.2 for detail). The curves of different colors represent different minimum DESI galaxies required in our DESI examination process. Upper right panel: similar to the upper left panel, but for the bias between CAMIRA photo-z z CAM and cluster redshift derived from DESI EDR z cl,DESI. Lower left panel: similar to the upper left panel, but for the scatter between CAMIRA photo-z z CAM and cluster redshift derived from DESI EDR z cl,DESI. Lower right panel: a comparison between spectroscopic redshift of BCGs z BCG and z cl,DESI. The bias and scatter are calculated by Equations (5) and (6), respectively. Outlier rate (OLR) is the fraction of redshift 4σ clipped when calculating the scatter as defined in M. Oguri et al. (2018).