adsabs/AstroCLIMB huggingface dataset:
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: The temporal variation of baselines for the 6 days of observation. The different color shows different feeds. The temporal baselines for each feed are centered by subtracting the mean across the full observation, individually. The positive peaks are due to the bright continuum sources, and the few negative spikes are bad data due to the strong RFI contamination.
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: The histogram of the residual TOD. The solid green curve represents the combined data histogram and the dashed-red line represents the best-fit Gaussian distribution. The gray-thin curves represent the histograms of different data blocks. Each curve is normalized by its total number of data points.
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: The TOD delay spectrum of different days. The top panel shows the delay spectra of the TOD before bandpass calibration, while the bottom panel shows the results after bandpass calibration. All the delay spectra are estimated using the XX polarization of the central beam data. The dashed vertical line marks the delay of τ ∼ 0.92 μ s, associated with the standing wave between the FAST feed and reflector.
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: The noise level of each feed. The results for lower and higher frequency bands are shown in red and blue markers. The feeds are split into three categories, i.e., the feeds in the central (Feed 01), the inner circle (Feed 02–Feed 07), and the outer circle (Feed 08–Feed 19) of the feed array. The horizontal lines indicate the mean noise level of each feed category.
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: The spectrum from a feed averaged over ∼30 min. The channels contaminated by RFI from GNSS are marked by the gray region. The allocated frequency channels used for the GPS, Galileo (GAL), Glonass (GLO), and Beidou (BD2/BD3) are marked in different colors (Teunissen & Montenbruck 2017).
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: Histogram statistic of the temperature difference before and after the RFI flagging.
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: Total intensity map of the surveyed regions. The top and bottom panels show the integrated intensity between 1050 and 1150 MHz and 1323–1450 MHz, respectively.
Paper: FAST Drift Scan Survey for HI Intensity Mapping: I. Preliminary Data Analysis
Authors: Yichao Li, Yougang Wang, Furen Deng, Wenxiu Yang, Wenkai Hu, Diyang Liu, Xinyang Zhao, Shifan Zuo, Shuanghao Shu, Jixia Li, Peter Timbie, Réza Ansari, Olivier Perdereau, Albert Stebbins, Laura Wolz, Fengquan Wu, Xin Zhang, Xuelei Chen
Figure caption: Waterfall plot of ∼4 hr TOD observed with Feed01 XX polarization. The blank region is the RFI-flagged data.
Paper: ALMA 0.5 kpc Resolution Spatially Resolved Investigations of Nuclear Dense Molecular Gas Properties in Nearby Ultraluminous Infrared Galaxies Based on HCN and HCO+ Three Transition Line Data
Authors: Masatoshi Imanishi, 昌俊 今西, Shunsuke Baba, 俊介 馬場, Kouichiro Nakanishi, 康一郎 中西, Takuma Izumi, 拓磨 泉
Figure caption: Comparison of the posteriors of gas parameters obtained for different regions in (a) IRAS 16090−0139, (b) IRAS 22491−1808, and (c) NGC 1614. Upper left: H2 gas density in cm−3. Upper right: H2 gas kinetic temperature in K. Lower left: HCO+ column density in cm−2. Lower right: HCN-to-HCO+ abundance ratio. The filled red, green, and blue histograms and the dashed and dotted line histograms correspond to the results for the central ≲0.5 kpc, ≲1 kpc, ≲2 kpc, and 0.5–1 kpc annular and 1–2 kpc annular regions, respectively. Note that the horizontal axis range of the HCN-to-HCO+ abundance ratio (lower right) is much narrower for NGC 1614 than for the other two ULIRGs.
Paper: ALMA 0.5 kpc Resolution Spatially Resolved Investigations of Nuclear Dense Molecular Gas Properties in Nearby Ultraluminous Infrared Galaxies Based on HCN and HCO+ Three Transition Line Data
Authors: Masatoshi Imanishi, 昌俊 今西, Shunsuke Baba, 俊介 馬場, Kouichiro Nakanishi, 康一郎 中西, Takuma Izumi, 拓磨 泉
Figure caption: Intensity-weighted mean velocity (moment 1) map of HCN and HCO+ lines created from the original beam-sized data (Table 3, columns (2)–(4)) taken in ALMA Cycle 7. The map of the HCO+ J = 4–3 line for IRAS 03250+1606 is not shown because the signal-to-noise ratios are too low. The peak position of the simultaneously taken continuum emission is shown as a cross. For each object, the field of view and velocity display range are set the same for all lines. The length of the solid vertical black bar in the first image of each object corresponds to 0.5 kpc. The beam size for each moment 1 map is shown as an open circle in the lower left region.
Paper: ALMA 0.5 kpc Resolution Spatially Resolved Investigations of Nuclear Dense Molecular Gas Properties in Nearby Ultraluminous Infrared Galaxies Based on HCN and HCO+ Three Transition Line Data
Authors: Masatoshi Imanishi, 昌俊 今西, Shunsuke Baba, 俊介 馬場, Kouichiro Nakanishi, 康一郎 中西, Takuma Izumi, 拓磨 泉
Figure caption: Adopted Gaussian fits (solid curved lines) of the HCN and HCO+ J = 2–1 emission lines extracted in the 0.5 kpc (left two panels), 1 kpc (middle two panels), and 2 kpc (right two panels) beam-sized spectra in the first row for each ULIRG. In the second row, the fits extracted from the spectra of 0.5–1 kpc (left two panels) and 1–2 kpc (middle two panels) annular regions are shown. For the LIRG NGC 1614, only the fits from the spectra of the central ≲0.5 kpc (left two panels), ≲1 kpc (middle two panels), and 0.5–1 kpc annular (right two panels) regions are displayed. The abscissa is the optical LSR velocity in km s−1, and the ordinate is the flux density in mJy. The horizontal thin dotted straight line indicates the zero-flux level.
Paper: ALMA 0.5 kpc Resolution Spatially Resolved Investigations of Nuclear Dense Molecular Gas Properties in Nearby Ultraluminous Infrared Galaxies Based on HCN and HCO+ Three Transition Line Data
Authors: Masatoshi Imanishi, 昌俊 今西, Shunsuke Baba, 俊介 馬場, Kouichiro Nakanishi, 康一郎 中西, Takuma Izumi, 拓磨 泉
Figure caption: Comparison of the velocity profile of HCN J = 4–3 (solid black) and HCO+ J = 4–3 (dotted red) lines for (a) IRAS 00091−0738 and (b) IRAS 22206−2715 extracted from the 0.5 kpc beam-sized spectra. The abscissa is the optical LSR velocity in km s−1, and the ordinate is the flux density in mJy. The expected velocity of the HCN-VIB J = 4–3 line at the adopted redshift (Table 1, column (2)) is indicated with a downward red arrow. The horizontal dotted straight black line indicates the zero-flux level.
Paper: ALMA 0.5 kpc Resolution Spatially Resolved Investigations of Nuclear Dense Molecular Gas Properties in Nearby Ultraluminous Infrared Galaxies Based on HCN and HCO+ Three Transition Line Data
Authors: Masatoshi Imanishi, 昌俊 今西, Shunsuke Baba, 俊介 馬場, Kouichiro Nakanishi, 康一郎 中西, Takuma Izumi, 拓磨 泉
Figure caption: Continuum spectral energy distribution from 60 μm to ∼2000 μm for four ULIRGs. Herschel 250 μm, 350 μm, and 500 μm (Clements et al. 2018) and IRAS 60 μm and 100 μm photometric measurements (Table 1) are displayed as filled black circles. Our ALMA 1 kpc beam-sized continuum flux measurements at 850–2000 μm (Table 4, column (7)) are plotted as open stars. The best-fit graybody curve derived by Clements et al. (2018), using only infrared data at ≲500 μm, is overplotted as the thick curved line after flux normalization at 250 μm. The object name and redshift are shown in each panel.
Paper: ALMA 0.5 kpc Resolution Spatially Resolved Investigations of Nuclear Dense Molecular Gas Properties in Nearby Ultraluminous Infrared Galaxies Based on HCN and HCO+ Three Transition Line Data
Authors: Masatoshi Imanishi, 昌俊 今西, Shunsuke Baba, 俊介 馬場, Kouichiro Nakanishi, 康一郎 中西, Takuma Izumi, 拓磨 泉
Figure caption: Same as Figure 13, but for (a) IRAS 00456−2904, (b) IRAS 01569−2939, (c) IRAS 03250+1606, (d) IRAS 00188−0856, (e) IRAS 01166−0844, and (f) IRAS 22206−2715. A subtle sign of an increasing HCN-to-HCO+ abundance ratio and/or an increasing temperature toward the innermost ≲0.5 kpc region is seen in panels (a)–(c), but not in panels (d)–(f).
Paper: Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
Authors: Helen Qu, Masao Sako
Figure caption: An illustration of our estimated cosmological biases arising from photo-z errors. (Top) A Hubble diagram zoomed-in on the neighborhood of z model = 1 showing a fiducial cosmology (green: w = −1, Ω m  = 0.3) and two biased cosmologies (pink: Δw = −0.03, orange: Δw = + 0.03). The dashed black line shows the distance modulus value at z model = 1 for the fiducial cosmology, μ model(z model = 1). The two dotted lines show the redshifts that correspond to the value of μ model(z model = 1) for the two biased cosmologies, i.e., μ model(z model = 1) = μ Δw=−0.03(z = 0.99528) = μ Δw=+0.03(z = 1.00486). The values of these redshifts are labeled on the redshift axis. We approximate the redshift error required to create a bias of, e.g., Δw = + 0.03, as the difference between the biased and fiducial redshift values, dz bias = 1 − 1.00486 = − 0.00486. (bottom) Δz = dz/(1 + z) values for various choices of Δw, compared to the mean binned residuals Δz produced by the mean point estimates of Photo-zSNthesis PDFs (pink, reproduced from Figure 7).
Paper: Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
Authors: Helen Qu, Masao Sako
Figure caption: Mean binned residuals Δz as a function of true redshift z true for the SDSS simulated and real SNe Ia samples. Predictions from this work have much lower biases as well as scatter compared to predictions from LCFIT+Z. We also show that two common methods of condensing redshift PDFs into point estimates, max(PDF) and mean(PDF), resulting in similar errors for our model. Dotted lines are plotted at Δz = ± 0.02 for reference.
Paper: Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
Authors: Helen Qu, Masao Sako
Figure caption: Mean binned residuals Δz as a function of true redshift z true for the PLAsTiCC simulated SNe Ia test set. Predictions from our model have much lower biases as well as scatter compared to predictions from LCFIT+Z. The max(PDF) and mean(PDF) point estimates for our model also agree quite well, resulting in similar Δz values. Dotted lines are plotted at Δz = ± 0.02 for reference.
Paper: Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
Authors: Helen Qu, Masao Sako
Figure caption: Mean binned residuals, ﹩{\rm{\Delta }}z\equiv \tfrac{{z}_{\mathrm{pred}}-{z}_{\mathrm{true}}}{1+{z}_{\mathrm{true}}}﹩, as a function of true redshift, z true, for the SDSS observed photometric SNe Ia sample. Errors from both Photo-zSNthesis and LCFIT+Z are shown with (darker) and without (lighter) a prior on z pred from the host galaxy photometric redshift. The lighter (no prior) curves are identical to those in Figure 9, with the max(PDF) curves omitted for clarity.
Paper: Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
Authors: Helen Qu, Masao Sako
Figure caption: Predicted redshift error, Δz, as a function of true redshift, z true, for the SDSS real SNe Ia sample with and without bias correction computed from the simulated results. The point estimates used in this figure are computed using the mean(PDF) method. Dotted lines are plotted at Δz = ± 0.02 for reference.
Paper: Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
Authors: Helen Qu, Masao Sako
Figure caption: Predicted vs. true redshifts for the PLAsTiCC simulated SNe Ia sample colored by the fraction of each sample represented by each point. (Left) Predictions from Photo-zSNthesis described in this work, (right) predictions from LCFIT+Z.
Paper: Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
Authors: Helen Qu, Masao Sako
Figure caption: Predicted vs. true redshifts for the SDSS simulated and real SNe Ia samples colored by the fraction of each sample represented by each point. (Top row) Predictions from Photo-zSNthesis described in this work, (bottom row) predictions from LCFIT+Z.
Paper: Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
Authors: Helen Qu, Masao Sako
Figure caption: Mean binned residuals, ﹩{\rm{\Delta }}z\equiv \tfrac{{z}_{\mathrm{pred}}-{z}_{\mathrm{true}}}{1+{z}_{\mathrm{true}}}﹩, as a function of true redshift, z true, for the DES3YR SNe Ia sample produced by a model trained on the PLAsTiCC data set. The max(PDF) and mean(PDF) methods of obtaining point estimates from Photo-zSNthesis PDFs are described in Section 4.1.1.
Paper: Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
Authors: Helen Qu, Masao Sako
Figure caption: Mean binned SALT2 color, c, as a function of predicted redshift, z pred, for the Photo-zSNthesis prediction outliers and non-outliers in the SDSS observed photometric SN Ia sample.
Paper: Can Cuspy Dark-matter-dominated Halos Hold Cored Stellar Mass Distributions?
Authors: Jorge Sánchez Almeida, Angel R. Plastino, Ignacio Trujillo
Figure caption: Cored baryon distribution in an NFW gravitational potential. Left panel: a polytrope of order m = 5 gives ρ(r), with the central density ρ(0) = 105 M ⊙ kpc−3 and a core radius r 0 = 1.4 kpc, chosen so that M ⋆ ∼ 106 M ⊙ (the red solid line). The NFW density profile that defines the overall gravitational potential through the Poisson equation (the gray line) has ρ  s /ρ(0) = 10 and r  s /r 0 = 4, which provides M ⋆/M p ≃ 1.2 × 10−4. The bullet symbol points out the total density at the virial radius, assuming a concentration of 30 (r vir = 30 r  s ). Right panel: baryon DF f needed to match baryon density and potential according to Eddington’s inversion method which reaches negative values (the dashed line) implying that this particular combination is unphysical.
Paper: Can Cuspy Dark-matter-dominated Halos Hold Cored Stellar Mass Distributions?
Authors: Jorge Sánchez Almeida, Angel R. Plastino, Ignacio Trujillo
Figure caption: Double power-law density profile (Equation (32)) that goes seamlessly from a Schuster–Plummer profile to an NFW profile when the inner slope (−c) goes from 0 to −1.
Paper: Internal and External Alignment of Carbonaceous Grains within the Radiative Torque Paradigm
Authors: Thiem Hoang, Vo Hong Minh Phan, Le Ngoc Tram
Figure caption: Left-hand panel: magnetic parameter δ nucl−DG in a medium of density n H = 101 cm−3, magnetic field strength B = 5 μG and radiation field with U = 1, γ = 0.3, and ﹩\bar{\lambda }=1.2\,\,\mu {\rm{m}}﹩. Results are shown for grains at low-J attractors with Ω = Ω T  (dashed lines) and high-J attractors with ﹩{\rm{\Omega }}=\max ({{\rm{\Omega }}}_{T},{{\rm{\Omega }}}_{\mathrm{RAT}})﹩ (solid lines) assuming different values of the nucleon number density n  n  = 1021 cm−3 (red), n  n  = 5 × 1021 cm−3 (green), and n  n  = 1022 cm−3 (blue). Right-hand panel: same as the left panel, but for the different magnetic field strengths B = 5 μG (red), B = 10 μG (green), and B = 100 μG (blue). Results are shown for a typical value of n  n  = 1022 cm−3.
Paper: Internal and External Alignment of Carbonaceous Grains within the Radiative Torque Paradigm
Authors: Thiem Hoang, Vo Hong Minh Phan, Le Ngoc Tram
Figure caption: Disruption size (a disr) as a function of radius from IRC +10216 for different assumptions of the maximum tensile strength (﹩{S}_{\max }﹩). The initial maximum grain size is adopted as 10 μm. Close to the central IRC +10216, the rotation damping is very efficient owing to an extremely dense and hot gas, making a low RAT-D efficiency. Farther into the envelope, in which n H and T gas substantially drop, the rotation damping becomes less efficient and hence the disruption is more efficient. One can see that smaller grains are disrupted for lower ﹩{S}_{\max }﹩. In the most outer part of the envelope, n H and T gas decrease even further but F IR ≫ 1 and U ≫ 1 are still satisfied, a disr could be computed analytically. In this case, a disr tends to increase due to lower radiation intensity coming from the central source. See Tram et al. (2020) for more details.
Paper: Internal and External Alignment of Carbonaceous Grains within the Radiative Torque Paradigm
Authors: Thiem Hoang, Vo Hong Minh Phan, Le Ngoc Tram
Figure caption: Left-hand panel: critical grain sizes for efficient IA for HAC grains aligned at high-J attractors via NR (﹩{a}_{\max ,\mathrm{aJ}}^{\mathrm{high}-J}(\mathrm{NR})﹩, dashed line) and minimum grain size for inelastic relaxation (﹩{a}_{\min ,\mathrm{aJ}}^{\mathrm{high}-J}(\mathrm{iER})﹩, solid lines) as a function of the radial distance from the central star for the IRC +10216. Results in the case of inelastic relaxation are shown for μ 10 Q 3 = 1. Right-hand panel: critical grain sizes for efficient IA for grains aligned at a low-J attractor via NR (﹩{a}_{\min ,\mathrm{aJ}}^{\mathrm{low}-{\text{}}J}(\mathrm{NR})﹩ and ﹩{a}_{\max ,\mathrm{aJ}}^{\mathrm{low}-{\text{}}J}(\mathrm{NR})﹩, orange-solid and dashed lines, respectively) and inelastic relaxation (﹩{a}_{\max ,\mathrm{aJ}}^{\mathrm{low}-{\text{}}J}(\mathrm{iER})﹩, green solid lines). We also marked regions of the parameter space where grains are expected to have the right IA with inelastic relaxation and NR (hatched and shaded areas, respectively).
Paper: Internal and External Alignment of Carbonaceous Grains within the Radiative Torque Paradigm
Authors: Thiem Hoang, Vo Hong Minh Phan, Le Ngoc Tram
Figure caption: Critical grain sizes for external alignment via k-RAT and B-RAT at both high-J (left-hand panel) and low-J (right-hand panel) attractors as functions of the radial distance from the center of the IRC +10216 envelope assuming 13C dominated grains.
Paper: Internal and External Alignment of Carbonaceous Grains within the Radiative Torque Paradigm
Authors: Thiem Hoang, Vo Hong Minh Phan, Le Ngoc Tram
Figure caption: The effect of RAT-D on the population of grains at low-J and high-J attractors for carbonaceous grains subject to a strong radiation field. The RAT-D removes large grains at high-J attractor of a > a disr, so that larger grains are aligned at low-J attractors. Large grains at low-J attractors tend to experience the k-RAT alignment (gray area) due to fast radiative precession. Grains experience the B-RAT alignment for low-J (green area) and high-J attractors (orange area).
Paper: Internal and External Alignment of Carbonaceous Grains within the Radiative Torque Paradigm
Authors: Thiem Hoang, Vo Hong Minh Phan, Le Ngoc Tram
Figure caption: Timescales of various processes involved in external alignment of HAC grains, including Larmor precession (τ B), radiative precession (τ  k ), electric dipole precession (τ  E ), and gas damping (τ gas) at different radial distances from the center of the IRC +10216 envelope. Grains aligned at both low-J and high-J attractors are considered.
Paper: Internal and External Alignment of Carbonaceous Grains within the Radiative Torque Paradigm
Authors: Thiem Hoang, Vo Hong Minh Phan, Le Ngoc Tram
Figure caption: Critical grain sizes for external alignment via k-RAT and B-RAT at both high-J (left-hand panel) and low-J (right-hand panel) attractors as functions of the radial distance from the center of the IRC +10216 assuming HAC grains.
Paper: Internal and External Alignment of Carbonaceous Grains within the Radiative Torque Paradigm
Authors: Thiem Hoang, Vo Hong Minh Phan, Le Ngoc Tram
Figure caption: Critical speed for MET-D with ﹩{S}_{\max }={10}^{6}﹩ erg cm−3 (yellow), ﹩{S}_{\max }={10}^{8}﹩ erg cm−3 (red), and ﹩{S}_{\max }={10}^{10}﹩ erg cm−3 (blue). The black line represents the expansion speed of the IRC +10216 envelope.
Paper: Testing Physical Scenarios for the Reflection Features of Type-1 AGNs using XMM-Newton and NuSTAR Simultaneous Observations
Authors: César Ivan Victoria-Ceballos, Omaira González-Martín, Josefa Masegosa, Anna Lia Longinotti, Donaji Esparza-Arredondo, Natalia Osorio-Clavijo
Figure caption: Example of the resulting X-ray spectra of the reflection models used in this analysis (see Section 3.2). Ionized and relativistic, ionized, and neutral models are shown in the left-hand, middle, and right-hand panels, respectively. For all models, the photon index Γ = 2, the high-energy cutoff E cut = 300, the abundance Z = Z ⊙, the iron abundance Z Fe = Z ⊙, the viewing angle toward the system i = 45°, the inner radius of the disk R in = 2r  g , the outer radius of the disk R out = 100r  g , the disk temperature T disk = 30,000 K, the index emissivity Index = 3, and the ionization parameter ξ = 1000, when the parameter is inside the model. In addition, for BORUS, the column density NHTor = 1024 cm−2, the angular size thTor = 45°; for GAUSS, the center and the width of the emission line are E line = 6.4 keV and σ = 0.01 keV, respectively; and for RELXILL, the spin of the black hole is set to a = 0.998.
Paper: Testing Physical Scenarios for the Reflection Features of Type-1 AGNs using XMM-Newton and NuSTAR Simultaneous Observations
Authors: César Ivan Victoria-Ceballos, Omaira González-Martín, Josefa Masegosa, Anna Lia Longinotti, Donaji Esparza-Arredondo, Natalia Osorio-Clavijo
Figure caption: Best fit to the 12 objects that prefer the hybrid model. We show the best fit (black solid line) to the data in the top panel and the ratio between model and data of the hybrid, neutral, and ionized models (marked with letters H, N, and I, respectively) in the bottom panels. We also show the reduced χ 2 of the corresponding fit.