adsabs/AstroCLIMB huggingface dataset:
Paper: Linking Extragalactic Transients and Their Host Galaxy Properties: Transient Sample, Multiwavelength Host Identification, and Database Construction
Authors: Yu-Jing Qin, Ann Zabludoff, Marina Kisley, Yuantian Liu, Iair Arcavi, Kobus Barnard, Peter Behroozi, K. Decker French, Curtis McCully, and Nirav Merchant
Figure caption: The empirical accuracy of new hosts from visual inspection. Here the accuracy is the fraction of correctly identified hosts in cases where the host, either correctly identified or not, is clearly visible and unambiguous. We show the dependence of accuracy on transient redshift, transient-host angular distance, transient type, and host optical-NIR magnitude in four separate panels. Except for the panel of transient type, we divide the sample into bins of equal sample size over the axis of interest and plot the per-bin accuracy and mean value there. Each colored accuracy curve represents the performance of an input feature set using the default Logistic Regression classifier. Redshift-dependent and redshift-independent feature sets are grouped into two subpanels for clarity. We calculate the accuracy for feature sets using detailed source properties with the subset of events whose best hosts (either correctly identified or not) have the catalogs cross-matched. Since these properties are not universally available, we further plot the fraction of best hosts with the required features available in the bottom subpanels to indicate the possible loss of accuracy. Furthermore, as transient type and redshift are independent of the best host, we also estimated the uncertainty of accuracy contributed by indecisive cases (“Unclear” and “Confusing” cases). The optimistic and pessimistic limits of accuracy are indicated as “error bars” of these points. Similar to Figure 7, for events without redshift, we plot their accuracy in the rightmost gray shaded area of the redshift panel.
Paper: Linking Extragalactic Transients and Their Host Galaxy Properties: Transient Sample, Multiwavelength Host Identification, and Database Construction
Authors: Yu-Jing Qin, Ann Zabludoff, Marina Kisley, Yuantian Liu, Iair Arcavi, Kobus Barnard, Peter Behroozi, K. Decker French, Curtis McCully, and Nirav Merchant
Figure caption: Examples of estimating confidence scores from baseline distributions. We compare the ranking scores of new host candidates (orange) to a baseline distribution (blue), i.e., the highest-ranking scores that nearby non-host objects can achieve by chance in a randomized mock transient sample. We use the cumulative percentage curve of the baseline distribution (red) to quote the ranking scores of new host candidates. True hosts, ideally, should have ranking scores at the positive tail of the baseline distribution. Their confidence scores are closer to 100%. Here we show the case for the redshift-independent default ranking function (top panel) and the redshift-dependent default ranking function in several redshift intervals (centered at z, with bin width Δz).
Paper: Linking Extragalactic Transients and Their Host Galaxy Properties: Transient Sample, Multiwavelength Host Identification, and Database Construction
Authors: Yu-Jing Qin, Ann Zabludoff, Marina Kisley, Yuantian Liu, Iair Arcavi, Kobus Barnard, Peter Behroozi, K. Decker French, Curtis McCully, and Nirav Merchant
Figure caption: The cumulative distribution of confidence scores for some host candidates identified during our visual inspection of new hosts (Appendix J). Successfully identified hosts (“OK”) usually have very high confidence scores. However, higher confidence scores do not guarantee more reliable hosts; misidentified hosts (“Failed: Identified”) still have higher confidence scores than the manually reassigned hosts in the same field (“Failed: Manual Fix”). When there are multiple possible hosts, the other equally possible host (“Confusing: Lowest”) also has high confidence scores, although not as high as the best host selected by the algorithm in the same field (“Confusing: Highest”). We see much lower confidence scores for the selected “host” when the vicinity contains multiple faint sources, yet no one stands out to be a clear and unambiguous host (“Multiple Faint”). Finally, the best candidate of possible hostless transients (“Hostless”) has much lower confidence scores than other candidates. They basically follow the baseline distribution. Results here are global confidence scores using the default ranking function in each field.
Paper: Linking Extragalactic Transients and Their Host Galaxy Properties: Transient Sample, Multiwavelength Host Identification, and Database Construction
Authors: Yu-Jing Qin, Ann Zabludoff, Marina Kisley, Yuantian Liu, Iair Arcavi, Kobus Barnard, Peter Behroozi, K. Decker French, Curtis McCully, and Nirav Merchant
Figure caption: The number of transients under each type label, illustrated with stacked bars to show the relative sample sizes. Here, labels with ellipses (…) only count events with any subtype classification under this parent type, while labels with asterisks (*) count events without any further refined subtype label in the hierarchy (i.e., unspecified as defined in Table 2). Events with multiple incompatible labels (i.e., labels that are not subtypes of each other) are accounted for multiple times. Type Ia supernova is the most representative type in our sample, followed by CC SNe (Type Ib, Ic, and II). Gamma-ray bursts dominate the remaining 7% of classified events. Relatively rare events, like superluminous supernovae (SLSNe) and tidal disruption events, only account for about 1% of all records.
Paper: Dust Extinction Law in Nearby Star-resolved Galaxies. I. M31 Traced by Supergiants
Authors: Yuxi Wang, Jian Gao, and Yi Ren
Figure caption: Extinction curves of the four Local Group galaxies: the MW, LMC, SMC, and M31. It presents the extinction law from UV (0.1  μm) to 1  μm, while the inset shows the extinction law from 0.6  μm to IR bands (5  μm). The black solid line shows the extinction law of the diffuse region in the MW. The orange dashed line and the yellow dashed line are the extinction curves in the SMC bar region and the SMC wing region, respectively (Gordon et al. 2003). The blue dashed–dotted line presents the LMC average extinction law, while the red dashed–dotted line is the extinction curve of the 30 Dor region in the LMC (Gordon et al. 2003). The cyan triangles show the MW-like extinction law with a weaker 2175 Å bump derived by Bianchi et al. (1996). The purple squares indicate the extinction near the bulge in M31 (Dong et al. 2014), while the green dotted line is the average of the four extinction curves derived by Clayton et al. (2015). The RV values of MCs are from Gordon et al. (2003).
Paper: Dust Extinction Law in Nearby Star-resolved Galaxies. I. M31 Traced by Supergiants
Authors: Yuxi Wang, Jian Gao, and Yi Ren
Figure caption: Color–color diagrams (CCDs) and color–magnitude diagrams (CMDs) for all LGGS or UKIRT sources (background gray dots) and the selected supergiants as extinction tracers. In panels (a) and (b), the red dots indicate all 27 O-type supergiants in the sample, while the blue dots are all 281 B-type supergiants in the sample. In panels (c) and (d), the red dots and the blue dots are the O-type supergiants and B-type supergiants with near-IR photometry in the sample. The green dashed–dotted line indicates the criteria for emission in near-IR bands, as described in Section 2.2. Near-IR photometry of sources within the green dashed–dotted line in panel (c) and on the left side of the green dashed–dotted line in panel (d) is adopted.
Paper: Dust Extinction Law in Nearby Star-resolved Galaxies. I. M31 Traced by Supergiants
Authors: Yuxi Wang, Jian Gao, and Yi Ren
Figure caption: The improved pair method applied in this work. The details of the observed data are described in Section 2. Intrinsic spectra from the model atmosphere and the model extinction curves derived from the dust model are introduced in Sections 3.1 and 3.2, respectively. The construction of the model SEDs is presented in Section 3.3. Section 3.4 shows the fitting technique.
Paper: Dust Extinction Law in Nearby Star-resolved Galaxies. I. M31 Traced by Supergiants
Authors: Yuxi Wang, Jian Gao, and Yi Ren
Figure caption: Example results for a B-type supergiant J004446.72+420515.8 in the sample. (a) The best-fitting model SED compared with the observed data. The fitting parameters α and AV and the corresponding RV and E(B − V) are presented. (b) The extinction curve derived from the dust model compared to the average extinction curve in Clayton et al. (2015) and those of the average LMC, the bar region in the SMC, and the MW. (c) Normalized intrinsic spectrum and best-fitting SED to the observed data with the effective temperature log (Teff) and the gravity log (g) marked. (d) One- and two-dimensional projections of the posterior probability distributions of the derived parameter ﹩[\alpha ,\,\mathrm{log}({T}_{\mathrm{eff}}),\,\mathrm{log}(g),\,{A}_{V}﹩].
Paper: Dust Extinction Law in Nearby Star-resolved Galaxies. I. M31 Traced by Supergiants
Authors: Yuxi Wang, Jian Gao, and Yi Ren
Figure caption: Distributions and correlations of the expectation values of the four fitting parameters ﹩[\alpha ,\mathrm{log}({T}_{\mathrm{eff}}),\mathrm{log}(g),{A}_{V}]﹩ and the two derived parameters [E(B − V), RV] for the selected tracers in the extinction sample. The two peaks in the ﹩\mathrm{log}(g)﹩ distribution indicate the O-type and B-type tracers, respectively. The relatively inapparent peaks for the O-type tracers are due to the small number.
Paper: Dust Extinction Law in Nearby Star-resolved Galaxies. I. M31 Traced by Supergiants
Authors: Yuxi Wang, Jian Gao, and Yi Ren
Figure caption: Extinction curves for the tracers (gray solid lines) in M31, of which the red solid line presents the average extinction curve. This shows the extinction laws from UV (0.1 μm) to 1 μm, and the inset shows those from 0.6 μm to near-IR bands (2 μm). The black dashed line shows the extinction law of the diffuse region in the MW. The orange dashed–dotted line, the yellow dashed line, and the blue dashed–dotted line are the extinction curves of the SMC bar and the SMC wing and the average extinction curve of the LMC, respectively (Gordon et al. 2003). The cyan triangles show the flat extinction curve in M31 derived by Bianchi et al. (1996). The purple squares indicate the extinction law around the bulge in M31 with R ≈ 2.4–2.5 (Dong et al. 2014). The green dotted line is the average of the four extinction curves derived by Clayton et al. (2015). The RV values of MCs are given by Gordon et al. (2003).
Paper: Dust Extinction Law in Nearby Star-resolved Galaxies. I. M31 Traced by Supergiants
Authors: Yuxi Wang, Jian Gao, and Yi Ren
Figure caption: Comparison of the dust size distributions. The red solid line indicates the average dust size distribution for M31 derived in this work. The black dashed line shows the corresponding dust size distribution for the diffuse region in the MW as fitting model extinction curves to the CCM extinction law (RV = 3.1). The yellow dotted line and the blue solid line are the typical MRN dust size distributions for silicate and graphite, respectively (Mathis et al. 1977).
Paper: Dust Extinction Law in Nearby Star-resolved Galaxies. I. M31 Traced by Supergiants
Authors: Yuxi Wang, Jian Gao, and Yi Ren
Figure caption: AV derived in this work against the dust mass surface density map in Draine et al. (2014) (the upper panel) and the derived AV vs. the theoretical ﹩\tilde{{A}_{V}}﹩ inferred from the dust emission in Draine et al. (2014) (the lower panel). The empirical relationship between ﹩\tilde{{A}_{V}}﹩ and the dust mass surface density inferred from the dust emission is ﹩\tilde{{A}_{V}}=0.74(\tfrac{{\sum }_{M{\rm{d}}}}{{10}^{5}M\odot \,{\mathrm{kpc}}^{-2}})﹩ (Draine & Li 2007).
Paper: Dust Extinction Law in Nearby Star-resolved Galaxies. I. M31 Traced by Supergiants
Authors: Yuxi Wang, Jian Gao, and Yi Ren
Figure caption: (a) Comparison of the extinction curves toward the individual sight lines derived in this work (red solid lines) and those (blue dashed–dotted lines for UV bands and blues squares for UBVRI) in Clayton et al. (2015). (b) Comparison of the model SEDs for the four tracers derived in this work with the HST/STIS spectra and the observed photometry (the symbol convention follows Figure 4). The blue dashed–dotted lines indicate the model SEDs constructed with the derived parameters in Clayton et al. (2015).
Paper: Dust Extinction Law in Nearby Star-resolved Galaxies. I. M31 Traced by Supergiants
Authors: Yuxi Wang, Jian Gao, and Yi Ren
Figure caption: The results of sensitivity tests are shown with density plots for model SEDs consisting of nine photometric points (UVW1, U, B, V, R, I, J. H, K) extracted from the grid of model SEDs constructed in this work. The blue dotted lines give the 1σ (67%) regions.
Paper: Dust Extinction Law in Nearby Star-resolved Galaxies. I. M31 Traced by Supergiants
Authors: Yuxi Wang, Jian Gao, and Yi Ren
Figure caption: Influence of the lack of UV or near-IR data on the derived α,  AV, and E(B − V). The red and blue dots indicate the tracers with near-IR data and UV data, respectively. The values of the x-axis for a certain red (blue) dot are the derived parameters [α,  AV,  E(B − V)] with all available photometric data considered, while the corresponding values of y-axis are the derived parameters ﹩[\widetilde{\alpha },\,\widetilde{{A}_{V}},\,\widetilde{E(B-V)}]﹩ when repeating the calculation process but without taking the available near-IR (UV) data into consideration. The deviation between the red (blue) dots and y = x presents the influence of the lack of near-IR (UV) data.
Paper: A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF
Authors: Tingting Xu, Chao Liu, Feng Wang, Weirong Huang, Hui Deng, Ying Mei, and Zhong Cao
Figure caption: The data-flow diagram of identifying variable sources for LAMOST sources.
Paper: A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF
Authors: Tingting Xu, Chao Liu, Feng Wang, Weirong Huang, Hui Deng, Ying Mei, and Zhong Cao
Figure caption: The statistical histograms of detection times and mean magnitude versus variable sources/standard stars distributions.
Paper: A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF
Authors: Tingting Xu, Chao Liu, Feng Wang, Weirong Huang, Hui Deng, Ying Mei, and Zhong Cao
Figure caption: The statistical histograms of sigma level versus variable sources/standard stars distributions.
Paper: A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF
Authors: Tingting Xu, Chao Liu, Feng Wang, Weirong Huang, Hui Deng, Ying Mei, and Zhong Cao
Figure caption: The statistical histograms of Q and Q2 versus variable sources/standard stars distributions.
Paper: A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF
Authors: Tingting Xu, Chao Liu, Feng Wang, Weirong Huang, Hui Deng, Ying Mei, and Zhong Cao
Figure caption: The distribution of standard deviation under three iterations for Kepler variable sources.
Paper: A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF
Authors: Tingting Xu, Chao Liu, Feng Wang, Weirong Huang, Hui Deng, Ying Mei, and Zhong Cao
Figure caption: The variability probabilities at different variability parameters.
Paper: A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF
Authors: Tingting Xu, Chao Liu, Feng Wang, Weirong Huang, Hui Deng, Ying Mei, and Zhong Cao
Figure caption: The performance of variability parameter Q at different variability probabilities.
Paper: A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF
Authors: Tingting Xu, Chao Liu, Feng Wang, Weirong Huang, Hui Deng, Ying Mei, and Zhong Cao
Figure caption: The ROC curves of the 10 variability parameters.
Paper: A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF
Authors: Tingting Xu, Chao Liu, Feng Wang, Weirong Huang, Hui Deng, Ying Mei, and Zhong Cao
Figure caption: The CaMD of the LAMOST variable source candidates. The G_BP—G_RP is the color of these candidates in the G band of Gaia DR2. The absolute Gaia magnitudes in the G band for individual sources are estimated using M_G = G + 5 + 5*﹩{\mathrm{log}}_{10}(\varpi /1000)﹩ , where G is the magnitude in the G band and ϖ is the parallax in milliarcseconds (Gaia Collaboration et al. 2018).
Paper: A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF
Authors: Tingting Xu, Chao Liu, Feng Wang, Weirong Huang, Hui Deng, Ying Mei, and Zhong Cao
Figure caption: The types distribution of variable sources. On the left is Chen et al. (2020), and on the right is our catalog cross-matched with Chen et al. (2020).
Paper: A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF
Authors: Tingting Xu, Chao Liu, Feng Wang, Weirong Huang, Hui Deng, Ying Mei, and Zhong Cao
Figure caption: The S/Ng distribution of LAMOST variable sources presented by Ofek et al. (2020).
Paper: A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF
Authors: Tingting Xu, Chao Liu, Feng Wang, Weirong Huang, Hui Deng, Ying Mei, and Zhong Cao
Figure caption: The correlation matrix of 10 variability parameters for variable source candidates in two bands of the ZTF (top: g band; bottom: r band).
Paper: ALMA High-frequency Long-baseline Campaign in 2017: An Investigation of Phase-referencing Cycle Times and Effective Baseline Lengths Using Band-to-band and In-band Phase Calibration Techniques
Authors: Luke T. Maud, Yoshiharu Asaki, William R. F. Dent, Akihiko Hirota, Edward B. Fomalont, Satoko Takahashi, Satoki Matsushita, Neil M. Phillips, Tsuyoshi Sawada, Stuartt Corder, and John Carpenter
Figure caption: The full observing sequence for an entire B2B and in-band test observation. One full observation is made of six blocks, divided up into the first DGC observation, in-band referencing, B2B referencing, the second in-band block, the second B2B block, and finally, the ending DGC block. The figure is illustrative and not accurate in terms of observing time per scan or the number of scans. Scan lengths in our observations are 9  s for all targets, regardless of observing at the high or low frequency. The individual scans are identified in the key to the bottom. This figure is reproduced from Maud et al. (2020).
Paper: ALMA High-frequency Long-baseline Campaign in 2017: An Investigation of Phase-referencing Cycle Times and Effective Baseline Lengths Using Band-to-band and In-band Phase Calibration Techniques
Authors: Luke T. Maud, Yoshiharu Asaki, William R. F. Dent, Akihiko Hirota, Edward B. Fomalont, Satoko Takahashi, Satoki Matsushita, Neil M. Phillips, Tsuyoshi Sawada, Stuartt Corder, and John Carpenter
Figure caption: Expected phase rms presented in path-length microns against the measurement timescale. The left scale bars show the phase rms at bands 7, 8, and 9 in degrees, while the right scale bars show the expected coherence. The bottom panel presents a zoom in of data with a maximal expected phase rms of 100  μm. The color relates to the target frequency: blue is band 7, purple is band 8, and yellow is band 9. The symbols are representative of the maximal baseline lengths: circles are for maximal baselines between 1.5  km and 3.7  km, squares are for 3.7  km to 8.5  km, while triangles are for baselines >8.5  km. Independent of the absolute phase rms value, maximal baseline length of the data, or the frequency bands, the phase rms all increase as a power law, following an average of ∼﹩\sqrt{{\rm{t}}{\rm{i}}{\rm{m}}{\rm{e}}}﹩.
Paper: ALMA High-frequency Long-baseline Campaign in 2017: An Investigation of Phase-referencing Cycle Times and Effective Baseline Lengths Using Band-to-band and In-band Phase Calibration Techniques
Authors: Luke T. Maud, Yoshiharu Asaki, William R. F. Dent, Akihiko Hirota, Edward B. Fomalont, Satoko Takahashi, Satoki Matsushita, Neil M. Phillips, Tsuyoshi Sawada, Stuartt Corder, and John Carpenter
Figure caption: Comparisons of the DGC source image coherence after calibration using 24, 48, 72, 96, and 120  s cycle times, compared with the expected coherence. The top panel calculates the expected coherence using the expected phase rms measured on the DGC over 33, 81, and 129  s timescales after scaling to the respective cycle time of the calibrated data. The bottom panel instead uses the residual phase rms, the variations measured to remain in the DGC high-frequency data after B2B phase referencing. The colors (bands) and symbols (baseline length) follow those in Figure 2. The dashed line is that of equality, whereas the dotted lines are differences of  ± 5% and  ± 10% respectively. Essentially, the expected coherence calculated using either the expected or residual phase rms is in strong agreement with the image coherence for all cycle times.
Paper: ALMA High-frequency Long-baseline Campaign in 2017: An Investigation of Phase-referencing Cycle Times and Effective Baseline Lengths Using Band-to-band and In-band Phase Calibration Techniques
Authors: Luke T. Maud, Yoshiharu Asaki, William R. F. Dent, Akihiko Hirota, Edward B. Fomalont, Satoko Takahashi, Satoki Matsushita, Neil M. Phillips, Tsuyoshi Sawada, Stuartt Corder, and John Carpenter
Figure caption: Image coherence as a function of phase-referencing cycle time (24, 48, 72, 96, 120 s) at bands 7, 8, and 9, for both in-band (left) and B2B (right) images that are calibrated with the same close <1.°67 calibrators. The colors (bands) and symbols (baseline length) follow those in Figure 2. For all observations, regardless of frequency or maximal baseline length, there is a clear decrease of coherence as a function of time. The black dashed line indicates theoretical expected coherence values, where the upper and lower lines are related to σϕ  =  30° and 90° at the 120  s cycle time and assume a ﹩\sqrt{{\rm{time}}}﹩ scaling to shorter times. Faster switching provides a significant improvement for low coherence, where the phase rms can be improved from a poor to reasonable value. There is little change for observations with already good phase rms (high coherence). Uncertainties are not included for plotting clarity but are of the order  ± 5% for bands 7 and 8, and typically  ± 20% for band 9.
Paper: ALMA High-frequency Long-baseline Campaign in 2017: An Investigation of Phase-referencing Cycle Times and Effective Baseline Lengths Using Band-to-band and In-band Phase Calibration Techniques
Authors: Luke T. Maud, Yoshiharu Asaki, William R. F. Dent, Akihiko Hirota, Edward B. Fomalont, Satoko Takahashi, Satoki Matsushita, Neil M. Phillips, Tsuyoshi Sawada, Stuartt Corder, and John Carpenter
Figure caption: Images of the target source J2228-0753 at band 8 using normal in-band phase referencing and calibrated with J2229-0832, separated by only 0.°68. From top to bottom are the images calibrated with 24, 72, and 120  s cycle times, while left and right can be considered stable and unstable conditions taken on 2017 August 19 and 20, respectively. The expected phase rms values using Table 2 and scaling by ﹩\sqrt{{\rm{time}}}﹩ are 15.°3, 30.°1, and 38.°4 and 43.°5, 76.°4, and 97.°1 at 24, 72, and 120  s cycle times for the stable and unstable conditions, respectively. The images are all scaled to their respective peak flux densities while the contours are at 1.2, 2.0, 4.0, and 8.0 mJy bm−1 and correspond approximately to 2σ–3σ, 4σ–5σ, 9σ–10σ, and 18σ–20σ.