adsabs/AstroCLIMB huggingface dataset:
Paper: ALMA Lensing Cluster Survey: Deep 1.2 mm Number Counts and Infrared Luminosity Functions at z ≃ 1–8
Authors: Seiji Fujimoto, Kotaro Kohno, Masami Ouchi, Masamune Oguri, Vasily Kokorev, Gabriel Brammer, Fengwu Sun, Jorge González-López, Franz E. Bauer, Gabriel B. Caminha, Bunyo Hatsukade, Johan Richard, Ian Smail, Akiyoshi Tsujita, Yoshihiro Ueda, Ryosuke Uematsu, Adi Zitrin, Dan Coe, Jean-Paul Kneib, Marc Postman, Keiichi Umetsu, Claudia del P. Lagos, Gergö Popping, Yiping Ao, Larry Bradley, Karina Caputi, Miroslava Dessauges-Zavadsky, Eiichi Egami, Daniel Espada, R. J. Ivison, Mathilde Jauzac, Kirsten K. Knudsen, Anton M. Koekemoer, Georgios E. Magdis, Guillaume Mahler, A. M. Muñoz Arancibia, Timothy Rawle, Kazuhiro Shimasaku, Sune Toft, Hideki Umehata, Francesco Valentino, Tao Wang, Wei-Hao Wang
Figure caption: MC simulation results for completeness. The red circles and arrows present the spatial size in half width at half maximum (HWHM) and upper limit evaluated from CASA IMFIT, respectively, for the ALCS sources whose structure is well resolved with S/Nnat ≥ 10. The white circles show the remaining ALCS sources whose spatial sizes are estimated from the lensing distortion based on the source redshifts and our fiducial lens models by assuming the intrinsic effective radius of 0.″15. As an example, the background color scale denotes the completeness of our sample selection for the blind sample (S/Nnat ≥ 5.0 or S/Ntap ≥ 4.5; Section 3.1) based on the ALCS map taken in M2129 whose data depth and synthesized beam size are the typical value among our 33 ALCS lensing clusters (Table 1).
Paper: ALMA Lensing Cluster Survey: Deep 1.2 mm Number Counts and Infrared Luminosity Functions at z ≃ 1–8
Authors: Seiji Fujimoto, Kotaro Kohno, Masami Ouchi, Masamune Oguri, Vasily Kokorev, Gabriel Brammer, Fengwu Sun, Jorge González-López, Franz E. Bauer, Gabriel B. Caminha, Bunyo Hatsukade, Johan Richard, Ian Smail, Akiyoshi Tsujita, Yoshihiro Ueda, Ryosuke Uematsu, Adi Zitrin, Dan Coe, Jean-Paul Kneib, Marc Postman, Keiichi Umetsu, Claudia del P. Lagos, Gergö Popping, Yiping Ao, Larry Bradley, Karina Caputi, Miroslava Dessauges-Zavadsky, Eiichi Egami, Daniel Espada, R. J. Ivison, Mathilde Jauzac, Kirsten K. Knudsen, Anton M. Koekemoer, Georgios E. Magdis, Guillaume Mahler, A. M. Muñoz Arancibia, Timothy Rawle, Kazuhiro Shimasaku, Sune Toft, Hideki Umehata, Francesco Valentino, Tao Wang, Wei-Hao Wang
Figure caption: Survey areas of ALCS (S/Nnat ≥ 4.0, PB ≥30%) and other large ALMA surveys in the literature (González-López et al. 2020; Casey et al. 2021; Gómez-Guijarro et al. 2022; Muñoz Arancibia et al. 2023). The light red line denotes the survey area before the lensing correction. The red dashed and solid lines represent the effect survey area after the lensing correction, assuming the redshift at z = 2.0 and z = 6.0, respectively, with our fiducial lens models. ALCS explores the unique parameter space toward faint and wide regimes, compared to previous ALMA surveys, demonstrating the power of the lensing support. In addition to the little difference in the results between z = 2.0 and z = 6.0, we confirm that the difference in the survey areas with different lens models is negligible in Appendix D. We scale the flux densities using the methods described in Section 5.2.
Paper: ALMA Lensing Cluster Survey: Deep 1.2 mm Number Counts and Infrared Luminosity Functions at z ≃ 1–8
Authors: Seiji Fujimoto, Kotaro Kohno, Masami Ouchi, Masamune Oguri, Vasily Kokorev, Gabriel Brammer, Fengwu Sun, Jorge González-López, Franz E. Bauer, Gabriel B. Caminha, Bunyo Hatsukade, Johan Richard, Ian Smail, Akiyoshi Tsujita, Yoshihiro Ueda, Ryosuke Uematsu, Adi Zitrin, Dan Coe, Jean-Paul Kneib, Marc Postman, Keiichi Umetsu, Claudia del P. Lagos, Gergö Popping, Yiping Ao, Larry Bradley, Karina Caputi, Miroslava Dessauges-Zavadsky, Eiichi Egami, Daniel Espada, R. J. Ivison, Mathilde Jauzac, Kirsten K. Knudsen, Anton M. Koekemoer, Georgios E. Magdis, Guillaume Mahler, A. M. Muñoz Arancibia, Timothy Rawle, Kazuhiro Shimasaku, Sune Toft, Hideki Umehata, Francesco Valentino, Tao Wang, Wei-Hao Wang
Figure caption: Integration of the 1.2 mm number counts as a function of integration limit S ALMA, limit. The red (black) solid and dashed lines indicate the integration of our fiducial Schechter function estimate with the ALCS (GOODS+HUDF) number counts down to the nominal detection limit of S limit  =  7 μ Jy (40 μJy) and its extrapolation beyond the detection limit, respectively. The magenta (black) horizontal dotted line denotes the CIB estimate in Odegard et al. (2019) by interpolating COBE/FIRAS measurements at 217 and 353 GHz according to the central frequency of the ALCS (GOODS+HUDF) survey after subtracting the Galactic foreground from a linear combination of Galactic HI and Hα. The shaded region shows the possible range of the interpolation of the CIB by taking the estimates from the lower and upper limits of the observed frequency range in the ALMA surveys.
Paper: ALMA Lensing Cluster Survey: Deep 1.2 mm Number Counts and Infrared Luminosity Functions at z ≃ 1–8
Authors: Seiji Fujimoto, Kotaro Kohno, Masami Ouchi, Masamune Oguri, Vasily Kokorev, Gabriel Brammer, Fengwu Sun, Jorge González-López, Franz E. Bauer, Gabriel B. Caminha, Bunyo Hatsukade, Johan Richard, Ian Smail, Akiyoshi Tsujita, Yoshihiro Ueda, Ryosuke Uematsu, Adi Zitrin, Dan Coe, Jean-Paul Kneib, Marc Postman, Keiichi Umetsu, Claudia del P. Lagos, Gergö Popping, Yiping Ao, Larry Bradley, Karina Caputi, Miroslava Dessauges-Zavadsky, Eiichi Egami, Daniel Espada, R. J. Ivison, Mathilde Jauzac, Kirsten K. Knudsen, Anton M. Koekemoer, Georgios E. Magdis, Guillaume Mahler, A. M. Muñoz Arancibia, Timothy Rawle, Kazuhiro Shimasaku, Sune Toft, Hideki Umehata, Francesco Valentino, Tao Wang, Wei-Hao Wang
Figure caption: Comparison of differential number counts at 1.2 mm with the simulation predictions. The red-filled circles, red line, red shaded region, and black dashed line are assigned as in Figure 9. The other colored lines show several model predictions for number counts (Lagos et al. 2020; Popping et al. 2020; Trayford et al. 2020; Bethermin et al. 2022). These model predictions are generally consistent with the individual data points of ALCS within 1σ–2σ.
Paper: ALMA Lensing Cluster Survey: Deep 1.2 mm Number Counts and Infrared Luminosity Functions at z ≃ 1–8
Authors: Seiji Fujimoto, Kotaro Kohno, Masami Ouchi, Masamune Oguri, Vasily Kokorev, Gabriel Brammer, Fengwu Sun, Jorge González-López, Franz E. Bauer, Gabriel B. Caminha, Bunyo Hatsukade, Johan Richard, Ian Smail, Akiyoshi Tsujita, Yoshihiro Ueda, Ryosuke Uematsu, Adi Zitrin, Dan Coe, Jean-Paul Kneib, Marc Postman, Keiichi Umetsu, Claudia del P. Lagos, Gergö Popping, Yiping Ao, Larry Bradley, Karina Caputi, Miroslava Dessauges-Zavadsky, Eiichi Egami, Daniel Espada, R. J. Ivison, Mathilde Jauzac, Kirsten K. Knudsen, Anton M. Koekemoer, Georgios E. Magdis, Guillaume Mahler, A. M. Muñoz Arancibia, Timothy Rawle, Kazuhiro Shimasaku, Sune Toft, Hideki Umehata, Francesco Valentino, Tao Wang, Wei-Hao Wang
Figure caption: HST/F160W 30″ × 30″ cutout images for seven ALCS sources falling in the intrinsically faintest millimeter regime of 0.01–0.06 mJy in the blind sample (S/Nnat > 5.0 or S/Ntap > 4.5), whose redshifts are spectroscopically confirmed or securely constrained by the lens models from its multiple image positions and their photometric redshifts. These secure redshift estimates mitigate the magnification uncertainty and do not support any strong flattening at ∼0.1 mJy (see Section 5.2). The green contour indicates the ALMA Band 6 continuum at 2σ, 3σ, 4σ, and 5σ. The white curves represent the contours for the lensing magnification of μ = 200.
Paper: ALMA Lensing Cluster Survey: Deep 1.2 mm Number Counts and Infrared Luminosity Functions at z ≃ 1–8
Authors: Seiji Fujimoto, Kotaro Kohno, Masami Ouchi, Masamune Oguri, Vasily Kokorev, Gabriel Brammer, Fengwu Sun, Jorge González-López, Franz E. Bauer, Gabriel B. Caminha, Bunyo Hatsukade, Johan Richard, Ian Smail, Akiyoshi Tsujita, Yoshihiro Ueda, Ryosuke Uematsu, Adi Zitrin, Dan Coe, Jean-Paul Kneib, Marc Postman, Keiichi Umetsu, Claudia del P. Lagos, Gergö Popping, Yiping Ao, Larry Bradley, Karina Caputi, Miroslava Dessauges-Zavadsky, Eiichi Egami, Daniel Espada, R. J. Ivison, Mathilde Jauzac, Kirsten K. Knudsen, Anton M. Koekemoer, Georgios E. Magdis, Guillaume Mahler, A. M. Muñoz Arancibia, Timothy Rawle, Kazuhiro Shimasaku, Sune Toft, Hideki Umehata, Francesco Valentino, Tao Wang, Wei-Hao Wang
Figure caption: MC simulation results for input and output flux densities as a function of output S/Nnat to correct the effects of the flux boosting and Eddington bias. We compare the four types of flux measurements (S nat, S tap, S ap, and S imf) as well as the photometry finally used in our analysis (S use) (Section 3.3). We find that S use deviates from the input value by ∼10% below S/Nnat = 8, where we apply the correction of 10% for our flux density measurement.
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption: The confusion matrices of different catalogs.
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption: Confusion matrix for the best performing model in 10 class.
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption: The confusion matrices of different bands.
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption: Meanvariance vs. logPeriodLS diagram for all LEAVES variables.
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption: Confusion matrix for the best performing model in seven class.
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption: Confusion matrix for the best performing model in binary classification.
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption: logMedianAbsDev vs. Gskew diagram for all LEAVES variables.
Paper: Investigating the Behavior and Spatiotemporal Variations of Green-line Emission in the Solar Corona
Authors: Jacob Oloketuyi, Yu Liu, Linhua Deng, Abouazza Elmhamdi, Fengrong Zhu, Ayodeji Ibitoye, Opeyemi Omole, Feiyang Sha, Qiang Liu
Figure caption: The north–south asymmetry. (a) The dominance trend showing the leading region. (b) Average mean values for north and south. (c) North–south asymmetry showing the leading phase across the northern and southern hemispheres. (d) Average mean values for rising and declining phases across north and south, where NR and SR represent the rising phase for the northern and southern hemispheres, while ND and SD represent the declining phase for the north and south, respectively.
Paper: Investigating the Behavior and Spatiotemporal Variations of Green-line Emission in the Solar Corona
Authors: Jacob Oloketuyi, Yu Liu, Linhua Deng, Abouazza Elmhamdi, Fengrong Zhu, Ayodeji Ibitoye, Opeyemi Omole, Feiyang Sha, Qiang Liu
Figure caption: The cross correlations between the solar north and south regions.
Paper: Investigating the Behavior and Spatiotemporal Variations of Green-line Emission in the Solar Corona
Authors: Jacob Oloketuyi, Yu Liu, Linhua Deng, Abouazza Elmhamdi, Fengrong Zhu, Ayodeji Ibitoye, Opeyemi Omole, Feiyang Sha, Qiang Liu
Figure caption: The evolutionary trends obtained from low and high latitudes of the Sun. Panel (a) shows the global trends for high (black) and low (red) latitudes. Panels (b) and (c) show trends obtained from the northern and southern hemispheres, respectively, where blue and magenta colors corresponds to high and low latitudes, respectively.
Paper: Investigating the Behavior and Spatiotemporal Variations of Green-line Emission in the Solar Corona
Authors: Jacob Oloketuyi, Yu Liu, Linhua Deng, Abouazza Elmhamdi, Fengrong Zhu, Ayodeji Ibitoye, Opeyemi Omole, Feiyang Sha, Qiang Liu
Figure caption: The cross correlations between the solar north and south regions across the solar cycles.
Paper: Investigating the Behavior and Spatiotemporal Variations of Green-line Emission in the Solar Corona
Authors: Jacob Oloketuyi, Yu Liu, Linhua Deng, Abouazza Elmhamdi, Fengrong Zhu, Ayodeji Ibitoye, Opeyemi Omole, Feiyang Sha, Qiang Liu
Figure caption: The evolutionary trends in the southern hemisphere obtained for high (blue) and low (pink) latitudes.
Paper: Investigating the Behavior and Spatiotemporal Variations of Green-line Emission in the Solar Corona
Authors: Jacob Oloketuyi, Yu Liu, Linhua Deng, Abouazza Elmhamdi, Fengrong Zhu, Ayodeji Ibitoye, Opeyemi Omole, Feiyang Sha, Qiang Liu
Figure caption: The PSD analysis of green-line coronal emissions for the global Sun (top) and the solar north (blue) and south (magenta) (bottom).
Paper: Investigating the Behavior and Spatiotemporal Variations of Green-line Emission in the Solar Corona
Authors: Jacob Oloketuyi, Yu Liu, Linhua Deng, Abouazza Elmhamdi, Fengrong Zhu, Ayodeji Ibitoye, Opeyemi Omole, Feiyang Sha, Qiang Liu
Figure caption: The evolutionary trends in the northern hemisphere obtained for high (blue) and low (pink) latitudes.
Paper: Investigating the Behavior and Spatiotemporal Variations of Green-line Emission in the Solar Corona
Authors: Jacob Oloketuyi, Yu Liu, Linhua Deng, Abouazza Elmhamdi, Fengrong Zhu, Ayodeji Ibitoye, Opeyemi Omole, Feiyang Sha, Qiang Liu
Figure caption: The evolutionary trends obtained for high (black) and low latitudes (pink) for solar cycles.
Paper: Investigating the Behavior and Spatiotemporal Variations of Green-line Emission in the Solar Corona
Authors: Jacob Oloketuyi, Yu Liu, Linhua Deng, Abouazza Elmhamdi, Fengrong Zhu, Ayodeji Ibitoye, Opeyemi Omole, Feiyang Sha, Qiang Liu
Figure caption: The PSD analysis of green-line coronal emissions at the high and low latitudes for (the global Sun (top), solar north (middle), and solar south (bottom).
Paper: Revisiting the Hβ Size–Luminosity Relation Using a Uniform Reverberation-mapping Analysis
Authors: Shu Wang, Jong-Hak Woo
Figure caption:  R BLR–L 5100 relation based on ICCF (left) and PyROA (right). The sample presented here includes positive lags from the literature parent sample (gray filled circles) as well as the 30 positive lags from SAMP (J.-H. Woo et al. 2024, gray open circles). The black filled and open circles represent the best-quality literature sample (see Section 3.5 for details) and the good-quality lags selected by J.-H. Woo et al. (2024, black open circles). The gray and brown dashed lines represent the best-fit relations for the combined total and combined best-quality subsample, respectively. The orange lines are 50 randomly drawn realizations from the MCMC samples for illustrating the dispersion of the relation. The best-fit slope β and the intrinsic scatter σ int are labeled in the legend.
Paper: Revisiting the Hβ Size–Luminosity Relation Using a Uniform Reverberation-mapping Analysis
Authors: Shu Wang, Jong-Hak Woo
Figure caption: Demonstration of our selection for the best-quality lag measurements. The literature parent sample (212 objects) is plotted on the ﹩{r}_{\max }﹩–p-values plane, and color coded by the f peak of the τ ICCF. Objects with ﹩{r}_{\max }\gt 0.55﹩, p-value ≤ 0.10 (as indicated by the gray dashed lines), and f peak ≥ 0.6 are selected as good-quality lag measurements. The histogram of both ﹩{r}_{\max }﹩ and p-values are also displayed in the bottom and right panel, respectively. In addition, we perform visual assessment on the lag quality. Those objects selected based on our visual inspection are denoted with circles with gray edges.
Paper: Revisiting the Hβ Size–Luminosity Relation Using a Uniform Reverberation-mapping Analysis
Authors: Shu Wang, Jong-Hak Woo
Figure caption: Upper: comparison between the lags originally reported in the literature (﹩{\tau }_{\mathrm{literature}}﹩) and the lags measured in this work based on ICCF (τ ICCF,this work). The gray dotted–dashed line represents the 1:1 correspondence. The red cross at the top left corner indicates the log scale average uncertainty of both axes. Lower: distribution of the difference between the lag uncertainty of ﹩{\tau }_{\mathrm{literature}}﹩ and the uncertainty of τ ICCF,thiswork. Lower and upper uncertainties are shown using blue and green color, respectively.
Paper: Revisiting the Hβ Size–Luminosity Relation Using a Uniform Reverberation-mapping Analysis
Authors: Shu Wang, Jong-Hak Woo
Figure caption: Comparison between τ ICCF, τ JAV, and τ PyROA. The gray dotted dashed line represents the 1:1 relation. The red crosses at the top left corner indicate the median uncertainties of x- and y-axis. The scatter in the 16th–84th percentile range is shown at the bottom right corner.
Paper: Investigating the Behavior and Spatiotemporal Variations of Green-line Emission in the Solar Corona
Authors: Jacob Oloketuyi, Yu Liu, Linhua Deng, Abouazza Elmhamdi, Fengrong Zhu, Ayodeji Ibitoye, Opeyemi Omole, Feiyang Sha, Qiang Liu
Figure caption: The evolutionary trends of green-line coronal emissions from 1939 to 2024. Panel (a) shows the global trend in black, while panel (b) shows the trends for the northern (blue) and southern (magenta) hemispheres.
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption:  Gskew vs. logPeriodLS diagram for all LEAVES variables. From short to long periods, these include δ Scuti stars (DSCT; purple), EW-type eclipsing binaries (EW; gray), RRc Lyrae (RRC; orange), RRab Lyrae (RRAB; yellow), EA-type eclipsing binaries (EA; red), rotational variables (ROT; fuchsia), Cepheids (CEP; green), Mira variables (M; cyan), and semiregular variables (SR; blue).
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption: Hierarchical taxonomy used in this work.
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption: The data-flow diagram of building LEAVES and identifying variables. Before using the data set to train the model, we preprocess the light-curve data. We provide the time, magnitude, and magnitude error of light curves as input and use feets (J. B. Cabral et al. 2018) to extract features. A subset is then selected from the original features to improve classification performance. Finally, we verify the compatibility of the data set and perform classification experiments on the variables using three ML classification algorithms: SVM, MLP, and RF.
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption: The architecture of LEAVES. We complete the part in the dashed line: merging light curves from different bands and different surveys; experimentally verifying the compatibility of the obtained data set; computing features from the light curves; training a light-curve classifier suitable for multiple bands and catalogs, which can be applied to the automatic classification of variable stars.
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption: Light-curve compilation for nonvariables. We determine the positions of nonvariable stars based on the location of the variable star and retrieve them from the database. Finally, data cleaning is performed.
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption: StetsonK_AC vs. logPeriodLS diagram for all LEAVES variables. StetsonK_AC is practical to separate Miras and SRs from others.
Paper: LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
Authors: Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao
Figure caption: Light-curve compilation for variables. We calculate the attributes required for the light curve, unify its time format, and perform data cleaning.
Paper: A Survey of Sulfur-bearing Molecular Lines toward the Dense Cores in 11 Massive Protoclusters
Authors: Mengyao Tang, Sheng-Li Qin, Tie Liu, Luis A. Zapata, Xunchuan Liu, Yaping Peng, Fengwei Xu, Chao Zhang, Ken'ichi Tatematsu
Figure caption: Spectra of the I14498-7 core are depicted. The observation is represented by the black stepped line in each panel. The spectrum exhibiting a non-Gaussian profile has been fitted with multiple velocity components, and the different components are shown as colored curves in each panel. The red stepped line represents the aggregated modeling of all velocity components. Molecular names and component numbers are indicated in the upper-left corner of each panel. Multiple transitions of H2CS and 34SO2 are also plotted. The quantum numbers (QNs) of transitions for H2CS and 34SO2 are presented in the panels, as well. The spectra of CS and NS were also presented with only the black stepped line, it is clear that the CS spectrum is double peaked and with obvious outflow wings, and the NS spectrum is blended by CO outflow wings. Therefore, the spectra of CS and NS are excluded from the analyses.
Paper: A Survey of Sulfur-bearing Molecular Lines toward the Dense Cores in 11 Massive Protoclusters
Authors: Mengyao Tang, Sheng-Li Qin, Tie Liu, Luis A. Zapata, Xunchuan Liu, Yaping Peng, Fengwei Xu, Chao Zhang, Ken'ichi Tatematsu
Figure caption: Two-dimensional KDE plots of molecular abundances, number of detected species, and temperatures vs. low- and high-mass core categories. Prestellar core candidates and protostellar cores are analyzed separately and shown in blue and red colors, respectively. The vertical axis in each panel shows the mass categories of low- and high-mass cores, while the horizontal axis in each panel displays a specific parameter used for KDE analysis. Some species such as 33SO, OC34S, SO2, 34SO2, 33SO2, and SO18O were not detected in prestellar core candidates, labeled as “No detection” in the panels.
Paper: A Survey of Sulfur-bearing Molecular Lines toward the Dense Cores in 11 Massive Protoclusters
Authors: Mengyao Tang, Sheng-Li Qin, Tie Liu, Luis A. Zapata, Xunchuan Liu, Yaping Peng, Fengwei Xu, Chao Zhang, Ken'ichi Tatematsu
Figure caption: Relationship between the abundance increasing factor F of SO2 and temperatures. The linear fitting is demonstrated as the blue dashed line. The fitting results are shown in the legend. The definition of the restoration factor can be found in Section 4.5. The F values of other studies are estimated by the abundances and gas temperatures collected from the literature (K. S. Wang et al. 2013; J. Holdship et al. 2019; P. Rivière-Marichalar et al. 2019; A. Fuente et al. 2021; T. Möller et al. 2021; L. Bouscasse et al. 2022; F. Fontani et al. 2023).