adsabs/AstroCLIMB huggingface dataset:
Paper: Quasar Winds Caught on Acceleration and Deceleration
Authors: Weimin Yi, P. B. Hall, Zunli Yuan, W. N. Brandt, D. P. Schneider, Zhicheng He, Jin-Ming Bai, Xue-Bing Wu
Figure caption: Multiepoch optical spectra (smoothed by a 7 pixel boxcar filter corresponding to ∼420 km s−1) of the four quasars having signatures of BAL acceleration, in which the spectral fluxes of J1238 and J1259 are recalibrated by V-band photometry using the first SDSS epoch as a benchmark. The MJDs of individual observations are given in the upper corner of each panel. Plus symbols mark telluric absorption.
Paper: Broadband Multiwavelength Study of LHAASO-detected Active Galactic Nuclei
Authors: Ze-Rui Wang, Rui Xue, Dingrong Xiong, Hai-Qin Wang, Lu-Ming Sun, Fang-Kun Peng, Jirong Mao
Figure caption: The optical depths (left panel), the intrinsic VHE spectra (middle panel), and the hypothetical extended intrinsic spectrum and the expected observed spectrum (right panel) of Mrk 421 (z = 0.031) for different EBL models. The optical depths taken from Finke et al. (2010), Domínguez et al. (2011), Finke et al. (2022), Gilmore et al. (2012), and Saldana-Lopez et al. (2021).
Paper: Broadband Multiwavelength Study of LHAASO-detected Active Galactic Nuclei
Authors: Ze-Rui Wang, Rui Xue, Dingrong Xiong, Hai-Qin Wang, Lu-Ming Sun, Fang-Kun Peng, Jirong Mao
Figure caption: One-zone proton-synchrotron modeling. The meanings of symbols and line styles are given in the legend of Mrk 421.
Paper: Broadband Multiwavelength Study of LHAASO-detected Active Galactic Nuclei
Authors: Ze-Rui Wang, Rui Xue, Dingrong Xiong, Hai-Qin Wang, Lu-Ming Sun, Fang-Kun Peng, Jirong Mao
Figure caption: The second fitting strategy (two zone) of the spine-layer model, the SEDs are reproduced by the superposition of emissions from two components. The meanings of symbols and line styles are given in the legend of Mrk 421.
Paper: Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions
Authors: Khaznah Alshammari, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: Stacked bar plot of MVTS populations for each of the five flare classes across different partitions of the SWAN-SF benchmark data set. Flare classes X, M, C, and B are taken and validated from GOES classification, while Q denotes flare-quiet and GOES A-class instances. The annotated populations stem from the current time-series slicing strategy of the SWAN-SF with a 12 hr observation window and a 24 hr prediction window. The sliced multivariate time series are labeled with the class of the strongest reported flare, if any, within the respective prediction window (Ahmadzadeh et al. 2021).
Paper: Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions
Authors: Khaznah Alshammari, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: Relationships between the training and testing partitions, and label description of each partition during training. In this directed graph, each edge/link (i, j) represents partition i is used for training (testing) while partition j is used for testing (training).
Paper: Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions
Authors: Khaznah Alshammari, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: Mean ± standard deviation test TSS score where partition 5 is used for training and validation, partition 4 for testing, and decision tree as the downstream classifier.
Paper: Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions
Authors: Khaznah Alshammari, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: Mean ± standard deviation test TSS score where partition 2 is used for training and validation, partition 5 for testing, and random forest as the downstream classifier.
Paper: Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions
Authors: Khaznah Alshammari, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: Mean ± standard deviation test TSS score where partition 1 is used for training and validation, partition 4 for testing, and kNN as the downstream classifier.
Paper: Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions
Authors: Khaznah Alshammari, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: Mean ± standard deviation test TSS score where partition 1 is used for training and validation, partition 4 for testing, and XGBoost as the downstream classifier.
Paper: Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions
Authors: Khaznah Alshammari, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: Mean ± standard deviation test TSS score where partition 5 is used for training and validation, partition 3 for testing, and SVM as the downstream classifier.
Paper: Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions
Authors: Khaznah Alshammari, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: Deep-learning-based MVTS feature selection framework.
Paper: A Closer Look at Small-scale Magnetic Flux Ropes in the Solar Wind at 1 au: Results from Improved Automated Detection
Authors: Hameedullah Farooki, Sung Jun Noh, Jeongwoo Lee, Haimin Wang, Hyomin Kim, Yasser Abduallah, Jason T. L. Wang, Yu Chen, Sergio Servidio, Francesco Pecora
Figure caption: Joint distribution of ﹩\hat{{\boldsymbol{z}}}﹩ azimuth ϕ (counterclockwise angle from the radial direction away from the Sun) and polar angle θ (angle from the normal direction in the RTN coordinate system) for various ranges of diameter. In the 2D histograms, darker values mean higher filling factors. The 2D histograms are generated with hexagonal binning to clearly show the shape. Furthermore, the vertical binning is by ﹩\cos (\theta )﹩ instead of θ (hence, the uneven spacing) because randomly oriented unit vectors naturally tend to values of θ closer to 90° (by a factor of ﹩\sin (\theta )﹩) but have uniformly distributed ﹩\cos (\theta )﹩. The sides show the histograms of ϕ (on top) and θ (on the right).
Paper: A Closer Look at Small-scale Magnetic Flux Ropes in the Solar Wind at 1 au: Results from Improved Automated Detection
Authors: Hameedullah Farooki, Sung Jun Noh, Jeongwoo Lee, Haimin Wang, Hyomin Kim, Yasser Abduallah, Jason T. L. Wang, Yu Chen, Sergio Servidio, Francesco Pecora
Figure caption: Comparison of the absolute value of the Walén slope with the distribution derived from PSP’s first six encounters by Chen & Hu (2022). Only the absolute value is shown because most early PSP encounters were during periods of antiradial magnetic field, so the Walén slope from the PSP observations was preferentially positive. For the Wind data, only samples with at least a 0.8 correlation are included in the histogram so that the threshold is the same as the one used by Chen & Hu (2022). Additionally, since we require that the Walén slope is less than 0.9, we do not include bins above 0.9. This figure shows that the peak of the distribution is the same. The differences in the other parts of the distribution may be due to the differences in plasma types observed by the two spacecraft.
Paper: A Closer Look at Small-scale Magnetic Flux Ropes in the Solar Wind at 1 au: Results from Improved Automated Detection
Authors: Hameedullah Farooki, Sung Jun Noh, Jeongwoo Lee, Haimin Wang, Hyomin Kim, Yasser Abduallah, Jason T. L. Wang, Yu Chen, Sergio Servidio, Francesco Pecora
Figure caption: (a) Histogram of the Walén slope. The black bars include all events, while the blue and red, respectively, represent events with extremely strong and not very strong correlations between  v  −  v  HT and  v  A. A positive (negative) Walén slope indicates a flow (anti)parallel to the magnetic field. (b) Histogram of the correlation between  v  −  v  FR and  v  A. (c) Histogram of velocity difference between 〈 v 〉 and  v  FR. The plot indicates a tendency for the velocity fluctuations to be sunward along the Parker spiral in the flux rope frame of reference.
Paper: S-PLUS: Photometric Recalibration with the Stellar Color Regression Method and an Improved Gaia XP Synthetic Photometry Method
Authors: Kai Xiao, Yang Huang, Haibo Yuan, Timothy C. Beers, Bowen Huang, Shuai Xu, Lin Yang, Felipe Almeida-Fernandes, Hélio D. Perottoni, Guilherme Limberg, William Schoenell, Tiago Ribeiro, Antonio Kanaan, Natanael Gomes de Oliveira
Figure caption: Two-dimensional polynomial fits of the intrinsic colors with respect to T eff and [Fe/H] for the calibration stars in the SCR method. The intrinsic colors include G BP − uJAVA, G BP − J0378, G BP − J0395, G BP − J0410, G BP − J0430, G BP − gSDSS, G BP − J0515, G RP − rSDSS, G RP − J0660, G RP − iSDSS, G RP − J0861, and G RP − zSDSS. The fit results after 3σ clipping are shown in the first column of panels, with the red and blue curves representing results for [Fe/H] = 0 and [Fe/H] = −1, respectively. The fit residuals are labeled in red. In the second to sixth columns of panels, the residuals are plotted against T eff, [Fe/H], extinction of E(G BP − G RP), G magnitude, and ﹩{({G}_{\mathrm{BP}}-{G}_{\mathrm{RP}})}_{0}﹩ color, respectively. Zero residuals are denoted by black dashed lines.
Paper: A Closer Look at Small-scale Magnetic Flux Ropes in the Solar Wind at 1 au: Results from Improved Automated Detection
Authors: Hameedullah Farooki, Sung Jun Noh, Jeongwoo Lee, Haimin Wang, Hyomin Kim, Yasser Abduallah, Jason T. L. Wang, Yu Chen, Sergio Servidio, Francesco Pecora
Figure caption: Flux rope coordinate system. ﹩\hat{{\boldsymbol{z}}}﹩ is the cylindrical direction along which ∂/∂z = 0. The perpendicular plane is divided into ﹩\hat{{\boldsymbol{x}}}﹩ and ﹩\hat{{\boldsymbol{y}}}﹩, defined so that the spacecraft only moves through the cross section along ﹩\hat{{\boldsymbol{x}}}﹩ minus the motion along ﹩\hat{{\boldsymbol{z}}}﹩. Therefore, the velocity of the flux rope in the spacecraft frame ( v  FR; assumed constant) is contained in the x–z plane. Since the spacecraft moves along ﹩\hat{{\boldsymbol{x}}}﹩ within the cross section, the x component of  v  FR must be negative since  v  FR is measured in the spacecraft frame of reference.
Paper: A Closer Look at Small-scale Magnetic Flux Ropes in the Solar Wind at 1 au: Results from Improved Automated Detection
Authors: Hameedullah Farooki, Sung Jun Noh, Jeongwoo Lee, Haimin Wang, Hyomin Kim, Yasser Abduallah, Jason T. L. Wang, Yu Chen, Sergio Servidio, Francesco Pecora
Figure caption: (a) Histogram of correlation between proton number density and magnetic field strength. (b) Histogram of the maximum B minus minimum B in the closed part of the reconstructed cross section. Demonstrates that unlike pure Alfvén waves, SMFRs have anticorrelated n  p  and B as well as a nonzero, measurable change in B.
Paper: A Closer Look at Small-scale Magnetic Flux Ropes in the Solar Wind at 1 au: Results from Improved Automated Detection
Authors: Hameedullah Farooki, Sung Jun Noh, Jeongwoo Lee, Haimin Wang, Hyomin Kim, Yasser Abduallah, Jason T. L. Wang, Yu Chen, Sergio Servidio, Francesco Pecora
Figure caption: Scatter plots of Ψ, Φ, and τ as functions of diameter. The shaded region, with a cutoff at 600 km s−1 × 30 s, indicates where the smallest sliding windows cannot detect SMFRs of that size for orientations too far from radial, so the reconstructions and derived parameters are less reliable in this range.
Paper: S-PLUS: Photometric Recalibration with the Stellar Color Regression Method and an Improved Gaia XP Synthetic Photometry Method
Authors: Kai Xiao, Yang Huang, Haibo Yuan, Timothy C. Beers, Bowen Huang, Shuai Xu, Lin Yang, Felipe Almeida-Fernandes, Hélio D. Perottoni, Guilherme Limberg, William Schoenell, Tiago Ribeiro, Antonio Kanaan, Natanael Gomes de Oliveira
Figure caption: Histograms of the number of standard stars for the XPSP method in each image. The bands are labeled in each panel.
Paper: High-dimensional Statistical Analysis and Its Application to an ALMA Map of NGC 253
Authors: Tsutomu T. Takeuchi, Kazuyoshi Yata, Kento Egashira, Makoto Aoshima, Aki Ishii, Suchetha Cooray, Kouichiro Nakanishi, Kotaro Kohno, Kai T. Kono
Figure caption: A sample distribution drawn from two typical probability density distributions. Left: a sample generated from a d-dimensional Gaussian distribution with a mean of  μ  = 0 and a covariance matrix ﹩{\tilde{{\rm{\Sigma }}}}_{d}={\tilde{I}}_{d}﹩ (﹩{\tilde{I}}_{d}﹩: a d-dimensional identity matrix). Right: a sample generated from a d-dimensional t-distribution with t  d (0, I  d , ν), with a mean of  μ  = 0, a covariance matrix I  d , and a degrees of freedom ν = 5.
Paper: High-dimensional Statistical Analysis and Its Application to an ALMA Map of NGC 253
Authors: Tsutomu T. Takeuchi, Kazuyoshi Yata, Kento Egashira, Makoto Aoshima, Aki Ishii, Suchetha Cooray, Kouichiro Nakanishi, Kotaro Kohno, Kai T. Kono
Figure caption: Responsible features to characterize PCs from the A-SPCA for the ALMA map of NGC 253, after the Doppler shift correction due to the systemic rotation. Stars and triangles represent PC1- and PC2-related responsible spectral features similar to those in Figure 10, while filled circles are PC3 related.
Paper: High-dimensional Statistical Analysis and Its Application to an ALMA Map of NGC 253
Authors: Tsutomu T. Takeuchi, Kazuyoshi Yata, Kento Egashira, Makoto Aoshima, Aki Ishii, Suchetha Cooray, Kouichiro Nakanishi, Kotaro Kohno, Kai T. Kono
Figure caption: Ratio of eigenvalues obtained by traditional PCA (﹩{\hat{\lambda }}_{i}﹩) and that obtained by high-dimensional (NR)PCA (﹩{\check{\lambda }}_{i}﹩). Horizontal axes represent the index of eigenvalues, and vertical axes show the ratio ﹩{\hat{\lambda }}_{i}/{\check{\lambda }}_{i}﹩ (i = 1, 2,...). The left panel of Figure 21 shows the result of the raw data (without Doppler velocity subtraction), while the right panel shows that of the Doppler-corrected data.
Paper: High-dimensional Statistical Analysis and Its Application to an ALMA Map of NGC 253
Authors: Tsutomu T. Takeuchi, Kazuyoshi Yata, Kento Egashira, Makoto Aoshima, Aki Ishii, Suchetha Cooray, Kouichiro Nakanishi, Kotaro Kohno, Kai T. Kono
Figure caption: The two-dimensional structure to map PC1, PC2, and PC3 obtained by NRPCA.
Paper: High-dimensional Statistical Analysis and Its Application to an ALMA Map of NGC 253
Authors: Tsutomu T. Takeuchi, Kazuyoshi Yata, Kento Egashira, Makoto Aoshima, Aki Ishii, Suchetha Cooray, Kouichiro Nakanishi, Kotaro Kohno, Kai T. Kono
Figure caption: Example of the Doppler shift-corrected molecular emission line, HCN(4–3). Red, green, and blue lines represent the corrected spectra so that the line center agrees with its rest-frame frequency.
Paper: High-dimensional Statistical Analysis and Its Application to an ALMA Map of NGC 253
Authors: Tsutomu T. Takeuchi, Kazuyoshi Yata, Kento Egashira, Makoto Aoshima, Aki Ishii, Suchetha Cooray, Kouichiro Nakanishi, Kotaro Kohno, Kai T. Kono
Figure caption: The region with velocity peculiarity in Figure 9.
Paper: Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model
Authors: Maria Giovanna Dainotti, Elias Taira, Eric Wang, Elias Lehman, Aditya Narendra, Agnieszka Pollo, Grzegorz M. Madejski, Vahe Petrosian, Malgorzata Bogdan, Apratim Dey, Shubham Bhardwaj
Figure caption: Scatter matrix plot of the data after the cleaning of the sample and variable transformation.
Paper: Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model
Authors: Maria Giovanna Dainotti, Elias Taira, Eric Wang, Elias Lehman, Aditya Narendra, Agnieszka Pollo, Grzegorz M. Madejski, Vahe Petrosian, Malgorzata Bogdan, Apratim Dey, Shubham Bhardwaj
Figure caption: Flowchart detailing each step of the pipeline. All yellow boxes indicate the number of GRBs in the training set at each step of the process. Gray boxes are preprocessing steps. Orange boxes indicate all steps involving model construction. Green boxes show all steps involving the generalization set predictions. Blue boxes show all the postprocessing steps with the training set.
Paper: Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model
Authors: Maria Giovanna Dainotti, Elias Taira, Eric Wang, Elias Lehman, Aditya Narendra, Agnieszka Pollo, Grzegorz M. Madejski, Vahe Petrosian, Malgorzata Bogdan, Apratim Dey, Shubham Bhardwaj
Figure caption: Scatter plot between z obs and z pred. Upper panel: predictions before removal of catastrophic outliers and bias correction. Middle panel: predictions after the removal of catastrophic outliers. Lower panel: predictions after the removal of catastrophic outliers and application of bias correction.
Paper: Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model
Authors: Maria Giovanna Dainotti, Elias Taira, Eric Wang, Elias Lehman, Aditya Narendra, Agnieszka Pollo, Grzegorz M. Madejski, Vahe Petrosian, Malgorzata Bogdan, Apratim Dey, Shubham Bhardwaj
Figure caption: The plot of the cross-validation results of RMSE and the r in the ﹩\mathrm{log}(z+1)﹩ of GLM (right) and GAM (left) formulae. Each dot on either plot represents a formula performance within the 10fCV. The red dots represent the formulae that were above the chosen RMSE and correlation.
Paper: Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model
Authors: Maria Giovanna Dainotti, Elias Taira, Eric Wang, Elias Lehman, Aditya Narendra, Agnieszka Pollo, Grzegorz M. Madejski, Vahe Petrosian, Malgorzata Bogdan, Apratim Dey, Shubham Bhardwaj
Figure caption: The missing data in our sample. The red boxes show the missing GRB data points, while the blue boxes indicate GRBs with no missing data for a given GRB variable presented in the top axis. The bottom axis shows the number of missing GRBs per variable. The left axis represents the number of observations that have missing data for a specific set of features. For example: there are 172 GRBs with no missing data, 13 GRBs with missing data in ﹩\mathrm{log}(\mathrm{NH})﹩ data, three GRBs with missing data in α, and so on. The right axis represents the number of features that are missing for that row.
Paper: Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model
Authors: Maria Giovanna Dainotti, Elias Taira, Eric Wang, Elias Lehman, Aditya Narendra, Agnieszka Pollo, Grzegorz M. Madejski, Vahe Petrosian, Malgorzata Bogdan, Apratim Dey, Shubham Bhardwaj
Figure caption: Histogram comparing the distributions of the training set z obs and the redshift predictions of the generalization set.
Paper: Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model
Authors: Maria Giovanna Dainotti, Elias Taira, Eric Wang, Elias Lehman, Aditya Narendra, Agnieszka Pollo, Grzegorz M. Madejski, Vahe Petrosian, Malgorzata Bogdan, Apratim Dey, Shubham Bhardwaj
Figure caption: Histograms of our chosen metrics on testing sets. Both correlation and RMSE are computed in the ﹩\mathrm{log}(z+1)﹩ scale. Left panel: correlation between predicted and observed redshift. Right panel: RMSE of the predicted redshifts.
Paper: Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model
Authors: Maria Giovanna Dainotti, Elias Taira, Eric Wang, Elias Lehman, Aditya Narendra, Agnieszka Pollo, Grzegorz M. Madejski, Vahe Petrosian, Malgorzata Bogdan, Apratim Dey, Shubham Bhardwaj
Figure caption: Plot of the relative influence of each predictor also including in each predictor the second-order variables.
Paper: Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model
Authors: Maria Giovanna Dainotti, Elias Taira, Eric Wang, Elias Lehman, Aditya Narendra, Agnieszka Pollo, Grzegorz M. Madejski, Vahe Petrosian, Malgorzata Bogdan, Apratim Dey, Shubham Bhardwaj
Figure caption: The weights assigned to the features by LASSO.
Paper: Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model
Authors: Maria Giovanna Dainotti, Elias Taira, Eric Wang, Elias Lehman, Aditya Narendra, Agnieszka Pollo, Grzegorz M. Madejski, Vahe Petrosian, Malgorzata Bogdan, Apratim Dey, Shubham Bhardwaj
Figure caption: Box plot for each GRB is shown for the generalization set. Error bars are shown for each prediction.
Paper: Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model
Authors: Maria Giovanna Dainotti, Elias Taira, Eric Wang, Elias Lehman, Aditya Narendra, Agnieszka Pollo, Grzegorz M. Madejski, Vahe Petrosian, Malgorzata Bogdan, Apratim Dey, Shubham Bhardwaj
Figure caption: Histogram distributions for α, β, and γ, showing the distribution before the outlier removal.
Paper: The ALCHEMI Atlas: Principal Component Analysis Reveals Starburst Evolution in NGC 253
Authors: Nanase Harada, David S. Meier, Sergio Martín, Sebastien Muller, Kazushi Sakamoto, Toshiki Saito, Mark D. Gorski, Christian Henkel, Kunihiko Tanaka, Jeffrey G. Mangum, Susanne Aalto, Rebeca Aladro, Mathilde Bouvier, Laura Colzi, Kimberly L. Emig, Rubén Herrero-Illana, Ko-Yun Huang, Kotaro Kohno, Sabine König, Kouichiro Nakanishi, Yuri Nishimura, Shuro Takano, Víctor M. Rivilla, Serena Viti, Yoshimasa Watanabe, Paul P. van der Werf, Yuki Yoshimura
Figure caption: Top: Velocity-integrated images of CO(1–0) in blue, H39α in red, and CH3OH(2 K –1 K ) around the rest frequency of 96.7 GHz in green. Rough positions of parts of the x 1 orbits and the full x 2 orbits farther inside are shown with dashed–dotted and dashed lines, respectively. Note that these are just some examples of the presumably large families of possible x 1 and x 2 orbits. There likely exists another x 2 orbit connecting GMCs 3–6 or 3–7 (see Levy et al. 2022) almost fully edge-on. The synthetic beam size is shown at the left bottom corner as a white circle. Bottom: The same as the top panel, but with HC3N(25–24) in red, and CN(3–2) in green. Intensities are scaled so that faint lines have similar dynamic ranges as other lines. We employed the algorithm by Lupton et al. (2004) to produce RGB images.
Paper: The ALCHEMI Atlas: Principal Component Analysis Reveals Starburst Evolution in NGC 253
Authors: Nanase Harada, David S. Meier, Sergio Martín, Sebastien Muller, Kazushi Sakamoto, Toshiki Saito, Mark D. Gorski, Christian Henkel, Kunihiko Tanaka, Jeffrey G. Mangum, Susanne Aalto, Rebeca Aladro, Mathilde Bouvier, Laura Colzi, Kimberly L. Emig, Rubén Herrero-Illana, Ko-Yun Huang, Kotaro Kohno, Sabine König, Kouichiro Nakanishi, Yuri Nishimura, Shuro Takano, Víctor M. Rivilla, Serena Viti, Yoshimasa Watanabe, Paul P. van der Werf, Yuki Yoshimura
Figure caption: Velocity-integrated images integrated from within 400 km s−1 of the systemic velocity with masking as described in Section 2.1. Contour levels are set at 10%, 30%, 50%, 70%, and 90% of their peak values. Maximum values of color scales are set for 120% of their peak values. We show more commonly observed species first, and transitions are in ascending order of frequency within the same species. The transition names are in the upper left corner, and the line rest frequency and the energy level in the upper state are also in each panel. The 1.″6 beam is in the bottom left corner. The plus signs indicate GMC locations. The images are corrected for the primary beam response patterns of the ALMA antennas.
Paper: The ALCHEMI Atlas: Principal Component Analysis Reveals Starburst Evolution in NGC 253
Authors: Nanase Harada, David S. Meier, Sergio Martín, Sebastien Muller, Kazushi Sakamoto, Toshiki Saito, Mark D. Gorski, Christian Henkel, Kunihiko Tanaka, Jeffrey G. Mangum, Susanne Aalto, Rebeca Aladro, Mathilde Bouvier, Laura Colzi, Kimberly L. Emig, Rubén Herrero-Illana, Ko-Yun Huang, Kotaro Kohno, Sabine König, Kouichiro Nakanishi, Yuri Nishimura, Shuro Takano, Víctor M. Rivilla, Serena Viti, Yoshimasa Watanabe, Paul P. van der Werf, Yuki Yoshimura
Figure caption: Same as Figure 2.