adsabs/AstroCLIMB huggingface dataset:
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: The detection rate of H I 21 cm absorbers from this study and previous literature across different redshift intervals. Only the intervening and associated H I 21 cm absorptions with integrated optical depth limits exceeding 0.5 km s−1 and 1 km s−1, respectively, are considered here. Detection rates from the same survey at various redshifts are linked with dashed lines. The detection rates for associated and intervening systems are indicated by open and filled points, respectively. The references for the literature are R. C. Vermeulen et al. (2003), N. Gupta et al. (2006), B. H. C. Emonts et al. (2010), Y. Chandola et al. (2011, 2013), S. J. Curran et al. (2011, 2017), J. R. Allison et al. (2012), J. N. H. S. Aditya et al. (2016, 2024), T. Yan et al. (2016), F. M. Maccagni et al. (2017), L. Ostorero et al. (2017), J. N. H. S. Aditya & N. Kanekar (2018b), J. N. H. S. Aditya (2019), K. Grasha et al. (2019), S. Murthy et al. (2021, 2022), R. Su et al. (2022), N. Kanekar et al. (2009, 2014), and N. Gupta et al. (2009, 2012).
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: Same as Figure 33, but for WISE magnitudes difference between W1 and W2 bands vs. the H I column density. The black dashed line represents a W1 − W2 value of 0.8.
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: The comoving absorption path length (ΔX) spanned by our data as a function of H I column density sensitivity. The results for spin temperature to source covering fraction ratios of 100 K and 1000 K are shown as blue and red lines, respectively.
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: The fitting results for the H I absorption of the HVS in the 3C 84 system using three, four, and five Gaussian models are displayed in the left, center, and right panels, respectively. The BIC values for each model are also provided in the corresponding panels.
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: Stack of H I absorption spectra of the associated, intervening, and all H I absorption samples detected in this work.
Paper: A Machine Learning–ready Data Set for Exoplanet Atmospheric Retrieval
Authors: Simone Zorzan, Frank Soboczenski, Molly D. O'Beirne, Michael D. Himes, Michael B. Lund, Julian C. van Eyken, Giada N. Arney, Geronimo L. Villanueva, Massimo Mascaro, Shawn D. Domagal-Goldman, Atilim Güneş Baydin
Figure caption: INARA data set Exoplanet Archive web interface: For each spectrum, the user can access the parameters used to generate the data (top panel), a graphical interface with a distribution of spectra across the space of the parameters (top-right plot), and plots for the selected spectrum (bottom-right plot). The table and plot interfaces provide many more functionalities via the icons in the gray bar at the top of each. For more information, refer to the documentation page at the Exoplanet Archive.
Paper: A Machine Learning–ready Data Set for Exoplanet Atmospheric Retrieval
Authors: Simone Zorzan, Frank Soboczenski, Molly D. O'Beirne, Michael D. Himes, Michael B. Lund, Julian C. van Eyken, Giada N. Arney, Geronimo L. Villanueva, Massimo Mascaro, Shawn D. Domagal-Goldman, Atilim Güneş Baydin
Figure caption: Detailed results of INARA in predicting one random planet’s values for H2O and CH4. The distribution is obtained by 600 predictions with dropout where nodes were randomly removed from the network. The black lines represent INARA’s median prediction, and the red lines and star represent, respectively, the real value in the dimensions of H2O and CH4, and in the two-dimensional space with one molecule per axis.
Paper: A Machine Learning–ready Data Set for Exoplanet Atmospheric Retrieval
Authors: Simone Zorzan, Frank Soboczenski, Molly D. O'Beirne, Michael D. Himes, Michael B. Lund, Julian C. van Eyken, Giada N. Arney, Geronimo L. Villanueva, Massimo Mascaro, Shawn D. Domagal-Goldman, Atilim Güneş Baydin
Figure caption: Overview of the distributed data generation and training setup. Once instantiated as a Docker container in the cloud, INARA communicates with a designated PSG VM to generate the data and store it in the cloud. INARA Docker containers are then able to read the data back in for ML training and inference purposes.
Paper: A Machine Learning–ready Data Set for Exoplanet Atmospheric Retrieval
Authors: Simone Zorzan, Frank Soboczenski, Molly D. O'Beirne, Michael D. Himes, Michael B. Lund, Julian C. van Eyken, Giada N. Arney, Geronimo L. Villanueva, Massimo Mascaro, Shawn D. Domagal-Goldman, Atilim Güneş Baydin
Figure caption: INARA prediction performance for the logarithm of the normalized abundance of H2O, CO2, O2, N2 and CH4 across 1000 test planets. We normalize values by subtracting the mean and dividing by the standard deviation across all planets. Each dot is the average of 600 runs with dropout for a single planet. Predicted vs. true values are plotted, with the diagonal line indicating perfect correspondence.
Paper: Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts
Authors: Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park
Figure caption: (Left) Comparison between the photometric redshifts measured with our random forest model (zphot,RF) and the spectroscopic redshifts (zspec). Orange cross symbols indicate the catastrophic outliers with ∣Δz∣/(1 + z) > 0.15. The quoted numbers are the accuracy metrics of the zphot,RF. (Right) Photometric redshifts measured from the SED fit (zphot,SED) vs. zphot,RF. For clarity, we display only 5% of the data.
Paper: Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts
Authors: Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park
Figure caption: Properties of spectroscopically observed sources in our NEPW sample as a function of spectroscopic redshifts. The top panels show the distribution of (a) all available spectroscopic redshifts and (b) spectroscopic redshifts measured in this study. The vertical dashed line indicates the median of the redshifts. The middle panel shows the RVSNUpy r-value distribution as a function of the spectroscopic redshift measured in this study. The bottom panel is for the Subaru/HSC r-band magnitudes as a function of (d) all available spectroscopic redshifts and (e) the spectroscopic redshifts measured in this study.
Paper: Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts
Authors: Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park
Figure caption: (Left) rms of the redshift measurements for training (blue) and test (orange) sets as a function of the depth. The shaded region displays the 1σ distributions of rms. The dashed vertical line denotes the depth of 25. (Right) rms of the test set as a function of the number of bootstrapped subtraining sets. The dashed vertical line denotes 1000 bootstrapped subtraining sets.
Paper: Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts
Authors: Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park
Figure caption: The field of view of our own spectroscopic survey of the NEPW field. The gray dots indicate the sources identified in the infrared range in the AKARI NEPW field survey. We plot 10% of all samples detected by AKARAI for clarity. Blue dots show the sources with spectroscopic redshifts measured in previous studies. Orange dots display the sources with spectroscopic redshifts measured from our MMT/Hectospec survey. The large black circles indicate the MMT/Hectospec survey fields.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Comparison of key physical quantities at 12002 resolution using different numerical schemes. All quantities are evaluated at ﹩x/{H}_{0}=\left({X}_{\,\rm{max}\,}/2\right)/{H}_{0}﹩, corresponding to the midpoint of the rising loop. Other remarks are the same as in Figure 5.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Comparison of key physical quantities at 24002 resolution using different numerical schemes. All quantities are evaluated at ﹩x/{H}_{0}=\left({X}_{\,\rm{max}\,}/2\right)/{H}_{0}﹩, corresponding to the midpoint of the rising loop. Other remarks are the same as in Figure 5.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: A schematic of the feedforward neural network. The IL (leftmost collection of nodes p1, …, pn) takes EB parameters as input; these parameters are then propagated to the hidden layer (middle collection of nodes h1…hm) and, in turn, to the OL (rightmost collection of nodes o1…oq). The mapping is uniquely defined by the set of weights w11…wnm, v11…vmq, and biases b1…bm, c1…cq added after summation of weighted connections. These weights and biases are determined by backpropagation from the known combinations of model parameters and synthesized observables.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: A diagnostic figure illustrating the results from RSCV. The figure consists of N plots, where N corresponds to the number of unique values for a given hyperparameter. Each plot features two histograms: the first histogram (solid black line) represents the entire sample, while the second (dashed red line) represents the subset with the specific value of the hyperparameter, shown above each histogram (in this case, the AF in the OL). The x-axis indicates the values of the chosen metric (here, the MAE averaged across all folds). The lower the value of the metric, the better the performance.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: Overlay of light curves generated for a test sample. For each phase, the median and 18th–84th percentile range of fluxes were computed. The red solid line represents the median light curve, while the shaded region between the red dashed lines indicates the percentile range. Each thin black line represents an individual light curve. Although only 5000 out of the 1,250,000 generated light curves are shown here, the statistics were computed for the entire sample. See Section 5.1 for more details.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: Corner plot of combined posterior distributions, comparing results from the bootstrap analysis for the final solution using ANNs (red) and PHOEBE (blue). Standard deviations are provided above each histogram. Black dots indicate the true parameter values used to generate the synthetic light curve.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: (Top) Phase-folded light curve of one synthetic data set with fitted models. The dashed red line represents the model obtained using an ANN, while the solid blue line represents the model obtained with PHOEBE. (Bottom) Residuals after subtracting the ANN model fluxes from the observed data.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: Histograms of the differences between the obtained parameter values and the true values derived using the bootstrapping method with PHOEBE. Black dashed lines denote the zero-point.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: Density histograms comparing the initial and filtered distributions of the main set of parameters during the step of generating forward models using PHOEBE.
Paper: Galaxy–Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting Two-dimensional Information from Multiband Images in Ground and Space Observations
Authors: Fucheng Zhong, Ruibiao Luo, Nicola R. Napolitano, Crescenzo Tortora, Rui Li, Xincheng Zhu, Valerio Busillo, L. V. E. Koopmans, Giuseppe Longo
Figure caption: The mean error of the foreground/background four-band magnitudes for 1000 mock KiDS and HST images in each bin, with a bin size of 1, is shown in the leftmost subpanel. The foreground magnitudes are depicted as filled symbols, while the background magnitudes are depicted as empty symbols. The error bars represent one standard deviation. The density distributions of the predicted magnitude residuals with M (median) values for the mock images are also presented on the right subpanel(s). In the leftmost subpanel, the x-axis represents the magnitudes calculated from Ff or Fb, while the y-axis represents the difference between the magnitudes calculated from ﹩{\hat{F}}_{f}﹩ or ﹩{\hat{F}}_{b}﹩ and those calculated from Ff or Fb.
Paper: Galaxy–Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting Two-dimensional Information from Multiband Images in Ground and Space Observations
Authors: Fucheng Zhong, Ruibiao Luo, Nicola R. Napolitano, Crescenzo Tortora, Rui Li, Xincheng Zhu, Valerio Busillo, L. V. E. Koopmans, Giuseppe Longo
Figure caption: The photometric redshift vs. the spectroscopic redshift for the foreground is shown. The mean relative redshift error is calculated as follows: δ = ∣photo-z − spec-z∣/(1 + spec-z). The photo-z is measured using magnitudes in four different ways: (1) using U-Net gri-band magnitudes (blue points), (2) using GAaP gri-band magnitudes (green down-triangles), (3) using U-Net optical magnitudes combined with NIR GAaP magnitudes (for bands other than the optical bands, orange left-triangles), and (4) using nine-band GAaP magnitudes (red right-triangles).
Paper: Galaxy–Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting Two-dimensional Information from Multiband Images in Ground and Space Observations
Authors: Fucheng Zhong, Ruibiao Luo, Nicola R. Napolitano, Crescenzo Tortora, Rui Li, Xincheng Zhu, Valerio Busillo, L. V. E. Koopmans, Giuseppe Longo
Figure caption: The optical-band magnitudes calculated from the U-Net flux are compared to the magnitudes provided by the KiDS catalog for 50 lens candidates. Those magnitudes include extinction corrections. The empty symbols label the background source magnitudes, while the filled symbols represent the foreground. The KiDS magnitudes are calculated using the optimal minimum GAaP flux for each source, and extinction corrections are also included.
Paper: Galaxy–Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting Two-dimensional Information from Multiband Images in Ground and Space Observations
Authors: Fucheng Zhong, Ruibiao Luo, Nicola R. Napolitano, Crescenzo Tortora, Rui Li, Xincheng Zhu, Valerio Busillo, L. V. E. Koopmans, Giuseppe Longo
Figure caption: The BELLS GALLERY lens reconstruction results. Labels are the same as those in Figure 4, but now, instead of mock data, columns 2 (Ff) and 4 (Fb) show the models of BELLS GALLERY. We select eight real BELLS GALLERY lenses to display here. The ﹩{\chi }_{1}^{2}﹩ in column 2 and ﹩{\chi }_{2}^{2}﹩ in column 4 are calculated by replacing ﹩{\hat{F}}_{f}﹩ with Ff and ﹩{\hat{F}}_{b}﹩ with Fb in Equations (14) and (15). The χ2 and residual ﹩{\hat{F}}_{n}﹩ from U-Net show a reconstruction quality comparable to the residual Fn from BELLS GALLERY.
Paper: Galaxy–Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting Two-dimensional Information from Multiband Images in Ground and Space Observations
Authors: Fucheng Zhong, Ruibiao Luo, Nicola R. Napolitano, Crescenzo Tortora, Rui Li, Xincheng Zhu, Valerio Busillo, L. V. E. Koopmans, Giuseppe Longo
Figure caption: Anomalous reconstruction images of the mock KiDS images are shown. The labels are identical to those in Figure 4. In rows 1 and 2, the foreground contains two Sérsic models; in rows 3 and 4, a very bright object is located very close to the background source; and in rows 5 and 6, the background source signal is overwhelmed by noise.
Paper: Galaxy–Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting Two-dimensional Information from Multiband Images in Ground and Space Observations
Authors: Fucheng Zhong, Ruibiao Luo, Nicola R. Napolitano, Crescenzo Tortora, Rui Li, Xincheng Zhu, Valerio Busillo, L. V. E. Koopmans, Giuseppe Longo
Figure caption: The optical-band magnitudes calculated from the U-Net flux ﹩{\hat{F}}_{f}+{\hat{F}}_{b}﹩ are compared to the magnitudes provided by the KiDS catalog for 50 lens candidates. The labels are identical to those in Figure 15.
Paper: Galaxy–Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting Two-dimensional Information from Multiband Images in Ground and Space Observations
Authors: Fucheng Zhong, Ruibiao Luo, Nicola R. Napolitano, Crescenzo Tortora, Rui Li, Xincheng Zhu, Valerio Busillo, L. V. E. Koopmans, Giuseppe Longo
Figure caption: In rows 1 and 2, the image is broken, with the broken part introduced by randomly selecting Fn from KiDS. In rows 3 and 4, the presence of a similar extraneous source and a weak background signal may have caused the misextraction of the background. In rows 5 and 6, some noise might have been misinterpreted as a background signal.
Paper: Galaxy–Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting Two-dimensional Information from Multiband Images in Ground and Space Observations
Authors: Fucheng Zhong, Ruibiao Luo, Nicola R. Napolitano, Crescenzo Tortora, Rui Li, Xincheng Zhu, Valerio Busillo, L. V. E. Koopmans, Giuseppe Longo
Figure caption: Modeling results for 20 real KiDS candidates. Labels are identical to those in Figure 6. Some candidates might be spiral galaxies, for instance, the case of (R.A. = 220.635, decl. = 0.292).
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Comparison of the parameters estimated by A. Belloche et al. (2013), labeled “test,” with the best-fitting parameters obtained in this work, labeled “pred.” Whenever the original study suggests multiple components, the component with the highest column density is compared. Each data point compares the parameters of a molecule at one state. The red and blue dots represent the results based on the peak-matching and χ2 loss functions, respectively.
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Line blending analysis of ethanol (C2H5OH) in SgrB2M-IRAM. The black solid lines represent the observed spectrum, and the black dashed lines indicate the C2H5OH spectral lines identified by A. Belloche et al. (2013). The blue lines show the best-fitting spectrum of C2H5OH obtained in the individual fitting phase. The red solid lines illustrate the spectrum of C2H5OH as a part of the combined spectrum. The final combined spectrum is shown as the yellow lines. While the final result shows a low recall of C2H5OH, most lines can be recovered in the individual fitting phase, implying line blending.
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Line identification of cyclopropenylidene (c-C3H2) by A. Belloche et al. (2013). The black solid lines represent the observed spectrum, and the black dashed lines indicate the spectral lines of c-C3H2 identified by A. Belloche et al. (2013). The purple lines represent the spectrum generated using the parameters suggested by A. Belloche et al. (2013). Each panel displays a peak from the model spectrum. These plots imply that absorption should be taken into account when fitting the c-C3H2 spectrum.
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Comparison of the parameters estimated by A. Belloche et al. (2013), labeled “test,” with the best-fitting parameters obtained in this work, labeled “pred.” Whenever the original study suggests multiple components, the component with the highest column density is compared. Each data point compares the parameters of a molecule at one state. Red and blue dots represent the results based on the peak-matching and χ2 loss functions, respectively.
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Best-fitting results for SgrB2N-IRAM. Black lines represent the observed spectrum. Left and right panels show the results using the peak-matching and χ2 loss functions, respectively. The peak-matching loss function is described in Section 2.4.
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Recall and precision of species identified in SgrB2N-IRAM. The red and blue bars represent the results using the peak-matching and χ2 loss functions, respectively. Definitions of recall and precision are provided in Section 4.1.