adsabs/AstroCLIMB huggingface dataset:
Paper: The Heating Efficiency of Hot Jupiters from a Data-driven Perspective
Authors: Sheng Jin, Dong-Hong Wu, Yi-Xuan Cao, Zi-Yi Guo
Figure caption: The distribution of stellar effective temperature vs. planetary radius for hot Jupiters (blue dots) with Teq in the ranges of 883–1565 K, 1565–1857 K, and 1869–2311 K, respectively. The red lines and red dashed lines show the fitted power-law relationships along with the 1σ regions of those relationships. The fitted power-law indices, γ (Rplanet, Tstar), for the three groups of hot Jupiters are 0.45 ±  0.08, 0.97 ±  0.15, and 0.43 ±  0.16, respectively.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit for Object 3 and the parameter posteriors with the same plotting convention as Figure 21.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit for Object 4 and the parameter posteriors with the same plotting convention as Figure 21.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit for Object 5 and the parameter posteriors with the same plotting convention as Figure 21.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit for Object 6 and the parameter posteriors with the same plotting convention as Figure 21.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit for Object 7 and the parameter posteriors with the same plotting convention as Figure 21.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The corner plot for Object 3, for model parameters and the log(L*) and log(Lacc) posteriors.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The corner plot for Object 6, for model parameters and the log(L*) and log(Lacc) posteriors.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The corner plot for Object 5, for model parameters and the log(L*) and log(Lacc) posteriors.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The corner plot for Object 4, for model parameters and the log(L*) and log(Lacc) posteriors.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit for Object 8 and the parameter posteriors with the same plotting convention as Figure 21.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit for Object 10 and the parameter posteriors with the same plotting convention as Figure 21.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit for Object 11 and the parameter posteriors with the same plotting convention as Figure 21.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit for Object 9 and the parameter posteriors with the same plotting convention as Figure 21.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The corner plot for Object 11, for model parameters and the log(L*) and log(Lacc) posteriors.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The corner plot for Object 8, for model parameters and the log(L*) and log(Lacc) posteriors.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The corner plot for Object 10, for model parameters and the log(L*) and log(Lacc) posteriors.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The corner plot for Object 7, for model parameters and the log(L*) and log(Lacc) posteriors.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The corner plot for Object 9, for model parameters and the log(L*) and log(Lacc) posteriors.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The corner plot for Object 15, for model parameters and the log(L*) and log(Lacc) posteriors.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit for Object 12 and the parameter posteriors with the same plotting convention as Figure 21.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit for Object 13 and the parameter posteriors with the same plotting convention as Figure 21.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit for Object 14 and the parameter posteriors with the same plotting convention as Figure 21.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit for Object 15 and the parameter posteriors with the same plotting convention as Figure 21.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The corner plot for Object 13, for model parameters and the log(L*) and log(Lacc) posteriors.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The corner plot for Object 12, for model parameters and the log(L*) and log(Lacc) posteriors.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The corner plot for Object 14, for model parameters and the log(L*) and log(Lacc) posteriors.
Paper: Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m s−1 RV Precision for APOGEE in the Plate Era
Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
Figure caption: The top panel shows how different estimates of S/N compare to the RV precision predicted using the observed flux uncertainties. The bottom two panels show fiber-to-fiber variation in predicted RV precision at APO (middle) and LCO (bottom), respectively. See the text for more.
Paper: Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m s−1 RV Precision for APOGEE in the Plate Era
Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
Figure caption: Radial velocity (RV) observations of HD 167858 by HARPS (top panel) and APOGEE (middle panel). The RVs from both the DRP (red x) and apMADGICS (blue circles) are shown for all available APOGEE visits. The bottom panel depicts the Lomb–Scargle periodogram of these RV observations, with shading indicating the ±1σ uncertainties on the periodogram, estimated by jackknife resampling.
Paper: Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m s−1 RV Precision for APOGEE in the Plate Era
Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
Figure caption: The offset in wavelength between the center of a Gaussian fit to the LSF model and the nominal center wavelength of the LSF model from the DRP, reported as a velocity shift in meters per second.
Paper: Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m s−1 RV Precision for APOGEE in the Plate Era
Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
Figure caption: Percentage of theoretical RV precision bound achieved by apMADGICS RV catalog as measured by scatter on repeat visits to a given star using the same fiber. Obtained via a ratio of the bottom-middle and bottom-left panels in Figure 8.
Paper: Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m s−1 RV Precision for APOGEE in the Plate Era
Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
Figure caption: Same as Figure 8 but vs. Teff and [X/H].
Paper: Bioverse: Giant Magellan Telescope and Extremely Large Telescope Direct Imaging and High-resolution Spectroscopy Assessment—Surveying Exo-Earth O2 and Testing the Habitable Zone Oxygen Hypothesis
Authors: Kevin K. Hardegree-Ullman, Dániel Apai, Sebastiaan Y. Haffert, Martin Schlecker, Markus Kasper, Jens Kammerer, Kevin Wagner
Figure caption: Output of the semianalytical AO simulations for ELT/PCS with the 128 actuator model. The left figure shows the simulated point-spread function (PSF) and the right figure shows the coronagraphic stellar image. There are two visible circular regions. The largest corresponds to the control radius of the AO system and the smaller one corresponds to the control radius of the non-common-path aberrations. The contrast within the inner radius is dominated by the wind-driven halo. Axes are shown in both milliarcseconds and λ/D, assuming λ = 765 nm and D = 39.3 m.
Paper: Bioverse: Giant Magellan Telescope and Extremely Large Telescope Direct Imaging and High-resolution Spectroscopy Assessment—Surveying Exo-Earth O2 and Testing the Habitable Zone Oxygen Hypothesis
Authors: Kevin K. Hardegree-Ullman, Dániel Apai, Sebastiaan Y. Haffert, Martin Schlecker, Markus Kasper, Jens Kammerer, Kevin Wagner
Figure caption: Coronagraphic encircled energy throughput (1.8λ/D diameter) as a function of angular separation for an 8 mag star. A fourth-order perfect coronagraph with the ELT aperture was used in these simulations. The top row shows the PSFs corresponding to the four orange points on the throughput curve from 0 mas (left) to 20 mas (right) separation. The PSF is deformed close to the inner working angle of the coronagraph.
Paper: New Stellar Parameters, Metallicities, and Elemental Abundance Ratios for 311 Metal-poor Stars
Authors: Sanil Mittal, Ian U. Roederer
Figure caption: Plot of Teff and log g values, color coded by [M/H] values. The size of the marker is given by the vt value. We also plot Padova Isochrones with [Fe/H] = −2.0; the red line for an age of 10 Gyr, and the dashed blue line for an age of 14 Gyr.
Paper: New Stellar Parameters, Metallicities, and Elemental Abundance Ratios for 311 Metal-poor Stars
Authors: Sanil Mittal, Ian U. Roederer
Figure caption: Comparison of NLTE and LTE abundances for different Fe ionization states. The top panel shows the difference between the LTE [Fe II/H] and NLTE-corrected [Fe I/H] values. The bottom panel shows the amount of NLTE corrections applied to [Fe I/H]. In both panels, the dashed line indicates a difference of 0.0. In the bottom panel, the stars with differences <0 are MS stars with log g > 4, where NLTE effects are expected to be minimal (e.g., K. Lind et al. 2012).
Paper: New Stellar Parameters, Metallicities, and Elemental Abundance Ratios for 311 Metal-poor Stars
Authors: Sanil Mittal, Ian U. Roederer
Figure caption: Comparison of our abundances with other metal-poor star samples. The references for the symbols are given in the panel legends and the main body of the text. The dashed line indicates the solar ratio in all of the panels.
Paper: New Stellar Parameters, Metallicities, and Elemental Abundance Ratios for 311 Metal-poor Stars
Authors: Sanil Mittal, Ian U. Roederer
Figure caption: Differences between our abundances and abundances published by I. U. Roederer et al. (2014). The dashed line indicates the solar ratio in all of the panels. Δ refers to the updated abundance minus previously published abundance. NLTE corrections have been applied to the O, Na, Mg, Si, K, and Fe abundances.
Paper: New Stellar Parameters, Metallicities, and Elemental Abundance Ratios for 311 Metal-poor Stars
Authors: Sanil Mittal, Ian U. Roederer
Figure caption: Elemental abundance ratios as a function of [Fe I/H]. The dashed line indicates the Solar ratio in all of the panels.
Paper: Bioverse: Giant Magellan Telescope and Extremely Large Telescope Direct Imaging and High-resolution Spectroscopy Assessment—Surveying Exo-Earth O2 and Testing the Habitable Zone Oxygen Hypothesis
Authors: Kevin K. Hardegree-Ullman, Dániel Apai, Sebastiaan Y. Haffert, Martin Schlecker, Markus Kasper, Jens Kammerer, Kevin Wagner
Figure caption: The S/N after a 1 hr integration for different planet contrasts and stellar magnitudes. The S/N = 5 detection limit is shown by the dashed gray line.
Paper: LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent Neural Networks in the Korea Microlensing Telescope Network
Authors: Javier Viaña, Kyu-Ha Hwang, Zoë de Beurs, Jennifer C. Yee, Andrew Vanderburg, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge
Figure caption: Illustration of the gravitational microlensing phenomenon used to detect exoplanets. As a foreground lens star passes in front of a more distant background star, the gravitational field of the lens star warps spacetime, magnifying the light from the background star. The diagram shows three stages of this event, where the lens star and its exoplanet's relative positions cause distinct distortions in the background star's light, as depicted by the warped grid lines. The resulting light curve, displayed in the inset, features two peaks: a primary one due to the lens star and a smaller secondary peak caused by the exoplanet.
Paper: LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent Neural Networks in the Korea Microlensing Telescope Network
Authors: Javier Viaña, Kyu-Ha Hwang, Zoë de Beurs, Jennifer C. Yee, Andrew Vanderburg, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge
Figure caption: Demonstration of the process to identify potential microlensing events by analyzing the light curves from the three different telescopes: CTIO, SAAO, and SSO. The point tbreak represents the time at which the algorithm identifies the start of the rising event, while tcut is the moment at which the data was evaluated. The point t0 corresponds to the peak of the microlensing event, assuming it is a genuine event. In this particular case it was the CTIO telescope that triggered the alert, and thus the specific time points tbreak, tcut, and t0 refer to the CTIO's time series data. The “unalerted” data represents the baseline flux before any indication of a microlensing event, while the “alerted” data shows a noticeable increase in flux. The fitted line over the alerted data highlights the trend in flux increase, which is used to detect the alert. The figure captures the progression of the event from its real beginning (tbegin) to its end (tend). This figure also shows how the microlensing pattern can be “seen” in all three telescopes, despite the fact that it is only alerted from the data of one of them.
Paper: LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent Neural Networks in the Korea Microlensing Telescope Network
Authors: Javier Viaña, Kyu-Ha Hwang, Zoë de Beurs, Jennifer C. Yee, Andrew Vanderburg, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge
Figure caption: Schematic of the branched Recurrent Neural Network architecture used to classify microlensing alerts based on time series data from three telescopes: CTIO, SAAO, and SSO. Each telescope's data is processed independently through a dedicated RNN branch that handles a vector of multiple features (relative time, flux, flux error, air mass, FWHM, PSF, and piecewise fitted flux). The figure also illustrates the key preprocessing steps, including time relativization, outlier removal, feature fitting, standardization, and padding, which prepare the data for input into the RNN. Additionally, the unrolled RNN is depicted, showing how hidden states are passed from one-time step to the next, capturing the temporal dependencies within the data. The figure highlights differences in observational data, such as the later start of SAAO observations and data gaps in SSO, which justify the use of separate RNN branches. The encoded outputs from each RNN branch are then combined and passed through dense layers to predict whether the event is a microlensing event. This architecture is designed to handle the varying quality and temporal resolution of data across the three telescopes effectively.
Paper: LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent Neural Networks in the Korea Microlensing Telescope Network
Authors: Javier Viaña, Kyu-Ha Hwang, Zoë de Beurs, Jennifer C. Yee, Andrew Vanderburg, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge
Figure caption: Light curves of a candidate found by AlertFinder on 2021 May 10 UT 06:00 (i.e., tcut = 9344.75) from each category: “Yes” (top), “No” (middle), “Maybe” (bottom). Left panels show the light curves as they would appear at tcut with the fitted broken line (black solid line). The right panels show the full 2021 light curves with smaller markers for data with t > tcut. The colors show data from different observatories: SAAO (data set with the strongest AlertFinder signal; blue), CTIO (red), and SSO (green). The vertical dashed line shows tbreak, and the vertical solid line shows tcut.
Paper: LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent Neural Networks in the Korea Microlensing Telescope Network
Authors: Javier Viaña, Kyu-Ha Hwang, Zoë de Beurs, Jennifer C. Yee, Andrew Vanderburg, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge
Figure caption: Comparison of the current and future pipelines for the KMTNet. The current pipeline, which processes around 5000 alerts per day, involves manual vetting of light curves and difference images by coauthor K.H.H. This process reduces the number of alerts to around 20 per day. The future pipeline, incorporating LensNet and additional work from Z. E. E. de Beurs (2025, in preparation), will process a higher volume of alerts—around 20,000 per day—while reducing the number of manually reviewed alerts.