adsabs/AstroCLIMB huggingface dataset:
Paper: Multibeam Blind Search of Targeted SETI Observations toward 33 Exoplanet Systems with FAST
Authors: Xiao-Hang Luan, Zhen-Zhao Tao, Hai-Chen Zhao, Bo-Lun Huang, Shi-Yu Li, Cong Liu, Hong-Feng Wang, Wen-Fei Liu, Tong-Jie Zhang, Vishal Gajjar, Dan Werthimer
Figure caption: Schematics of the MBCM blind search mode. The first row shows three examples of allowed signals, and the bottom row shows three examples of forbidden signals.
Paper: Multibeam Blind Search of Targeted SETI Observations toward 33 Exoplanet Systems with FAST
Authors: Xiao-Hang Luan, Zhen-Zhao Tao, Hai-Chen Zhao, Bo-Lun Huang, Shi-Yu Li, Cong Liu, Hong-Feng Wang, Wen-Fei Liu, Tong-Jie Zhang, Vishal Gajjar, Dan Werthimer
Figure caption: The distributions of frequency, drift rate, and S/N in YY polarization.
Paper: Measuring the Obliquities of the TRAPPIST-1 Planets with MAROON-X
Authors: Madison Brady, Jacob L. Bean, Andreas Seifahrt, David Kasper, Rafael Luque, Ansgar Reiners, Björn Benneke, Gudmundur Stefánsson, Julian Stürmer
Figure caption: The observed RVs of TRAPPIST-1, stacked and stretched in time and RV amplitude according to the best-fit model to the planets with a shared obliquity and zero α. The time and RV coordinates have been stretched to show what each individual planets’ RM curves would look like if they had the same period and RM amplitude. The planets are each plotted with different colors to highlight which systems were the most and least constraining with respect to the RM fit. The best-fitting model itself is shown as a gray dashed line, with the binned data overplotted in black.
Paper: Multibeam Blind Search of Targeted SETI Observations toward 33 Exoplanet Systems with FAST
Authors: Xiao-Hang Luan, Zhen-Zhao Tao, Hai-Chen Zhao, Bo-Lun Huang, Shi-Yu Li, Cong Liu, Hong-Feng Wang, Wen-Fei Liu, Tong-Jie Zhang, Vishal Gajjar, Dan Werthimer
Figure caption: An event detected at 1404.050 MHz toward HD 180617 (NBS 210421). This figure shows XX polarization of all 19 beams.
Paper: Measuring the Obliquities of the TRAPPIST-1 Planets with MAROON-X
Authors: Madison Brady, Jacob L. Bean, Andreas Seifahrt, David Kasper, Rafael Luque, Ansgar Reiners, Björn Benneke, Gudmundur Stefánsson, Julian Stürmer
Figure caption: A corner plot of ﹩v\,\sin \,i﹩ and λ for our preferred TRAPPIST-1 model (the planets share their obliquity and α = 0). The transit times and linear trends have no visible correlations and are thus not shown.
Paper: Multibeam Blind Search of Targeted SETI Observations toward 33 Exoplanet Systems with FAST
Authors: Xiao-Hang Luan, Zhen-Zhao Tao, Hai-Chen Zhao, Bo-Lun Huang, Shi-Yu Li, Cong Liu, Hong-Feng Wang, Wen-Fei Liu, Tong-Jie Zhang, Vishal Gajjar, Dan Werthimer
Figure caption: Statistics of the signals present only in Beams 4 and 9 of XX and YY. The blue dots represent signals with zero drift rates and the red dots represent signals with non-zero drift rates. The red star represents NBS 210421. In these particular targets, the frequencies of all signals form an evenly spaced regularity, which reveals an unknown instrumental RFI. NBS 210421 and the other four signals of HD 180617 are included in this regularity.
Paper: Multibeam Blind Search of Targeted SETI Observations toward 33 Exoplanet Systems with FAST
Authors: Xiao-Hang Luan, Zhen-Zhao Tao, Hai-Chen Zhao, Bo-Lun Huang, Shi-Yu Li, Cong Liu, Hong-Feng Wang, Wen-Fei Liu, Tong-Jie Zhang, Vishal Gajjar, Dan Werthimer
Figure caption: The other four signals of HD 180617 are highly consistent with the characteristics of NBS 210421. The left column is XX polarization, and the right column is YY polarization. The beam coverage, drift rate, and polarization characteristics are consistent with NBS 210421. These frequencies are different from 1404.050 MHz by multiples of 10.23 MHz.
Paper: Multibeam Blind Search of Targeted SETI Observations toward 33 Exoplanet Systems with FAST
Authors: Xiao-Hang Luan, Zhen-Zhao Tao, Hai-Chen Zhao, Bo-Lun Huang, Shi-Yu Li, Cong Liu, Hong-Feng Wang, Wen-Fei Liu, Tong-Jie Zhang, Vishal Gajjar, Dan Werthimer
Figure caption: Waterfall plots of Beams 4 and 9 for HD 180617 NBS 210421 in XX and YY found by the de-drifting algorithm.
Paper: Comet 108P/Ciffreo: The Blob
Authors: Yoonyoung Kim, David Jewitt, Jane Luu, Jing Li, Max Mutchler
Figure caption: Temporal development of 108P. Each panel shows a region 30″ × 30″ with North to the top and east to the left. The image from February 27 is the composite of B, V and R filter data. All others are R-band images from the NOT except that of February 5, which shows an F350LP image from HST (HST images from February 10 and 13 are nearly identical to that from February 5 and are not separately shown).
Paper: Comet 108P/Ciffreo: The Blob
Authors: Yoonyoung Kim, David Jewitt, Jane Luu, Jing Li, Max Mutchler
Figure caption: (Left) UT 2022 February 5 image spatially filtered to suppress the coma by subtracting the median signal computed within a set of nucleus-centered nested annuli. (Right) Best-fit Monte Carlo model on the same date. The nucleus location is marked with a cross in each panel. Isophotal contours are overlaid. The projected pole (i.e., jet) direction is shown, as well as the projected antisolar (−S) and negative heliocentric velocity (−V) vectors. The model shows the turnaround of particles ejected from 2021 August to December.
Paper: Comet 108P/Ciffreo: The Blob
Authors: Yoonyoung Kim, David Jewitt, Jane Luu, Jing Li, Max Mutchler
Figure caption: HST image showing 108P on UT 2022 February 5 with isophotal contours overlaid. A 104 km scale bar is shown, as well as the projected antisolar (−S) and negative heliocentric velocity (−V) vectors. Numbered structures are (1) the central nucleus, (2) jet, (3) detached blob, and (4) extended envelope.
Paper: Comet 108P/Ciffreo: The Blob
Authors: Yoonyoung Kim, David Jewitt, Jane Luu, Jing Li, Max Mutchler
Figure caption: Plane-of-sky separation between the nucleus and the blob as a function of the date of observation, expressed as Day of Year (DOY = 1 on UT 2022 January 1). The solid line shows the time dependence of the separation computed from our model.
Paper: Comet 108P/Ciffreo: The Blob
Authors: Yoonyoung Kim, David Jewitt, Jane Luu, Jing Li, Max Mutchler
Figure caption: (Top) Composite of four 150 s exposures through the R filter taken on UT 2022 January 31 at the NOT. (Bottom) The same composite convolved with a 1.″5 FWHM Gaussian and contoured to show the tail of 108P. Antisolar (−S) and negative heliocentric velocity vectors (−V) are shown, together with the cardinal directions and a scale bar. The filled black circle marks the location of the nucleus.
Paper: Comet 108P/Ciffreo: The Blob
Authors: Yoonyoung Kim, David Jewitt, Jane Luu, Jing Li, Max Mutchler
Figure caption: (Left) Syndynes for the UT 2022 February 5 image, showing the paths of particles with β = 0.1, 0.03, 0.01, 0.003, 0.001, and 0.0003 and (right) synchrones computed for ejection 15, 30, 45, 60, 75, and 90 days prior to the date of observation.
Paper: Comet 108P/Ciffreo: The Blob
Authors: Yoonyoung Kim, David Jewitt, Jane Luu, Jing Li, Max Mutchler
Figure caption: Subsolar latitude (red line) of 108P as a function of time, together with the heliocentric distance (black line) on the right axis. We assumed a pole orientation of α pol = 200° and δ pol = 0° with an obliquity of 90°. Vertical lines indicate the dates of the northern solstice, inferred nucleus–blob separation L = 0, and first detection of the blob.
Paper: Comet 108P/Ciffreo: The Blob
Authors: Yoonyoung Kim, David Jewitt, Jane Luu, Jing Li, Max Mutchler
Figure caption: Archival ZTF images of 108P taken on UT 2021 November–December, showing the emergence of the blob. A 10″ (7100 km) scale bar is shown, as well as the projected antisolar (−S) and heliocentric velocity (V) vectors.
Paper: Identifying Active Galactic Nuclei at z ∼ 3 from the HETDEX Survey Using Machine Learning
Authors: Valentina Tardugno Poleo, Steven L. Finkelstein, Gene Leung, Erin Mentuch Cooper, Karl Gebhardt, Daniel J. Farrow, Eric Gawiser, Greg Zeimann, Donald P. Schneider, Leah Morabito, Daniel Mock, Chenxu Liu
Figure caption: Schematic of an autoencoder neural network. The latent space contains a lower-dimensional representation of the network’s inputs. We implemented an autoencoder to reduce the dimensionality of the spectra of our photoz sample, resulting in a more manageable data set that was later projected onto two-dimensions through t-SNE and separated into clusters. This approach allowed for the removal of contaminants from our sample and a measurement of AGN fractions.
Paper: Identifying Active Galactic Nuclei at z ∼ 3 from the HETDEX Survey Using Machine Learning
Authors: Valentina Tardugno Poleo, Steven L. Finkelstein, Gene Leung, Erin Mentuch Cooper, Karl Gebhardt, Daniel J. Farrow, Eric Gawiser, Greg Zeimann, Donald P. Schneider, Leah Morabito, Daniel Mock, Chenxu Liu
Figure caption: Training and validation loss curves. During training, we sought to minimize the training error while avoiding overfitting. Hence, the training stopped after 200 epochs on a decreasing training error and a plateauing validation error.
Paper: Identifying Active Galactic Nuclei at z ∼ 3 from the HETDEX Survey Using Machine Learning
Authors: Valentina Tardugno Poleo, Steven L. Finkelstein, Gene Leung, Erin Mentuch Cooper, Karl Gebhardt, Daniel J. Farrow, Eric Gawiser, Greg Zeimann, Donald P. Schneider, Leah Morabito, Daniel Mock, Chenxu Liu
Figure caption: t-SNE plot of the combined training (colored circles) and photoz (black triangles) samples. t-SNE allows for a visualization of our samples in a two-dimensional plane, revealing where each spectrum lies in relationship to the others. Astronomical objects form clusters with others of their kind, a feature that allowed us to employ a clustering algorithm to label our photoz sample. Most of the spectra from the photoz sample appeared to be concentrated around the high-redshift galaxies and AGN from the training sample, which was to be expected based on our selection criteria.
Paper: Identifying Active Galactic Nuclei at z ∼ 3 from the HETDEX Survey Using Machine Learning
Authors: Valentina Tardugno Poleo, Steven L. Finkelstein, Gene Leung, Erin Mentuch Cooper, Karl Gebhardt, Daniel J. Farrow, Eric Gawiser, Greg Zeimann, Donald P. Schneider, Leah Morabito, Daniel Mock, Chenxu Liu
Figure caption: Example spectra for an AGN (upper left), a star (upper right), a low redshift galaxy (bottom left), and a high-redshift galaxy (bottom right). The blue spectra represent normalized data extracted from the HETDEX survey. The orange spectra represent reconstructed spectra obtained from running the thirty-dimensional encoding through the decoder network. Comparing the HETDEX spectra to their corresponding reconstructed spectra allows for a visualization of how well the encoding preserves information. While the key features of the AGN and stars appeared to be preserved, narrow emission lines from galaxies were not reconstructed adequately. This made the separation between low and high-redshift galaxies less prominent (see Section 6).
Paper: Identifying Active Galactic Nuclei at z ∼ 3 from the HETDEX Survey Using Machine Learning
Authors: Valentina Tardugno Poleo, Steven L. Finkelstein, Gene Leung, Erin Mentuch Cooper, Karl Gebhardt, Daniel J. Farrow, Eric Gawiser, Greg Zeimann, Donald P. Schneider, Leah Morabito, Daniel Mock, Chenxu Liu
Figure caption: Clusters identified through Gaussian mixture models on the t-SNE of the combined training and photoz samples. The four pairs of ellipses represent two standard deviations away from the mean of each cluster, as estimated by the sklearn.mixture.GaussianMixture class. The clustering algorithm appears to adequately capture the clusters formed by each group of astronomical objects. Clustering allowed us to classify the photoz spectra and therefore remove contaminants and find an AGN fraction.
Paper: Identifying Active Galactic Nuclei at z ∼ 3 from the HETDEX Survey Using Machine Learning
Authors: Valentina Tardugno Poleo, Steven L. Finkelstein, Gene Leung, Erin Mentuch Cooper, Karl Gebhardt, Daniel J. Farrow, Eric Gawiser, Greg Zeimann, Donald P. Schneider, Leah Morabito, Daniel Mock, Chenxu Liu
Figure caption: AGN fraction as a function of r-band absolute magnitude and an interpolated curve. The two brightest bins have an AGN fraction of 0.8 ± 0.1. The AGN fraction drops to 0.37 ± 0.03 at a magnitude of −23.5, and approaches zero at the faintest bins. Our calculated AGN fractions imply a shallower faint-end slope for the AGN luminosity function at redshift three.
Paper: The PEPSI Exoplanet Transit Survey (PETS). II. A Deep Search for Thermal Inversion Agents in KELT-20 b/MASCARA-2 b with Emission and Transmission Spectroscopy 
Authors: Marshall C. Johnson, Ji Wang, 王 吉, Anusha Pai Asnodkar, Aldo S. Bonomo, B. Scott Gaudi, Thomas Henning, Ilya Ilyin, Engin Keles, Luca Malavolta, Matthias Mallonn, Karan Molaverdikhani, Valerio Nascimbeni, Jennifer Patience, Katja Poppenhaeger, Gaetano Scandariato, Everett Schlawin, Evgenya Shkolnik, Daniela Sicilia, Alessandro Sozzetti, Klaus G. Strassmeier, Christian Veillet, Fei Yan
Figure caption: Graphical summary of our limits on the abundances of inversion agents. For each species, the left-pointing arrow shows our upper limit set upon the VMR, while the error bar shows the range in altitudes which contribute 68% of the emission or absorption from that species within the PEPSI bandpass at the VMR limit. The filled point for each species is from the emission data, and the empty points from the transmission data. The vertical lines show the VMRs expected from a chemical equilibrium calculation at 1 bar, assuming that this is the quenching pressure and that the VMR is constant above this point. Solid lines assume solar metallicity, and dotted lines 10× solar. The colors of the lines correspond to those of the data points for each species. If the data point is to the left of the colored bar, the observation is in conflict with the expectation.
Paper: The PEPSI Exoplanet Transit Survey (PETS). II. A Deep Search for Thermal Inversion Agents in KELT-20 b/MASCARA-2 b with Emission and Transmission Spectroscopy 
Authors: Marshall C. Johnson, Ji Wang, 王 吉, Anusha Pai Asnodkar, Aldo S. Bonomo, B. Scott Gaudi, Thomas Henning, Ilya Ilyin, Engin Keles, Luca Malavolta, Matthias Mallonn, Karan Molaverdikhani, Valerio Nascimbeni, Jennifer Patience, Katja Poppenhaeger, Gaetano Scandariato, Everett Schlawin, Evgenya Shkolnik, Daniela Sicilia, Alessandro Sozzetti, Klaus G. Strassmeier, Christian Veillet, Fei Yan
Figure caption: Results of the tests of the systematics correction methods. Lines of the same color show the same method and PEPSI arm: blue arm SYSREM (blue), red arm SYSREM (red), red arm molecfit (dark red), and red arm molecfit + SYSREM (orange). Circles and stars show the 2021 May 1 data set, squares and diamonds the 2021 May 18 data set, and triangles and Xs the 2019 May 1 data set; the latter data set is the transmission data. The x-axis shows the number of systematics that have been removed with SYSREM. The plots show the results for TiO (upper left), VO (upper right), FeH (lower left), and CaH (lower right). Injections resulting in peaks below 4σ (the horizontal dashed line) are not considered to be recovered but are shown for completeness. The arm/method combination resulting in the highest S/N for each data set is highlighted with a black circle. In all cases for the red arm using only molecfit outperforms using SYSREM only, and in many cases using both offers further improvements.
Paper: The PEPSI Exoplanet Transit Survey (PETS). II. A Deep Search for Thermal Inversion Agents in KELT-20 b/MASCARA-2 b with Emission and Transmission Spectroscopy 
Authors: Marshall C. Johnson, Ji Wang, 王 吉, Anusha Pai Asnodkar, Aldo S. Bonomo, B. Scott Gaudi, Thomas Henning, Ilya Ilyin, Engin Keles, Luca Malavolta, Matthias Mallonn, Karan Molaverdikhani, Valerio Nascimbeni, Jennifer Patience, Katja Poppenhaeger, Gaetano Scandariato, Everett Schlawin, Evgenya Shkolnik, Daniela Sicilia, Alessandro Sozzetti, Klaus G. Strassmeier, Christian Veillet, Fei Yan
Figure caption: Shifted and combined CCFs for TiO, in emission (left panels) and transmission (right panels). The top panels show the data, and the bottom panels the data with a signal from the minimum VMR that can be recovered at >4σ injected.
Paper: The PEPSI Exoplanet Transit Survey (PETS). II. A Deep Search for Thermal Inversion Agents in KELT-20 b/MASCARA-2 b with Emission and Transmission Spectroscopy 
Authors: Marshall C. Johnson, Ji Wang, 王 吉, Anusha Pai Asnodkar, Aldo S. Bonomo, B. Scott Gaudi, Thomas Henning, Ilya Ilyin, Engin Keles, Luca Malavolta, Matthias Mallonn, Karan Molaverdikhani, Valerio Nascimbeni, Jennifer Patience, Katja Poppenhaeger, Gaetano Scandariato, Everett Schlawin, Evgenya Shkolnik, Daniela Sicilia, Alessandro Sozzetti, Klaus G. Strassmeier, Christian Veillet, Fei Yan
Figure caption: Shifted and combined CCFs for emission from Fe I . We detect the Fe I emission at a significance of 16.9σ, reproducing the results of Yan et al. (2022b), Borsa et al. (2022), and Kasper et al. (2022).
Paper: The PEPSI Exoplanet Transit Survey (PETS). II. A Deep Search for Thermal Inversion Agents in KELT-20 b/MASCARA-2 b with Emission and Transmission Spectroscopy 
Authors: Marshall C. Johnson, Ji Wang, 王 吉, Anusha Pai Asnodkar, Aldo S. Bonomo, B. Scott Gaudi, Thomas Henning, Ilya Ilyin, Engin Keles, Luca Malavolta, Matthias Mallonn, Karan Molaverdikhani, Valerio Nascimbeni, Jennifer Patience, Katja Poppenhaeger, Gaetano Scandariato, Everett Schlawin, Evgenya Shkolnik, Daniela Sicilia, Alessandro Sozzetti, Klaus G. Strassmeier, Christian Veillet, Fei Yan
Figure caption: Same as Figure 8 but for VO. We show the results for both injecting and recovering the ExoMol McKemmish et al. (2016) line list.
Paper: The PEPSI Exoplanet Transit Survey (PETS). II. A Deep Search for Thermal Inversion Agents in KELT-20 b/MASCARA-2 b with Emission and Transmission Spectroscopy 
Authors: Marshall C. Johnson, Ji Wang, 王 吉, Anusha Pai Asnodkar, Aldo S. Bonomo, B. Scott Gaudi, Thomas Henning, Ilya Ilyin, Engin Keles, Luca Malavolta, Matthias Mallonn, Karan Molaverdikhani, Valerio Nascimbeni, Jennifer Patience, Katja Poppenhaeger, Gaetano Scandariato, Everett Schlawin, Evgenya Shkolnik, Daniela Sicilia, Alessandro Sozzetti, Klaus G. Strassmeier, Christian Veillet, Fei Yan
Figure caption: Same as Figure 8 but for CaH.
Paper: The PEPSI Exoplanet Transit Survey (PETS). II. A Deep Search for Thermal Inversion Agents in KELT-20 b/MASCARA-2 b with Emission and Transmission Spectroscopy 
Authors: Marshall C. Johnson, Ji Wang, 王 吉, Anusha Pai Asnodkar, Aldo S. Bonomo, B. Scott Gaudi, Thomas Henning, Ilya Ilyin, Engin Keles, Luca Malavolta, Matthias Mallonn, Karan Molaverdikhani, Valerio Nascimbeni, Jennifer Patience, Katja Poppenhaeger, Gaetano Scandariato, Everett Schlawin, Evgenya Shkolnik, Daniela Sicilia, Alessandro Sozzetti, Klaus G. Strassmeier, Christian Veillet, Fei Yan
Figure caption: Same as Figure 8 but for FeH.
Paper: Exploring the Recycling Model of Phobos Formation: Rubble-pile Satellites 
Authors: Gustavo Madeira, Sébastien Charnoz, Yun Zhang, Ryuki Hyodo, Patrick Michel, Hidenori Genda, Silvia Giuliatti Winter
Figure caption: Snapshots of tidal disruption processes for two structures with different cohesion distributions. Time proceeds from left to right in the three frames separated by the vertical dashed lines. The time t, orbital elements (a,  e, and  f), and corresponding orbital distance are indicated by the white text on the top of each frame. The style of the box boundary indicates the initial condition (white solid), the homogeneous cohesion case (blue dotted), and the heterogeneous cohesion case (green solid). The line of sight is in a direction perpendicular to the orbital plane, and the light rays are ejected from Mars (e.g., in the direction from left to right) to illustrate Mars’ direction.
Paper: Exploring the Recycling Model of Phobos Formation: Rubble-pile Satellites 
Authors: Gustavo Madeira, Sébastien Charnoz, Yun Zhang, Ryuki Hyodo, Patrick Michel, Hidenori Genda, Silvia Giuliatti Winter
Figure caption: Disruption location, in Mars radius (R M), of a rubble-pile satellite as a function of its mass/equivalent spherical radius. The red, black, and blue lines correspond to the cases with friction angle ϕ = 25°, 40°, and 80°, respectively. The dotted–dashed vertical line shows the mass and radius of Phobos.
Paper: Exploring the Recycling Model of Phobos Formation: Rubble-pile Satellites 
Authors: Gustavo Madeira, Sébastien Charnoz, Yun Zhang, Ryuki Hyodo, Patrick Michel, Hidenori Genda, Silvia Giuliatti Winter
Figure caption: Temporal evolution of the (a) semimajor axis and (b) physical radius of Phobos (k 2/Q = 10−6) under tidal effects. The evolution of the satellite before reaching the RRL (dotted horizontal line) is given by the solid black line, with the black dot showing the instant Phobos would be destroyed. The dashed–dotted line shows what the evolution of the satellite would have been if it had not been torn apart by tidal forces. A hypothetical case in which Phobos shrinks due to tidal effects is shown by the solid red line.
Paper: Exploring the Recycling Model of Phobos Formation: Rubble-pile Satellites 
Authors: Gustavo Madeira, Sébastien Charnoz, Yun Zhang, Ryuki Hyodo, Patrick Michel, Hidenori Genda, Silvia Giuliatti Winter
Figure caption: Evolution of disk surface density and satellite mass for the fifth and sixth cycles of our standard model. The disk is shown as a solid blue line with surface density given on the left scale, and satellites are given by black dots with mass on the right scale. The vertical dashed lines show the locations of the RRL and FRL. Figure 4 also shows this evolution. A copy can be obtained at https://www.ipgp.fr/en/directory/madeira.
Paper: Exploring the Recycling Model of Phobos Formation: Rubble-pile Satellites 
Authors: Gustavo Madeira, Sébastien Charnoz, Yun Zhang, Ryuki Hyodo, Patrick Michel, Hidenori Genda, Silvia Giuliatti Winter
Figure caption: Semimajor axis of satellites as a function of time for the same simulation as in Figure 4. Each dot stands for a satellite obtained in the simulation but at different times. The color represents the satellite’s mass. Satellites with masses similar to Phobos are in green. The horizontal dotted lines provide the location of the RRL, FRL, and 2:1 ILR with FRL. The vertical dotted lines delimit the beginning and end of the cycles.