adsabs/AstroCLIMB huggingface dataset:
Paper: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
Authors: Charlotte Fairman, Hannah R. Wakeford, Ryan J. MacDonald
Figure caption: Median retrieved spectra for the HST and Spitzer observations of HD 209458b (left) and WASP-39b (right) for a lower wavelength data cutoff of ﹩{\lambda }_{\min }﹩. Shown are ﹩{\lambda }_{\min }﹩ values from 0.3 μm (dark blue) to 1.1 μm (dark red) in intervals of 0.1 μm. The observed, multi-instrument transmission spectrum is overlaid (black diamonds). The retrieved spectrum is plotted at R = 100 for clarity.
Paper: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
Authors: Charlotte Fairman, Hannah R. Wakeford, Ryan J. MacDonald
Figure caption: Retrieved median model parameters and 1σ errors for HD 209458b (top) and WASP-39b (bottom) from observations spanning a wavelength range ﹩{\lambda }_{\min }﹩ to 4.5 μm. ﹩{\lambda }_{\min }﹩ takes values from 0.3 (dark blue) to 1.1 μm (dark red) in intervals of 0.1 μm.
Paper: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
Authors: Charlotte Fairman, Hannah R. Wakeford, Ryan J. MacDonald
Figure caption: Retrieved median transmission spectra for the population (continued). See Figure 3 for the caption.
Paper: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
Authors: Charlotte Fairman, Hannah R. Wakeford, Ryan J. MacDonald
Figure caption: Median retrieved parameters and 1σ errors across the population. Only parameters from the base retrieval are displayed (HAT-P-11b, WASP-12b, and WASP-121b contain additional parameters in their retrievals). The retrieval setup of each planet can be found in Table 3. Results for each wavelength range, ﹩{\lambda }_{\min }﹩ = 0.3 μm, ﹩{\lambda }_{\min }﹩ = 0.6 μm, and ﹩{\lambda }_{\min }﹩ = 1.1 μm, are displayed in purple, blue, and orange, respectively. Planets are displayed in order of their retrieved Rayleigh enhancement factor, ﹩\mathrm{log}a﹩, with the planet with the lowest value of ﹩\mathrm{log}a﹩ at the top.
Paper: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
Authors: Charlotte Fairman, Hannah R. Wakeford, Ryan J. MacDonald
Figure caption: Retrieved transmission spectrum for HAT-P-11b. Retrieved median spectra for each wavelength range, ﹩{\lambda }_{\min }﹩ = 0.3 μm, ﹩{\lambda }_{\min }﹩ = 0.6 μm, and ﹩{\lambda }_{\min }﹩ = 1.1 μm, are displayed in purple, blue, and orange, respectively. Light and dark shaded regions correspond to the 1 and 2σ bounds. The data points show the observed spectrum, where the marker colors correspond to data above the minimum wavelength of the three retrievals, following the same color scheme as the retrieved spectra. The y-axis scale is expressed in atmospheric scale heights, H = k  B  T/μ g, where T is taken as the retrieved temperature from the ﹩{\lambda }_{\min }﹩ = 0.3 μm retrieval.
Paper: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
Authors: Charlotte Fairman, Hannah R. Wakeford, Ryan J. MacDonald
Figure caption: IC (measured in nats) broken down by parameter group for ﹩{\lambda }_{\min }﹩ = 0.6–0.3 μm. Top: breakdown of parameters by R  p,ref, T, alkali species, H2O, carbon species, cloud parameters, high-temperature species (TiO/VO), and stellar parameters. Bottom: breakdown of IC change per cloud parameter. Planets are ordered from highest to lowest (left to right) retrieved Rayleigh enhancement factor, ﹩\mathrm{log}a﹩.
Paper: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
Authors: Charlotte Fairman, Hannah R. Wakeford, Ryan J. MacDonald
Figure caption: IC (measured in nats) broken down by parameter group for ﹩{\lambda }_{\min }﹩ = 1.1–0.3 μm. Top: breakdown of parameters by R  P,ref, T, alkali species, H2O, carbon species, cloud parameters, high-temperature species (TiO/VO), and stellar parameters. Bottom: breakdown of IC change per cloud parameter. Planets are ordered from highest to lowest (left to right) retrieved Rayleigh enhancement factor, ﹩\mathrm{log}a﹩.
Paper: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
Authors: Charlotte Fairman, Hannah R. Wakeford, Ryan J. MacDonald
Figure caption: Retrieved scattering slope, γ, against T eq. The purple and gray lines show the slopes for soot and tholin aerosols, respectively, from Ohno & Kawashima (2020) for two different aerosol mass fluxes (F). The models do not extend beyond 2250 K and we find no clear trend to any single aerosol profile across the full population of our study.
Paper: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
Authors: Charlotte Fairman, Hannah R. Wakeford, Ryan J. MacDonald
Figure caption: IC (measured in nats) between the posterior and prior distributions for ﹩{\lambda }_{\min }﹩ = 0.3, 0.6, and 1.1 μm retrievals. Left panel: change in IC with the number of data points. Right panel: change in IC with data wavelength cutoff. Planets are ordered in terms of their retrieved Rayleigh enhancement factor, ﹩\mathrm{log}a﹩, with the planet with the highest value of ﹩\mathrm{log}a﹩ (HD 189733b) in yellow and the lowest value of ﹩\mathrm{log}a﹩ (WASP-17b) in purple.
Paper: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
Authors: Charlotte Fairman, Hannah R. Wakeford, Ryan J. MacDonald
Figure caption: Retrieved cloud fraction, ﹩{\bar{\phi }}_{\mathrm{clouds}}﹩, trends with retrieved water abundance (left) and planetary equilibrium temperature (right). Dashed and dotted lines represent the solar abundance values for temperatures < 1200 K and > 1200 K calculated by Madhusudhan (2012), respectively. The planets are colored by equilibrium temperature from the hottest (WASP-12b) in yellow to the coldest (HAT-P-11b) in purple.
Paper: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
Authors: Charlotte Fairman, Hannah R. Wakeford, Ryan J. MacDonald
Figure caption: Retrieved limb temperatures plotted against equilibrium temperatures (purple). Plotted in green are GCM limb average temperatures for 11 planets from Kataria et al. (2016). The gray dashed line marks where limb temperature is equal to planetary equilibrium temperature. Linear fits and 1σ errors are found for the retrieved limb temperatures and GCM limb temperatures, shown by the purple and green lines and shading, respectively.
Paper: Identification of the Top TESS Objects of Interest for Atmospheric Characterization of Transiting Exoplanets with JWST
Authors: Benjamin J. Hord, Eliza M.-R. Kempton, Thomas M. Evans-Soma, David W. Latham, David R. Ciardi, Diana Dragomir, Knicole D. Colón, Gabrielle Ross, Andrew Vanderburg, Zoe L. de Beurs, Karen A. Collins, Cristilyn N. Watkins, Jacob Bean, Nicolas B. Cowan, Tansu Daylan, Caroline V. Morley, Jegug Ih, David Baker, Khalid Barkaoui, Natalie M. Batalha, Aida Behmard, Alexander Belinski, Zouhair Benkhaldoun, Paul Benni, Krzysztof Bernacki, Allyson Bieryla, Avraham Binnenfeld, Pau Bosch-Cabot, François Bouchy, Valerio Bozza, Rafael Brahm, Lars A. Buchhave, Michael Calkins, Ashley Chontos, Catherine A. Clark, Ryan Cloutier, Marion Cointepas, Kevin I. Collins, Dennis M. Conti, Ian J. M. Crossfield, Fei Dai, Jerome P. de Leon, Georgina Dransfield, Courtney Dressing, Adam Dustor, Gilbert Esquerdo, Phil Evans, Sergio B. Fajardo-Acosta, Jerzy Fiołka, Raquel Forés-Toribio, Antonio Frasca, Akihiko Fukui, Benjamin Fulton, Elise Furlan, Tianjun Gan, Davide Gandolfi, Mourad Ghachoui, Steven Giacalone, Emily A. Gilbert, Michaël Gillon, Eric Girardin, Erica Gonzales, Ferran Grau Horta, Joao Gregorio, Michael Greklek-McKeon, Pere Guerra, J. D. Hartman, Coel Hellier, Ian Helm, Krzysztof G. Hełminiak, Thomas Henning, Michelle L. Hill, Keith Horne, Andrew W. Howard, Steve B. Howell, Daniel Huber, Giovanni Isopi, Emmanuel Jehin, Jon M. Jenkins, Eric L. N. Jensen, Marshall C. Johnson, Andrés Jordán, Stephen R. Kane, John F. Kielkopf, Vadim Krushinsky, Sławomir Lasota, Elena Lee, Pablo Lewin, John H. Livingston, Jack Lubin, Michael B. Lund, Franco Mallia, Christopher R. Mann, Giuseppi Marino, Nataliia Maslennikova, Bob Massey, Rachel Matson, Elisabeth Matthews, Andrew W. Mayo, Tsevi Mazeh, Kim K. McLeod, Edward J. Michaels, Teo Močnik, Mayuko Mori, Georgia Mraz, Jose A. Muñoz, Norio Narita, Krupa Natarajan, Louise Dyregaard Nielsen, Hugh Osborn, Enric Palle, Aviad Panahi, Riccardo Papini, Peter Plavchan, Alex S. Polanski, Adam Popowicz, Francisco J. Pozuelos, Samuel N. Quinn, Don J. Radford, Phillip A. Reed, Howard M. Relles, Malena Rice, Paul Robertson, Joseph E. Rodriguez, Lee J. Rosenthal, Ryan A. Rubenzahl, Nicole Schanche, Joshua Schlieder, Richard P. Schwarz, Ramotholo Sefako, Avi Shporer, Alessandro Sozzetti, Gregor Srdoc, Chris Stockdale, Alexander Tarasenkov, Thiam-Guan Tan, Mathilde Timmermans, Eric B. Ting, Judah Van Zandt, JP Vignes, Ian Waite, Noriharu Watanabe, Lauren M. Weiss, Justin Wittrock, George Zhou, Carl Ziegler, Shay Zucker
Figure caption: A schematic outline of our analysis procedure. From the initial query of the Exoplanet Archive and generation of the best-in-class sample, each target went through every step of the procedure to check for factors that could indicate a false positive to arrive at a final disposition. Not every vetting step applied to every target due to lack of follow-up, so each vetting step was applied when possible but skipped when not. The sections where each step is discussed more in-depth are listed in parentheses.
Paper: Identification of the Top TESS Objects of Interest for Atmospheric Characterization of Transiting Exoplanets with JWST
Authors: Benjamin J. Hord, Eliza M.-R. Kempton, Thomas M. Evans-Soma, David W. Latham, David R. Ciardi, Diana Dragomir, Knicole D. Colón, Gabrielle Ross, Andrew Vanderburg, Zoe L. de Beurs, Karen A. Collins, Cristilyn N. Watkins, Jacob Bean, Nicolas B. Cowan, Tansu Daylan, Caroline V. Morley, Jegug Ih, David Baker, Khalid Barkaoui, Natalie M. Batalha, Aida Behmard, Alexander Belinski, Zouhair Benkhaldoun, Paul Benni, Krzysztof Bernacki, Allyson Bieryla, Avraham Binnenfeld, Pau Bosch-Cabot, François Bouchy, Valerio Bozza, Rafael Brahm, Lars A. Buchhave, Michael Calkins, Ashley Chontos, Catherine A. Clark, Ryan Cloutier, Marion Cointepas, Kevin I. Collins, Dennis M. Conti, Ian J. M. Crossfield, Fei Dai, Jerome P. de Leon, Georgina Dransfield, Courtney Dressing, Adam Dustor, Gilbert Esquerdo, Phil Evans, Sergio B. Fajardo-Acosta, Jerzy Fiołka, Raquel Forés-Toribio, Antonio Frasca, Akihiko Fukui, Benjamin Fulton, Elise Furlan, Tianjun Gan, Davide Gandolfi, Mourad Ghachoui, Steven Giacalone, Emily A. Gilbert, Michaël Gillon, Eric Girardin, Erica Gonzales, Ferran Grau Horta, Joao Gregorio, Michael Greklek-McKeon, Pere Guerra, J. D. Hartman, Coel Hellier, Ian Helm, Krzysztof G. Hełminiak, Thomas Henning, Michelle L. Hill, Keith Horne, Andrew W. Howard, Steve B. Howell, Daniel Huber, Giovanni Isopi, Emmanuel Jehin, Jon M. Jenkins, Eric L. N. Jensen, Marshall C. Johnson, Andrés Jordán, Stephen R. Kane, John F. Kielkopf, Vadim Krushinsky, Sławomir Lasota, Elena Lee, Pablo Lewin, John H. Livingston, Jack Lubin, Michael B. Lund, Franco Mallia, Christopher R. Mann, Giuseppi Marino, Nataliia Maslennikova, Bob Massey, Rachel Matson, Elisabeth Matthews, Andrew W. Mayo, Tsevi Mazeh, Kim K. McLeod, Edward J. Michaels, Teo Močnik, Mayuko Mori, Georgia Mraz, Jose A. Muñoz, Norio Narita, Krupa Natarajan, Louise Dyregaard Nielsen, Hugh Osborn, Enric Palle, Aviad Panahi, Riccardo Papini, Peter Plavchan, Alex S. Polanski, Adam Popowicz, Francisco J. Pozuelos, Samuel N. Quinn, Don J. Radford, Phillip A. Reed, Howard M. Relles, Malena Rice, Paul Robertson, Joseph E. Rodriguez, Lee J. Rosenthal, Ryan A. Rubenzahl, Nicole Schanche, Joshua Schlieder, Richard P. Schwarz, Ramotholo Sefako, Avi Shporer, Alessandro Sozzetti, Gregor Srdoc, Chris Stockdale, Alexander Tarasenkov, Thiam-Guan Tan, Mathilde Timmermans, Eric B. Ting, Judah Van Zandt, JP Vignes, Ian Waite, Noriharu Watanabe, Lauren M. Weiss, Justin Wittrock, George Zhou, Carl Ziegler, Shay Zucker
Figure caption: All of the JWST exoplanet targets that are approved for transmission or emission spectroscopy observations in Cycles 1 and 2 across planetary equilibrium temperature and radius. Yellow stars represent approved JWST targets that were discovered by TESS while those represented by blue circles represent planets not discovered by TESS. As evidenced by the plot, TESS-discovered planets constitute a large proportion of approved JWST Cycle 1 and 2 targets for transmission or emission spectroscopy and cover a wide range of parameter space.
Paper: Ages of “Singles” versus “Multis”: Predictions for Dynamical Sculpting over Gyr in the Kepler Sample
Authors: Christopher Lam, Sarah Ballard
Figure caption: Constraints on sculpting parameters Θ resulting from four injection–recovery tests: fast (steep sculpting slope r), moderate, slow (shallow sculpting slope r), and no sculpting. The top row depicts the injected ground-truth sculpting law in red, the recovered best-fit model in black, and all models with ﹩{\rm{\Delta }}{\rm{log}}({ \mathcal L })\lt 20﹩ of the best-fit model in blue. We note that, in all injection–recovery tests, we recover a relatively tight range of present-day intact fractions, within 6% of the ground truth. The bottom two rows depict two 2D slices of Θ parameter space from which the sculpting laws are constructed: the sculpting slope r versus the intact fraction p8 at 108 yr, and the sculpting slope r versus the sculpting cutoff timescale c. Pairs contributing to a “favored” Θ are shaded in light blue, while darker blue regions correspond to pairs that do not appear in any “favored” Θ. We mark the ground-truth Θtrue's location in these 2D slices with a red star, corresponding to the red sculpting law in the top row.
Paper: Ages of “Singles” versus “Multis”: Predictions for Dynamical Sculpting over Gyr in the Kepler Sample
Authors: Christopher Lam, Sarah Ballard
Figure caption: We split the Kepler planet-host sample into young and old populations along different thresholds (x-axis). The y-axis shows the multi-to-non-multi ratio of each “young” or “old” population, where multis are systems with three or more planets and non-multis are systems with one or two planets. If there is evidence for sculpting, we would expect the older population to have a lower ratio after a certain young–old threshold. We construct 95th percentile confidence envelopes by bootstrapping 100 times. We observe no significant difference in the multi-to-non-multi ratio between young and old systems, and we find that this lack of age dependence does not change when we disaggregate by stellar metallicity.
Paper: Ages of “Singles” versus “Multis”: Predictions for Dynamical Sculpting over Gyr in the Kepler Sample
Authors: Christopher Lam, Sarah Ballard
Figure caption: Top: We show sculpting laws with log(﹩{ \mathcal L })﹩ ≥ −34.5, or the best log(﹩{ \mathcal L })﹩ minus 20. These models represent <3% of the models we run through the forward-modeling pipeline. Bottom: Here, we show the transit multiplicity yields for these favored models. The blue envelope illustrates the full range of these favored model yields for each transit multiplicity bin, and the red stars represent the Kepler transit multiplicity for FGK dwarfs, using the koi_disposition planet vetting criterion.
Paper: Ages of “Singles” versus “Multis”: Predictions for Dynamical Sculpting over Gyr in the Kepler Sample
Authors: Christopher Lam, Sarah Ballard
Figure caption: Eccentricity and mutual inclination distributions of simulated planetary systems based on one of our highest likelihood models. Each dot is a system. The contours represent the 1σ and 2σ populations for our simulated multis (R ∼ Rayleigh(0.06), in blue) and singles (R ∼ Rayleigh(0.24), in red), as prescribed by Van Eylen et al. (2019). The distribution of points overlaps with the hotter end of the AMD-stable model from He et al. (2020).
Paper: Ages of “Singles” versus “Multis”: Predictions for Dynamical Sculpting over Gyr in the Kepler Sample
Authors: Christopher Lam, Sarah Ballard
Figure caption: Observed Kepler transit multiplicities using different planet false-positive indicators. For “koi_disposition,” we keep planets marked as “CONFIRMED” or “CANDIDATE” in the koi_disposition column from the KOI Cumulative List Table on the Exoplanet Archive. For “koi_score,” we keep planets with a score of greater than 0.5 in the koi_score column in the KOI Cumulative List Table on the Exoplanet Archive. Finally, for “fpp_prob,” we keep planets with a score of less than 0.5 in the “Probability to be Any of the Considered Astrophysical False Positive Scenarios” column in the Kepler False Positive Probabilities Table on the Exoplanet Archive.
Paper: Ages of “Singles” versus “Multis”: Predictions for Dynamical Sculpting over Gyr in the Kepler Sample
Authors: Christopher Lam, Sarah Ballard
Figure caption: We run the same experiment as in Figure 3, comparing the multi-to-non-multi ratio of different “young” and “old” subsamples, but now disaggregating by stellar type. We find that disaggregating by stellar type does not change the full-sample picture of a lack of difference in the multi-to-non-multi ratio between young and old systems. Since K dwarfs comprise only a small part of our sample, it is subject to shot noise.
Paper: Ages of “Singles” versus “Multis”: Predictions for Dynamical Sculpting over Gyr in the Kepler Sample
Authors: Christopher Lam, Sarah Ballard
Figure caption: Distributions of stellar effective temperature (top) and stellar age (bottom) after all cuts to the sample are made. Vertical, dotted, red lines indicate the medians (5903 K and 5.17 Gyr, respectively).
Paper: Ages of “Singles” versus “Multis”: Predictions for Dynamical Sculpting over Gyr in the Kepler Sample
Authors: Christopher Lam, Sarah Ballard
Figure caption: Top: We compare the age–multiplicity relations of two favored models to that of Chen et al. (2021). The two models were chosen such that one has the greatest change in P intact over the relevant timescale (pink; “most sculpting”) and the other has the least change in P intact over the same timescale (blue; “least sculpting”). The boxes contain the middle 50% age uncertainties; the whiskers denote the 5th and 95th age percentiles. Using our sample, we find no significant change in multiplicity with age. Bottom: We conduct the same comparison running the same models through a stellar sample redrawn using 10% age errors, showing that it requires an idealized scenario of exquisitely measured stellar ages to produce a better match to the Chen et al. (2021) result.
Paper: Ages of “Singles” versus “Multis”: Predictions for Dynamical Sculpting over Gyr in the Kepler Sample
Authors: Christopher Lam, Sarah Ballard
Figure caption: We show four different 2D slices of the free parameter space, where each cell is colored in light blue if the best sculpting law in that two-tuple group of models has a log likelihood greater than or equal to −34.5, and dark blue otherwise. Top left: Our forward-modeling pipeline disfavors sculpting laws that have shallow or zero slope, unless they are sufficiently compensated by a low intact fraction at 0.1 Gyr. A p8 of 0 is categorically disfavored. Top right: Sculpting laws that have steep slopes and cutoff timescales beyond 0.6 Gyr are disfavored. Bottom left: We disfavor sculpting laws that have cutoff timescales shorter than 0.6 Gyr and an intact fraction at 0.1 Gyr that is insufficiently low to offset the overproduction of multis attendant with short-lived sculpting. Bottom right: We appear to favor a planet-host fraction of 0.4 and 0.5.
Paper: Ages of “Singles” versus “Multis”: Predictions for Dynamical Sculpting over Gyr in the Kepler Sample
Authors: Christopher Lam, Sarah Ballard
Figure caption: We show the same series of four injection–recovery tests depicted in Figure 6, but with log-uniformly distributed mean ages and uniformly distributed 10% age errors, instead of those derived from isochrones and provided in the Berger et al. (2020b) Gaia-Kepler cross-match. We find that slightly less of the free parameter space is ruled out by the pipeline, but most of the prominent features noted in the previous injection–recovery tests (e.g., disfavored low p8 if the ground truth is not low p8) persist. One feature that does change is that the lower right corner of shallow sculpting laws with high intact fractions at 0.1 Gyr can no longer be ruled out among moderate and slower sculpting laws.
Paper: Ages of “Singles” versus “Multis”: Predictions for Dynamical Sculpting over Gyr in the Kepler Sample
Authors: Christopher Lam, Sarah Ballard
Figure caption: We plot favored models (those models with log likelihood within 10 of the maximum likelihood model) as a function of the present-day intact fractions from which their planet populations were drawn. Each dot represents a realization of a sculpting model’s fraction of Kepler FGK dwarfs that host STIPs and the associated log likelihood. Using the koi_disposition indicator, the intact fraction peaks in likelihood at 8%, and the present-day intact fraction among favored models ranges from 4% to 13%. We show that our models are robust to the choice of KOI indicator, with the favored intact fractions overlapping between all three indicators used.
Paper: Ages of “Singles” versus “Multis”: Predictions for Dynamical Sculpting over Gyr in the Kepler Sample
Authors: Christopher Lam, Sarah Ballard
Figure caption: We show the same series of four injection–recovery tests depicted in Figure 6, but with uniformly distributed 10% age errors, instead of those derived from isochrones and provided in the Berger et al. (2020b) Gaia-Kepler cross-match. We find that, besides a slight improvement in the ability to rule out marginally higher p8, there is no significant difference between what we can recover with our current age estimates and what we can recover in the idealized case of 10% age errors.
Paper: Parallax Effect in Microlensing Events Due to Free-floating Planets
Authors: Parisa Sangtarash, Sedighe Sajadian
Figure caption: Left panels: two examples of microlensing light curves due to FFPs in which the parallax effects are not discernible. Right panels: their lens–source relative trajectories projected on the plane of the sky without (black solid lines,  u  ⊙) and with (blue dashed lines,  u  o) considering the parallax effect. In these panels, the dark red dotted–dashed lines are  δ   u . The index r for parameters refers to their values in the real models.
Paper: Parallax Effect in Microlensing Events Due to Free-floating Planets
Authors: Parisa Sangtarash, Sedighe Sajadian
Figure caption: Similar to Figure 1. In these two short-duration microlensing light curves the parallax changes their widths significantly. The index b for t E and ρ ⋆ refers to their values in the best-fitted simple microlensing models. The best-fitted light curves are depicted by green dotted curves. In the titles “dof” refers to the degrees of freedom.
Paper: Parallax Effect in Microlensing Events Due to Free-floating Planets
Authors: Parisa Sangtarash, Sedighe Sajadian
Figure caption: The light purple histograms are the normalized distributions of some parameters due to all simulated microlensing events, which are described in Section 3. The normalized distributions of the events whose light curves are (and are not) affected by parallax effects are shown with green (and black) histograms. Green histograms are with Δχ 2 > 100 and black ones with Δχ 2 < 100 as the criteria.
Paper: Parallax Effect in Microlensing Events Due to Free-floating Planets
Authors: Parisa Sangtarash, Sedighe Sajadian
Figure caption: Similar to Figure 3. In these two microlensing events the parallax-induced deviations are significant, because in these events the lens object is close to the observer, which produces large π rel values.