adsabs/AstroCLIMB huggingface dataset:
Paper: A Mid-infrared Study of Directly Imaged Planetary-mass Companions Using Archival Spitzer/IRAC Images
Authors: Raquel A. Martinez, Adam L. Kraus
Figure caption:  K  s –[8.0] vs. K  s –[3.6] color (left panel) and [3.6]–[8.0] vs. K  s –[3.6] color (right panel) for our sample companions, the Paper I wide-orbit PMC sample, and the young Taurus and Upper Sco brown dwarfs, depicted as in Figures 4, 8, and 9. We show both of these color spaces to take advantage of better-constrained ground-based K-band contrasts for some companions that were marginally detected by our pipeline at [3.6]. We also include the expected color–color sequence of 5 Myr BT-Settl and AMES-Dusty isochrones. The disk-bearing objects are clear outliers in these particular color–color spaces, providing a criterion to say that DH Tau B, 2M0441 B, ROXs 42B b, and SR 12 c appear to host disks.
Paper: A Mid-infrared Study of Directly Imaged Planetary-mass Companions Using Archival Spitzer/IRAC Images
Authors: Raquel A. Martinez, Adam L. Kraus
Figure caption: The [3.6]–[8.0] color as a function of spectral type for our sample.We include the wide-orbit PMC sample from Paper I and the young Taurus and Upper Sco brown dwarfs, depicted as in Figures 4 and 8. Also included are the field and YMG member polynomial sequences of Dupuy & Liu (2012; solid line, dark gray) and Faherty et al. (2016; dashed line, light gray). The dynamic range is refined enough in color space for the disk-bearing objects to clearly sit above the disk-free objects.
Paper: A Mid-infrared Study of Directly Imaged Planetary-mass Companions Using Archival Spitzer/IRAC Images
Authors: Raquel A. Martinez, Adam L. Kraus
Figure caption: Contrast limits determined from the stacked IRAC channel 4 images of our sample. The dashed blue line indicates the contrast curves prior to PSF subtraction as a function of separation from the primary in arcseconds. The solid blue line indicates the corresponding contrast curve after the median two-source PSF model has been subtracted. The top of each left y-axis lists the primary magnitude as measured by the pipeline. The contrast curves are presented in terms of apparent magnitudes, as well as mass calculated by using the Gaia EDR3 parallax distance estimates of Bailer-Jones et al. (2021), literature age determinations, and BT-Settl isochrones (Allard et al. 2012).
Paper: Colors of Irregular Satellites of Saturn with the Dark Energy Camera
Authors: José Peña, Cesar Fuentes
Figure caption: Color–color plot for values from Table 4. In red are Hyrrokkin and Kari, bodies whose colors had never been measure before (note that Skoll and Loge had not been measured before either, but they were measure in r and i only). The error bars were obtained by propagating the individual errors when computing the mean magnitudes and the colors.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Diagram of the Gated Recurrent Unit (GRU) cell and layer used in this paper. Each element x  t  of a sequence x is processed by the cell along with the hidden state h  t  from the previous sequence element, generating an output y  t . The outputs y  t  form a sequence y. The GRU cell may learn through training to extract information from x and embed it into y. The so-called activation functions f 1 and f 2 are typically taken to be the sigmoid function and tanh function, respectively.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Creation of a sequence of unfolded ribbons referred to as  r  starting from a data cube of N readouts.  r  is the concatenation of the unfolded ribbons  r   i  contained in the chosen row. The ribbons are ordered as they are read from the detector in order to keep the correlations in the noise intact.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Distribution of errors on the predictions of the neural network over an entire test image, split into three categories of pixels: no flux (<1 e −), low flux (1 e − to 20 e −), and high flux (20 e − to 60 e −). The distribution of errors resulting from ramp fitting is also displayed. In all cases, the histogram for the neural network predictions is significantly narrower than that of ramp fitting, showing that the image constructed using the neural network is closer to the scene than is the image constructed by ramp fitting.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: (Top row) 2D comparison between one of the test science images and its reconstruction done using the neural network and ramp fitting. A region of 500 × 500 pixels is displayed. The image constructed by the neural network is significantly closer to the scene than is the image constructed by ramp fitting. It is also smoother than the image constructed by ramp fitting, due to the use of spatial correlations by the neural network. (Bottom row) 2D comparison between the absolute error on the reconstruction of the image done using the neural network and ramp fitting. The same region of 500 × 500 pixels is displayed
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Creation of the neural network’s inputs  x  from three consecutive r. The inputs x contain data for three rows of the data cube. The neural network takes in x as input, and returns predictions for the flux of pixels in the middle row.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Architecture of the neural network, and the most important hyperparameters. The numbers surrounding boxes and vectors represent their dimensions. Given as input an array x containing data collected on three rows of a data cube, it uses a bidirectional GRU layer, followed by three convolutional layers and another GRU layer to generate a point estimate for the flux of pixels contained in the middle row of x, which it returns as y. By generating predictions for each row of a science iteratively, the entire image may be constructed.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Representation of our concept of ribbons and unfolded ribbons, and how they are obtained from a data cube of N readouts collected by an NIR array. The ribbon contains all pixels that are read from the detector simultaneously, and the corresponding left and right reference pixels. For visualization purposes, the array is shrunk significantly so that it contains only a few pixels.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: 2D comparison between one of the test science images and the reconstruction of one of the test science images done using the neural network and ramp fitting. Three regions of 30 × 30 pixels are displayed. The image constructed by the neural network is significantly closer to the scene than is the image constructed by ramp fitting.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Distribution of errors on the predictions of the neural network over an entire test image. The distribution of errors resulting from ramp fitting is also displayed. The histogram for the neural network predictions is significantly narrower than that of ramp fitting.
Paper: The μ Arae Planetary System: Radial Velocities and Astrometry
Authors: G. F. Benedict, B. E. McArthur, E. P. Nelan, R. Wittenmyer, R. Barnes, H. Smotherman, J. Horner
Figure caption: Reduced proper-motion diagram for 3200 stars in a 1° field centered on μ Arae, #3 on the plot. Star identifications are in Table 6, and proper motions are from Gaia DR2. For a given spectral type, giants and subgiants have more negative H  K  values and are redder than dwarfs in (J − K). The small plus sign at the lower left represents a typical (J − K) error of 0.04 mag and H  K  error of 0.17 mag.
Paper: The μ Arae Planetary System: Radial Velocities and Astrometry
Authors: G. F. Benedict, B. E. McArthur, E. P. Nelan, R. Wittenmyer, R. Barnes, H. Smotherman, J. Horner
Figure caption: HST FGS parallaxes compared with Gaia EDR3 parallaxes. The linear regression assumes no errors for Gaia. Comparison plots only targets with Gaia RUWE < 1.4, ID numbers from Table 1 in Benedict et al. (2017): 5, 8, 10, 12, 13, 14, 15, 16, 17, 20, 21, 23, 24, 26, 27, 28, 29, 30, 56, 57, 58, 59, 60, 61, 62, 64, 67, 72, 73, 74, 75, 76, 77, 82, 83, 84, 85, 90, 92, 98, 99, 103, 104, and 105. The HST residuals have an rms value 0.82 mas.
Paper: The μ Arae Planetary System: Radial Velocities and Astrometry
Authors: G. F. Benedict, B. E. McArthur, E. P. Nelan, R. Wittenmyer, R. Barnes, H. Smotherman, J. Horner
Figure caption: Histograms of x and y residuals obtained by deriving the coefficients of Equations (2)–(5) from 654 reference star measures, while modeling reference star parallax and proper motion. The priors for this model had the published EDR3 errors. Distributions are fit with Gaussians, with standard deviations, σ, indicated in each panel.
Paper: The μ Arae Planetary System: Radial Velocities and Astrometry
Authors: G. F. Benedict, B. E. McArthur, E. P. Nelan, R. Wittenmyer, R. Barnes, H. Smotherman, J. Horner
Figure caption: CDFs for the entire inclination set from the 6th Visual Binary Star Catalog (WDS), inclinations for HST-measured binary stars from Benedict et al. (2016), and exoplanet perturbation inclinations (Table 11). K-S test results (Table 12) indicate that neither our exoplanet inclination distributions nor the HST binary distributions are drawn from the same parent population as the 6th Catalog binary inclination population.
Paper: The μ Arae Planetary System: Radial Velocities and Astrometry
Authors: G. F. Benedict, B. E. McArthur, E. P. Nelan, R. Wittenmyer, R. Barnes, H. Smotherman, J. Horner
Figure caption: Normalized Lomb–Scargle periodograms (Zechmeister & Kürster 2009) of μ Arae astrometry residuals obtained by applying the coefficients of Equations (2)–(5) to μ Arae, solving only for μ Arae proper motion and parallax (top), and the window function for the μ Arae observation sequence (bottom). Vertical lines indicate RV-determined periods for (left to right) components e, b, and d. We find no significant power in the residuals at any component period.
Paper: The μ Arae Planetary System: Radial Velocities and Astrometry
Authors: G. F. Benedict, B. E. McArthur, E. P. Nelan, R. Wittenmyer, R. Barnes, H. Smotherman, J. Horner
Figure caption: Estimated detection thresholds using Lomb–Scargle periodograms for each component, with perturbation amplitude indicated. A power level of 10 yields a false-positive level of 1%.
Paper: OGLE-2016-BLG-1093Lb: A Sub-Jupiter-mass Spitzer Planet Located in the Galactic Bulge
Authors: In-Gu Shin, Jennifer C. Yee, Kyu-Ha Hwang, Andrew Gould, Andrzej Udalski, Ian A. Bond, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Youn Kil Jung, Hyoun Woo Kim, Yoon-Hyun Ryu, Yossi Shvartzvald, 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, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radek Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof Ulaczyk, Charles A. Beichman, Geoffery Bryden, Sebastiano Calchi Novati, Sean Carey, B. Scott Gaudi, Calen B. Henderson, Wei Zhu, Fumio Abe, Richard K. Barry, David P. Bennett, Aparna Bhattacharya, Hirosane Fujii, Akihiko Fukui, Yuki Hirao, Yoshitaka Itow, Rintaro Kirikawa, Naoki Koshimoto, Iona Kondo, Yutaka Matsubara, Sho Matsumoto, Shota Miyazaki, Yasushi Muraki, Greg Olmschenk, Arisa Okamura, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Stela Ishitani Silva, Takahiro Sumi, Daisuke Suzuki, Taiga Toda, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama
Figure caption: Light curve with the best-fit model (see Table 2). The left panels show the full view of the light curve from both the ground and Spitzer. The top right panels show just the ground-based data for the anomaly part of the light curve (caustic crossings at t 1 and t 2), which is induced by the planetary system. For comparison, we present the 1L1S model light curve (dashed red line) with residuals. The bottom right panel presents the caustic geometry of the event.(The data used to create this figure are available.)
Paper: OGLE-2016-BLG-1093Lb: A Sub-Jupiter-mass Spitzer Planet Located in the Galactic Bulge
Authors: In-Gu Shin, Jennifer C. Yee, Kyu-Ha Hwang, Andrew Gould, Andrzej Udalski, Ian A. Bond, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Youn Kil Jung, Hyoun Woo Kim, Yoon-Hyun Ryu, Yossi Shvartzvald, 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, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radek Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof Ulaczyk, Charles A. Beichman, Geoffery Bryden, Sebastiano Calchi Novati, Sean Carey, B. Scott Gaudi, Calen B. Henderson, Wei Zhu, Fumio Abe, Richard K. Barry, David P. Bennett, Aparna Bhattacharya, Hirosane Fujii, Akihiko Fukui, Yuki Hirao, Yoshitaka Itow, Rintaro Kirikawa, Naoki Koshimoto, Iona Kondo, Yutaka Matsubara, Sho Matsumoto, Shota Miyazaki, Yasushi Muraki, Greg Olmschenk, Arisa Okamura, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Stela Ishitani Silva, Takahiro Sumi, Daisuke Suzuki, Taiga Toda, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama
Figure caption: The (π E,E, π E,N) contours of the APRX models. Each color indicates ﹩{\rm{\Delta }}{\chi }_{(\mathrm{chain}-\mathrm{best})}^{2}\leqslant {n}^{2}﹩, where n = 1 (red), 2 (yellow), 3 (green), 4 (light blue), 5 (blue), and 6 (purple), respectively.
Paper: SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
Authors: Julie Imig, Jon A. Holtzman, Renbin Yan, Daniel Lazarz, Yanping Chen, Lewis Hill, Daniel Thomas, Claudia Maraston, Moire K. M. Prescott, Guy S. Stringfellow, Dmitry Bizyaev, Rachael L. Beaton, Niv Drory
Figure caption: Performance of the neural network over a range of different temperature bins, from coolest (left column) to hottest (right column), demonstrating the neural network’s ability to reproduce the flux of any given pixel in every spectrum of the training set. Top row: one-to-one plots comparing the true flux values in the training set spectra (f true) to the flux generated by the neural network (f  nn ). Middle row: histograms showing the the distribution of spectral flux error (f  nn  − f true) calculated for every pixel in every spectrum in the training set. Bottom row: cumulative distribution function of the absolute spectral error ∣(f  nn  − f true)∣.
Paper: SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
Authors: Julie Imig, Jon A. Holtzman, Renbin Yan, Daniel Lazarz, Yanping Chen, Lewis Hill, Daniel Thomas, Claudia Maraston, Moire K. M. Prescott, Guy S. Stringfellow, Dmitry Bizyaev, Rachael L. Beaton, Niv Drory
Figure caption: Stellar spectra comparing two different synthetic libraries, Bohlin et al. (2017; blue) and Allende Prieto et al. (2018; red), compared to an empirical MaStar spectrum (black) with similar parameters from ASPCAP. The top panel shows an M-dwarf star with stellar parameters T eff = 3500 K, ﹩\mathrm{log}g﹩ = 4.5 dex, [Fe/H] = [α/Fe] = 0.0. The bottom panel depicts an F-type star with T eff = 6000 K, ﹩\mathrm{log}g\,﹩ = 4.5 dex, [Fe/H] = [α/Fe] = 0.0. All spectra have been normalized to a relative flux by dividing out a median value from a defined wavelength window. Residuals ((f synth − f MaStar)/f MaStar) are shown underneath each example.
Paper: SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
Authors: Julie Imig, Jon A. Holtzman, Renbin Yan, Daniel Lazarz, Yanping Chen, Lewis Hill, Daniel Thomas, Claudia Maraston, Moire K. M. Prescott, Guy S. Stringfellow, Dmitry Bizyaev, Rachael L. Beaton, Niv Drory
Figure caption: Results from a cross-validation test, training the neural network on 90% of the reference set and reserving the last 10% for validation. Each panel shows a different parameter, highlighting the neural network’s ability to accurately fit the remaining 10% of spectra. Points are colored by the reduced χ 2 value of the fits. Median offsets and scatter for each parameter are listed in Table 1.
Paper: SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
Authors: Julie Imig, Jon A. Holtzman, Renbin Yan, Daniel Lazarz, Yanping Chen, Lewis Hill, Daniel Thomas, Claudia Maraston, Moire K. M. Prescott, Guy S. Stringfellow, Dmitry Bizyaev, Rachael L. Beaton, Niv Drory
Figure caption: Example of the pseudo-continuum normalization procedure described in Section 2.2. Top: an unnormalized spectrum (black line) and a smoothed version of the spectrum created with a 400 pixel window running median (red line), which is the estimate of the pseudo-continuum. Bottom: resulting normalized spectrum, made by dividing out the pseudo-continuum from the original spectrum.
Paper: SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
Authors: Julie Imig, Jon A. Holtzman, Renbin Yan, Daniel Lazarz, Yanping Chen, Lewis Hill, Daniel Thomas, Claudia Maraston, Moire K. M. Prescott, Guy S. Stringfellow, Dmitry Bizyaev, Rachael L. Beaton, Niv Drory
Figure caption: ASPCAP parameter distribution of the APOGEE–MaStar overlap sample in a Kiel diagram (left) and the [α/Fe]–[Fe/H] plane (right). Bottom row: reported precision in the ASPCAP parameters for the APOGEE–MaStar overlap sample.
Paper: SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
Authors: Julie Imig, Jon A. Holtzman, Renbin Yan, Daniel Lazarz, Yanping Chen, Lewis Hill, Daniel Thomas, Claudia Maraston, Moire K. M. Prescott, Guy S. Stringfellow, Dmitry Bizyaev, Rachael L. Beaton, Niv Drory
Figure caption: Full reference set used to train the neural network, shown in a Kiel diagram (left) and the [α/Fe]–[Fe/H] plane (right). The training set includes parameters from ASPCAP (star points), and extends to hotter temperatures through the addition of randomly drawn synthetic spectra (round points).
Paper: Colors of Irregular Satellites of Saturn with the Dark Energy Camera
Authors: José Peña, Cesar Fuentes
Figure caption: Our colors compared to colors from other works. In light blue, colors from Grav et al. (2003), in red from Buratti et al. (2005), in green from Grav & Bauer (2007), and in yellow from Graykowski & Jewitt (2018). In black is the one-to-one line.
Paper: Colors of Irregular Satellites of Saturn with the Dark Energy Camera
Authors: José Peña, Cesar Fuentes
Figure caption: Same as Figure 2 but comparing data from Grav & Bauer (2007; which contains the greatest number of SIrrs colors in past works) against Grav et al. (2003; in red), Buratti et al. (2005; in blue), and Graykowski & Jewitt (2018; in green).
Paper: The μ Arae Planetary System: Radial Velocities and Astrometry
Authors: G. F. Benedict, B. E. McArthur, E. P. Nelan, R. Wittenmyer, R. Barnes, H. Smotherman, J. Horner
Figure caption: RV values from the sources listed in Table 2 plotted on the final RV four-component orbit (Table 4). All RV input errors have been increased by a factor of 1.4 to achieve a near-unity χ 2. Residuals are plotted in the top panel. We note the rms RV residual value in the plot.
Paper: The μ Arae Planetary System: Radial Velocities and Astrometry
Authors: G. F. Benedict, B. E. McArthur, E. P. Nelan, R. Wittenmyer, R. Barnes, H. Smotherman, J. Horner
Figure caption: Positions of μ Arae (3) and astrometric reference stars (20–27) in FGS 1r FOV coordinates.
Paper: The μ Arae Planetary System: Radial Velocities and Astrometry
Authors: G. F. Benedict, B. E. McArthur, E. P. Nelan, R. Wittenmyer, R. Barnes, H. Smotherman, J. Horner
Figure caption:  μ Arae and the astrometric reference stars (20–27) identified in Table 6.
Paper: OGLE-2016-BLG-1093Lb: A Sub-Jupiter-mass Spitzer Planet Located in the Galactic Bulge
Authors: In-Gu Shin, Jennifer C. Yee, Kyu-Ha Hwang, Andrew Gould, Andrzej Udalski, Ian A. Bond, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Youn Kil Jung, Hyoun Woo Kim, Yoon-Hyun Ryu, Yossi Shvartzvald, 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, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radek Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof Ulaczyk, Charles A. Beichman, Geoffery Bryden, Sebastiano Calchi Novati, Sean Carey, B. Scott Gaudi, Calen B. Henderson, Wei Zhu, Fumio Abe, Richard K. Barry, David P. Bennett, Aparna Bhattacharya, Hirosane Fujii, Akihiko Fukui, Yuki Hirao, Yoshitaka Itow, Rintaro Kirikawa, Naoki Koshimoto, Iona Kondo, Yutaka Matsubara, Sho Matsumoto, Shota Miyazaki, Yasushi Muraki, Greg Olmschenk, Arisa Okamura, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Stela Ishitani Silva, Takahiro Sumi, Daisuke Suzuki, Taiga Toda, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama
Figure caption: The (π E,E, π E,N) contours of SPRX models. Top panels show SPRX contours of Joint Spitzer + Ground cases. Bottom panels show SPRX contours of Spitzer-“only” cases. Right and left panels present (u 0 < 0) and (u 0 > 0) cases, respectively. The black stars indicate the best-fit (π E,E, π E,N) values of Joint Spitzer+ Ground cases. The labeled black plus signs indicate the (π E,E, π E,N) locations of the four minima shown in Figure 4.
Paper: OGLE-2016-BLG-1093Lb: A Sub-Jupiter-mass Spitzer Planet Located in the Galactic Bulge
Authors: In-Gu Shin, Jennifer C. Yee, Kyu-Ha Hwang, Andrew Gould, Andrzej Udalski, Ian A. Bond, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Youn Kil Jung, Hyoun Woo Kim, Yoon-Hyun Ryu, Yossi Shvartzvald, 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, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radek Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof Ulaczyk, Charles A. Beichman, Geoffery Bryden, Sebastiano Calchi Novati, Sean Carey, B. Scott Gaudi, Calen B. Henderson, Wei Zhu, Fumio Abe, Richard K. Barry, David P. Bennett, Aparna Bhattacharya, Hirosane Fujii, Akihiko Fukui, Yuki Hirao, Yoshitaka Itow, Rintaro Kirikawa, Naoki Koshimoto, Iona Kondo, Yutaka Matsubara, Sho Matsumoto, Shota Miyazaki, Yasushi Muraki, Greg Olmschenk, Arisa Okamura, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Stela Ishitani Silva, Takahiro Sumi, Daisuke Suzuki, Taiga Toda, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama
Figure caption: Four solutions from Spitzer-“only” fitting. Left panels show the trajectories of the source as seen from the ground (black line with arrow) and Spitzer (red line with arrows) over the full span of the observations from Spitzer (epochs of observations shown as purple circles). The caustics are shown as a black, closed curve. Middle panels show a zoom-in of the caustic region. Right panels show the Spitzer light curve (purple points) with the best-fit model light curve (red line) for each solution. The “a” and “b” solutions are disfavored relative to the “A” and “B” solutions; lettering corresponds to the minima indicated in Figure 3.