adsabs/AstroCLIMB huggingface dataset:
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: Learning performance evolution for the three-class classification task over 5000 epochs. The plot illustrates the weighted “No,” “Maybe,” and “Yes” category accuracies for both the training (Tr.) and validation (Vl.) sets. Similar to the two-class task, the model demonstrates an initial steep learning curve, stabilizing around the 2000th epoch. The inset highlights the performance between 65% and 85%, focusing on the accuracy values from epoch 4000 to 5000, where the model achieves a consistent performance across all categories.
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 data augmentation process applied to the flux time series and associated features for the training of the microlensing event classification model. The figure shows multiple augmented flux instances of the same real data from a given instrument, where the time series has been cropped from the left (historic data) and right (ascending flux data) sides. The percentages indicate the extent of the data removed in the cropping of each side. This augmentation simulates varying levels of prior knowledge (left cropping) and available microlensing alert information (right cropping), ensuring the model is robust to different observational scenarios.
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: Learning performance evolution for the binary classification task over 3000 epochs. The graph shows the weighted, No, and “Yes”/“Maybe” category accuracies for the training (Tr.) and validation (Vl.) sets. The model initially experiences a sharp increase in accuracy for both categories, followed by a gradual learning process that stabilizes around the 1000th epoch. The inset zooms in on the accuracy range between 75% and 90%, providing a closer view of the learning performance between epochs 2500 and 3000.
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: Examples of a real microlensing event (top) compared to two common types of false positives: a bleed trail (center) and a diffraction spike (bottom). The left panels show the original images, and the right panels show the difference images. A magenta circle indicates the location of the catalog star on which the candidate was detected. White arrows indicate the relevant feature causing the false positive.
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: Map of the KMTNet BLG observing fields, showing the distribution of the four CCDs per field, with each field color coded according to its observational cadence, ranging from 0.5 hr (purple) to 5.0 hr (yellow). The fields are plotted in galactic coordinates, longitude (ℓ), and latitude (b). Credit: Matthew Penny.
Paper: Giant Molecular Clouds in RCW 106 (G333): Galactic Mini-starbursts and Massive Star Formation Induced by Supersonic Cloud–Cloud Collisions
Authors: Mikito Kohno, 樹人 河野, Rin I. Yamada, 麟 山田, Kengo Tachihara, 研悟 立原, Shinji Fujita, 真司 藤田, Rei Enokiya, 玲依 榎谷, Kazuki Tokuda, 一起 徳田, Asao Habe, 朝男 羽部, Hidetoshi Sano, 栄俊 佐野, Takahiro Hayakawa, 貴敬 早川, Fumika Demachi, 史夏 出町, Takuto Ito, 拓冬 伊藤, Kisetsu Tsuge, 紀節 柘植, Atsushi Nishimura, 淳 西村, Masato I. N. Kobayashi, 将人 小林, Hiroaki Yamamoto, 宏昭 山本, Yasuo Fukui, 康雄 福井
Figure caption: (a) The Herschel three-color composite image of the RCW 106 GMC complex. Blue, green, and red show the Herschel/PACS 70 μm, Herschel/PACS 160 μm, and Herschel/SPIRE 350 μm distributions, respectively (S. Molinari et al. 2016). (b) The Herschel close-up image of RCW 106 East (G333). The red cross marks are the positions of the massive star-forming regions in RCW 106 East (I. Bains et al. 2006). (c) The Herschel close-up image of RCW 106 West. (d) The NANTEN2 12CO J = 1–0 integrated intensity map of the RCW 106 GMC complex. The integrated velocity range is from −70 km s−1 to −30 km s−1.
Paper: Giant Molecular Clouds in RCW 106 (G333): Galactic Mini-starbursts and Massive Star Formation Induced by Supersonic Cloud–Cloud Collisions
Authors: Mikito Kohno, 樹人 河野, Rin I. Yamada, 麟 山田, Kengo Tachihara, 研悟 立原, Shinji Fujita, 真司 藤田, Rei Enokiya, 玲依 榎谷, Kazuki Tokuda, 一起 徳田, Asao Habe, 朝男 羽部, Hidetoshi Sano, 栄俊 佐野, Takahiro Hayakawa, 貴敬 早川, Fumika Demachi, 史夏 出町, Takuto Ito, 拓冬 伊藤, Kisetsu Tsuge, 紀節 柘植, Atsushi Nishimura, 淳 西村, Masato I. N. Kobayashi, 将人 小林, Hiroaki Yamamoto, 宏昭 山本, Yasuo Fukui, 康雄 福井
Figure caption: (a) The large-scale longitude–velocity diagram of 12CO J = 1–0 obtained by NANTEN. The red and green dotted lines indicate the Scutum–Centaurus and Norma arm, respectively (J. Koda et al. 2016; J. P. Vallée 2017). The integrated latitude range is from −1﹩\mathop{.}\limits^{\unicode{x000B0}}﹩0 to 1﹩\mathop{.}\limits^{\unicode{x000B0}}﹩0. The yellow dotted square shows the area in panel (b). (b) Detailed longitude–velocity diagram focusing on the RCW 106 GMC complex of 12CO J = 1–0 obtained by NANTEN2. The integrated latitude range is from −0﹩\mathop{.}\limits^{\unicode{x000B0}}﹩8 to 0﹩\mathop{.}\limits^{\unicode{x000B0}}﹩5. The red-dotted rectangle and yellow arrow show the bridge feature at RCW 106 West. The velocity space is smoothed in 9 channels by the hanning window. The lowest contour and interval are 2 K degree and 1 K degree, respectively.
Paper: An Embedded Disk (eDisk) in the IceAge: Investigating the Jet and Outflow from Ced 110 IRS4
Authors: Mayank Narang, Nagayoshi Ohashi, John J. Tobin, M. K. McClure, Jes K. Jørgensen, Jinshi Sai (Insa Choi)
Figure caption: (a) The molecular H2 0–0 S(1) line, with contours of 0–0 S(4) and 0–0 S(7) overlaid on top. The H2 0–0 S(4) and H2 0–0 S(7) contours are at 10% of the peak flux density (3.55 × 10–4 erg s–1 cm–2 sr–1 and 8.5 × 10−4 erg s−1 cm−2 sr−1, respectively) of the emission. (b) The edge detected (in red) on top of the H2 0–0 S(4) in color scale. (c) The outflow width as a function of distance from the protostar for the three (H2 0–0 S(1), S(4), and (7)) transitions.
Paper: An Embedded Disk (eDisk) in the IceAge: Investigating the Jet and Outflow from Ced 110 IRS4
Authors: Mayank Narang, Nagayoshi Ohashi, John J. Tobin, M. K. McClure, Jes K. Jørgensen, Jinshi Sai (Insa Choi)
Figure caption: The morphology of the H2 0–0 S line from S(1) to S(8) covering the MIRI MRS. All images are cropped to the same spatial scale of 5﹩\mathop{.}\limits^{^{\prime\prime} }﹩7 × 5﹩\mathop{.}\limits^{^{\prime\prime} }﹩7 and have the same color scale. The ALMA 1.3 mm position for Ced 110 IRS4A is shown as a blue circle, and that for IRS4B is shown as a lime circle. The scale bar corresponding to 100 au is shown in the lower left corner. The MIRI FWHM based on D. D. Law et al. (2023) is shown in the lower right corner as a red circle.
Paper: An Embedded Disk (eDisk) in the IceAge: Investigating the Jet and Outflow from Ced 110 IRS4
Authors: Mayank Narang, Nagayoshi Ohashi, John J. Tobin, M. K. McClure, Jes K. Jørgensen, Jinshi Sai (Insa Choi)
Figure caption: (a) The [Fe II] jet at 5.35 μm shown in blue, overlaid with a red rectangle that denotes the region considered for making the PV diagram. The black filled circle is the ALMA continuum position. (b) The PV diagram for the [Fe II] line at 5.34 μm as a function of the distance from the central protostar along the jet axis.
Paper: An Embedded Disk (eDisk) in the IceAge: Investigating the Jet and Outflow from Ced 110 IRS4
Authors: Mayank Narang, Nagayoshi Ohashi, John J. Tobin, M. K. McClure, Jes K. Jørgensen, Jinshi Sai (Insa Choi)
Figure caption: The molecular H2 0–0 S(7) line with the NIRCam F150W emission, the SBLB 1.3 mm ALMA continuum, and the ALMA 12CO observations. The MIRI FWHM based on D. D. Law et al. (2023) is shown in the lower left corner as a red circle, while the NIRCam/ALMA beam is shown as a blue ellipse.
Paper: An Embedded Disk (eDisk) in the IceAge: Investigating the Jet and Outflow from Ced 110 IRS4
Authors: Mayank Narang, Nagayoshi Ohashi, John J. Tobin, M. K. McClure, Jes K. Jørgensen, Jinshi Sai (Insa Choi)
Figure caption: The moment 1 map for the [Fe II] line at 5.34 μm. The contours mark the velocities in km s−1.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Illustration of our new postprocessing technique 4S. To overcome the limitations of PCA, 4S makes use of three innovations: (1) a right reason mask to prevent oversubtraction (bottom left box), (2) a loss function that is invariant to the signal of the planet (bottom right box), and (3) a regularization based on domain knowledge to prevent overfitting. The main steps ① to ③ are explained in detail in Section 4.4.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Results of our fake planet experiments for data set #5 (HD 22049; see Table 1). On the left, example residuals are shown for fake planets at ▴ = (1.5λ/D, 7.5 mag), ■ = (2.5λ/D, 9.5 mag) and ﹩\unicode{x02605}﹩ = (11 λ/D, 13 mag). In the case of PCA, we tune the number of principal components to get the deepest contrast (optimal PCA). For 4S, we use the same setup with λ = 100 for all separations. An interactive version of the plot is available on our documentation page. The plot on the right shows contrast curves of 4S and PCA with different numbers of principal components. Each contrast curve was obtained by computing a contrast grid using the Python package applefy and is based on 945 fake planet experiments. The dotted black line gives the cADI contrast, which is equivalent to a simple subtraction of the temporal median. Note that for large numbers of components and small separations, none of the inserted fake planets reach an S/N > 5.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Variation in planet visibility as a function of principal components (K = 20, K = 60, K = 120, K = 300). The shown data set is data set #5 (HD 22049) in Table 1. More details on data preprocessing are given in Section 5. Two fake planets have been inserted into the data: one positioned at ∼3λ/D separation from the star with a contrast brightness of 9 mag (white circle), and one at ∼10λ/D with a contrast brightness of 12.5 mag (white dashed circle). The inner planet reaches the highest S/N at ∼K = 60 components (left panel, solid line) and the outer planet at ∼K = 300 components (left panel, dashed line). This example highlights the challenge of choosing the right number of principal components within a single data set.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Illustration of how to compute saliency maps for PCA. For each pixel position in the noise estimate, we can compute one saliency map. Each saliency map highlights the areas in the science frame that are used to obtain the noise estimate at the respective position. Since the noise model of PCA is linear, every pixel in the noise estimate is a weighted sum of the pixels in the input image. The saliency map shows the absolute value of these weights. Two examples of such saliency maps are shown in the middle row (300 principal components). The example used in the text is marked in orange. For large numbers of principal components, the saliency map converges to the shape of the PSF. A gallery of saliency maps for different separations and numbers of principal components is given in Figure 17. The saliency maps should not be confused with the component vectors u1, . . . , uK shown in Figure 1.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Residual images of the AF Lep data set (#12 in Table 1) taken with VLT-NACO in 2011. The left and middle panels show the residuals obtained with PCA with K = 20 and K = 60 components, respectively. The right panel shows the residual of 4S, which reveals the giant exoplanet AF Lep b at S/N = 6.8, 11 yr before its discovery. For better visibility, we filter the residual images with a Gaussian kernel (σ = 0.8 pixels). The S/N is calculated on the raw residuals before applying the Gaussian filter. An interactive version of the plot for different setups of PCA and 4S is available in our online documentation. A gallery of all PCA and 4S residuals is given in Figure 18.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Saliency maps for 4S, computed for data sets #4 (HD 22049; left column) and #7 (HD 40136; right column). The saliency maps show which information from the science frames xt is used by 4S to estimate the noise at the position marked by the white plus sign. In contrast to PCA (see Figure 3), 4S successfully learns the point-symmetric signatures of the speckle noise. The red star marks the center of the frame.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Retained flux after postprocessing with PCA as a function of planet brightness and separation from the star. The grid was computed using applefy. Each value in the grid is the result of three fake planet experiments with planets inserted at different position angles. A significant fraction of the planet signal is lost, especially close to the star.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Convergence of the saliency map to the shape of the PSF as a function of the number of principal components used. At closer separations from the star, the convergence is faster, resulting in a greater loss of the planet signal with a smaller number of components.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Relative improvement in contrast of 4S over PCA, accumulated over the 11 data sets shown in Table 1. The blue line shows the median improvement, the gray boxes give the interquartile range (IQR), and the whiskers extend to points that lie within 1.5 IQR. The points marked as circles are outliers.
Paper: Testing Cluster Membership of Planetary Nebulae with High-precision Proper Motions. II. HST Observations of PHR J1315–6555 in the Open Cluster AL 1 (ESO 96-SC04)
Authors: Andrea Bellini, Howard E. Bond, Kailash C. Sahu
Figure caption: Left: observed mF814W vs. mF555W − mF814W CMD of stars in the HST field. The chosen proper-motion-selected reference cluster members are highlighted in red, and the black points are stars considered to be nonmembers. An arrow shows the direction of the reddening vector. Center: DR-corrected CMD. The MS of AL 1 is now narrower than in the uncorrected CMD. Right: spatial map of the DR corrections, centered on the B25 candidate PNN. Each star is color-coded according to the shift of nearby reference stars along the reddening vector direction with respect to the average reddening over the whole field. The color bar at the top indicates the color-coding of the points, giving the local value of E(B − V) relative to the mean E(B − V) across the field. A circle of radius ﹩5\mathop{.}\limits^{\unicode{x02033}}6﹩ centered on B25 indicates the location of the PN. The plus sign to its southwest marks the center of the AL 1 cluster. See text for details and discussion.
Paper: Testing Cluster Membership of Planetary Nebulae with High-precision Proper Motions. II. HST Observations of PHR J1315–6555 in the Open Cluster AL 1 (ESO 96-SC04)
Authors: Andrea Bellini, Howard E. Bond, Kailash C. Sahu
Figure caption: CMD for members of the cluster AL 1 in the HST/WFC3 F555W and F814W bandpasses. Red points are stars whose HST-based PMs place them inside the ellipses in Figure 4; blue points are bright stars that are saturated in all but two short epoch 1 exposures, also selected to have Gaia DR3 PMs lying inside the Figure 4 ellipses. Filled circles plot the photometry for the two candidate nuclei of PHR J1315, color-coded as indicated in the legend at the top. All photometry has been corrected for DR, as discussed in the text. Magenta error bars show the mean uncertainties in magnitude and color, including uncertainties in the DR corrections, as a function of magnitude. The green line is a theoretical isochrone calculated for parameters given in its legend.
Paper: Testing Cluster Membership of Planetary Nebulae with High-precision Proper Motions. II. HST Observations of PHR J1315–6555 in the Open Cluster AL 1 (ESO 96-SC04)
Authors: Andrea Bellini, Howard E. Bond, Kailash C. Sahu
Figure caption: (a) HST-based vector-point diagram of sources in the analyzed field. The red ellipse, enclosing the compact clump of stars, is used to define AL 1 cluster membership. Its semiminor and major axes are 3 times the observed velocity dispersion of AL 1 stars along the R.A. and decl. directions, respectively. The candidate central stars F19 (green) and B25 (orange) are marked using filled circles. (b) Enlarged view of the vector-point diagram around the locus of AL 1 cluster members. Here F19 and B25 are shown with error bars. Both stars have PMs statistically consistent with cluster membership. See text for details.
Paper: Testing Cluster Membership of Planetary Nebulae with High-precision Proper Motions. II. HST Observations of PHR J1315–6555 in the Open Cluster AL 1 (ESO 96-SC04)
Authors: Andrea Bellini, Howard E. Bond, Kailash C. Sahu
Figure caption: Central region of the nebula, enlarged from Figure 1. Two candidate central stars are marked: “F19” from V. Fragkou et al. (2019a), and our proposed nucleus, labeled “B25.” The height of frame is ﹩4\mathop{.}\limits^{\unicode{x02033}}35﹩.
Paper: Testing Cluster Membership of Planetary Nebulae with High-precision Proper Motions. II. HST Observations of PHR J1315–6555 in the Open Cluster AL 1 (ESO 96-SC04)
Authors: Andrea Bellini, Howard E. Bond, Kailash C. Sahu
Figure caption: False-color HST image of the PN PHR J1315−6555. Created from WFC3 frames in F814W (I; red), F555W (V; green), and F502N ([O III] λ5007; blue). North is at the top, east on the left. The height of frame is 12″.
Paper: Testing Cluster Membership of Planetary Nebulae with High-precision Proper Motions. II. HST Observations of PHR J1315–6555 in the Open Cluster AL 1 (ESO 96-SC04)
Authors: Andrea Bellini, Howard E. Bond, Kailash C. Sahu
Figure caption: Heavily stretched rendition of the [O III] λ5007 WFC3 image. Both candidate central stars from Figure 2 are marked. A dashed cyan line lies along the approximate axis of the bipolar nebula, perpendicular to its narrow waist. Our candidate central star B25 is closer to this axis than is the F19 candidate.