adsabs/AstroCLIMB huggingface dataset:
Paper: Climbing the Cliffs: Classifying Young Stellar Objects in the Cosmic Cliffs JWST Data Using a Probabilistic Random Forest
Authors: B. L. Crompvoets, J. Di Francesco, H. Teimoorinia, T. Preibisch
Figure caption: Comparison of the spectral index of Spitzer-identified YSOs, determined from Spitzer data (y-axis) and JWST data (x-axis). Colored segments show the spectral indices in Spitzer associated with each class, with class labels on the right.
Paper: Climbing the Cliffs: Classifying Young Stellar Objects in the Cosmic Cliffs JWST Data Using a Probabilistic Random Forest
Authors: B. L. Crompvoets, J. Di Francesco, H. Teimoorinia, T. Preibisch
Figure caption: The relative performance of the PRF vs. the RF models; see text for details on this metric. The vertical gray line marks the 50% threshold line.
Paper: Climbing the Cliffs: Classifying Young Stellar Objects in the Cosmic Cliffs JWST Data Using a Probabilistic Random Forest
Authors: B. L. Crompvoets, J. Di Francesco, H. Teimoorinia, T. Preibisch
Figure caption: The trend of the number of YSOs with the cut in probability that determines if a given object is a YSO. There are no cYSOs with exactly 100% probability. The gray horizontal line marks the upper limit of YSOs we expect within the region from previous works, and the gray vertical line marks the 50% threshold.
Paper: Climbing the Cliffs: Classifying Young Stellar Objects in the Cosmic Cliffs JWST Data Using a Probabilistic Random Forest
Authors: B. L. Crompvoets, J. Di Francesco, H. Teimoorinia, T. Preibisch
Figure caption: The confusion matrices for the validation set using a cut of 50% probability for an object to be a YSO based on the RF results (top) and PRF results (bottom).
Paper: Climbing the Cliffs: Classifying Young Stellar Objects in the Cosmic Cliffs JWST Data Using a Probabilistic Random Forest
Authors: B. L. Crompvoets, J. Di Francesco, H. Teimoorinia, T. Preibisch
Figure caption: The trends of various metrics with a variation of the cut in probability that determines if a given object is a YSO. There are no YSOs with exactly 100% probability.
Paper: Climbing the Cliffs: Classifying Young Stellar Objects in the Cosmic Cliffs JWST Data Using a Probabilistic Random Forest
Authors: B. L. Crompvoets, J. Di Francesco, H. Teimoorinia, T. Preibisch
Figure caption: Histograms of the spectral index (see Equation (3)) of the full population of identified cYSOs (maroon) and the Spitzer-identified YSOs which make up the training set (gray), normalized for comparison.
Paper: Climbing the Cliffs: Classifying Young Stellar Objects in the Cosmic Cliffs JWST Data Using a Probabilistic Random Forest
Authors: B. L. Crompvoets, J. Di Francesco, H. Teimoorinia, T. Preibisch
Figure caption: The relative importance of each color to the classification model, based on the feature_importances_ metric of the PRF method that uses all available data.
Paper: Climbing the Cliffs: Classifying Young Stellar Objects in the Cosmic Cliffs JWST Data Using a Probabilistic Random Forest
Authors: B. L. Crompvoets, J. Di Francesco, H. Teimoorinia, T. Preibisch
Figure caption: ROC curves for each model with threshold based on the probability of an object being a YSO.
Paper: Climbing the Cliffs: Classifying Young Stellar Objects in the Cosmic Cliffs JWST Data Using a Probabilistic Random Forest
Authors: B. L. Crompvoets, J. Di Francesco, H. Teimoorinia, T. Preibisch
Figure caption: Color–magnitude and color–color diagrams of cYSOs (maroon stars), substellar cYSOs (salmon squares), Spitzer-identified YSOs (navy circles), and contaminants (gray circles with color gradient specifying the error of the photometry).
Paper: Climbing the Cliffs: Classifying Young Stellar Objects in the Cosmic Cliffs JWST Data Using a Probabilistic Random Forest
Authors: B. L. Crompvoets, J. Di Francesco, H. Teimoorinia, T. Preibisch
Figure caption: Binned surface density with column density contours overlaid for the full cYSO population (top) and that of the substellar cYSO population (bottom). The surface density is normalized to the total number of objects in that bin to emphasize regions of YSO enhancement. The column density contours are laid in steps of A V  = 0.5 mag.
Paper: Climbing the Cliffs: Classifying Young Stellar Objects in the Cosmic Cliffs JWST Data Using a Probabilistic Random Forest
Authors: B. L. Crompvoets, J. Di Francesco, H. Teimoorinia, T. Preibisch
Figure caption: Welch’s t-test investigation of the population to which the substellar objects belong. In this instance “YSOs” are those cYSOs above the substellar threshold, and “contaminants” are either above or below the substellar threshold depending on if they’re being compared to the “YSOs” or the substellar population, respectively.
Paper: Climbing the Cliffs: Classifying Young Stellar Objects in the Cosmic Cliffs JWST Data Using a Probabilistic Random Forest
Authors: B. L. Crompvoets, J. Di Francesco, H. Teimoorinia, T. Preibisch
Figure caption: Diagram of [F090W]–[F200W] color vs. F200W magnitude for cYSOs and contaminants (symbols are as in Figure 13) overlaid with pre-MS object isochrones from PARSEC models at an age of 2 Myr and various foreground extinctions.
Paper: Automatic Search for Low-surface-brightness Galaxies from Sloan Digital Sky Survey Images Using Deep Learning
Authors: Zengxu Liang, Zhenping Yi, Wei Du, Meng Liu, Yuan Liu, Junjie Wang, Xiaoming Kong, Yude Bu, Hao Su, Hong Wu
Figure caption: Three anomalous objects and their anomaly scores. (a) A star with an extremely high anomaly score (2.3465). (b) and (c) A compact galaxy group and a planetary nebula (PN A66 73), respectively.
Paper: Automatic Search for Low-surface-brightness Galaxies from Sloan Digital Sky Survey Images Using Deep Learning
Authors: Zengxu Liang, Zhenping Yi, Wei Du, Meng Liu, Yuan Liu, Junjie Wang, Xiaoming Kong, Yude Bu, Hao Su, Hong Wu
Figure caption: The distribution of surface brightness in the B band for the training samples and the identified samples.
Paper: Automatic Search for Low-surface-brightness Galaxies from Sloan Digital Sky Survey Images Using Deep Learning
Authors: Zengxu Liang, Zhenping Yi, Wei Du, Meng Liu, Yuan Liu, Junjie Wang, Xiaoming Kong, Yude Bu, Hao Su, Hong Wu
Figure caption: The distributions of half-light radius in the g band and r band for the training samples and the identified samples.
Paper: Automatic Search for Low-surface-brightness Galaxies from Sloan Digital Sky Survey Images Using Deep Learning
Authors: Zengxu Liang, Zhenping Yi, Wei Du, Meng Liu, Yuan Liu, Junjie Wang, Xiaoming Kong, Yude Bu, Hao Su, Hong Wu
Figure caption: The color–magnitude diagrams of the training samples and identified samples. The red dashed line represents the dividing line between “red” sequence galaxies (above the line) and “blue” cloud galaxies (below the line).
Paper: Automatic Search for Low-surface-brightness Galaxies from Sloan Digital Sky Survey Images Using Deep Learning
Authors: Zengxu Liang, Zhenping Yi, Wei Du, Meng Liu, Yuan Liu, Junjie Wang, Xiaoming Kong, Yude Bu, Hao Su, Hong Wu
Figure caption: The distribution of the B-band absolute magnitude (M  B ) for the training samples (red) and the identified samples (black).
Paper: Automatic Search for Low-surface-brightness Galaxies from Sloan Digital Sky Survey Images Using Deep Learning
Authors: Zengxu Liang, Zhenping Yi, Wei Du, Meng Liu, Yuan Liu, Junjie Wang, Xiaoming Kong, Yude Bu, Hao Su, Hong Wu
Figure caption: The distribution of axis ratios (b/a) in the g band and r band for the training samples and the identified samples.
Paper: Automatic Search for Low-surface-brightness Galaxies from Sloan Digital Sky Survey Images Using Deep Learning
Authors: Zengxu Liang, Zhenping Yi, Wei Du, Meng Liu, Yuan Liu, Junjie Wang, Xiaoming Kong, Yude Bu, Hao Su, Hong Wu
Figure caption: The sky distribution of 37,536 LSB galaxy candidates in equatorial coordinates.
Paper: Automatic Search for Low-surface-brightness Galaxies from Sloan Digital Sky Survey Images Using Deep Learning
Authors: Zengxu Liang, Zhenping Yi, Wei Du, Meng Liu, Yuan Liu, Junjie Wang, Xiaoming Kong, Yude Bu, Hao Su, Hong Wu
Figure caption: The distribution of the redshift for the training samples (red) and the identified samples (black).
Paper: Automatic Search for Low-surface-brightness Galaxies from Sloan Digital Sky Survey Images Using Deep Learning
Authors: Zengxu Liang, Zhenping Yi, Wei Du, Meng Liu, Yuan Liu, Junjie Wang, Xiaoming Kong, Yude Bu, Hao Su, Hong Wu
Figure caption: (a) Eight LSB galaxies from the training set. (b) 12 model-detected LSB galaxy candidates.
Paper: JWST-TST High Contrast: Achieving Direct Spectroscopy of Faint Substellar Companions Next to Bright Stars with the NIRSpec Integral Field Unit
Authors: Jean-Baptiste Ruffio, Marshall D. Perrin, Kielan K. W. Hoch, Jens Kammerer, Quinn M. Konopacky, Laurent Pueyo, Alex Madurowicz, Emily Rickman, Christopher A. Theissen, Shubh Agrawal, Alexandra Z. Greenbaum, Brittany E. Miles, Travis S. Barman, William O. Balmer, Jorge Llop-Sayson, Julien H. Girard, Isabel Rebollido, Rémi Soummer, Natalie H. Allen, Jay Anderson, Charles A. Beichman, Andrea Bellini, Geoffrey Bryden, Néstor Espinoza, Ana Glidden, Jingcheng Huang, Nikole K. Lewis, Mattia Libralato, Dana R. Louie, Sangmo Tony Sohn, Sara Seager, Roeland P. van der Marel, Hannah R. Wakeford, Laura L. Watkins, Marie Ygouf, C. Matt Mountain
Figure caption: Effect of the effective temperature of a companion on the high-contrast sensitivity of the NIRSpec IFU for HD 19467. (Left) 5σ detection limits as a function of the assumed effective temperature for the model. While lower effective temperatures achieve better detection limits in terms of the flux ratio, this does not mean that cooler companions are easier to detect. This is because they will also be significantly fainter than their warmer counterpart, everything else being equal. The limits are expressed in terms of the companion-to-star flux ratio and they are taken as the median value at each separation. This uses the HD 19467 data set, which means a stellar K-band apparent magnitude of K = 5.4, and a total integration time of ﹩\sim 35\,\mathrm{minutes}﹩. The 5σ companion-to-star flux ratio detection limits are here defined as the median value at each separation. The region within 0.″3 is grayed out because the current implementation of the method is not valid there, but it may theoretically be achieved. (Right) BT-Settl atmospheric models at each temperature used to fit for the companion (Allard et al. 2003). Each model is normalized to 1 Jy in F444W. The NIRCam contrast curve is from Carter et al. (2023) scaled by the square root of the exposure time and stellar brightness. A machine-readable table of the detection limits as a function of the companion effective temperature is available in the following filters: F356W, F444W, Lp, and Mp.(The data used to create this figure are available in the online article.)(The data used to create this figure are available.)
Paper: JWST-TST High Contrast: Achieving Direct Spectroscopy of Faint Substellar Companions Next to Bright Stars with the NIRSpec Integral Field Unit
Authors: Jean-Baptiste Ruffio, Marshall D. Perrin, Kielan K. W. Hoch, Jens Kammerer, Quinn M. Konopacky, Laurent Pueyo, Alex Madurowicz, Emily Rickman, Christopher A. Theissen, Shubh Agrawal, Alexandra Z. Greenbaum, Brittany E. Miles, Travis S. Barman, William O. Balmer, Jorge Llop-Sayson, Julien H. Girard, Isabel Rebollido, Rémi Soummer, Natalie H. Allen, Jay Anderson, Charles A. Beichman, Andrea Bellini, Geoffrey Bryden, Néstor Espinoza, Ana Glidden, Jingcheng Huang, Nikole K. Lewis, Mattia Libralato, Dana R. Louie, Sangmo Tony Sohn, Sara Seager, Roeland P. van der Marel, Hannah R. Wakeford, Laura L. Watkins, Marie Ygouf, C. Matt Mountain
Figure caption: Companion detection maps. From left to right: companion flux map, flux error map, S/N map, and S/N value histogram. The top panels are products of the FM and the bottom panels are obtained from RDI. The absolute fluxes are expressed for the F444W filter but the fits include the entire 2.9–5.3 μm NIRSpec spectral range. The scales and color bars for the flux and S/N maps are identical between the FM and RDI to allow for cross-comparison. The flux error maps illustrate the spatial variations of the sensitivity due to the 2D profile of the JWST PSF such as the diffraction spikes. The similarity of S/N histograms to a Gaussian distribution is a way to evaluate the meaningfulness of the S/N values and the validity of the 5σ  detection limits in Figure 2.
Paper: JWST-TST High Contrast: Achieving Direct Spectroscopy of Faint Substellar Companions Next to Bright Stars with the NIRSpec Integral Field Unit
Authors: Jean-Baptiste Ruffio, Marshall D. Perrin, Kielan K. W. Hoch, Jens Kammerer, Quinn M. Konopacky, Laurent Pueyo, Alex Madurowicz, Emily Rickman, Christopher A. Theissen, Shubh Agrawal, Alexandra Z. Greenbaum, Brittany E. Miles, Travis S. Barman, William O. Balmer, Jorge Llop-Sayson, Julien H. Girard, Isabel Rebollido, Rémi Soummer, Natalie H. Allen, Jay Anderson, Charles A. Beichman, Andrea Bellini, Geoffrey Bryden, Néstor Espinoza, Ana Glidden, Jingcheng Huang, Nikole K. Lewis, Mattia Libralato, Dana R. Louie, Sangmo Tony Sohn, Sara Seager, Roeland P. van der Marel, Hannah R. Wakeford, Laura L. Watkins, Marie Ygouf, C. Matt Mountain
Figure caption: RDI PSF subtraction in the point cloud. Similar to Figure 7, but for fitting of a reference-star observation instead of a simulated PSF at 4.705 μm. (Left) The fit is done in one sector and one wavelength at a time. The sector illustrated here includes the companion HD 19467 B. As in Figure 16, the dots show the spatial sampling locations. (Middle left) Linear interpolation of the 2D point cloud of the reference-star data set, which is sampled onto the 2D point cloud of the science data. (Middle right) Linear interpolation of the 2D point-cloud residuals, showing the data minus the best-fit model. The companion HD 19467 B is visible in the residual map as a bright yellow point source. (Right) Scatter plot of the data, model, and residuals that better represent the nature of the fit. The residuals were offset for clarity.
Paper: JWST-TST High Contrast: Achieving Direct Spectroscopy of Faint Substellar Companions Next to Bright Stars with the NIRSpec Integral Field Unit
Authors: Jean-Baptiste Ruffio, Marshall D. Perrin, Kielan K. W. Hoch, Jens Kammerer, Quinn M. Konopacky, Laurent Pueyo, Alex Madurowicz, Emily Rickman, Christopher A. Theissen, Shubh Agrawal, Alexandra Z. Greenbaum, Brittany E. Miles, Travis S. Barman, William O. Balmer, Jorge Llop-Sayson, Julien H. Girard, Isabel Rebollido, Rémi Soummer, Natalie H. Allen, Jay Anderson, Charles A. Beichman, Andrea Bellini, Geoffrey Bryden, Néstor Espinoza, Ana Glidden, Jingcheng Huang, Nikole K. Lewis, Mattia Libralato, Dana R. Louie, Sangmo Tony Sohn, Sara Seager, Roeland P. van der Marel, Hannah R. Wakeford, Laura L. Watkins, Marie Ygouf, C. Matt Mountain
Figure caption: Topology of the companion signal in a NIRSpec detector image (file jw01414004001_02101_00001_nrs2_cal.fits). (Left) Full NRS2 detector image. The arrow points at the region of the star that is also partially saturated. The area of the IFU slices that includes the bulk of the companion signal is shaded in pink. (Right) Zoom in the highlighted companion area. The pixels that are within 0.″1 of the companion position are painted in red. The dashed orange cut is the row data shown in Figure 11.
Paper: JWST-TST High Contrast: Achieving Direct Spectroscopy of Faint Substellar Companions Next to Bright Stars with the NIRSpec Integral Field Unit
Authors: Jean-Baptiste Ruffio, Marshall D. Perrin, Kielan K. W. Hoch, Jens Kammerer, Quinn M. Konopacky, Laurent Pueyo, Alex Madurowicz, Emily Rickman, Christopher A. Theissen, Shubh Agrawal, Alexandra Z. Greenbaum, Brittany E. Miles, Travis S. Barman, William O. Balmer, Jorge Llop-Sayson, Julien H. Girard, Isabel Rebollido, Rémi Soummer, Natalie H. Allen, Jay Anderson, Charles A. Beichman, Andrea Bellini, Geoffrey Bryden, Néstor Espinoza, Ana Glidden, Jingcheng Huang, Nikole K. Lewis, Mattia Libralato, Dana R. Louie, Sangmo Tony Sohn, Sara Seager, Roeland P. van der Marel, Hannah R. Wakeford, Laura L. Watkins, Marie Ygouf, C. Matt Mountain
Figure caption: Joint FM of the starlight and companion signal in NIRSpec detector images. (Top panel) Data and model for a single row of the detector image shown in Figure 10. (Second panel) Decomposition of the starlight model as a linear combination of 40 spline subcomponents. (Third and fourth panels) Model contribution from the left and right principal components. (Bottom panel) Residuals of the FM fit compared to the flux error from the JWST science calibration pipeline.
Paper: JWST-TST High Contrast: Achieving Direct Spectroscopy of Faint Substellar Companions Next to Bright Stars with the NIRSpec Integral Field Unit
Authors: Jean-Baptiste Ruffio, Marshall D. Perrin, Kielan K. W. Hoch, Jens Kammerer, Quinn M. Konopacky, Laurent Pueyo, Alex Madurowicz, Emily Rickman, Christopher A. Theissen, Shubh Agrawal, Alexandra Z. Greenbaum, Brittany E. Miles, Travis S. Barman, William O. Balmer, Jorge Llop-Sayson, Julien H. Girard, Isabel Rebollido, Rémi Soummer, Natalie H. Allen, Jay Anderson, Charles A. Beichman, Andrea Bellini, Geoffrey Bryden, Néstor Espinoza, Ana Glidden, Jingcheng Huang, Nikole K. Lewis, Mattia Libralato, Dana R. Louie, Sangmo Tony Sohn, Sara Seager, Roeland P. van der Marel, Hannah R. Wakeford, Laura L. Watkins, Marie Ygouf, C. Matt Mountain
Figure caption: Identification and modeling of a possible stray light in NIRSpec. (Left) Artifact in the bottom right of the NRS1 NIRSpec detector images (file jw01414004001_02101_00001_nrs1_cal.fits). The artifact was highlighted by subtracting the best-fit starlight model and dividing the residuals by the pixel flux errors resulting in a pixel S/N map. (Right) Individual left and right principal components used in the FM for the same file. The left principal components highlight some edge instabilities of the spline model of the continuum. The right principal components feature the detector artifact shown in the left panel.
Paper: JWST-TST High Contrast: Achieving Direct Spectroscopy of Faint Substellar Companions Next to Bright Stars with the NIRSpec Integral Field Unit
Authors: Jean-Baptiste Ruffio, Marshall D. Perrin, Kielan K. W. Hoch, Jens Kammerer, Quinn M. Konopacky, Laurent Pueyo, Alex Madurowicz, Emily Rickman, Christopher A. Theissen, Shubh Agrawal, Alexandra Z. Greenbaum, Brittany E. Miles, Travis S. Barman, William O. Balmer, Jorge Llop-Sayson, Julien H. Girard, Isabel Rebollido, Rémi Soummer, Natalie H. Allen, Jay Anderson, Charles A. Beichman, Andrea Bellini, Geoffrey Bryden, Néstor Espinoza, Ana Glidden, Jingcheng Huang, Nikole K. Lewis, Mattia Libralato, Dana R. Louie, Sangmo Tony Sohn, Sara Seager, Roeland P. van der Marel, Hannah R. Wakeford, Laura L. Watkins, Marie Ygouf, C. Matt Mountain
Figure caption: Best-fit centroid of TYC 4433-1800-1 extracted by fitting a model PSF from WebbPSF to the 2D point cloud at each wavelength. The centroid features a 0.″02 trend across the 3–5 μm wavelength range. The high-frequency quasiperiodic fluctuations are likely caused by systematics coming from the spatial undersampling of the data combined with the imperfect PSF model. Note that the average offsets in R.A. and decl. not being zero are not a concern for this early commissioning data set; it is the systematic trend versus wavelength that we draw attention to. The corresponding spectrum of the star is shown in Figure 8.
Paper: JWST-TST High Contrast: Achieving Direct Spectroscopy of Faint Substellar Companions Next to Bright Stars with the NIRSpec Integral Field Unit
Authors: Jean-Baptiste Ruffio, Marshall D. Perrin, Kielan K. W. Hoch, Jens Kammerer, Quinn M. Konopacky, Laurent Pueyo, Alex Madurowicz, Emily Rickman, Christopher A. Theissen, Shubh Agrawal, Alexandra Z. Greenbaum, Brittany E. Miles, Travis S. Barman, William O. Balmer, Jorge Llop-Sayson, Julien H. Girard, Isabel Rebollido, Rémi Soummer, Natalie H. Allen, Jay Anderson, Charles A. Beichman, Andrea Bellini, Geoffrey Bryden, Néstor Espinoza, Ana Glidden, Jingcheng Huang, Nikole K. Lewis, Mattia Libralato, Dana R. Louie, Sangmo Tony Sohn, Sara Seager, Roeland P. van der Marel, Hannah R. Wakeford, Laura L. Watkins, Marie Ygouf, C. Matt Mountain
Figure caption: Definition of the sectors used for RDI. The dots represent the spatial sampling of the nine-dither HD 19467 data set. The blue-shaded region is the sector that is shown in Figure 17, which includes HD 19467 B at the marked location. The data within a 0.″3 radius of the star are not included in RDI and are shown as the hatched region.
Paper: JWST-TST High Contrast: Achieving Direct Spectroscopy of Faint Substellar Companions Next to Bright Stars with the NIRSpec Integral Field Unit
Authors: Jean-Baptiste Ruffio, Marshall D. Perrin, Kielan K. W. Hoch, Jens Kammerer, Quinn M. Konopacky, Laurent Pueyo, Alex Madurowicz, Emily Rickman, Christopher A. Theissen, Shubh Agrawal, Alexandra Z. Greenbaum, Brittany E. Miles, Travis S. Barman, William O. Balmer, Jorge Llop-Sayson, Julien H. Girard, Isabel Rebollido, Rémi Soummer, Natalie H. Allen, Jay Anderson, Charles A. Beichman, Andrea Bellini, Geoffrey Bryden, Néstor Espinoza, Ana Glidden, Jingcheng Huang, Nikole K. Lewis, Mattia Libralato, Dana R. Louie, Sangmo Tony Sohn, Sara Seager, Roeland P. van der Marel, Hannah R. Wakeford, Laura L. Watkins, Marie Ygouf, C. Matt Mountain
Figure caption: Detection maps as a function of the model effective temperature corresponding to Figure 14. (Left) Companion S/N maps. (Right) Histograms of the S/N values after masking HD 19467 B.
Paper: SuperBIT Superpressure Flight Instrument Overview and Performance: Near-diffraction-limited Astronomical Imaging from the Stratosphere
Authors: Ajay S. Gill, Steven J. Benton, Christopher J. Damaren, Spencer W. Everett, Aurelien A. Fraisse, John W. Hartley, David Harvey, Bradley Holder, Eric M. Huff, Mathilde Jauzac, William C. Jones, David Lagattuta, Jason S.-Y. Leung, Lun Li, Thuy Vy T. Luu, Richard Massey, Jacqueline E. McCleary, Johanna M. Nagy, C. Barth Netterfield, Emaad Paracha, Susan F. Redmond, Jason D. Rhodes, Andrew Robertson, L. Javier Romualdez, Jürgen Schmoll, Mohamed M. Shaaban, Ellen L. Sirks, Georgios N. Vassilakis, André Z. Vitorelli
Figure caption: Time stream of the instrument current, the solar array voltage, and the battery voltage over the diurnal cycle. The three-stage (bulk charge, absorption, and float) battery charging algorithm employed by the charge controller is shown. It took ∼5.5 (7) hr to fully charge the batteries at gondola latitudes of ∼40° south (60° south).
Paper: SuperBIT Superpressure Flight Instrument Overview and Performance: Near-diffraction-limited Astronomical Imaging from the Stratosphere
Authors: Ajay S. Gill, Steven J. Benton, Christopher J. Damaren, Spencer W. Everett, Aurelien A. Fraisse, John W. Hartley, David Harvey, Bradley Holder, Eric M. Huff, Mathilde Jauzac, William C. Jones, David Lagattuta, Jason S.-Y. Leung, Lun Li, Thuy Vy T. Luu, Richard Massey, Jacqueline E. McCleary, Johanna M. Nagy, C. Barth Netterfield, Emaad Paracha, Susan F. Redmond, Jason D. Rhodes, Andrew Robertson, L. Javier Romualdez, Jürgen Schmoll, Mohamed M. Shaaban, Ellen L. Sirks, Georgios N. Vassilakis, André Z. Vitorelli
Figure caption: The variation of the primary mirror temperatures over 5.5 days, showing a phase shift with Sun elevation, and a variation of up to 1.°5C within the mirror itself at a given time. The notation for the sensors is t_prxcy, where x is the relative radial distance from the center of the primary mirror and y is the column number.