adsabs/AstroCLIMB huggingface dataset:
Paper: Reassessing the Evidence for Time Variability in the Atmosphere of the Exoplanet HAT-P-7 b
Authors: Maura Lally, Andrew Vanderburg
Figure caption: Measurements of the phase offset recovered from the process described in Section 4.4. The average phase curve of HAT-P-7 b was divided out of the Kepler light curve and, instead, a phase curve shift varying from ±60 deg was injected. The plot shows several recovered phase offsets from the same injected light curve but processed with different spline spacings. The dotted line shows the true value of the injected phase offset. As expected from our previous tests and shown in Figure 4, spline spacings shorter than the period of HAT-P-7 b do attenuate real variations; however, with a basis spline spaced at the period of HAT-P-7 b or longer, our analysis does not flatten away real phase variations.
Paper: Reassessing the Evidence for Time Variability in the Atmosphere of the Exoplanet HAT-P-7 b
Authors: Maura Lally, Andrew Vanderburg
Figure caption: Measurements of the phase (top) and amplitude (bottom) of HAT-P-7 b’s phase curve over time. Our measurements are shown in bold points, plotted with the phase offsets and amplitudes from Armstrong et al. (2016) shown in open points. The vertical dotted lines show where the Kepler light curve is broken into quarters. The χ 2 per degree of freedom value is shown for each result. Though our result appears to show significant atmospheric variability, the identified variations may be due to unrelated factors.
Paper: Reassessing the Evidence for Time Variability in the Atmosphere of the Exoplanet HAT-P-7 b
Authors: Maura Lally, Andrew Vanderburg
Figure caption: Comparison between our measurements of the phase (left) and amplitude (right) of HAT-P-7 b’s phase curve with those from Armstrong et al. (2016). The dashed gray diagonal lines in each plot show a one-to-one slope, while the solid black line shows the slope from a fit accommodating the error bars in both axes. The fitted slope and uncertainty appears on the top left of each plot. The phases and amplitudes we measure in each light-curve segment are fairly similar to those measured by Armstrong et al. (2016).
Paper: Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey
Authors: Ilya Straumit, Andrew Tkachenko, Sarah Gebruers, Jeroen Audenaert, Maosheng Xiang, Eleonora Zari, Conny Aerts, Jennifer A. Johnson, Juna A. Kollmeier, Hans-Walter Rix, Rachael L. Beaton, Jennifer L. Van Saders, Johanna Teske, Alexandre Roman-Lopes, Yuan-Sen Ting, Carlos G. Román-Zúñiga
Figure caption: Effect of the T eff and ﹩\mathrm{log}\,g﹩ parameter changes on the appearance of stellar spectra of late A- to early F-type stars in the wavelength range of the APOGEE instrument. The top and bottom rows show the effect at significantly subsolar ([M/H] = −0.8 dex) and solar ([M/H] = 0.0 dex) metallicity, respectively. Left column: blue, orange, and green lines represent models with T eff = 8000, 9000, and 10,000 K, respectively; ﹩\mathrm{log}\,g﹩ and ﹩v\sin i﹩ are kept fixed at 4.0 dex and 150 km s−1, respectively. Right column: blue, orange, and green lines represent models with ﹩\mathrm{log}\,g﹩ = 3.5, 4.0, and 4.5 dex, respectively; T eff and ﹩v\sin i﹩ are kept fixed at 9000 K and 150 km s−1, respectively.
Paper: Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey
Authors: Ilya Straumit, Andrew Tkachenko, Sarah Gebruers, Jeroen Audenaert, Maosheng Xiang, Eleonora Zari, Conny Aerts, Jennifer A. Johnson, Juna A. Kollmeier, Hans-Walter Rix, Rachael L. Beaton, Jennifer L. Van Saders, Johanna Teske, Alexandre Roman-Lopes, Yuan-Sen Ting, Carlos G. Román-Zúñiga
Figure caption: Differences between the parameters inferred with the ZETA-PAYNE algorithm from the simulated noiseless APOGEE spectra and their true values (black dots), as a function of the true T eff (left) and ﹩v\sin i﹩ (right). From top to bottom: the differences are shown for T eff, ﹩\mathrm{log}\,g﹩, ﹩v\sin i﹩, [M/H], and radial velocity (RV). The internal uncertainties per parameter bin are shown with the magenta full circles with error bars; definitions of the individual parameter bins is given in Table 2.
Paper: Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey
Authors: Ilya Straumit, Andrew Tkachenko, Sarah Gebruers, Jeroen Audenaert, Maosheng Xiang, Eleonora Zari, Conny Aerts, Jennifer A. Johnson, Juna A. Kollmeier, Hans-Walter Rix, Rachael L. Beaton, Jennifer L. Van Saders, Johanna Teske, Alexandre Roman-Lopes, Yuan-Sen Ting, Carlos G. Román-Zúñiga
Figure caption: Comparison between atmospheric parameters derived from the medium-resolution near-IR APOGEE and high-resolution optical HERMES spectra. From left to right: T eff, ﹩\mathrm{log}\,g﹩, and [M/H] of the star. The dashed and solid lines represent the one-to-one correspondence and the best-fit linear regression model, respectively. The error bars shown reflect 1σ statistical uncertainties of the fit. See the text for details.
Paper: Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey
Authors: Ilya Straumit, Andrew Tkachenko, Sarah Gebruers, Jeroen Audenaert, Maosheng Xiang, Eleonora Zari, Conny Aerts, Jennifer A. Johnson, Juna A. Kollmeier, Hans-Walter Rix, Rachael L. Beaton, Jennifer L. Van Saders, Johanna Teske, Alexandre Roman-Lopes, Yuan-Sen Ting, Carlos G. Román-Zúñiga
Figure caption: Comparison between stellar parameters of 20 Kepler SPB-type stars as inferred from their high-resolution optical spectra in Gebruers et al. (2021) and in this study. From left to right: T eff, ﹩\mathrm{log}\,g﹩, ﹩v\sin i﹩, and [M/H]. The solid black line indicates the one-to-one correspondence between the parameters and is shown to help guide the eye. The shown error bars reflect 1σ statistical uncertainties and do not include the internal uncertainty reported in Section 4.
Paper: Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey
Authors: Ilya Straumit, Andrew Tkachenko, Sarah Gebruers, Jeroen Audenaert, Maosheng Xiang, Eleonora Zari, Conny Aerts, Jennifer A. Johnson, Juna A. Kollmeier, Hans-Walter Rix, Rachael L. Beaton, Jennifer L. Van Saders, Johanna Teske, Alexandre Roman-Lopes, Yuan-Sen Ting, Carlos G. Román-Zúñiga
Figure caption: Synthetic spectra computed for T eff = 13,000 K, ﹩\mathrm{log}g=4.0﹩ dex, ﹩v\sin i=100﹩ km s−1, and [M/H] = 0.0 dex and varying microturbulent velocity ξ from 2 to 10 km s−1.
Paper: Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey
Authors: Ilya Straumit, Andrew Tkachenko, Sarah Gebruers, Jeroen Audenaert, Maosheng Xiang, Eleonora Zari, Conny Aerts, Jennifer A. Johnson, Juna A. Kollmeier, Hans-Walter Rix, Rachael L. Beaton, Jennifer L. Van Saders, Johanna Teske, Alexandre Roman-Lopes, Yuan-Sen Ting, Carlos G. Román-Zúñiga
Figure caption: Comparison between stellar parameters inferred with the ZETA-PAYNE algorithm from the HERMES high-resolution and LAMOST low-resolution optical spectra of a sample of 18 SPB stars from Gebruers et al. (2021). The dashed black line indicates the one-to-one correspondence between the parameters and is shown to help guide the eye. The error bars reflect 1σ statistical uncertainties of the fit and do not account for the internal parameter uncertainties.
Paper: Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey
Authors: Ilya Straumit, Andrew Tkachenko, Sarah Gebruers, Jeroen Audenaert, Maosheng Xiang, Eleonora Zari, Conny Aerts, Jennifer A. Johnson, Juna A. Kollmeier, Hans-Walter Rix, Rachael L. Beaton, Jennifer L. Van Saders, Johanna Teske, Alexandre Roman-Lopes, Yuan-Sen Ting, Carlos G. Román-Zúñiga
Figure caption: The same as Figure 6 but for the simulated noiseless BOSS data.
Paper: Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey
Authors: Ilya Straumit, Andrew Tkachenko, Sarah Gebruers, Jeroen Audenaert, Maosheng Xiang, Eleonora Zari, Conny Aerts, Jennifer A. Johnson, Juna A. Kollmeier, Hans-Walter Rix, Rachael L. Beaton, Jennifer L. Van Saders, Johanna Teske, Alexandre Roman-Lopes, Yuan-Sen Ting, Carlos G. Román-Zúñiga
Figure caption: Top: the T eff–﹩v\sin i﹩ distribution of the 1000 simulated spectra that are used to estimate the internal uncertainty. Bottom: comparison of the training (crosses) and test (dots) grids. See the text for details.
Paper: Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey
Authors: Ilya Straumit, Andrew Tkachenko, Sarah Gebruers, Jeroen Audenaert, Maosheng Xiang, Eleonora Zari, Conny Aerts, Jennifer A. Johnson, Juna A. Kollmeier, Hans-Walter Rix, Rachael L. Beaton, Jennifer L. Van Saders, Johanna Teske, Alexandre Roman-Lopes, Yuan-Sen Ting, Carlos G. Román-Zúñiga
Figure caption: Score as a function of iteration number during the training of a neural network.
Paper: Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey
Authors: Ilya Straumit, Andrew Tkachenko, Sarah Gebruers, Jeroen Audenaert, Maosheng Xiang, Eleonora Zari, Conny Aerts, Jennifer A. Johnson, Juna A. Kollmeier, Hans-Walter Rix, Rachael L. Beaton, Jennifer L. Van Saders, Johanna Teske, Alexandre Roman-Lopes, Yuan-Sen Ting, Carlos G. Román-Zúñiga
Figure caption: Left: normalized synthetic spectra of (from top to bottom) OBAF-type stars computed with the GSSP software package in the APOGEE wavelength range. Vertical dashed lines in the top plot indicate positions of hydrogen lines of the Brackett series, while vertical solid lines mark positions of the He II lines at 1.5722 μm and 1.6923 μm, whose wavelengths are taken from Lenorzer et al. (2004). Right: normalized synthetic spectra for the same stellar parameters in the BOSS wavelength range (only part of the full range is shown for clarity).
Paper: Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey
Authors: Ilya Straumit, Andrew Tkachenko, Sarah Gebruers, Jeroen Audenaert, Maosheng Xiang, Eleonora Zari, Conny Aerts, Jennifer A. Johnson, Juna A. Kollmeier, Hans-Walter Rix, Rachael L. Beaton, Jennifer L. Van Saders, Johanna Teske, Alexandre Roman-Lopes, Yuan-Sen Ting, Carlos G. Román-Zúñiga
Figure caption: From top to bottom: synthetic APOGEE (left) or BOSS (right) spectrum of a B-type star, random fifth-order Chebyshev polynomial (model of the unknown response function), and a product of the synthetic spectrum with the model of the response function.
Paper: Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey
Authors: Ilya Straumit, Andrew Tkachenko, Sarah Gebruers, Jeroen Audenaert, Maosheng Xiang, Eleonora Zari, Conny Aerts, Jennifer A. Johnson, Juna A. Kollmeier, Hans-Walter Rix, Rachael L. Beaton, Jennifer L. Van Saders, Johanna Teske, Alexandre Roman-Lopes, Yuan-Sen Ting, Carlos G. Román-Zúñiga
Figure caption: Left: a quasi-random grid of synthetic APOGEE spectra, projected onto the T eff–﹩\mathrm{log}\,g﹩ plane. Right: an additional random sample of models to increase the density of the grid in the low T eff–﹩v\sin i﹩ region. The probability density functions for T eff and ﹩v\sin i﹩ from which the points are sampled are shown in the left and bottom panels, respectively.
Paper: A Census of the Circumstellar Disk Populations in the Sco-Cen Complex 
Authors: K. L. Luhman
Figure caption:                       ﹩{M}_{{G}_{\mathrm{RP}}}﹩ vs. G RP − J for candidate members of Sco-Cen that lack IR excesses from disks (left) and that do exhibit IR excesses (right). Among the stars with disks, those that are unusually faint for their color may be seen in scattered light due to an edge-on disk (open circles).
Paper: A Census of the Circumstellar Disk Populations in the Sco-Cen Complex 
Authors: K. L. Luhman
Figure caption: Excess fractions vs. spectral type in Upper Sco and UCL/LCC for full, transitional, and evolved disks (left) and debris and evolved transitional disks (right, Tables 3 and 4). For each band, data are shown only down to the latest spectral type at which most candidate members are detected. The data for Upper Sco and UCL/LCC are offset slightly left and right, respectively, to avoid overlap. The triangles represent 1σ upper limits.
Paper: A Census of the Circumstellar Disk Populations in the Sco-Cen Complex 
Authors: K. L. Luhman
Figure caption: Extinction-corrected IR color excesses for the WISE sources from Table 1 that have excesses. The boundaries used for assigning disk classes are shown in the bottom diagram (red solid lines).
Paper: A Census of the Circumstellar Disk Populations in the Sco-Cen Complex 
Authors: K. L. Luhman
Figure caption: Extinction-corrected IR colors vs. spectral type for the WISE sources from Table 1. For stars that lack spectroscopy, spectral types have been estimated from photometry (Luhman 2021). In each diagram, the tight sequence of blue colors corresponds to stellar photospheres. The thresholds used for identifying color excesses from disks are indicated (red solid lines).
Paper: A Census of the Circumstellar Disk Populations in the Sco-Cen Complex 
Authors: K. L. Luhman
Figure caption: Extinction-corrected IR color excesses for candidate members of four populations in Sco-Cen. The boundaries from Figure 2 used for assigning disk classes are indicated (red solid lines).
Paper: A Census of the Circumstellar Disk Populations in the Sco-Cen Complex 
Authors: K. L. Luhman
Figure caption: Fractions of candidate members of Upper Sco and UCL/LCC with primordial disks (full, transitional, and evolved) as a function of spectral type (Table 5). The boundaries of the spectral type bins were chosen to correspond approximately to logarithmic intervals of mass.
Paper: A Census of the Circumstellar Disk Populations in the Sco-Cen Complex 
Authors: K. L. Luhman
Figure caption:                       ﹩{M}_{{G}_{\mathrm{RP}}}﹩ vs. G BP − G RP and G RP − J for candidate members of Upper Sco and UCL/LCC that lack disks (black points) and that have full disks (red points).
Paper: A Census of Stars and Disks in Corona Australis 
Authors: T. L. Esplin, K. L. Luhman
Figure caption: Map of the fields toward Corona Australis that have been imaged by IRAC on Spitzer (Table 2) and FourStar on Magellan. The known members are plotted with symbols based on their extinction. The Corona Australis cloud is represented by an extinction map (gray scale; Dobashi et al. 2013).
Paper: A Census of Stars and Disks in Corona Australis 
Authors: T. L. Esplin, K. L. Luhman
Figure caption: ΔM  K  vs. A  J  for members of Corona Australis that are near the cloud (b < − 17.3). The metric is defined as the difference between the extinction-corrected M  K  and the median value for Upper Sco members at a given spectral type (Esplin et al. 2018). Higher values of ΔM  K  correspond to older ages.
Paper: A Census of Stars and Disks in Corona Australis 
Authors: T. L. Esplin, K. L. Luhman
Figure caption: Proper motion offsets vs. parallax for members of Corona Australis with σ  π  < 0.5 and RUWE < 1.6 (Table 1). Different symbols are used for stars embedded in the cloud (A  J  > 1, b < −17.°3) and all other sources (A  J  ≤ 1 or b ≥ − 17.°3).
Paper: A Census of Stars and Disks in Corona Australis 
Authors: T. L. Esplin, K. L. Luhman
Figure caption: Color–magnitude diagrams of members of Corona Australis with spectral types ≤ M6 (blue points) and >M6 (red circles) within the field imaged by IRAC in multiple epochs (left) and the field imaged by FourStar (right; see Figure 6). Sources with unknown status are indicated (gray points). The completeness limits for these data are marked (dashed lines).
Paper: A Census of Stars and Disks in Corona Australis 
Authors: T. L. Esplin, K. L. Luhman
Figure caption: Top: Galactic Cartesian coordinates for members of Corona Australis with σ  π  < 0.5 and RUWE < 1.6 (Table 1). Bottom: stars that have radial velocity measurements are plotted in diagrams of U, V, and W vs. X, Y, and Z, respectively. The average errors are indicated.
Paper: A Census of Stars and Disks in Corona Australis 
Authors: T. L. Esplin, K. L. Luhman
Figure caption: Relative proper motions based on multi-epoch imaging from IRAC and FourStar for Corona Australis, consisting of previously known members (black points), new members from our spectroscopic survey (red circles), and all other sources (gray points). The typical errors are indicated. Sources within 1σ of the blue circle were selected as candidate members.
Paper: A Census of Stars and Disks in Corona Australis 
Authors: T. L. Esplin, K. L. Luhman
Figure caption: Box diagrams of the interquartile range of ΔM  K  calculated for individual members in three samples of Corona Australis members. The error bars represent the errors on the medians, which have been estimated via bootstrapping.
Paper: A Census of Stars and Disks in Corona Australis 
Authors: T. L. Esplin, K. L. Luhman
Figure caption: Examples of IR spectra of new members of Corona Australis (Table 4). The spectra have been dereddened to match the slopes of young standards from Luhman et al. (2017).(The data used to create this figure are available.)
Paper: A Census of Stars and Disks in Corona Australis 
Authors: T. L. Esplin, K. L. Luhman
Figure caption: Extinction-corrected CMDs of the previously known members of Corona Australis (points) and new members from our spectroscopic survey (circles). We have selected candidate members based on positions above the solid boundaries.
Paper: A Census of Stars and Disks in Corona Australis 
Authors: T. L. Esplin, K. L. Luhman
Figure caption: Examples of optical spectra of new members of Corona Australis (Table 4). The spectra have been smoothed to a resolution of 13 Å.(The data used to create this figure are available.)
Paper: Accurate Photometry of Saturated Stars Using the Point-spread-function Wing Technique with Spitzer
Authors: Kate Y. L. Su, G. H. Rieke, M. Marengo, Everett Schlawin
Figure caption: Final mosaics at 3.6 μm for the 12 stars in this study. The green circle in each panel marks a radius of 100″ circle centered at the star. The stars from left to right and top to bottom are Sirius, β Gem, Procyon, HD 30652, HD 102870, HD 142860, HD 19373, HD 126660, HD 215648, HD 173667, HD 10476, and HD 165459. The intercept of the three blue lines in the Sirius data (top left panel) marks the best-guess centroid position using the diffraction spikes.
Paper: Accurate Photometry of Saturated Stars Using the Point-spread-function Wing Technique with Spitzer
Authors: Kate Y. L. Su, G. H. Rieke, M. Marengo, Everett Schlawin
Figure caption: Relative radial profiles using 49 different center positions for Sirius (thin colored lines) with the left panel for 3.6 μm data and right for 4.5 μm. In each panel, the thick black line is the profile that has the minimum deviation from the average profile within the range of 30″–180″ marked by the gray arrow.
Paper: A Census of Stars and Disks in Corona Australis 
Authors: T. L. Esplin, K. L. Luhman
Figure caption: Extinction-corrected IR color excesses for adopted members of Corona Australis. If a source lacked [4.5] or [24] photometry, the W2 or W4 data is shown instead. The bottom two diagrams include boundaries that are used to distinguish full disks from disks in more advanced stages of evolution (dashed lines).
Paper: Accurate Photometry of Saturated Stars Using the Point-spread-function Wing Technique with Spitzer
Authors: Kate Y. L. Su, G. H. Rieke, M. Marengo, Everett Schlawin
Figure caption: Example of the difference images between HD 10476 and Sirius at six different shift positions from (a) to (e) as the shift position gets closer to the best registered position (f) where the prominent diffraction spikes disappear.
Paper: A Census of Stars and Disks in Corona Australis 
Authors: T. L. Esplin, K. L. Luhman
Figure caption: Histograms of spectral types for four samples of adopted members of Corona Australis. The completeness limits are indicated (dashed lines).
Paper: The MKID Pipeline: A Data Reduction and Analysis Pipeline for UVOIR MKID Data
Authors: Sarah Steiger, John I. Bailey, Nicholas Zobrist, Noah Swimmer, Rupert Dodkins, Kristina K. Davis, Benjamin A. Mazin
Figure caption: A block diagram of the MKID Pipeline. Processing steps are shown as rectangles with calibration steps in green, product generation steps in orange, and processes that are a part of the calibration data product (CDP) subsystem in blue. Files containing observing data (in various formats) are depicted by purple (input) and yellow (processed) rhombuses. User inputs are in purple. Calibration data products themselves are shown as blue cylinder faces. Rectangles with the wavy bottom edge represent files where the purple are user input configuration files and the yellow are the files representing calibrated output data products. All calibration steps are optional with the exception of the bolded “Metadata Attachment” and “Wavelength Calibration” steps, without which product generation cannot occur. The dashed stages of the pipeline may be completely omitted. The bracketed numbers in the “Calibration Pipeline” boxes denote pipeline stages that must completed to generate the respective CDPs. Flat-fielding can be accomplished via reference to wavelength calibration data, denoted by the dashed arrow.
Paper: The MKID Pipeline: A Data Reduction and Analysis Pipeline for UVOIR MKID Data
Authors: Sarah Steiger, John I. Bailey, Nicholas Zobrist, Noah Swimmer, Rupert Dodkins, Kristina K. Davis, Benjamin A. Mazin
Figure caption: Top: Percent variability in pixel response before and after applying the flat-field calibration. This is calculated by subtracting and then dividing the median counts registered on the detector from each pixel. The structure seen in this MEC data is dominated by vignetting from the optical system. Bottom: Histograms of the percent variability with the uncorrected pixel response shown in blue and the corrected pixel response shown in orange.
Paper: The MKID Pipeline: A Data Reduction and Analysis Pipeline for UVOIR MKID Data
Authors: Sarah Steiger, John I. Bailey, Nicholas Zobrist, Noah Swimmer, Rupert Dodkins, Kristina K. Davis, Benjamin A. Mazin
Figure caption: Photon time stream where the red arrows denote locations of identified cosmic rays. Excluding all photon data obtained in a 10 μs window around each event would eliminate a total of 0.00195 s, <0.01% of the exposure. Any missed cosmic rays would contribute no more than a single photon per pixel in an astrophysical source.
Paper: The MKID Pipeline: A Data Reduction and Analysis Pipeline for UVOIR MKID Data
Authors: Sarah Steiger, John I. Bailey, Nicholas Zobrist, Noah Swimmer, Rupert Dodkins, Kristina K. Davis, Benjamin A. Mazin
Figure caption: Resolution image at 1.1 μm for the detector in MEC as of the time of this publication. The median energy resolution (R) across the array at this wavelength is 3.93 excluding dead pixels. This particular detector has three defective feed lines (each containing 2000 pixels), which results in the large strip of dead (R = 0) pixels seen to the left-hand side of the image
Paper: The MKID Pipeline: A Data Reduction and Analysis Pipeline for UVOIR MKID Data
Authors: Sarah Steiger, John I. Bailey, Nicholas Zobrist, Noah Swimmer, Rupert Dodkins, Kristina K. Davis, Benjamin A. Mazin
Figure caption: Single pixel count rate histograms for each laser wavelength, as well as the calibration solution fit (bottom right). The small Gaussian bump at low phases is likely to be due to an IR leak around 2.7 μm in the filter stack of the instrument (MEC) used to take this data.
Paper: The MKID Pipeline: A Data Reduction and Analysis Pipeline for UVOIR MKID Data
Authors: Sarah Steiger, John I. Bailey, Nicholas Zobrist, Noah Swimmer, Rupert Dodkins, Kristina K. Davis, Benjamin A. Mazin
Figure caption: Subset of an MKID array with hot, cold, and dead pixels labeled. The threshold method was used in the determination of the pixel flags with default settings.