adsabs/AstroCLIMB huggingface dataset:
Paper: Age Distribution of Exoplanet Host Stars: Chemical and Kinematic Age Proxies from GAIA DR3
Authors: C. Swastik, Ravinder K. Banyal, Mayank Narang, Athira Unni, Bihan Banerjee, P. Manoj, T. Sivarani
Figure caption: Variation of δ[Mg/Fe] = [Mg/Fe]GAIA-[Mg/Fe]HARPS as a function of effective temperature. A quadratic polynomial best describes the trends of δ[Mg/Fe] with T eff. The best-fit polynomial is represented by the blue line in the top figure, by the coefficients (p0, p1, p2) = (1.12e–07, -1.34e–03, 4.05), while the figure at the bottom shows the residuals in the approximation of the trend by the above polynomial. The green-dashed line shows the linear fit for the data.
Paper: PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information
Authors: Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael Zemcov
Figure caption: Reconstruction accuracy for diffuse cirrus (top) and CIB galaxies (bottom) in mock SPIRE observations, as a function of Fourier component truncation scale (ranging from ﹩{\theta }_{\mathrm{FC}}^{\min }=5^{\prime} ﹩ to 40″). The different colors show how the results change upon increasing the level of cirrus signal. The top axes indicate the approximate angular truncation scale corresponding to different N FC. The dashed lines in the top figure show the residual rms levels obtained from fitting Fourier components to the same cirrus realizations but without CIB injected.
Paper: PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information
Authors: Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael Zemcov
Figure caption: PGM for PCAT-DE. The top level of the PGM shows hyperparameters (α,  μ   S ,  σ   S , η, β( S )) that characterize priors over the point-source parameters { x ,  S , N src} and diffuse component colors { β   t }. These parameters are then used to generate model images M  D  that are compared with the data. The diamonds and circles indicate variables that are fixed and floated, respectively.
Paper: Detecting and Characterizing Mg II Absorption in DESI Survey Validation Quasar Spectra
Authors: Lucas Napolitano, Agnesh Pandey, Adam D. Myers, Ting-Wen Lan, Abhijeet Anand, Jessica Aguilar, Steven Ahlen, David M. Alexander, David Brooks, Rebecca Canning, Chiara Circosta, Axel De La Macorra, Peter Doel, Sarah Eftekharzadeh, Victoria A. Fawcett, Andreu Font-Ribera, Juan Garcia-Bellido, Satya Gontcho A Gontcho, L. Le Guillou, Julien Guy, Klaus Honscheid, Stephanie Juneau, T. Kisner, Martin Landriau, Aaron M. Meisner, Ramon Miquel, J. Moustakas, Will J. Percival, J. Xavier Prochaska, Michael Schubnell, Gregory Tarlé, B. A. Weaver, Benjamin Weiner, Zhimin Zhou, Hu Zou, Siwei Zou
Figure caption: Redshift-space distributions of all of the detected absorbers. Contours are kernel density estimates of the distribution. Marginalized histograms of each redshift population are also presented. The quasar redshift histogram is plotted alongside the redshift histogram of all DESI QSOs for reference, and both are scaled by density.
Paper: PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information
Authors: Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael Zemcov
Figure caption: Component separation results for a mock CIB realization with injected cirrus dust (at the highest level, 8×-Planck) and SPIRE-like noise (σ inst = 1 mJy beam−1). The columns show the data signal (left), the median PCAT model (middle), and the corresponding residuals (right).
Paper: Detecting and Characterizing Mg II Absorption in DESI Survey Validation Quasar Spectra
Authors: Lucas Napolitano, Agnesh Pandey, Adam D. Myers, Ting-Wen Lan, Abhijeet Anand, Jessica Aguilar, Steven Ahlen, David M. Alexander, David Brooks, Rebecca Canning, Chiara Circosta, Axel De La Macorra, Peter Doel, Sarah Eftekharzadeh, Victoria A. Fawcett, Andreu Font-Ribera, Juan Garcia-Bellido, Satya Gontcho A Gontcho, L. Le Guillou, Julien Guy, Klaus Honscheid, Stephanie Juneau, T. Kisner, Martin Landriau, Aaron M. Meisner, Ramon Miquel, J. Moustakas, Will J. Percival, J. Xavier Prochaska, Michael Schubnell, Gregory Tarlé, B. A. Weaver, Benjamin Weiner, Zhimin Zhou, Hu Zou, Siwei Zou
Figure caption: Two example spectra showing genuine Mg II absorption systems that appear to be physically impossible due to poor pipeline redshifts. The detected Mg II absorption is indicated by the gray-outlined area. The associated error spectrum is shown in orange.
Paper: PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information
Authors: Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael Zemcov
Figure caption: Mock realizations of CIB and cirrus at SPIRE resolution with 1 mJy beam−1 instrument noise. In this limit, confused point sources are the dominant agent reducing our modeling precision. The different panels show levels of cirrus consistent with the median “clean-sky” Planck sample (top left) and progressively more contaminated fields.
Paper: Detecting and Characterizing Mg II Absorption in DESI Survey Validation Quasar Spectra
Authors: Lucas Napolitano, Agnesh Pandey, Adam D. Myers, Ting-Wen Lan, Abhijeet Anand, Jessica Aguilar, Steven Ahlen, David M. Alexander, David Brooks, Rebecca Canning, Chiara Circosta, Axel De La Macorra, Peter Doel, Sarah Eftekharzadeh, Victoria A. Fawcett, Andreu Font-Ribera, Juan Garcia-Bellido, Satya Gontcho A Gontcho, L. Le Guillou, Julien Guy, Klaus Honscheid, Stephanie Juneau, T. Kisner, Martin Landriau, Aaron M. Meisner, Ramon Miquel, J. Moustakas, Will J. Percival, J. Xavier Prochaska, Michael Schubnell, Gregory Tarlé, B. A. Weaver, Benjamin Weiner, Zhimin Zhou, Hu Zou, Siwei Zou
Figure caption: Distribution of rest-frame EW values for the (2796 Å) and (2803 Å) Mg II lines. Contours are kernel density estimates of the distribution. Marginalized histograms are also presented.
Paper: PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information
Authors: Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael Zemcov
Figure caption: Histograms of pixelwise residuals between cirrus (top), CIB galaxies (bottom), and the posterior mean of each respective model component.
Paper: Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE). I. Using Grid-based Nested Sampling in Coronagraphy Observation Simulations for H2O
Authors: Natasha Latouf, Avi M. Mandell, Geronimo L. Villanueva, Michael Dane Moore, Nicholas Susemiehl, Vincent Kofman, Michael D. Himes
Figure caption: The full modern-Earth high-resolution spectrum from the spectral grid in Susemiehl et al. (2023), with a zero cloud fraction. We show the full resolution (R) of 500 (shown in pink, top panel), the spectra binned to R = 140 that are used in the nested sampling simulations conducted in this work, (shown in purple, middle panel), and a spectrum at R = 70 for reference (shown in dark purple, bottom panel). We also present the bandpass centers used in this study as dots (only in the top panel), and the first and last 20% bandpasses shaded in gray. We note that bandpasses overlap heavily with each other, so we only show the first and last for clarity.
Paper: M67 Blue Stragglers with High-resolution Infrared Spectroscopy
Authors: K. E. Brady, C. Sneden, C. A. Pilachowski, Melike Afşar, G. N. Mace, D. T. Jaffe, N. M. Gosnell, R. Seifert
Figure caption: Chemical abundance vs. effective temperature. The BSSs from our analysis are shown as black circles. BSS error bars are one standard deviation. M67 turnoff star abundances from Souto et al. (2019) are shown as blue squares and from Shetrone & Sandquist (2000) as green squares. Uncertainties from Souto et al. (2019) are shown in the upper right corner of each panel. M67 BSS abundances from Bertelli Motta et al. (2018) are blue circles and from Shetrone & Sandquist (2000) are green circles. Data from the literature are unfilled symbols.
Paper: M67 Blue Stragglers with High-resolution Infrared Spectroscopy
Authors: K. E. Brady, C. Sneden, C. A. Pilachowski, Melike Afşar, G. N. Mace, D. T. Jaffe, N. M. Gosnell, R. Seifert
Figure caption: Optical CMD of M67 with designated members by G15 and photometry from Gaia DR2. RF cluster membership probabilities from Gao (2018) are indicated with the symbol shade of blue as defined by the color palate, with the exception of S277, S997, and S975, whose RV membership probabilities are from G15. The program stars are circled.
Paper: M67 Blue Stragglers with High-resolution Infrared Spectroscopy
Authors: K. E. Brady, C. Sneden, C. A. Pilachowski, Melike Afşar, G. N. Mace, D. T. Jaffe, N. M. Gosnell, R. Seifert
Figure caption: A sample spectral region in the H band. The spectra are arranged in order of decreasing T eff from top to bottom. Rotational broadening of the spectral lines can be seen in S1031, S975, and S1195. The derived rotational velocities are shown in Table 3.
Paper: M67 Blue Stragglers with High-resolution Infrared Spectroscopy
Authors: K. E. Brady, C. Sneden, C. A. Pilachowski, Melike Afşar, G. N. Mace, D. T. Jaffe, N. M. Gosnell, R. Seifert
Figure caption: IR CMD of M67 with designated members from G15 and photometry from 2MASS (Cutri et al. 2003). The program stars are circled.
Paper: Three-dimensional MHD Simulations of the Magnetic Pileup at Mars
Authors: M. Wang, Z. J. Guan, L. Xie, J. Y. Lu, X. Xu, Y. Wei, Z. Zhou, L. Chai, J. Wang, Q. Chang, H. X. Zhang, B. H. Qu, H. Y. Sui, J. Q. Zhang, F. H. Qiao, L. Li
Figure caption: IMF components influence on the magnetic pileup at Mars represented by the difference in magnetic field intensity, dB  t , on the X–Y (upper panels) and X–Z (bottom panels) planes in the MSO coordinate system for P  d  = 1.06 nPa (n = 4.0 cm−3, V = −400.0 km s−1). Panels (a)–(h) display dB  t  under the steady state between the IMF conditions of zero intensity (IMF = 0) and B  X  = 3 nT (B  Y  = B  Z  = 0  nT) (panels (a) and (e)), B  Y  = 3 nT (B  X  = B  Z  = 0  nT) (panels (b) and (f)), B  Z  = 3 nT (B  X  = B  Y  = 0  nT) (panels (c) and (g)), and B  Z  = −3 nT (B  X  = B  Y  = 0  nT) (panels (d) and (h)). The black dashed lines represent the locations of the magnetic pileup boundary and the bow shock for Vignes et al. (2000) models.
Paper: Three-dimensional MHD Simulations of the Magnetic Pileup at Mars
Authors: M. Wang, Z. J. Guan, L. Xie, J. Y. Lu, X. Xu, Y. Wei, Z. Zhou, L. Chai, J. Wang, Q. Chang, H. X. Zhang, B. H. Qu, H. Y. Sui, J. Q. Zhang, F. H. Qiao, L. Li
Figure caption: The difference in magnetic field intensity, dB  t , on the X–Y (upper panels) and X–Z (bottom panels) planes in the MSO coordinate system for P  d  = 1.06 nPa (n = 4.0 cm−3, V = −400.0 km s−1). The black dashed lines stand for the locations of the magnetic pileup boundary and the bow shock for Vignes et al. (2000) models.
Paper: Automated SpectroPhotometric Image REDuction (ASPIRED)
Authors: Marco C. Lam, Robert J. Smith, Iair Arcavi, Iain A. Steele, Josh Veitch-Michaelis, Lukasz Wyrzykowski
Figure caption: Top: a significantly tilted spectrum is used to illustrate how cross-correlation is used to find the shift between neighboring subspectra with a resampling factor of 1.0. The light gray, dark gray, and black boxes are three example subspectra. Bottom left: the three subspectra are summed in the dispersion direction to generate the three profiles that are cross-correlated to compute the shift. Bottom right: the cross-correlated function for the light gray subspectrum relative to the dark gray subspectrum (plotted in alternating light and dark gray dashes), and the one for the black subspectrum relative to the dark gray subspectrum (plotted in alternating dark gray and black dashes).
Paper: Automated SpectroPhotometric Image REDuction (ASPIRED)
Authors: Marco C. Lam, Robert J. Smith, Iair Arcavi, Iain A. Steele, Josh Veitch-Michaelis, Lukasz Wyrzykowski
Figure caption: A two-dimensional spectrum from Las Cumbres/FLOYDS is used to demonstrate the rectification procedure. The dotted white line is aligned in the spatial direction to serve as a visual guide. Top: the trimmed image of a FLOYDS spectrum. Middle: the two-dimensional spectrum is resampled in the spatial direction based on the polynomial function of the trace. Bottom: the two-dimensional spectrum is resampled in the dispersion direction. The shifts and scales are found by cross-correlating the subspectra divided in the spatial direction. This is perpendicular to the tracing process.
Paper: Automated SpectroPhotometric Image REDuction (ASPIRED)
Authors: Marco C. Lam, Robert J. Smith, Iair Arcavi, Iain A. Steele, Josh Veitch-Michaelis, Lukasz Wyrzykowski
Figure caption: The same spectrum as in Figure 2. Top: the regions used for source and sky extractions are marked by the red and green boundaries. The light shade of orange hash marks the region that is used for spectral extraction. The two boxes show the two columns of pixels fitted with profiles in the bottom half of the figure (the boxes are inflated for clarity). Bottom: the independently normalized electron counts across the two slices; the much higher baseline on the right indicates a lower signal-to-background ratio. The vertical black dashed line is the centroid (trace) of the spectrum, the two red dashed lines mark the regions of the source, and the pairs of green dashed lines on each side of the centroid show the regions used for sky extractions, respectively. The black line is the measured spectral profile (data), and the blue line is the fitted LOWESS line-spread function (model), offset for clarity. The right panel illustrates how an extraction over a nonrectified two-dimensional spectrum can lead to an increased sky-background level.
Paper: Automated SpectroPhotometric Image REDuction (ASPIRED)
Authors: Marco C. Lam, Robert J. Smith, Iair Arcavi, Iain A. Steele, Josh Veitch-Michaelis, Lukasz Wyrzykowski
Figure caption: Example extractions of some special cases. Each set of spectra uses the same normalization. All spectra are optimally extracted using the H86 algorithm. The GTC/OSIRIS (top set in blue hue) spectra were flux calibrated in the shorter wavelengths where there are a lot of absorption features. With a bad sensitivity curve, we expect many small bump features in the spectra and that their continuum would not agree in the overlapping wavelength range, as polynomials tend to run away at the edges. The TNG/DOLORES (middle set in red hue) spectra were of two targets where the tracing was done simultaneously and then the extraction done sequentially. The WHT/ISIS and ACAM (bottom set in green hue) data demonstrate extracting a target with a very low S/N. The independently flux-calibrated spectra agree well with each other in the overlapping wavelength ranges.
Paper: Automated SpectroPhotometric Image REDuction (ASPIRED)
Authors: Marco C. Lam, Robert J. Smith, Iair Arcavi, Iain A. Steele, Josh Veitch-Michaelis, Lukasz Wyrzykowski
Figure caption: Fractional residual in the calibrated flux of BD+33 2642 using Hiltner 102 as a standard star taken with LT/SPRAT. Each gray line depicts the calibration residual between the median spectrum of the eight spectra taken on different nights. The blue area shows the one standard deviation of the scatter in the residual. The spikes above 5% residual are at the Hγ line at 4340 Å and the oxygen telluric features at 6869 and 7605 Å. The residuals that are larger on the two ends are expected as the S/Ns drop with the detector efficiency on either side.
Paper: Automated SpectroPhotometric Image REDuction (ASPIRED)
Authors: Marco C. Lam, Robert J. Smith, Iair Arcavi, Iain A. Steele, Josh Veitch-Michaelis, Lukasz Wyrzykowski
Figure caption: Top: the VLT/FORS2 spectrum and the trace of the standard star LTT 7379. Middle: the extracted spectra in electron counts using the three extraction methods are very similar. Bottom: the uncertainties with the three extraction methods in units of electron counts. The top-hat method is clearly noisier than the two optimal methods. Extractions using the Horne86 and Marsh89 algorithms produce almost identical outputs.
Paper: Automated SpectroPhotometric Image REDuction (ASPIRED)
Authors: Marco C. Lam, Robert J. Smith, Iair Arcavi, Iain A. Steele, Josh Veitch-Michaelis, Lukasz Wyrzykowski
Figure caption: From top to bottom are the comparison spectra taken with VLT/FORS2, Las Cumbres/FLOYDS, LT/SPRAT, and Gemini/GMOS in long-slit mode. The published FORS2 spectrum was reduced using MOLLY (green), powered by the STARLINK library. The other three (red) were reduced using the respective IRAF-based pipelines. All spectra reduced with ASPIRED (blue) are optimally extracted using the H86 algorithm and resampled to match the respective published spectra. Telluric correction is applied using the default setting.
Paper: Automated SpectroPhotometric Image REDuction (ASPIRED)
Authors: Marco C. Lam, Robert J. Smith, Iair Arcavi, Iain A. Steele, Josh Veitch-Michaelis, Lukasz Wyrzykowski
Figure caption: Extinction curves measured at Roque de los Muchachos Observatory (2420 m; blue), Maunakea Observatories (4205 m; orange), Cerro Paranal Observatory (2635 m; green), and La Silla Observatory (2400 m; red). The extinction table of La Silla Observatory terminates at 9000 Å. The rise at the red end of the curve is an undesired artifact due to extrapolation using a cubic spline. We are explicitly plotting this range to serve as a warning since we opt to preserve the raw data as provided without appending fake data points.
Paper: Automated SpectroPhotometric Image REDuction (ASPIRED)
Authors: Marco C. Lam, Robert J. Smith, Iair Arcavi, Iain A. Steele, Josh Veitch-Michaelis, Lukasz Wyrzykowski
Figure caption: Top: the extracted standard spectrum in electron counts (blue), the literature spectrum in units of flux density (orange), and the sensitivity function (y-axis on the right; black). Bottom: the literature spectrum (same as the one above) is plotted in orange, and the calibrated observed spectrum is plotted in green.
Paper: Automated SpectroPhotometric Image REDuction (ASPIRED)
Authors: Marco C. Lam, Robert J. Smith, Iair Arcavi, Iain A. Steele, Josh Veitch-Michaelis, Lukasz Wyrzykowski
Figure caption: The native wavelength calibration diagnostic plots from RASCAL. Top: the arc is plotted in blue, the fitted peaks are marked by the orange dots. Middle: the residual plot shows the difference between the fitted peaks and the true wavelengths. Bottom: the pixel–wavelength function (green) is overplotted with the fitted peaks (orange).
Paper: On the Origins of Extreme Velocity Stars as Revealed by Large-scale Galactic Surveys
Authors: Qing-Zheng Li, Yang Huang, Xiao-Bo Dong, Hua-Wei Zhang, Timothy C. Beers, Zhen Yuan
Figure caption: Ejection ratios for 88 HVSs and candidates from the literature and the current work, as a function of spectral type, color-coded by metallicity. Here we adopt the escape velocity curve from Williams et al. (2017) to calculate the ejection ratio. As mentioned in Section 3.3, the 52 late-type stars found in this work are mariginally HVS candidates given their large velocity uncertainties. The ejection ratio is defined to be the ratio of the total or radial velocity in the Galactic rest frame to the local escape velocity of a star. Note that the [Fe/H] values for white dwarf and early-type stars are assumed to be Solar ([Fe/H] = zero). The open asterisk marks the average ejection ratio of the corresponding spectral type. The inset shows the chemical distribution of the late-type stars discovered in the current work in the [α/Fe]–[Fe/H] plane.
Paper: On the Origins of Extreme Velocity Stars as Revealed by Large-scale Galactic Surveys
Authors: Qing-Zheng Li, Yang Huang, Xiao-Bo Dong, Hua-Wei Zhang, Timothy C. Beers, Zhen Yuan
Figure caption: The density distribution of v  ϕ  vs. [Fe/H] for our HiVel candidates after the two selection steps. The solid-black lines represent the isodensity contours, and the black-dotted line is the criterion we use to separate the disk population (upper right) and halo population (lower left). The red stars mark the final HVS candidates discovered in this work, some of which (blue-filled circles) were identified by Li et al. (2021). The inset shows the distributions of total velocity, V GSR, for the disk (black) and halo (red) HiVel candidates.
Paper: On the Origins of Extreme Velocity Stars as Revealed by Large-scale Galactic Surveys
Authors: Qing-Zheng Li, Yang Huang, Xiao-Bo Dong, Hua-Wei Zhang, Timothy C. Beers, Zhen Yuan
Figure caption: Distribution of the sources of our final HiVel sample in the plane of V GSR and Galactocentric distance r. Red dots denote the halo population and blue dots denote the disk population. The dashed line is the Galactic escape velocity curve. The large dots with ±1σ error bars represent the 52 HVS candidates with at least one group of P ub ≥ 50%, calculated from Monte Carlo simulations assuming the model Galactic escape velocity curves (dashed lines) derived from different Milky Way’s potentials or the measured one (black-dashed line), as indicated in the top right-hand corner. The green and yellow large dots mark those candidates with all five groups of P ub greater than 50 and 80%, respectively.
Paper: On the Origins of Extreme Velocity Stars as Revealed by Large-scale Galactic Surveys
Authors: Qing-Zheng Li, Yang Huang, Xiao-Bo Dong, Hua-Wei Zhang, Timothy C. Beers, Zhen Yuan
Figure caption: The final HiVel sample shown in the log g–T eff diagram (left-hand panel) and the diagram of ﹩{M}_{{G}_{0}}﹩ vs. color ﹩{({G}_{\mathrm{BP}}-{G}_{\mathrm{RP}})}_{0}﹩ (right-hand panel). The color is coded by metallicity, as labeled in the respective right sides. The larger dots are HVS candidates. The two solid lines in the panels represent stellar isochrones from PARSEC (Bressan et al. 2012; Marigo et al. 2017), with [M/H] = −1.85 and age = 9 Gyr for the left-hand line in each panel, and [M/H] = −0.60 and age = 14 Gyr for the right-hand line in each panel.
Paper: On the Origins of Extreme Velocity Stars as Revealed by Large-scale Galactic Surveys
Authors: Qing-Zheng Li, Yang Huang, Xiao-Bo Dong, Hua-Wei Zhang, Timothy C. Beers, Zhen Yuan
Figure caption: Hammer projection in R.A. and decl. of the final HiVel stars. Blue dots denote the disk population, red dots the halo population, and green-solid circles indicate our HVS candidates. The thick black line indicates the disk plane of the MW, with the large black dot and X-symbol marking the locations of the Galactic center and anticenter, respectively. The locations of M31, the LMC, and the Sgr dSph are also marked with their respective annotations.
Paper: On the Origins of Extreme Velocity Stars as Revealed by Large-scale Galactic Surveys
Authors: Qing-Zheng Li, Yang Huang, Xiao-Bo Dong, Hua-Wei Zhang, Timothy C. Beers, Zhen Yuan
Figure caption: The v los variations (the maximum minus the minimum value) for our 3776 HiVel candidates with multiple observations. For reference, the red-vertical line marks a difference of 15 km s−1.
Paper: Imaging the Milky Way with Millihertz Gravitational Waves
Authors: Kaitlyn Szekerczes, Scott Noble, Cecilia Chirenti, James Ira Thorpe
Figure caption: Comparison of GBMCMC 1σ errors and coherent diffraction limit estimates of sky position errors for 10026 Galactic binaries identified in a simulated 12mo LISA observation. The left panel shows scatter plot of latitude and longitude for all sources with reference lines for different scale factors K. The right panel shows histogram of scale factors for latitude and longitude measurements.