adsabs/AstroCLIMB huggingface dataset:
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: Hexbin map of stars in the in the [Mg/Fe] vs. [Fe/H] plane as a function of R and ∣Z∣, color coded by median age, produced similarly to J. Imig et al. (2023, Figure 9). The number in the top-right corner of each panel is the number of stars in that spatial bin we have ages for, using a training density threshold of 3 × 109. This figure is repeated in Appendix B.3 with different density thresholds to illustrate how changes to the threshold affect the map.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: Comparisons of model-derived ages with other age catalogs. Left: scatterplot comparing the DistMass ages from A. Stone-Martinez et al. (2024) to our model’s maximum-likelihood ages. Center: scatterplot comparing J. T. Mackereth et al. (2019) ages to our model’s ages. Right: scatterplot comparing H. W. Leung et al. (2023) ages to our model’s ages. In each subfigure, the black line represents the one-to-one correspondence between the two age estimates. The color gradient indicates the training space density of our model for each star.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: MRD of model-derived ages compared to asteroseismic ages in the test set, binned by ﹩\mathrm{log}\,g﹩ and [Fe/H]. A high MRD indicates that there is a larger scatter in the residual of the StarFlow ages vs. the asteroseismic test-set ages. In general we see that the MRD is lower for stars at higher ﹩\mathrm{log}\,g﹩ and [Fe/H].
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: The parameter space coverage of the model for (top) training space density > 108, (center) training space density > 5 × 108, and (bottom) training space density > 1010.
Paper: Updated Mass, Eccentricity, and Tidal Heating Constraints for the Earth-sized Planet LP 791-18 d
Authors: Michael Greklek-McKeon, Heather A. Knutson, W. Garrett Levine, Renyu Hu, Morgan Saidel, Jonathan Gomez Barrientos, Konstantin Batygin, Björn Benneke
Figure caption: Detrended Palomar/WIRC light curves for the three transit observations of LP 791-18 c (upper panels) and residuals after the best-fit transit light curve has been subtracted (lower panels). Unbinned data are shown as gray circles, with 10 minute binned points overplotted as black circles. The best joint-fit transit models are overplotted as red lines, with red shading to indicate the 1σ uncertainties on the transit shape.
Paper: Updated Mass, Eccentricity, and Tidal Heating Constraints for the Earth-sized Planet LP 791-18 d
Authors: Michael Greklek-McKeon, Heather A. Knutson, W. Garrett Levine, Renyu Hu, Morgan Saidel, Jonathan Gomez Barrientos, Konstantin Batygin, Björn Benneke
Figure caption: Posterior distributions for the masses and eccentricities of LP 791-18 d and c from a fit to the updated set of TTV observations in the case of a tidally damped free eccentricity prior (red), and a uniform eccentricity prior (blue). If we assume that the eccentricities are damped, then the observations are consistent with e = 0 and the planetary masses are slightly lower. If the eccentricities are not damped, the planetary masses are slightly higher.
Paper: Updated Mass, Eccentricity, and Tidal Heating Constraints for the Earth-sized Planet LP 791-18 d
Authors: Michael Greklek-McKeon, Heather A. Knutson, W. Garrett Levine, Renyu Hu, Morgan Saidel, Jonathan Gomez Barrientos, Konstantin Batygin, Björn Benneke
Figure caption: TTVs from the ground-based follow-up campaign of P23 (black circles) including the high-precision Spitzer observations (gray squares), and our new Palomar/WIRC follow-up observations (orange stars), with 100 random posterior draws from our damped eccentricity TTV model (red) and free eccentricity TTV model (blue) for LP 791-18 c (top panel) and d (bottom panel). Our Palomar/WIRC timing measurements for LP 791-18 c are the most precise TTV observations of this system so far, a factor of 1.5–3× more precise than Spitzer, and drive the TTV model constraints for LP 791-18 d.
Paper: Updated Mass, Eccentricity, and Tidal Heating Constraints for the Earth-sized Planet LP 791-18 d
Authors: Michael Greklek-McKeon, Heather A. Knutson, W. Garrett Levine, Renyu Hu, Morgan Saidel, Jonathan Gomez Barrientos, Konstantin Batygin, Björn Benneke
Figure caption: Planetary equilibrium temperature vs. tidal heat flux per unit mass, adapted from Figure 5 of D. Z. Seligman et al. (2024), showing the most promising rocky planets for tidal volcanism. Heat fluxes are normalized by the uncertain tidal quality parameters, point sizes are scaled by planet size, and color indicates the favorability for atmospheric characterization through transmission spectroscopy (E. M. R. Kempton et al. 2018). The difference in tidal heat flux per unit mass for LP 791-18 d spans more than three orders of magnitude for the damped vs. nondamped eccentricity states.
Paper: Updated Mass, Eccentricity, and Tidal Heating Constraints for the Earth-sized Planet LP 791-18 d
Authors: Michael Greklek-McKeon, Heather A. Knutson, W. Garrett Levine, Renyu Hu, Morgan Saidel, Jonathan Gomez Barrientos, Konstantin Batygin, Björn Benneke
Figure caption: The posterior probability density distribution of planetary eccentricities for LP 791-18 d and c with a fit to the TTV data of P23 using a tidally damped-state eccentricity prior and parameterizing e and ω as ﹩\sqrt{e}\cos (\omega )﹩ and ﹩\sqrt{e}\sin (\omega )﹩.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: Additional examples of test-set age posterior recoveries using the same format as Figure 2. Each panel shows a test-set star with a different asteroseismic age: (top) a young star (≈2.5 Gyr), (middle) a slightly older star (≈4.5 Gyr), and (bottom) a middle-age star (≈6.5 Gyr). These examples illustrate how the model recovers age posteriors with varying shape and uncertainty across the age range. Black curves show the full posterior, orange lines indicate the maximum-likelihood age, and dashed lines mark the 1σ range. Red dashed lines show the asteroseismic age.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: Galactic age maps using a threshold of (top) training space density > 108, (center) training space density > 5 × 108, and (bottom) training space density > 1010.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: α–metallicity plane maps using a threshold of (top) training space density > 108, (center) training space density > 5 × 108, and (bottom) training space density > 1010.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Comparison of uncertainties using simulated data. Left: histograms of the ratio σtotal/σB for all three stellar parameters show that the total error is nearly equal to the Bayesian error. Right: histograms of the ratio σNN/σB show that neural network-induced error is typically much smaller than the Bayesian error. This confirms that the dominant uncertainty originates from the Bayesian estimates.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Comparison between NN and Bayesian estimates for Mr, Ar, and [Fe/H] using real observational data. The dashed red line indicates the ideal 1:1 correlation. The data set includes 147,064 stars with a minimum magnitude’s SNR of 5 in all bands.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Residuals between NN predictions and true values plotted against u magnitude (top), Mr (middle), and differences in [Fe/H] (bottom) for real observational data. The full red line represents the median residuals, the dashed red line represents the standard deviation of the residuals, and the density of residuals is shown on a color scale.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Performance of the NN algorithm for Mr using real observational data. Analogous to Figure 3 except the residuals are defined as (NN – Bayesian).
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Performance of the NN algorithm for Ar using real observational data. Analogous to Figure 4 except the residuals are defined as (NN – Bayesian).
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Performance of the NN algorithm for [Fe/H] using real observational data. Analogous to Figure 5 except the residuals are defined as (NN – Bayesian).
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Histograms of residuals between NN and Bayesian estimates for Mr (left), Ar (middle), and [Fe/H] (right) using real observational data, normalized by predicted uncertainties. The red curves represent a normal distribution (N(0, 1)) for comparison.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Same as Figure 26 but for real observational data. The distributions are broader, but the same trend holds: the total uncertainty is dominated by the Bayesian method, while the neural network adds only a small contribution.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Histograms of residuals between NN and ground truth estimates for Mr (left), Ar (middle), and [Fe/H] (right), normalized by predicted uncertainties. The red curves show a normal distribution (N(0, 1)) for comparison.
Paper: No Influence of Passing Stars on Paleoclimate Reconstructions Over the Past 56 Million Years
Authors: Richard E. Zeebe, David M. Hernandez
Figure caption: Divergence times, τ’s, for ensemble runs (N = 3 × 128; blue and green symbols) relative to our reference astronomical solution (standard model) ZB18a (see Figure 3). Random stellar parameters for the three sets of 128 solutions are based on J. Heisler et al. (1987), J. García-Sánchez et al. (2001), and airball. All τ ≥ 56 Myr, except for the symbol in parentheses (τ < 56 Myr), indicating one run (solution S80) with a randomly generated (hypothetical), exceptional close encounter at t ≃ −1.2 Myr and ﹩{d}_{{\rm{\min }}}\simeq 699﹩ au, for which there is no evidence in Gaia DR3 (see text and C. A. L. Bailer-Jones 2022). Also shown are τ’s from runs including stellar flybys but omitting the lunar contribution and J2 (red triangles, △, N = 16), resembling the approach of N. A. Kaib & S. N. Raymond (2024). Light red triangles (⊳, N = 16): no stellar flybys, no lunar contribution and J2, but different initial positions for Earth (see Figure 4 in R. E. Zeebe 2023a). Purple diamonds (N = 16): first 16 ensemble runs repeated including random variations in the initial mean anomaly ﹩{{ \mathcal M }}_{0}﹩ of each body (see Section 5.3). For more information on τ and solar system chaos, see R. E. Zeebe (2023a) and R. E. Zeebe & I. J. Kocken (2024).
Paper: No Influence of Passing Stars on Paleoclimate Reconstructions Over the Past 56 Million Years
Authors: Richard E. Zeebe, David M. Hernandez
Figure caption: Example of divergence time τ. (a) Earth’s orbital eccentricity, e⊕, for solutions SHD7977 and our standard model ZB18a. The setup for SHD7977 includes randomly passing stars, as well as a (hypothetical) very close encounter with HD 7977 (﹩{d}_{\min }=3896﹩ au, t = −2.86 Myr). Notable differences between the two solutions appear around t = −70 Myr. (b) Difference in Earth’s orbital eccentricity on a log-y scale, ﹩{\mathrm{log}}_{10}| {\rm{\Delta }}{e}_{\oplus }| ﹩, between solutions SHD7977 and ZB18a over the past 100 Myr. The arrow indicates τ ≃ 70 Myr (τ is taken positive), when max∣Δe⊕∣ irreversibly crosses ∼10% of mean e⊕ (∼0.027 × 0.1, dashed horizontal line). Importantly, τ ≃ 70 Myr is far beyond the PETM age (∼56 Ma, vertical orange line).
Paper: No Influence of Passing Stars on Paleoclimate Reconstructions Over the Past 56 Million Years
Authors: Richard E. Zeebe, David M. Hernandez
Figure caption: Illustration of impulse gradient per unit mass (Equation (2)) for stellar flybys as a function of stellar mass (M⋆) and minimum passing distance (d). The upper d limit shown is 1 pc = 206, 265 au (see Section 3.1). The colored surface uses a stellar speed v = 50 km s−1. Orange dots indicate example values for 1800 flybys based on randomly generated stellar parameters (including v) after J. Heisler et al. (1987), see Section 3; 1800 flybys is an estimated number of stars passing the solar system within 100 Myr. The figure’s sole purpose is to illustrate the effect of different stellar parameters for very close, strong encounters (green-blue to green surface area) vs. average random flybys, which are weak (orange dots).
Paper: No Influence of Passing Stars on Paleoclimate Reconstructions Over the Past 56 Million Years
Authors: Richard E. Zeebe, David M. Hernandez
Figure caption: Random stellar flyby parameters generated following J. Heisler et al. (1987), J. García-Sánchez et al. (2001), and airball (top, middle, bottom row). Shown are example histograms for 1800 flybys each. Note the logarithmic ordinate for mass. J. García-Sánchez et al. (2001) tends to yield larger stellar masses at somewhat higher frequency, mostly due to a generally higher number density ni at a given mass (see text). The largest ﹩{d}_{{\rm{\min }}}﹩ considered here is 1 pc, as stars are injected into a sphere around the Sun with ﹩{r}_{{\rm{\max }}}=1﹩ pc (see Section 3.1).
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: The CHILES Con Pol field, defined as the 20% (blue circle) and 50% (orange circle) of the VLA primary beam at 1.4 GHz, overlaid on the COSMOS HST coverage (A. M. Koekemoer et al. 2007). In addition, to provide context for the extensive multiwavelength coverage in the COSMOS field, we present the footprints of just a few of the many surveys the JWST COSMOS-Web field-of-view is presented in red (C. M. Casey et al. 2023), the AxTEC submillimeter survey is presented in magenta (I. Aretxaga et al. 2011), the CFHT NUV survey is presented in black (P. Capak et al. 2007), and the GAMA G10 region of reprocessed COSMOS photometry and spectroscopic is shown in green (S. K. Andrews et al. 2017).
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: The primary beam corrected CHILES Continuum Polarization total intensity map at the resolution of 4﹩\mathop{.}\limits^{\unicode{x02033}}﹩5 x 4﹩\mathop{.}\limits^{\unicode{x02033}}﹩0, for the red-dashed square, of side length 8﹩\mathop{.}\limits^{{\rm{^{\prime} }}}﹩6, indicated in the previous Figure 5.
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: The primary beam corrected CHILES Continuum Polarization total intensity map at the resolution of 4﹩\mathop{.}\limits^{\unicode{x02033}}﹩5 x 4﹩\mathop{.}\limits^{\unicode{x02033}}﹩0, out to the 20% point of the VLA at 1.4 GHz. The central red-dashed square, of side length 8﹩\mathop{.}\limits^{{\rm{^{\prime} }}}﹩6, corresponds to the region shown in the following Figure 6.
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: The total intensity radio continuum images of a ﹩3^{\prime} \rm{}\,\rm{}\,\times 3^{\prime} \rm{}\,\rm{}\,﹩ region for a detailed comparison among the CHILES Con Pol (top left), MIGHTEE high (θ = 5″; upper right) and low resolution (θ = 8″. 6; lower left I. Heywood et al. 2022), and VLA-COSMOS 3 GHz (θ = 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩75; lower right V. Smolčić et al. 2017) data. The intensity ranges shown are between −1 and +20 μJy for the first three panels and −1 and +10 μJy for the VLA-COSMOS 3 GHz image. The VLA-COSMOS 1.4 GHz survey data (E. Schinnerer et al. 2007) is too shallow (σ ≈ 10 μJy) to offer a meaningful comparison and is not shown.
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: The distribution of pixel values for the total intensity image over five blank sky regions 39′, 44′, 48′, 49′, and 57′ away from the phase center (shown here in descending plots) with sizes of 3﹩^{\prime} ﹩ × 3﹩^{\prime} ﹩. The distributions are shown for the range ±4 μJy beam−1 range with the bin size dictated by the Freedman–Diaconis rule. The red-dashed lines indicate the Gaussian fits to the distributions used in estimating the rms noise.
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: The mean of the pixel-value distribution as a function of distance from field center for the simulated CHILES Con Pol observations of flux densities below 5 μJy (black markers), and for the CHILES Con Pol maps presented in this work (red markers).
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: Left: The percentages of total visibilities as a function of baseline length, with the data put into bins of width 100 m. Right: The percentages of total visibilities, using the same bins as the plot to the left, as a function of the angular scale to which they are sensitive to emission.
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: The PSF response in each of the four sub-bands, whose center frequencies are indicated in the upper right corner of each panel, with a common color scale across each panel of −0.1 to 0.1.
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: Left: The percentages of total visibilities flagged per each 128 MHz spectral window, and per the consecutive VLA-B configurations. Right: The percent of total visibilities flagged per the consecutive VLA-B configurations.
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: The percent of the total visibilities flagged for the entire CHILES Con Pol database. The four panels of the plot represent the split for the four spectral windows used in the observations.
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: Euclidean-normalized source counts. The 1.4 GHz P(D) distribution derived from the CHILES Con Pol data for the pixel ranges of 0.1–10 μJy beam−1 (left panel) and 0.1–60 μJy beam−1 (right panel) are shown by black-dashed lines. The source count derived from the CHILES Con Pol component catalog is shown by large black dots (H. B. Gim et al. 2025). They are compared with the published P(D) (K. J. Mitchell & J. J. Condon 1985; F. N. Owen & G. E. Morrison 2008; J. J. Condon et al. 2012; T. Mauch et al. 2020) and compilations of number counts by M. Bondi et al. (2008) and A. M. Matthews et al. (2021).
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: The 1.4 GHz total intensity radio continuum images for an interesting likely AGN within the COSMOS (top) a 15 mJy source, the brightest source within CHILES Con Pol, (middle), and the “Earmuffs” galaxy (bottom), for CHILES Con Pol, MIGHTEE (I. Heywood et al. 2022), and VLA-COSMOS (E. Schinnerer et al. 2007), from left to right. The color bar on the far right applies to all images and each map has contours at the ±2n σ for n values 1–11, with σ corresponding to their respective reported rms sensitivities.
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: Comparisons of Euclidean-normalized source counts with MeerKAT DEEP2 survey. Blue and red solid lines indicate the source counts obtained from T. Mauch et al. (2020) and A. M. Matthews et al. (2021) for the DEEP2 field observed at the MeerKAT, respectively, while the black-dashed line shows the source count obtained using our P(D) model to the MeerKAT DEEP2 image.
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: The posterior probability distributions of the four model parameters such as α, γ, μ, and σ0 are presented in 2D corner plots for models with an input pixel-value range of [−10, +10] in the left and [−60, +60] μJy beam−1 in the right panel, respectively.
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: The 5σ detection limit, assuming a 1 μJy noise level, for the CHILES Con Pol Survey for radio continuum derived SFR and the 1.4 GHz luminosity as a function of redshift.