adsabs/AstroCLIMB huggingface dataset:
Paper: OGLE-2016-BLG-1093Lb: A Sub-Jupiter-mass Spitzer Planet Located in the Galactic Bulge
Authors: In-Gu Shin, Jennifer C. Yee, Kyu-Ha Hwang, Andrew Gould, Andrzej Udalski, Ian A. Bond, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Youn Kil Jung, Hyoun Woo Kim, Yoon-Hyun Ryu, Yossi Shvartzvald, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radek Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof Ulaczyk, Charles A. Beichman, Geoffery Bryden, Sebastiano Calchi Novati, Sean Carey, B. Scott Gaudi, Calen B. Henderson, Wei Zhu, Fumio Abe, Richard K. Barry, David P. Bennett, Aparna Bhattacharya, Hirosane Fujii, Akihiko Fukui, Yuki Hirao, Yoshitaka Itow, Rintaro Kirikawa, Naoki Koshimoto, Iona Kondo, Yutaka Matsubara, Sho Matsumoto, Shota Miyazaki, Yasushi Muraki, Greg Olmschenk, Arisa Okamura, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Stela Ishitani Silva, Takahiro Sumi, Daisuke Suzuki, Taiga Toda, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama
Figure caption: The (π E,E, π E,N) contours of cheap–SPRX models with SPRX contours for comparison. Top panels show cheap–SPRX contours of Spitzer-“only” cases. Middle panels show cheap–SPRX contours of Joint Spitzer + Ground cases. Bottom panels show the SPRX contours for comparison. The right and left panels present the (u 0 < 0) and (u 0 > 0) cases, respectively. The circles indicate ∣π E∣ values (solid lines) measured from the cheap–SPRX with their 1σ errors (dashed lines). The blue and black stars indicate the best-fit values of SPRX and cheap–SPRX models, respectively.
Paper: OGLE-2016-BLG-1093Lb: A Sub-Jupiter-mass Spitzer Planet Located in the Galactic Bulge
Authors: In-Gu Shin, Jennifer C. Yee, Kyu-Ha Hwang, Andrew Gould, Andrzej Udalski, Ian A. Bond, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Youn Kil Jung, Hyoun Woo Kim, Yoon-Hyun Ryu, Yossi Shvartzvald, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radek Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof Ulaczyk, Charles A. Beichman, Geoffery Bryden, Sebastiano Calchi Novati, Sean Carey, B. Scott Gaudi, Calen B. Henderson, Wei Zhu, Fumio Abe, Richard K. Barry, David P. Bennett, Aparna Bhattacharya, Hirosane Fujii, Akihiko Fukui, Yuki Hirao, Yoshitaka Itow, Rintaro Kirikawa, Naoki Koshimoto, Iona Kondo, Yutaka Matsubara, Sho Matsumoto, Shota Miyazaki, Yasushi Muraki, Greg Olmschenk, Arisa Okamura, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Stela Ishitani Silva, Takahiro Sumi, Daisuke Suzuki, Taiga Toda, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama
Figure caption: The KMTNet color–magnitude diagrams. The red, blue, and green circles indicate positions of the red giant clump centroid (RGC), source, and blend, respectively. The orange box shows selected stars used to construct the color–color relation shown in Section 4.1.
Paper: OGLE-2016-BLG-1093Lb: A Sub-Jupiter-mass Spitzer Planet Located in the Galactic Bulge
Authors: In-Gu Shin, Jennifer C. Yee, Kyu-Ha Hwang, Andrew Gould, Andrzej Udalski, Ian A. Bond, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Youn Kil Jung, Hyoun Woo Kim, Yoon-Hyun Ryu, Yossi Shvartzvald, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radek Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof Ulaczyk, Charles A. Beichman, Geoffery Bryden, Sebastiano Calchi Novati, Sean Carey, B. Scott Gaudi, Calen B. Henderson, Wei Zhu, Fumio Abe, Richard K. Barry, David P. Bennett, Aparna Bhattacharya, Hirosane Fujii, Akihiko Fukui, Yuki Hirao, Yoshitaka Itow, Rintaro Kirikawa, Naoki Koshimoto, Iona Kondo, Yutaka Matsubara, Sho Matsumoto, Shota Miyazaki, Yasushi Muraki, Greg Olmschenk, Arisa Okamura, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Stela Ishitani Silva, Takahiro Sumi, Daisuke Suzuki, Taiga Toda, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama
Figure caption: Constraints on the microlens parallax vector without including the color constraint. The top panels show Spitzer-“only” cases. The bottom panels show Spitzer+ Ground cases. The star mark indicates the best-fit parallax with including the color constraint. These differ significantly from the constraints in Figure 3 because the correlated noise in the Spitzer light curve can be fit by the planetary model if an arbitrary source flux is allowed.
Paper: OGLE-2016-BLG-1093Lb: A Sub-Jupiter-mass Spitzer Planet Located in the Galactic Bulge
Authors: In-Gu Shin, Jennifer C. Yee, Kyu-Ha Hwang, Andrew Gould, Andrzej Udalski, Ian A. Bond, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Youn Kil Jung, Hyoun Woo Kim, Yoon-Hyun Ryu, Yossi Shvartzvald, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radek Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof Ulaczyk, Charles A. Beichman, Geoffery Bryden, Sebastiano Calchi Novati, Sean Carey, B. Scott Gaudi, Calen B. Henderson, Wei Zhu, Fumio Abe, Richard K. Barry, David P. Bennett, Aparna Bhattacharya, Hirosane Fujii, Akihiko Fukui, Yuki Hirao, Yoshitaka Itow, Rintaro Kirikawa, Naoki Koshimoto, Iona Kondo, Yutaka Matsubara, Sho Matsumoto, Shota Miyazaki, Yasushi Muraki, Greg Olmschenk, Arisa Okamura, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Stela Ishitani Silva, Takahiro Sumi, Daisuke Suzuki, Taiga Toda, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama
Figure caption: Spitzer light curve in instrumental flux units. The solid vertical line indicates the peak of the light curve as observed from Earth (t 0 ≡ t 0,⊕), and the dotted vertical line is +t E away.
Paper: OGLE-2016-BLG-1093Lb: A Sub-Jupiter-mass Spitzer Planet Located in the Galactic Bulge
Authors: In-Gu Shin, Jennifer C. Yee, Kyu-Ha Hwang, Andrew Gould, Andrzej Udalski, Ian A. Bond, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Youn Kil Jung, Hyoun Woo Kim, Yoon-Hyun Ryu, Yossi Shvartzvald, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radek Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof Ulaczyk, Charles A. Beichman, Geoffery Bryden, Sebastiano Calchi Novati, Sean Carey, B. Scott Gaudi, Calen B. Henderson, Wei Zhu, Fumio Abe, Richard K. Barry, David P. Bennett, Aparna Bhattacharya, Hirosane Fujii, Akihiko Fukui, Yuki Hirao, Yoshitaka Itow, Rintaro Kirikawa, Naoki Koshimoto, Iona Kondo, Yutaka Matsubara, Sho Matsumoto, Shota Miyazaki, Yasushi Muraki, Greg Olmschenk, Arisa Okamura, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Stela Ishitani Silva, Takahiro Sumi, Daisuke Suzuki, Taiga Toda, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama
Figure caption: The contours of the ρ * parameter. The color scheme is identical to Figure 2.
Paper: Pyspeckit: A Spectroscopic Analysis and Plotting Package
Authors: Adam Ginsburg, Vlas Sokolov, Miguel de Val-Borro, Erik Rosolowsky, Jaime E. Pineda, Brigitta M. Sipőcz, Jonathan D. Henshaw
Figure caption: Gaussian decomposition of a two-component spectrum performed by ScousePy using pyspeckit. The top-left panel shows the spectrum (black), the same spectrum smoothed with a Gaussian 1D kernel (dotted black line) using astropy.convolution.Gaussian1DKernel, which is used for the derivative spectroscopy (right panel), the location and amplitude of the initial guesses determined from derivative spectroscopy (black lollipops) and supplied to specfit, the best-fitting model solution (green and magenta lines indicate individual components and the total model), the residual spectrum after subtracting the model (orange line), and the 3σ noise level determined from ScousePy.getnoise. The top-right panel shows the first to fourth derivatives of the smoothed spectrum (dotted black line). These derivatives are used to estimate the number of components and their amplitudes, widths, and centroids (ScousePy.DSpec). The information panel at the bottom shows the output information from pyspeckit. This information includes the model parameters and their uncertainties and goodness-of-fit statistics. The buttons on the right can be used to control pyspeckit either noninteractively using “fit (dspec)” or interactively using “fit (manual).”
Paper: Pyspeckit: A Spectroscopic Analysis and Plotting Package
Authors: Adam Ginsburg, Vlas Sokolov, Miguel de Val-Borro, Erik Rosolowsky, Jaime E. Pineda, Brigitta M. Sipőcz, Jonathan D. Henshaw
Figure caption: Error estimate figure for the default NH3 model. The panels are labeled as in Figure 3. The centroid velocity panel is excluded from this plot because it is independent of the other parameters, exhibiting no degeneracy. The most relevant panel is the ntot0 vs. tex0 panel, which plots the column N against the excitation temperature T ex: both of these parameters govern the peak amplitude of the spectrum, so they are degenerate. The vertical and horizontal dashed lines represent the δ χ 2 = 1 and ±1σ computed errors, respectively. Their intersection points also intersect the blue marginalized δ χ 2 curve in all of the displayed cases, indicating that the approximation of the errors using the χ 2/n = 1 assumption is excellent. However, the asymmetry seen in the blue T ex curve suggests that the approximation for that parameter is likely to break down in some cases because the true errors are asymmetric.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Left) An unprocessed dark CDS captured by a NIR array on an H4RG detector. Correlated noise is present in the form of horizontal bands. The standard deviation in pixel value is ∼28e −. (Center) The CDS after it has been corrected using left and right reference pixels. (Right) The CDS after top and bottom reference pixels were used to match amplifier levels. The standard deviation in pixel value is ∼17e −. Correlated noise structures are still visible, showing that current methods are unsuccessful at removing the entirety of the correlated noise.
Paper: Pyspeckit: A Spectroscopic Analysis and Plotting Package
Authors: Adam Ginsburg, Vlas Sokolov, Miguel de Val-Borro, Erik Rosolowsky, Jaime E. Pineda, Brigitta M. Sipőcz, Jonathan D. Henshaw
Figure caption: Error estimate figure. In all panels, the vertical dashed lines show the estimated 1σ errors from the optimizer, while the horizontal dashed lines show the value Δχ 2 = 1, which corresponds to the 68% confidence interval for that parameter. In the off-diagonal panels, contours are shown at Δχ 2 = 1, 2.3, 6.2, 11.8 (black dashed, red, blue dotted, green dashed–dotted), corresponding to 39.3%, 68%, 95%, and 99.5% confidence regions (1σ for a single parameter, then 1σ, 2σ, and 3σ for two parameters). See Appendix A for details and interpretation.
Paper: Pyspeckit: A Spectroscopic Analysis and Plotting Package
Authors: Adam Ginsburg, Vlas Sokolov, Miguel de Val-Borro, Erik Rosolowsky, Jaime E. Pineda, Brigitta M. Sipőcz, Jonathan D. Henshaw
Figure caption: Ammonia model profile fit to a synthetic spectrum. The parameter values and errors are shown in the upper right. The associated error estimate triangle diagram is shown in Figure 5. The correct parameters are T R = T ex = 35, N = 15, σ  v  = 2, and v = 0, all of which are reasonably recovered. However, note that T ex > T R is generally nonphysical, yet the allowed parameter space for T ex includes such values. This two-panel plot was produced automatically using the pyspeckit.wrappers.fitnh3.plot_nh3 command.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Observed RON decay on low-flux NIRPS data compared to the expected decay in the absence of correlated noise. The observed decay is significantly worse due to the presence of correlated noise in the data.
Paper: Pyspeckit: A Spectroscopic Analysis and Plotting Package
Authors: Adam Ginsburg, Vlas Sokolov, Miguel de Val-Borro, Erik Rosolowsky, Jaime E. Pineda, Brigitta M. Sipőcz, Jonathan D. Henshaw
Figure caption: One-dimensional Gaussian profile fit to a synthetic spectrum. The parameter values and errors are shown in the upper right. The number of significant figures displayed in both the value and the error is automatically set to one digit more than the last significant digit in the error.
Paper: Pyspeckit: A Spectroscopic Analysis and Plotting Package
Authors: Adam Ginsburg, Vlas Sokolov, Miguel de Val-Borro, Erik Rosolowsky, Jaime E. Pineda, Brigitta M. Sipőcz, Jonathan D. Henshaw
Figure caption: An example plotted spectrum showing the automated unit labeling and errors. The errors are shown with the ‘‘fill’’ style and represent symmetric 1 − σ Gaussian errors.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Presence of biases in spectrum extractions on portions of the first test image. It compares the true spectrum to the spectrum extracted using the neural network and using ramp fitting. The two panels represent the worst case scenario in terms of systematic bias in the spectrum obtained using the neural network.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Extracted spectra on portions of the first test image. Compared are the true spectrum and the extracted spectrum using the neural network and using ramp fitting. The spectra obtained using the neural network are considerably less noisy than the extraction done using ramp fitting, and they do not show any systematic bias. On the top and bottom panels, the standard error to the scene is reduced by a factor of 1.73 and 1.55, respectively , showing that the reduction in per-pixel error has translated into a reduction of error at the spectrum level.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Portion of spectrum for a Fabry–Perot science image extracted using the neural network. The acceptable accuracy of the extracted spectrum confirms that the neural network is learning statistical properties of the time-correlated noise rather than memorizing and recreating spectral features it has seen during training.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Small section of spectra extracted for 10 realizations of a unique astrophysical scene, using both the neural network and ramp fitting. We compare the standard deviation σ of the 10 spectra. Using the neural network, σ is reduced by a factor of 1.93 when averaged over all spectral pixels, signifying that the spectra extracted using the neural network have less variation.
Paper: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Authors: Guillaume Payeur, Étienne Artigau, Laurence Perreault Levasseur, René Doyon
Figure caption: Example of a simulation of the RON decay in the case of no correlated noise vs. the theoretical decay for an array receiving no flux. The difference between the two decays is small.
Paper: SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
Authors: Julie Imig, Jon A. Holtzman, Renbin Yan, Daniel Lazarz, Yanping Chen, Lewis Hill, Daniel Thomas, Claudia Maraston, Moire K. M. Prescott, Guy S. Stringfellow, Dmitry Bizyaev, Rachael L. Beaton, Niv Drory
Figure caption: Parameter results from the semi-empirical neural network model (red points) and the synthetically trained neural network model (blue points) discussed in Section 5.2, both compared to the ASPCAP parameters for the training set stars, shown as a scatter plot (left panels) and in a histogram (right panels). The synthetic neural network has larger scatter in all parameters, most consequential for cool dwarf stars where the biggest difference between synthetic and empirical spectra is observed. This motivates our choice to use the semi-empirical model for our final results presented in Section 4.
Paper: Reflected Spectroscopy of Small Exoplanets II: Characterization of Terrestrial Exoplanets
Authors: Mario Damiano, Renyu Hu
Figure caption: Parameterization of the volume mixing ratio (VMR) vertical profile of the condensing gas (i.e., water vapor) in this study. At the bottom of the atmosphere, we include a reflecting surface characterized by two free parameters: the surface pressure and the surface albedo. The nonconstant VMR allows for the presence of clouds where the gas condenses (i.e., decreases the gas VMR). The four parameters that define the VMR profile and thus the cloud density profile highlighted in the schematics are also free parameters of the model.
Paper: Reflected Spectroscopy of Small Exoplanets II: Characterization of Terrestrial Exoplanets
Authors: Mario Damiano, Renyu Hu
Figure caption: Modeled spectra of an atmosphere that has a surface pressure of 105 Pa (1 bar) and composition similar to modern Earth’s atmosphere. Solid, dashed, and dotted lines correspond to low cloud, high cloud, no cloud scenarios, and the three colors within each group correspond to an assumed temperature of 200, 250, and 300 K. The orbital phase is π/3. The surface albedo is 0.2. The spectra are not sensitive to the assumed temperature.
Paper: Reflected Spectroscopy of Small Exoplanets II: Characterization of Terrestrial Exoplanets
Authors: Mario Damiano, Renyu Hu
Figure caption: Simulated data and the best-fit spectra of the Earth-like scenario. Combinations of different S/N and optical-band spectral resolutions have been considered to generate the four different cases. The blue lines depict the best-fit spectra from the retrieval, and the orange line depicts the best-fit spectrum without fitting the NIR band.
Paper: Reflected Spectroscopy of Small Exoplanets II: Characterization of Terrestrial Exoplanets
Authors: Mario Damiano, Renyu Hu
Figure caption: Simulated data of the dry CO2-dominated atmosphere scenario (red points) and the best-fit models with (blue line) and without the NIR band (orange line).
Paper: Reflected Spectroscopy of Small Exoplanets II: Characterization of Terrestrial Exoplanets
Authors: Mario Damiano, Renyu Hu
Figure caption: Simulated data of the Archean Earth-like scenario (red points) and the best-fit models with (blue line) and without the NIR band (orange line).
Paper: Reflected Spectroscopy of Small Exoplanets II: Characterization of Terrestrial Exoplanets
Authors: Mario Damiano, Renyu Hu
Figure caption: Simulated data of the CO2-dominated atmosphere with O2 and clouds scenario (red points) and best-fit models with (blue line) and without the NIR band (orange line).
Paper: Reflected Spectroscopy of Small Exoplanets II: Characterization of Terrestrial Exoplanets
Authors: Mario Damiano, Renyu Hu
Figure caption: Full posterior distributions of the Archean Earth-like scenario with (blue lines) and without (orange lines) the NIR wavelength band. The red lines in the corner plots refer to the true value used to simulate the data.
Paper: Reflected Spectroscopy of Small Exoplanets II: Characterization of Terrestrial Exoplanets
Authors: Mario Damiano, Renyu Hu
Figure caption: Full posterior distributions (corner plots) of the Earth-like scenario. The different cases explore a combination of different optical-band spectral resolution and S/N. The red lines in the corner plots refer to the true value used to simulate the data.
Paper: Reflected Spectroscopy of Small Exoplanets II: Characterization of Terrestrial Exoplanets
Authors: Mario Damiano, Renyu Hu
Figure caption: Full posterior distributions of the Earth-like scenario with (blue lines) and without (orange lines) the NIR wavelength band in 1.0–1.8 μm. The red lines in the corner plots refer to the true value used to simulate the data.
Paper: Reflected Spectroscopy of Small Exoplanets II: Characterization of Terrestrial Exoplanets
Authors: Mario Damiano, Renyu Hu
Figure caption: Full posterior distribution of the dry CO2-dominated atmosphere without clouds scenario with (blue lines) and without (orange lines) the NIR wavelength band. The red lines in the corner plots refer to the true value used to simulate the data.
Paper: Reflected Spectroscopy of Small Exoplanets II: Characterization of Terrestrial Exoplanets
Authors: Mario Damiano, Renyu Hu
Figure caption: Full posterior distribution of the CO2-dominated atmosphere with O2 and clouds scenario with (blue lines) and without (orange lines) the NIR wavelength band. The red lines in the corner plots refer to the true value used to simulate the data.
Paper: SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
Authors: Julie Imig, Jon A. Holtzman, Renbin Yan, Daniel Lazarz, Yanping Chen, Lewis Hill, Daniel Thomas, Claudia Maraston, Moire K. M. Prescott, Guy S. Stringfellow, Dmitry Bizyaev, Rachael L. Beaton, Niv Drory
Figure caption: Comparison between fits for an observed MaStar spectrum (black line) with results from the data-driven (semi-empirical) neural network model (red) and a model-spectra-driven (synthetic) neural network model (blue) described in Section 5.2. Results are shown for three different stars in different parameter regimes: a cool dwarf (top), a warm dwarf (middle), and a cool giant (bottom). Select derived parameters for both models are listed in the legend. The semi-empirical model is a better fit to the observed spectrum in all three cases, with the largest discrepancy seen in the cool dwarf star. This motivates our choice to use the semi-empirical model for our final results presented in Section 4.
Paper: SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
Authors: Julie Imig, Jon A. Holtzman, Renbin Yan, Daniel Lazarz, Yanping Chen, Lewis Hill, Daniel Thomas, Claudia Maraston, Moire K. M. Prescott, Guy S. Stringfellow, Dmitry Bizyaev, Rachael L. Beaton, Niv Drory
Figure caption: Examining the spread in parameter results while varying the resolution of spectra fit with our method. Each row represents a different parameter, from top to bottom: T eff, ﹩\mathrm{log}g﹩, [Fe/H], [α/Fe], ﹩\mathrm{log}({v}_{\mathrm{micro}})﹩. Each color represents the results from one of the four resolution catalogs: the 99.5% resolution catalog (red), the 90% resolution catalog (yellow), the 75% resolution catalog (green), and the 60% resolution catalog (blue). The median is calculated among the results from the four catalogs. The right column is the same spread flattened into a histogram.
Paper: SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
Authors: Julie Imig, Jon A. Holtzman, Renbin Yan, Daniel Lazarz, Yanping Chen, Lewis Hill, Daniel Thomas, Claudia Maraston, Moire K. M. Prescott, Guy S. Stringfellow, Dmitry Bizyaev, Rachael L. Beaton, Niv Drory
Figure caption: Color–magnitude diagrams using Gaia G-band magnitude and dereddened (G  BP  − G  RP ) color. The points are colored by each of the parameters we derive in this work. Left to right and to top to bottom: T eff, log g, [Fe/H], [α/Fe], and log(v micro).
Paper: SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
Authors: Julie Imig, Jon A. Holtzman, Renbin Yan, Daniel Lazarz, Yanping Chen, Lewis Hill, Daniel Thomas, Claudia Maraston, Moire K. M. Prescott, Guy S. Stringfellow, Dmitry Bizyaev, Rachael L. Beaton, Niv Drory
Figure caption: One-to-one comparison between our derived parameters and values from LAMOST (top row) and SEGUE (bottom row). Left column: T eff. Middle column: ﹩\mathrm{log}g﹩. Right column: [Fe/H]. The one-to-one line is drawn in gray as a guide.