adsabs/AstroCLIMB huggingface dataset:
Paper: Flaring Activity for Low-mass Stars in the β Pictoris Moving Group
Authors: Jordan N. Ealy, Joshua E. Schlieder, Thaddeus D. Komacek, Emily A. Gilbert
Figure caption: Flares injected by AltaiPony are shown in blue with a portion of the underlying light curve for TIC 269077806 shown in dark green. By running the flare-finding algorithm on this data set, we can compare the characteristics of the injected flares to the recovered to develop scaling ratios. These ratios can then be used on the original extracted flares to scale them to their likely intrinsic distribution and shape.
Paper: Flaring Activity for Low-mass Stars in the β Pictoris Moving Group
Authors: Jordan N. Ealy, Joshua E. Schlieder, Thaddeus D. Komacek, Emily A. Gilbert
Figure caption: Due to sustained starspot coverage and fast rotation periods of active low-mass stars, their light curves have distinct patterns. The sharp peaks superimposed on the spot modulation are flares. The varied amplitude, duration, and temporal distribution offer insight into the mechanisms generating them.
Paper: Flaring Activity for Low-mass Stars in the β Pictoris Moving Group
Authors: Jordan N. Ealy, Joshua E. Schlieder, Thaddeus D. Komacek, Emily A. Gilbert
Figure caption: All rotation periods within this sample were derived from a sector of TESS observations. We use each light curve (left) with the power spectrum from a Lomb–Scargle periodogram (inset). The maximum power returned is used to phase-fold the light curve (right).
Paper: Uncertainty of Line-of-sight Velocity Measurements of Faint Stars from Low- and Medium-resolution Optical Spectra
Authors: László Dobos, Alexander S. Szalay, Tamás Budavári, Evan N. Kirby, Robert H. Lupton, Rosemary F. G. Wyse
Figure caption: Comparison of the error estimates and bias for a model with atmospheric parameters ﹩\left[{\rm{M}}/{\rm{H}}\right]=-1.5﹩, T eff = 5500 K, and ﹩\mathrm{log}\,g=3.5﹩, for the case of fitting v los plus the atmospheric parameters, both with and without simulated wavelength calibration error when fitting of arms B + MR in combination. In panels (a) and (b), the blue dots indicate the asymptotic error calculated from the Fisher matrix at the maximum value of the significance function, the red dots show the standard deviation of Monte Carlo samples drawn from the Bayesian posterior, and the red curve shows the empirical error calculated from an ensemble of simulations, all in logarithmic bins of S/N. For this particular stellar type, different error estimates start to diverge below S/N < 6 and the error estimator from the Fisher matrix is no longer a good measure of the uncertainty. Panels (c)–(f) show the skewness and the kurtosis of the Monte Carlo samples drawn from the Bayesian posterior, to demonstrate that the posterior marginalized for the atmospheric parameters is no longer Gaussian at low S/N. In panels (g) and (h), the red curve indicates the expectation value of the bias Δv los, as determined from a Monte Carlo sample of the likelihood function, while the blue curve shows the same bias determined from the maximum a posteriori method. Both bias curves are calculated in logarithmic bins of S/N. The bias shows no significant trend over this S/N range, but its large scatter at low S/N is obvious. These plots are qualitatively the same for lower temperature, but more luminous, metal-poor giant stars.
Paper: Uncertainty of Line-of-sight Velocity Measurements of Faint Stars from Low- and Medium-resolution Optical Spectra
Authors: László Dobos, Alexander S. Szalay, Tamás Budavári, Evan N. Kirby, Robert H. Lupton, Rosemary F. G. Wyse
Figure caption: Results from Monte Carlo sampling of the posterior for two different realizations of the model with atmospheric parameters ﹩\left[{\rm{M}}/{\rm{H}}\right]=-1.5﹩, T eff = 4750 K, and ﹩\mathrm{log}\,g=2.0﹩, and S/Ns (measured per resolution element in the MR arm) of S/N = 11.76 (top) and S/N = 2.33 (bottom). The first three panels of each row show scatterplots of the deviations of the atmospheric parameters from the model input vs. Δv los. The colors indicate samples from three different Monte Carlo chains. The rightmost panel of each row shows histograms of Δv los for each Monte Carlo chain in color, whereas the black histogram is the combined histogram of all samples. In the top left of each scatterplot, the correlation coefficient of the two variables is printed. The small values of the Pearson correlation coefficient indicate that v los does not correlate with the atmospheric parameters. In the corner of the histograms, the values of the skewness s and the kurtosis k (0 for a Gaussian distribution) of the Monte Carlo samples are indicated. Note the different scales on the axes.
Paper: Uncertainty of Line-of-sight Velocity Measurements of Faint Stars from Low- and Medium-resolution Optical Spectra
Authors: László Dobos, Alexander S. Szalay, Tamás Budavári, Evan N. Kirby, Robert H. Lupton, Rosemary F. G. Wyse
Figure caption: The variance and bias of line-of-sight velocity fits to simulations of a star with atmospheric parameters ﹩\left[{\rm{M}}/{\rm{H}}\right]=-1.5﹩, T eff = 4750 K, and ﹩\mathrm{log}g=2.0﹩, with and without flux correction. The statistics are calculated from an ensemble of simulations in logarithmic bins of S/N. The blue curves indicate the results from fitting a matching template with the same parameters as the input model, whereas the red curves show the result from fits where we optimized for the atmospheric parameters as well. The rest of the curves, as indicated by the color given in the legend, show the results when we used templates that are one or two grid points away from the input model, on the synthetic stellar spectrum grid. Solid (dashed) lines indicate a deviation in the positive (negative) direction from the input parameters for ﹩\left[{\rm{M}}/{\rm{H}}\right]﹩, T eff, and ﹩\mathrm{log}g﹩. See the text for detailed discussion.
Paper: Uncertainty of Line-of-sight Velocity Measurements of Faint Stars from Low- and Medium-resolution Optical Spectra
Authors: László Dobos, Alexander S. Szalay, Tamás Budavári, Evan N. Kirby, Robert H. Lupton, Rosemary F. G. Wyse
Figure caption: Comparison of the typical empirical line-of-sight velocity error, without any instrumental systematics, for the models listed in Table 2 using the Subaru PFS B (thin blue curves), R (thin red curves), and MR (thin gray curves) arms, as well as the combination of B + R (thick black curves) and B + MR (thick red curves). The curves show the standard deviation of the line-of-sight velocity error Δv los, with respect to the model input, in logarithmic bins of S/N. For purposes of easy comparison, the values of S/N on the horizontal axes are always the 95th percentile S/N in the MR arm only, even when the fitting was done using different arms or a combination of arms.
Paper: Uncertainty of Line-of-sight Velocity Measurements of Faint Stars from Low- and Medium-resolution Optical Spectra
Authors: László Dobos, Alexander S. Szalay, Tamás Budavári, Evan N. Kirby, Robert H. Lupton, Rosemary F. G. Wyse
Figure caption: S/N per pixel for three different stellar types (from top to bottom: solar metallicity M giant, metal-poor M dwarf, and metal-poor K giant). The 95th percentile S/N is plotted in red and the median S/N in blue, whereas the dashed black line indicates the S/N as defined by Stoehr et al. (2008). See the text for discussion.
Paper: Uncertainty of Line-of-sight Velocity Measurements of Faint Stars from Low- and Medium-resolution Optical Spectra
Authors: László Dobos, Alexander S. Szalay, Tamás Budavári, Evan N. Kirby, Robert H. Lupton, Rosemary F. G. Wyse
Figure caption: Location of the simulated observations in the plane of atmospheric parameters (left) and the Subaru Hyper-Suprime Cam color–magnitude diagram (right). We plot a selection of PARSEC isochrones for reference, as well as the locations of the models we simulated. Note that the models were chosen to be aligned with the synthetic spectrum grid, thus may not lie on the isochrones. See Table 2 for a detailed list of parameters.
Paper: JWST COMPASS: A NIRSpec/G395H Transmission Spectrum of the Sub-Neptune TOI-836c
Authors: Nicole L. Wallack, Natasha E. Batalha, Lili Alderson, Nicholas Scarsdale, Jea I. Adams Redai, Artyom Aguichine, Munazza K. Alam, Peter Gao, Angie Wolfgang, Natalie M. Batalha, James Kirk, Mercedes López-Morales, Sarah E. Moran, Johanna Teske, Hannah R. Wakeford, Nicholas F. Wogan
Figure caption: The Allan variance plots for both detectors for the Eureka! reduction for each of the spectroscopic bins fit with a systematic model of the form in Equation (1). Each colored line represents a different bin, and the black line represents the normalized standard error. We have normalized the rms values for the data in order to show that the bins follow a 1/﹩\sqrt{N}﹩ scaling and that there is little excess red noise in the residuals of the fits to the binned light curves.
Paper: JWST COMPASS: A NIRSpec/G395H Transmission Spectrum of the Sub-Neptune TOI-836c
Authors: Nicole L. Wallack, Natasha E. Batalha, Lili Alderson, Nicholas Scarsdale, Jea I. Adams Redai, Artyom Aguichine, Munazza K. Alam, Peter Gao, Angie Wolfgang, Natalie M. Batalha, James Kirk, Mercedes López-Morales, Sarah E. Moran, Johanna Teske, Hannah R. Wakeford, Nicholas F. Wogan
Figure caption: The Allan variance plots for both detectors for each reduction are shown. We show the actual noise and expected noise for NRS1 in blue and for NRS2 in red. In the absence of red noise, the residuals would follow the solid lines. We see residual correlated noise in both detectors for all reductions, but especially for NRS1.
Paper: JWST COMPASS: A NIRSpec/G395H Transmission Spectrum of the Sub-Neptune TOI-836c
Authors: Nicole L. Wallack, Natasha E. Batalha, Lili Alderson, Nicholas Scarsdale, Jea I. Adams Redai, Artyom Aguichine, Munazza K. Alam, Peter Gao, Angie Wolfgang, Natalie M. Batalha, James Kirk, Mercedes López-Morales, Sarah E. Moran, Johanna Teske, Hannah R. Wakeford, Nicholas F. Wogan
Figure caption: We show the time in minutes from the predicted midtransit time of 59991.7252 BMJD (T 0), the a/R *, and the inclination from the fits to our white-light curves. The contours show the posterior distributions from the MCMC fits to the Eureka! reduction, and the best-fit values from the fits to the other reductions are shown as points above the distribution for each parameter (as we did not run an MCMC for the other reductions). All of our reductions agree on the presence of an offset in the times of transit between NRS1 and NRS2.
Paper: JWST COMPASS: A NIRSpec/G395H Transmission Spectrum of the Sub-Neptune TOI-836c
Authors: Nicole L. Wallack, Natasha E. Batalha, Lili Alderson, Nicholas Scarsdale, Jea I. Adams Redai, Artyom Aguichine, Munazza K. Alam, Peter Gao, Angie Wolfgang, Natalie M. Batalha, James Kirk, Mercedes López-Morales, Sarah E. Moran, Johanna Teske, Hannah R. Wakeford, Nicholas F. Wogan
Figure caption: White-light curves for each reduction with the best-fit models and associated residuals shown as both a time series and histogram. NRS1 (blue) and NRS2 (red) are shown separately for each reduction. The Eureka! reduction is shown at the top, ExoTiC-JEDI in the middle, and Aesop at the bottom.
Paper: JWST COMPASS: A NIRSpec/G395H Transmission Spectrum of the Sub-Neptune TOI-836c
Authors: Nicole L. Wallack, Natasha E. Batalha, Lili Alderson, Nicholas Scarsdale, Jea I. Adams Redai, Artyom Aguichine, Munazza K. Alam, Peter Gao, Angie Wolfgang, Natalie M. Batalha, James Kirk, Mercedes López-Morales, Sarah E. Moran, Johanna Teske, Hannah R. Wakeford, Nicholas F. Wogan
Figure caption: The expected errors on our transmission spectrum simulated from PandExo (Batalha et al. 2017) with our measured errors for each of our three reductions. We achieve comparable precisions between our three reductions and generally are not able to achieve errors as expected from PandExo, likely due to the noise structure in our observation that only utilizes three groups.
Paper: JWST COMPASS: A NIRSpec/G395H Transmission Spectrum of the Sub-Neptune TOI-836c
Authors: Nicole L. Wallack, Natasha E. Batalha, Lili Alderson, Nicholas Scarsdale, Jea I. Adams Redai, Artyom Aguichine, Munazza K. Alam, Peter Gao, Angie Wolfgang, Natalie M. Batalha, James Kirk, Mercedes López-Morales, Sarah E. Moran, Johanna Teske, Hannah R. Wakeford, Nicholas F. Wogan
Figure caption: The Allan variance plots for both detectors for the Eureka! reductions where we include 13 engineering parameters in the systematic model are shown in colors, while the corresponding curves without the engineering parameters are shown in gray for each detector (the gray curves are identical to those shown in the left panel of Figure 3). In the absence of red noise, the residuals would follow the black lines. While there is still residual correlated noise, the amount of red noise has been reduced.
Paper: Evidence for Primordial Alignment: Insights from Stellar Obliquity Measurements for Compact Sub-Saturn Systems
Authors: Brandon T. Radzom, Jiayin Dong, Malena Rice, Xian-Yu Wang, Samuel W. Yee, Tyler R. Fairnington, Cristobal Petrovich, Songhu Wang
Figure caption: Sky-projected stellar obliquity λ vs. stellar effective temperature T eff for all sub-Saturns considered in this work, with 1σ error bars shown. The subpanel in the top right displays the resultant distributions of the number of misaligned sub-Saturns found in compact multiplanet systems (dashed line) and the number of misaligned isolated sub-Saturns after performing 100,000 random draws (bars), with statistical significance (2.6σ) indicated. In both panels, orange corresponds to sub-Saturns in compact multiplanet systems, while blue corresponds to isolated sub-Saturns.
Paper: Evidence for Primordial Alignment: Insights from Stellar Obliquity Measurements for Compact Sub-Saturn Systems
Authors: Brandon T. Radzom, Jiayin Dong, Malena Rice, Xian-Yu Wang, Samuel W. Yee, Tyler R. Fairnington, Cristobal Petrovich, Songhu Wang
Figure caption: Keplerian signal-subtracted RVs (top panels) and RM model residuals (bottom panels) for TOI-5126 (left) and TOI-5398 (right). In the top panels, the best-fit RM models are shown with red dashed lines, and their uncertainties are indicated by gray shadows, while the planets’ spin–orbit configurations are depicted in the bottom-left portion of the plot. Note that the NEID RV measurement obtained ≈1 hr prior to ingress for TOI 5398b was taken on 2022 April 10 via 300 s exposure as part of our out-of-transit follow-up for this system. All NEID data used to generate this figure are available in a machine-readable format as the data behind the figure.(The data used to create this figure are available in the online article.)(The data used to create this figure are available.)
Paper: Archetype-based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey
Authors: Abhijeet Anand, Julien Guy, Stephen Bailey, John Moustakas, J. Aguilar, S. Ahlen, A. S. Bolton, A. Brodzeller, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, Biprateep Dey, K. Fanning, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, S. Juneau, D. Kirkby, T. Kisner, A. Kremin, A. Lambert, M. Landriau, L. Le Guillou, M. Manera, A. Meisner, R. Miquel, E. Mueller, G. Niz, N. Palanque-Delabrouille, W. J. Percival, C. Poppett, F. Prada, A. Raichoor, M. Rezaie, G. Rossi, E. Sanchez, E. F. Schlafly, D. Schlegel, M. Schubnell, D. Sprayberry, G. Tarlé, C. Warner, B. A. Weaver, R. Zhou, H. Zou
Figure caption: The redshift difference between single-epoch and deep coadded spectra of the same LRG targets. The Δχ 2 values are taken from single-epoch observations. The vertical dashed line (Δχ 2 > 15) shows the boundary to define good redshift, and the horizontal dashed line defines the catastrophic redshift failure, i.e., ∣Δv∣ > 1000 km s−1. The left panel shows the results for Redrock (without archetypes), and the right panel shows the results for our archetype approach (with a prior of σ a = 0.1 on polynomial coefficients). In both panels, the blue dots show the accepted redshifts (N a) while the red crosses show the rejected redshifts (N r). To guide the readers, the bottom-right quadrant in each subpanel represents good performance, showing that individual night redshifts match with deep coadded redshifts of the same spectrum, while the upper right shows the redshifts that differ from the deep coadded spectrum and also are flagged as very robust redshift in the pipeline. There are some rejected redshifts in the bottom-right quadrant. That is because we also reject LRGs with z > 1.5. Archetype performs ∼20% better in reducing catastrophic redshift failure.
Paper: Archetype-based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey
Authors: Abhijeet Anand, Julien Guy, Stephen Bailey, John Moustakas, J. Aguilar, S. Ahlen, A. S. Bolton, A. Brodzeller, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, Biprateep Dey, K. Fanning, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, S. Juneau, D. Kirkby, T. Kisner, A. Kremin, A. Lambert, M. Landriau, L. Le Guillou, M. Manera, A. Meisner, R. Miquel, E. Mueller, G. Niz, N. Palanque-Delabrouille, W. J. Percival, C. Poppett, F. Prada, A. Raichoor, M. Rezaie, G. Rossi, E. Sanchez, E. F. Schlafly, D. Schlegel, M. Schubnell, D. Sprayberry, G. Tarlé, C. Warner, B. A. Weaver, R. Zhou, H. Zou
Figure caption: The redshift difference between single-epoch observation and deep coadded spectra of same ELG_LOP targets. The good redshift selection criteria are defined in terms of [O II] SNR and Δχ 2 where both quantities are taken from single-epoch observations (see Table 2). We only include spectra that have valid [O II] line strength (i.e., [O II]flux>0 and ﹩{\sigma }_{{[{\rm{O}}\,\mathrm{II}]}_{\mathrm{flux}}}\gt 0﹩). The vertical dashed line (X > 0, see Table 2) shows the boundary to define good redshift, and the horizontal dashed line defines the catastrophic redshift failure, i.e., ∣Δv∣ > 1000  km s−1. The left panel shows the results for Redrock (without archetypes), and the right panel shows the results for our archetype approach (with a prior of σ a = 0.1 on polynomial coefficients). In both panels, the blue dots show the accepted redshifts (N a) while the red crosses show the rejected redshifts (N r). To guide the readers, the bottom-right quadrant in each subpanel represents good performance, showing that individual night redshifts match with deep coadded redshifts of the same spectrum, while the upper right shows the redshifts that differ from the deep coadded spectrum and also are flagged as very robust redshift in the pipeline. Archetype performs ∼50% better in reducing catastrophic redshift failure.
Paper: Archetype-based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey
Authors: Abhijeet Anand, Julien Guy, Stephen Bailey, John Moustakas, J. Aguilar, S. Ahlen, A. S. Bolton, A. Brodzeller, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, Biprateep Dey, K. Fanning, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, S. Juneau, D. Kirkby, T. Kisner, A. Kremin, A. Lambert, M. Landriau, L. Le Guillou, M. Manera, A. Meisner, R. Miquel, E. Mueller, G. Niz, N. Palanque-Delabrouille, W. J. Percival, C. Poppett, F. Prada, A. Raichoor, M. Rezaie, G. Rossi, E. Sanchez, E. F. Schlafly, D. Schlegel, M. Schubnell, D. Sprayberry, G. Tarlé, C. Warner, B. A. Weaver, R. Zhou, H. Zou
Figure caption: A schematic of our archetype based per-camera polynomial fitting. The coadded or single-exposure spectra are input to the redrock that uses previously estimated PCA-based templates of several spectral types to perform the redshift scan. We retain the N zbest redshifts corresponding to the least N χ 2 values after the preliminary scan. Subsequently, we model the input spectra using Equation (1) at each of these N zbest redshift (in a loop), employing each archetype (in another loop) to determine the final best z associated with the least χ 2. The final output is the best-fit redshift, along with the spectral type and index of the best-fit archetype.
Paper: Archetype-based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey
Authors: Abhijeet Anand, Julien Guy, Stephen Bailey, John Moustakas, J. Aguilar, S. Ahlen, A. S. Bolton, A. Brodzeller, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, Biprateep Dey, K. Fanning, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, S. Juneau, D. Kirkby, T. Kisner, A. Kremin, A. Lambert, M. Landriau, L. Le Guillou, M. Manera, A. Meisner, R. Miquel, E. Mueller, G. Niz, N. Palanque-Delabrouille, W. J. Percival, C. Poppett, F. Prada, A. Raichoor, M. Rezaie, G. Rossi, E. Sanchez, E. F. Schlafly, D. Schlegel, M. Schubnell, D. Sprayberry, G. Tarlé, C. Warner, B. A. Weaver, R. Zhou, H. Zou
Figure caption: The redshift difference between single-exposure and deep coadded spectra of same BGS_FAINT (top) and BGS_BRIGHT (bottom) targets. The Δχ 2 values are taken from single-epoch observations. The vertical dashed line (Δχ 2 > 40) shows the boundary to define confident redshift, and the horizontal dashed line defines the catastrophic redshift failure, i.e., ∣Δv∣ > 1000  km s−1. The left panel shows the results for Redrock (without archetype), and the right panel shows the results for our archetype approach (with a prior of σ a = 0.1 on polynomial coefficients). In both panels, the blue dots show the accepted redshifts (N a) while the red crosses show the rejected redshifts (N r). To guide the readers, the bottom-right quadrant in each subpanel represents good performance, showing that individual night redshifts match with deep coadded redshifts of the same spectrum, while the upper right shows the redshifts that differ from the deep coadded spectrum and also are flagged as very robust redshift in the pipeline.
Paper: Archetype-based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey
Authors: Abhijeet Anand, Julien Guy, Stephen Bailey, John Moustakas, J. Aguilar, S. Ahlen, A. S. Bolton, A. Brodzeller, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, Biprateep Dey, K. Fanning, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, S. Juneau, D. Kirkby, T. Kisner, A. Kremin, A. Lambert, M. Landriau, L. Le Guillou, M. Manera, A. Meisner, R. Miquel, E. Mueller, G. Niz, N. Palanque-Delabrouille, W. J. Percival, C. Poppett, F. Prada, A. Raichoor, M. Rezaie, G. Rossi, E. Sanchez, E. F. Schlafly, D. Schlegel, M. Schubnell, D. Sprayberry, G. Tarlé, C. Warner, B. A. Weaver, R. Zhou, H. Zou
Figure caption: First: PCA-template-based best-fit model (shown in colored solid lines) of a galaxy spectrum. Second: zoomed version of PCA model of the same spectrum. We can see the unphysical negative absorption line fitting for [O II] (λ ∼ 6200 Å) and Hβ (λ ∼ 8080 Å) lines. In fact, Redrock (without archetypes) misidentifies [O II] doublet as [O III] . Third: archetype-based best-fit model of the same spectrum. Fourth: zoomed version of archetype-based per-camera model of the same spectrum. The new model is more physical, and the redshift is more accurate, as confirmed by visual inspection of [O II] doublet. The black curve is the observed flux, the purple curve shows the error spectra in both panels, and the model is shown in solid color lines.