adsabs/AstroCLIMB huggingface dataset:
Paper: Deep Attention-based Supernovae Classification of Multiband Light Curves
Authors: Óscar Pimentel, Pablo A. Estévez, Francisco Förster
Figure caption: Gaussian distribution fits for the conditional observation-error distribution (from the training set ﹩{{ \mathcal D }}_{\mathrm{train}}﹩ of an arbitrary fold split). The green border (a) corresponds to the band g. The red border (b) corresponds to the band r.
Paper: Deep Attention-based Supernovae Classification of Multiband Light Curves
Authors: Óscar Pimentel, Pablo A. Estévez, Francisco Förster
Figure caption: Four examples of misclassified SN multiband light curves by the BRF baseline trained with real data (training-set=[r]) that were correctly classified by the P-TimeModAttn model.
Paper: Deep Attention-based Supernovae Classification of Multiband Light Curves
Authors: Óscar Pimentel, Pablo A. Estévez, Francisco Förster
Figure caption: Examples of the normalized attention scores for the P-TimeModAttn model after pretraining. The bigger the shadow circle, the higher the attention score of an observation. (a) SLSN type. (b) SNII type. (c) SNIa type. (d) SNIbc type.
Paper: Deep Attention-based Supernovae Classification of Multiband Light Curves
Authors: Óscar Pimentel, Pablo A. Estévez, Francisco Förster
Figure caption: Examples of multiband light-curve reconstructions for the P-TimeModAttn model after the pretraining process. Dashed lines are used for the reconstructed light curves. (a) SLSN type. (b) SNII type. (c) SNIa type. (d) SNIbc type.
Paper: Photometric Confirmation and Characterization of the Ennomos Collisional Family in the Jupiter Trojans
Authors: Ian Wong, Michael E. Brown
Figure caption: A portion of the distribution of L5 Trojan proper elements ﹩(a{{\prime} }_{p},{e}_{p},\sin {i}_{p}﹩) taken from Holt et al. (2020b), where ﹩a{{\prime} }_{p}﹩ is the modified semimajor axis, defined in Section 2. Background objects are shown in gray. The blue dots and red squares correspond to the Ennomos family members that are tabulated by Nesvorný et al. (2015) and Rozehnal et al. (2016), respectively. The latter grouping, which served as the master target list for our photometric observations, contains significantly more objects and extends to higher proper inclinations and lower proper semimajor axes.
Paper: Photometric Confirmation and Characterization of the Ennomos Collisional Family in the Jupiter Trojans
Authors: Ian Wong, Michael E. Brown
Figure caption: Histogram of measured g − i color for the purported Ennomos family members observed in our program (blue), alongside the color distribution of Trojans with H < 12.3 mag that were observed by the Sloan Digital Sky Survey (green). The vertical lines denote the mean g − i colors for the less-red and red Trojan subpopulations, as well as the solar color. While the majority of candidate family members have colors that are indistinguishable from the background population, there is a discernible cluster of objects that are significantly bluer than the background. The yellow-shaded region corresponds to g − i < 0.68 (i.e., the color selection criterion that we stipulate for the true Ennomos collisional family).
Paper: ODNet: A Convolutional Neural Network for Asteroid Occultation Detection
Authors: Dorian Cazeneuve, Franck Marchis, Guillaume Blaclard, Paul A. Dalba, Victor Martin, Joe Asencio
Figure caption: ROC curve of model including real data during the training and including only artificial data. A perfect model would be represented as a right angle at the top left corner: we see that as we input only five real observations in the test set, our area under curve (AUC) goes from 83% to 87%. Thus, more real data permits a more precise CNN. Note that this ROC curve is not from the same model as the model in the one in Table 2; it is an example for the enhancement of the model depending on the training data.
Paper: ODNet: A Convolutional Neural Network for Asteroid Occultation Detection
Authors: Dorian Cazeneuve, Franck Marchis, Guillaume Blaclard, Paul A. Dalba, Victor Martin, Joe Asencio
Figure caption: Visualization of ODNet’s model chosen. We can observe the 4D data (2 × 20 × 32 × 32) being flattened by the consecutive convolution and max-pooling layers, then, the five layers of neurons treat the sample to conclude if it contains or not an occultation. The user is informed of the output via one last neuron. The output neuron is a number from 0 to 1, seen as a probability of the event “presence of an occultation in the set presented.” Between each layers of convolution, there is a 3D max pooling, of size 2 × 2 × 2.
Paper: ODNet: A Convolutional Neural Network for Asteroid Occultation Detection
Authors: Dorian Cazeneuve, Franck Marchis, Guillaume Blaclard, Paul A. Dalba, Victor Martin, Joe Asencio
Figure caption: Patroclus’s observation seen by ODNet. From top to bottom: star occulted—reference star—raw output from ODNet—cleaner output.
Paper: ODNet: A Convolutional Neural Network for Asteroid Occultation Detection
Authors: Dorian Cazeneuve, Franck Marchis, Guillaume Blaclard, Paul A. Dalba, Victor Martin, Joe Asencio
Figure caption: Note that during the observation, the background intensity can vary, but in the same way on the TaSt and RfSt, meaning that if the S/N of one star is affected, it is in the same way affected for the other. This way, environmental perturbations are not counted as anomalies but regular events that always happen. Such an event is observed in real observations with high-altitude clouds, or nearby punctual light pollution due to, as an example, a car’s light.
Paper: ODNet: A Convolutional Neural Network for Asteroid Occultation Detection
Authors: Dorian Cazeneuve, Franck Marchis, Guillaume Blaclard, Paul A. Dalba, Victor Martin, Joe Asencio
Figure caption: Terminology of the simulated set. A frame contains the stars in the FOV of the eVscope. The snippet is an image containing a star, artificial or not. A stack is a set of N snippets, permitting to look at the evolution of a star through time. A sequence is a combination of the TaSt and RfSt stacks: this is the input that is fed to ODNet.
Paper: ODNet: A Convolutional Neural Network for Asteroid Occultation Detection
Authors: Dorian Cazeneuve, Franck Marchis, Guillaume Blaclard, Paul A. Dalba, Victor Martin, Joe Asencio
Figure caption: Public occultation report for Misa seen by the legacy code (top) and the same observation seen by ODNet (bottom). We can see that on simple occultation (no wind nor cloud), ODNet performs as good as the legacy code.
Paper: ODNet: A Convolutional Neural Network for Asteroid Occultation Detection
Authors: Dorian Cazeneuve, Franck Marchis, Guillaume Blaclard, Paul A. Dalba, Victor Martin, Joe Asencio
Figure caption: Example of low S/N star, mixed with its background. The star is at the exact center of the frame.
Paper: JWST PEARLS. Prime Extragalactic Areas for Reionization and Lensing Science: Project Overview and First Results
Authors: Rogier A. Windhorst, Seth H. Cohen, Rolf A. Jansen, Jake Summers, Scott Tompkins, Christopher J. Conselice, Simon P. Driver, Haojing Yan, Dan Coe, Brenda Frye, Norman Grogin, Anton Koekemoer, Madeline A. Marshall, Rosalia O'Brien, Nor Pirzkal, Aaron Robotham, Russell E. Ryan, Christopher N. A. Willmer, Timothy Carleton, Jose M. Diego, William C. Keel, Paolo Porto, Caleb Redshaw, Sydney Scheller, Stephen M. Wilkins, S. P. Willner, Adi Zitrin, Nathan J. Adams, Duncan Austin, Richard G. Arendt, John F. Beacom, Rachana A. Bhatawdekar, Larry D. Bradley, Tom Broadhurst, Cheng Cheng, Francesca Civano, Liang Dai, Hervé Dole, Jordan C. J. D'Silva, Kenneth J. Duncan, Giovanni G. Fazio, Giovanni Ferrami, Leonardo Ferreira, Steven L. Finkelstein, Lukas J. Furtak, Hansung B. Gim, Alex Griffiths, Heidi B. Hammel, Kevin C. Harrington, Nimish P. Hathi, Benne W. Holwerda, Rachel Honor, Jia-Sheng Huang, Minhee Hyun, Myungshin Im, Bhavin A. Joshi, Patrick S. Kamieneski, Patrick Kelly, Rebecca L. Larson, Juno Li, Jeremy Lim, Zhiyuan Ma, Peter Maksym, Giorgio Manzoni, Ashish Kumar Meena, Stefanie N. Milam, Mario Nonino, Massimo Pascale, Andreea Petric, Justin D. R. Pierel, Maria del Carmen Polletta, Huub J. A. Röttgering, Michael J. Rutkowski, Ian Smail, Amber N. Straughn, Louis-Gregory Strolger, Andi Swirbul, James A. A. Trussler, Lifan Wang, Brian Welch, J. Stuart B. Wyithe, Min Yun, Erik Zackrisson, Jiashuo Zhang, Xiurui Zhao
Figure caption: Comparison of the JWST NIRCam sky-SB values observed in our four PEARLS fields to models. Black triangles indicate the PEARLS sky-SB measurements of Section 5. The smaller (inner) error bars reflect the variation of the object-free sky-SB measurements within the NIRCam detectors, while the larger error bars reflect the median value of all sky-rms values across the images in each filter. These error bars include the ZP and other sky-SB uncertainties of Appendix B.3. Models are plotted for: TNJ1338 (upper left in purple), El Gordo (upper right in green), VV191 (lower left in red), and the JWIDF (lower right in blue). Short-dashed lines indicate the Zodiacal sky-SB from L2 as predicted by the Spitzer model (Appendix C). Zodiacal light is the highest amplitude component for all targets. Dotted–dashed lines indicate the JWST SL model from Figure 4 of Rigby et al. (2022); long-dashed lines indicate the DGL levels predicted by the IRSA model (Appendix C); black-dotted lines indicate the JWST thermal contributions to the sky-SB predicted by the ETC; solid colored lines are the sum of all four components. The SL level in each field was scaled down by factors f ≃ 0.5–1.0 compared to the Rigby et al. (2022) SL amplitude to obtain a best fit of the sum of the four model components to our 3.5–4.5 μm observations, where the sky-SB is lowest. Details are given in Section 5 and Appendix C.
Paper: JWST PEARLS. Prime Extragalactic Areas for Reionization and Lensing Science: Project Overview and First Results
Authors: Rogier A. Windhorst, Seth H. Cohen, Rolf A. Jansen, Jake Summers, Scott Tompkins, Christopher J. Conselice, Simon P. Driver, Haojing Yan, Dan Coe, Brenda Frye, Norman Grogin, Anton Koekemoer, Madeline A. Marshall, Rosalia O'Brien, Nor Pirzkal, Aaron Robotham, Russell E. Ryan, Christopher N. A. Willmer, Timothy Carleton, Jose M. Diego, William C. Keel, Paolo Porto, Caleb Redshaw, Sydney Scheller, Stephen M. Wilkins, S. P. Willner, Adi Zitrin, Nathan J. Adams, Duncan Austin, Richard G. Arendt, John F. Beacom, Rachana A. Bhatawdekar, Larry D. Bradley, Tom Broadhurst, Cheng Cheng, Francesca Civano, Liang Dai, Hervé Dole, Jordan C. J. D'Silva, Kenneth J. Duncan, Giovanni G. Fazio, Giovanni Ferrami, Leonardo Ferreira, Steven L. Finkelstein, Lukas J. Furtak, Hansung B. Gim, Alex Griffiths, Heidi B. Hammel, Kevin C. Harrington, Nimish P. Hathi, Benne W. Holwerda, Rachel Honor, Jia-Sheng Huang, Minhee Hyun, Myungshin Im, Bhavin A. Joshi, Patrick S. Kamieneski, Patrick Kelly, Rebecca L. Larson, Juno Li, Jeremy Lim, Zhiyuan Ma, Peter Maksym, Giorgio Manzoni, Ashish Kumar Meena, Stefanie N. Milam, Mario Nonino, Massimo Pascale, Andreea Petric, Justin D. R. Pierel, Maria del Carmen Polletta, Huub J. A. Röttgering, Michael J. Rutkowski, Ian Smail, Amber N. Straughn, Louis-Gregory Strolger, Andi Swirbul, James A. A. Trussler, Lifan Wang, Brian Welch, J. Stuart B. Wyithe, Min Yun, Erik Zackrisson, Jiashuo Zhang, Xiurui Zhao
Figure caption: Same as Figure 9 for the ground-based+HST 1.6 and 2.2 μm and Spitzer 3.5 and 4.5 μm counts with JWST NIRCam counts in F150W, F200W, F356W and F444W as brown/olive filled circles in the JWIDF and El Gordo noncluster module.
Paper: JWST PEARLS. Prime Extragalactic Areas for Reionization and Lensing Science: Project Overview and First Results
Authors: Rogier A. Windhorst, Seth H. Cohen, Rolf A. Jansen, Jake Summers, Scott Tompkins, Christopher J. Conselice, Simon P. Driver, Haojing Yan, Dan Coe, Brenda Frye, Norman Grogin, Anton Koekemoer, Madeline A. Marshall, Rosalia O'Brien, Nor Pirzkal, Aaron Robotham, Russell E. Ryan, Christopher N. A. Willmer, Timothy Carleton, Jose M. Diego, William C. Keel, Paolo Porto, Caleb Redshaw, Sydney Scheller, Stephen M. Wilkins, S. P. Willner, Adi Zitrin, Nathan J. Adams, Duncan Austin, Richard G. Arendt, John F. Beacom, Rachana A. Bhatawdekar, Larry D. Bradley, Tom Broadhurst, Cheng Cheng, Francesca Civano, Liang Dai, Hervé Dole, Jordan C. J. D'Silva, Kenneth J. Duncan, Giovanni G. Fazio, Giovanni Ferrami, Leonardo Ferreira, Steven L. Finkelstein, Lukas J. Furtak, Hansung B. Gim, Alex Griffiths, Heidi B. Hammel, Kevin C. Harrington, Nimish P. Hathi, Benne W. Holwerda, Rachel Honor, Jia-Sheng Huang, Minhee Hyun, Myungshin Im, Bhavin A. Joshi, Patrick S. Kamieneski, Patrick Kelly, Rebecca L. Larson, Juno Li, Jeremy Lim, Zhiyuan Ma, Peter Maksym, Giorgio Manzoni, Ashish Kumar Meena, Stefanie N. Milam, Mario Nonino, Massimo Pascale, Andreea Petric, Justin D. R. Pierel, Maria del Carmen Polletta, Huub J. A. Röttgering, Michael J. Rutkowski, Ian Smail, Amber N. Straughn, Louis-Gregory Strolger, Andi Swirbul, James A. A. Trussler, Lifan Wang, Brian Welch, J. Stuart B. Wyithe, Min Yun, Erik Zackrisson, Jiashuo Zhang, Xiurui Zhao
Figure caption: Parameters of the 0.9–4.5 μm galaxy counts and integrated galaxy light (IGL) as derived from Figures 9–10. The top panel shows the AB-magnitude level at which the normalized differential counts peak (see middle panels in Figures 9–10). This is where most of the discrete IGL is generated at each wavelength. Second panel shows the peak SB-value of the IGL derived from the middle panels of Figures 9–10. Third panel shows the width around the peak magnitude or interquartile (i.e., 25%–75%) range where 50% of the discrete IGL is generated. We refer to this range as the “IGL FWHM.” The bottom panel shows the total IGL values (in units of nW m−2 sr−1) of the converging integrals in the right panels of Figures 9–10. Error bars were determined by combining the NIRCam ZP uncertainties of Appendix B.1 with the uncertainties in transforming the NIRCam flux scale to the fiducial flux scale of the VISTA/IRAC filters in Appendix B.2 (see Sections 4.5–4.6).
Paper: JWST PEARLS. Prime Extragalactic Areas for Reionization and Lensing Science: Project Overview and First Results
Authors: Rogier A. Windhorst, Seth H. Cohen, Rolf A. Jansen, Jake Summers, Scott Tompkins, Christopher J. Conselice, Simon P. Driver, Haojing Yan, Dan Coe, Brenda Frye, Norman Grogin, Anton Koekemoer, Madeline A. Marshall, Rosalia O'Brien, Nor Pirzkal, Aaron Robotham, Russell E. Ryan, Christopher N. A. Willmer, Timothy Carleton, Jose M. Diego, William C. Keel, Paolo Porto, Caleb Redshaw, Sydney Scheller, Stephen M. Wilkins, S. P. Willner, Adi Zitrin, Nathan J. Adams, Duncan Austin, Richard G. Arendt, John F. Beacom, Rachana A. Bhatawdekar, Larry D. Bradley, Tom Broadhurst, Cheng Cheng, Francesca Civano, Liang Dai, Hervé Dole, Jordan C. J. D'Silva, Kenneth J. Duncan, Giovanni G. Fazio, Giovanni Ferrami, Leonardo Ferreira, Steven L. Finkelstein, Lukas J. Furtak, Hansung B. Gim, Alex Griffiths, Heidi B. Hammel, Kevin C. Harrington, Nimish P. Hathi, Benne W. Holwerda, Rachel Honor, Jia-Sheng Huang, Minhee Hyun, Myungshin Im, Bhavin A. Joshi, Patrick S. Kamieneski, Patrick Kelly, Rebecca L. Larson, Juno Li, Jeremy Lim, Zhiyuan Ma, Peter Maksym, Giorgio Manzoni, Ashish Kumar Meena, Stefanie N. Milam, Mario Nonino, Massimo Pascale, Andreea Petric, Justin D. R. Pierel, Maria del Carmen Polletta, Huub J. A. Röttgering, Michael J. Rutkowski, Ian Smail, Amber N. Straughn, Louis-Gregory Strolger, Andi Swirbul, James A. A. Trussler, Lifan Wang, Brian Welch, J. Stuart B. Wyithe, Min Yun, Erik Zackrisson, Jiashuo Zhang, Xiurui Zhao
Figure caption: ((a) Top left): part of the JWIDF image before 1/f removal; ((b) top right): same part of the JWIDF image after 1/f removal with the ProFound-based 1/f subtraction code. (Please magnify these figures to see the details.) ((c) Middle left): row-wise 1/f pattern subtracted in the x-direction. The inset color bars show the relative level of the corrections applied. The imprint of the four 5512 × 2048 pixel sections read by the ASIC is apparent; ((d) middle right): column-wise pattern subtracted in the y-direction; ((e) bottom): comparison of the Willott 1/f-removal algorithm compared to the ProFound-based 1/f subtraction. The two algorithms leave similar noise imprints on the resulting sky-SB at the level of ≲0.02–0.04 MJy sr−1, where 1.0 MJy sr−1 typically corresponds to a 5σ source of AB ∼ 28 mag.
Paper: Photometric Confirmation and Characterization of the Ennomos Collisional Family in the Jupiter Trojans
Authors: Ian Wong, Michael E. Brown
Figure caption: Color map of the proper element distribution from Figure 1, color-coded based on the measured g − i colors. The empty white dots indicate targets that are on the Rozehnal et al. (2016) candidate Ennomos family member list but were not imaged as part of our multiband photometric observations. Note the dense concentration of objects with blue–neutral colors at low proper inclination and high proper semimajor axis, which corresponds to the true extent of the Ennomos collisional family (yellow-shaded regions). The location of 4709 Ennomos and 17492 Hippasos are marked by the pink and purple diamonds, respectively.
Paper: Deep Attention-based Supernovae Classification of Multiband Light Curves
Authors: Óscar Pimentel, Pablo A. Estévez, Francisco Förster
Figure caption: A simplified example of a dot-attention mechanism given an arbitrary attention head h. In order to better illustrate the alignments, the query and key vectors are represented as pointing-arrow two-dimensional vectors (e.g., ﹩{{\boldsymbol{q}}}_{i,j}^{(h)}={\left[.707,.707\right]}^{T}﹩). Those two-dimensional vectors (and the value vectors) are the projected vectors, from the input vectors, obtained by using the associated learnable linear projections and bias vectors for the attention head h. The attention scores are based on the scaled dot product between the query and key vectors. The value vectors are represented as color codes, explaining the final color used for the context vector ﹩{{\boldsymbol{c}}}_{i,j}^{(h)}﹩. This operation can be extended and parallelized for an arbitrary number H of attention heads.
Paper: Deep Attention-based Supernovae Classification of Multiband Light Curves
Authors: Óscar Pimentel, Pablo A. Estévez, Francisco Förster
Figure caption: Diagram for the proposed autoencoder model (TimeModAttn), which is composed of an encoder ψ (shown on the left side of the diagram) and a decoder ψ (shown on the right side of the diagram). There are two variations for the encoder: serial and parallel. For the band operator, the notation Φ(b=1) is used for the band g, and Φ(b=2) is used for the band r. An arbitrary number of N  L  stacked layers is shown for the MHSelfAttn multilayer sequence processing. Optional tensor dimensional transformations, related to the model implementation, are also shown (e.g., (N,L,1+B)>(N,L,D)). N stands for the mini-batch size, L stands for the sequence steps tensor dimension, B stands for the number of bands, and D stands for the representation-vector dimension.
Paper: Deep Attention-based Supernovae Classification of Multiband Light Curves
Authors: Óscar Pimentel, Pablo A. Estévez, Francisco Förster
Figure caption: Self-attention example diagram. The key  k   i,j , value  v   i,j , and query  q   i,j  vector relationships are shown for the first, second, (L − 1)th, and Lth sequence steps. For simplicity, we assume a single attention head mechanism and omit the attention head superscript ﹩{\left(\cdot \right)}^{(h)}﹩. This operation is causal as each context vector  c   i,j  computation only depends on the current and previous sequence steps. All query, key, and value vectors come from the same sequence.
Paper: Deep Attention-based Supernovae Classification of Multiband Light Curves
Authors: Óscar Pimentel, Pablo A. Estévez, Francisco Förster
Figure caption: Proposed model architecture based on an autoencoder ﹩\left\{{\boldsymbol{\psi }},{\boldsymbol{\varphi }}\right\}﹩, which is composed of an encoder ψ and a decoder ψ. The representation vector   z  i serves as input to a classifier  λ.
Paper: Photometric Confirmation and Characterization of the Ennomos Collisional Family in the Jupiter Trojans
Authors: Ian Wong, Michael E. Brown
Figure caption: Cumulative absolute magnitude distributions of the confirmed Ennomos family members (blue circles), along with the magnitude distributions of the Eurybates collisional family (red squares) and current L5 background population (black triangles). The gray circles show the magnitude distribution of the full list of 104 candidate Ennomos family members. Significant catalog incompleteness in the candidate family member data sets begins at roughly H = 14.0. The shape of the Ennomos family magnitude distribution is poorly constrained due to the small sample size. Meanwhile, the Eurybates family magnitude distribution is significantly steeper than the background Trojan population.
Paper: Deep Attention-based Supernovae Classification of Multiband Light Curves
Authors: Óscar Pimentel, Pablo A. Estévez, Francisco Förster
Figure caption: Examples of synthetic SN multiband light curves (before the observation-time re-offset). The SPM posterior samples (k  s  = 32) from MCMC are shown as continuous-time curves for each band. Empirical observation fluxes are shown as color circles with observation-error bars. Synthetic observation fluxes, using a random SPM posterior sample, are shown as black outlined circles. (a) SLSN type. (b) SNII type. (c) SNIa type. (d) SNIbc type.
Paper: Deep Attention-based Supernovae Classification of Multiband Light Curves
Authors: Óscar Pimentel, Pablo A. Estévez, Francisco Förster
Figure caption: Class population distribution of SN types (from the original data set ﹩{{ \mathcal D }}_{})﹩.
Paper: KORG: A Modern 1D LTE Spectral Synthesis Package
Authors: Adam J. Wheeler, Matthew W. Abruzzo, Andrew R. Casey, Melissa K. Ness
Figure caption: Portions of a synthetic solar spectrum generated with KORG, MOOG, TURBOSPECTRUM, and SME, as well as the continuum generated with an empty line list from 4000 to 10000 Å. All wavelengths are vacuum. We emphasize that the structure in the blue and ultraviolet in the KORG continuum is due to resonances in the metal bound–free cross sections, not lines. The residuals (other–KORG) are shown underneath the rectified flux for each wavelength region. The level of agreement between KORG and the other codes is similar to their agreement with each other. See the text for a discussion of the discrepancies.
Paper: KORG: A Modern 1D LTE Spectral Synthesis Package
Authors: Adam J. Wheeler, Matthew W. Abruzzo, Andrew R. Casey, Melissa K. Ness
Figure caption: Same as Figure 5, but showing the synthetic spectrum of an HD499330-like star.
Paper: KORG: A Modern 1D LTE Spectral Synthesis Package
Authors: Adam J. Wheeler, Matthew W. Abruzzo, Andrew R. Casey, Melissa K. Ness
Figure caption: Derivatives of a synthesized solar spectrum with respect to various abundances, ﹩\tfrac{\partial { \mathcal F }}{\partial A(X)}﹩. Vertical offsets have been applied, but the relative scaling is preserved, which is why some derivative spectra appear flat here (but show fluctuations at a subpercent level).
Paper: KORG: A Modern 1D LTE Spectral Synthesis Package
Authors: Adam J. Wheeler, Matthew W. Abruzzo, Andrew R. Casey, Melissa K. Ness
Figure caption: The Hummer & Mihalas (1988) occupation probability correction factor, w, for several values of the primary quantum number, n, in hydrogen, evaluated at each layer in the solar atmosphere (indexed by optical depth at 5000 Å). While the formalism affects transitions involving outer energy levels, those which do not, e.g., H  α , are unaffected. The results are similar for the other atmospheres considered in Section 3.
Paper: KORG: A Modern 1D LTE Spectral Synthesis Package
Authors: Adam J. Wheeler, Matthew W. Abruzzo, Andrew R. Casey, Melissa K. Ness
Figure caption: Major sources of opacity at the solar photosphere, defined here at the MARCs model atmosphere layers where the optical depth at 5000 Å is most nearly unity.