adsabs/AstroCLIMB huggingface dataset:
Paper: The Athena++ Adaptive Mesh Refinement Framework: Multigrid Solvers for Self-gravity
Authors: Kengo Tomida, James M. Stone
Figure caption: The error compared to the analytic solution of the stable Jeans wave test. The blue dashed line shows the result using fully converged solutions for self-gravity, while the orange solid line shows the result using one sweep of FMG for self-gravity. The black dashed–dotted line shows the second-order convergence.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: Flowchart of the forecasting model. Three CNN networks (ResNet-18, SqueezeNet, and AlexNet) were chosen to forecast flares using EUV images of six wavelengths generated by the AIA equipment as the input. The input to each CNN is the images of the active regions in different wavelengths. The output of each CNN is the forecast of whether a flare will erupt in the next 48 hr. During model testing, the feature map was visualized to intuitively understand the region of interest of the network. Finally, the output results of the multiple wavelengths were fused to obtain the final forecast.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: ResNet-18 structure. ResNet-18 includes the convolutional, maximum pooling, average pooling, and FC layers. The EUV images of the active regions of six wavelengths were used as the input of the network to generate six different corresponding forecasting models and provide the respective forecasting results.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: Fire module. This module consists of the Squeeze and Expand modules.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: Flow of generating a data set.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: AlexNet structure. AlexNet contains a convolution layer, a maximum pooling layer, and an FC layer. The AIA images of the active regions of the six EUV wavelengths are respectively used as the input of the network to generate six different forecasting models and provide their respective forecasting results.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: SqueezeNet structure. SqueezeNet contains a convolution layer, a maximum pooling layer, an average pooling layer, an FC layer, and a Fire module. The AIA images of the active regions of the six EUV wavelengths are respectively used as the input of the network to generate six different forecasting models and provide their respective forecasting results.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: Six wavelengths of the EUV images of the active region. This active region sample is called NOAA 11429, which was obtained on 2012 March 7.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: The ROC charts for the single-wavelength data on three networks: (a) ResNet-18, (b) SqueezeNet, and (c) AlexNet.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: The AUC charts for the single-wavelength data on three networks: (a) ResNet-18, (b) SqueezeNet, and (c) AlexNet.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: The TSS charts for the single-wavelength data on three networks: (a) ResNet-18, (b) SqueezeNet, and (c) AlexNet.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: The AUC (a) and ROC (b) charts of the combined forecasting model with multiwavelength results on SqueezeNet.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: The AUC (a) and ROC (b) charts of the combined forecasting model with multiwavelength results on ResNet-18.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: The AUC (a) and ROC (b) charts of the combined forecasting model with multiwavelength results on AlexNet.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Top: full-resolution (R = 80,000) spectrum of Altair (the 860L segment) before (black) and after (red) the telluric correction. The three smaller panels show the spectrum in three different spectral ranges with the best-fit Earth atmospheric model (red). Middle and bottom: the same as in the top panels, but for HD 162305 with R = 20,000.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: The process of echelle order merging. Top: raw orders before applying corrections for blaze shift and scattered light. Middle: orders after correction. Orders are highlighted in black or red for visualization purposes. Bottom: spectrum after merging of the corrected orders.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Distribution of stellar spectral types in the UVES-POP library. Spectral type identification was done mostly using the SIMBAD database (Wenger et al. 2000) or, in rare cases, based on stellar atmospheric parameters (see Section 4).
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Merging of nonoverlapping spectral segments for Altair. The black line shows the spectra prior to merging. The red line shows the right segment renormalized by the merging procedure. A synthetic spectrum used for the merging of nonoverlapping segments is shown in gray.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Top: raw UVES spectral flat field in the 860L segment with strong fringes. Bottom: a single flat-field order with fringes. The red line shows the best-fitting B-spline, which we considered as the blaze function approximation.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Transmission curves of the photometric bands used for the spectrophotometric correction. Explanation of abbreviations and parameters of the bands are presented in Table 3.
Paper: Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images
Authors: Dezhi Sun, Xin Huang, Zhongrui Zhao, Long Xu
Figure caption: Visualization of the feature maps of the six wavelengths in the same active region. This active region sample is NOAA 12422 on 2015 September 27.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Visualization of the final stage of the minimization method used for the fitting of a spectrum of Procyon by grids of stellar atmosphere models with the two discrete parameters (T eff, ﹩\mathrm{log}\,g﹩ assuming solar metallicity and [α/Fe]) and continuous kinematic parameters (v rad, ﹩v\sin i﹩) and a multiplicative continuum. The middle panel shows a 3D plot of the second-order approximation surface for the χ 2 profile. The projection of the χ 2 profile is shown in the same colors; the black circles mark the grid nodes in which the χ 2 values were calculated. The positions of the nodes selected for the approximation are also shown on a 3D surface. The right and left panels present two small portions of a spectrum around Hβ and the Mg b triplet, their best-fitting models and fitting residuals in every node marked in the middle panel in the decreasing order of T eff and ﹩\mathrm{log}\,g﹩. The input data are shown in black, the best-fitting model is shown in red, the residuals are in blue, the residuals in the grid nodes selected for the approximation are shown in green in the same flux scale, and the spectral lines with the most noticeable changes from the parameters of stellar atmospheres are highlighted in yellow.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Positions of the UVES-POP stars in the [α/Fe]–[Fe/H] plane with T eff color-coded. Only the stars with [α/Fe] determined in our study are shown. The underlying number density plot shows positions of stars from the third release of the GALAH survey (Buder et al. 2021), with the high S/N and quality criteria for both [α/Fe] and [Fe/H] applied to the input catalog. The black dashed line shows a separation between thin and thick disks obtained by considering the bimodal [α/Fe] distribution at different [Fe/H], which will be described in detail in S. B. Borisov et al. (in preparation), where the authors also assume that objects of the Galactic halo have [Fe/H] ≤ –1.1 dex. The red solid line delineates the area that contains extremely [α/Fe]-rich stars (see Section 4.2); the candidate “y-exαfe” stars are shown as four-pointed stars.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Examples of 12 resolution-degraded UVES-POP spectra (red) with Gaia BP/RP spectra (black) overplotted. All spectra are normalized to unity at the maximal flux of the convolved UVES-POP spectrum. Below each plot we show a panel with the fractional flux differences between the two spectra. The left six panels present the stars with a satisfactory agreement between UVES-POP and Gaia; the right six panels show stars with obvious calibration issues in the Gaia BP/RP spectra. None of the 12 stars have significant variability according to SIMBAD.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Comparison of atmospheric parameters T eff, log g, and [Fe/H] between this work and the literature. Stars that are presented in more than one catalog are shown multiple times. Pulsating variable stars are marked by squares.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Spectrophotometric correction for the UVES-POP spectrum of HD 75063. Top: spectrum before correction (black line) and after correction (red line). Blue regions demonstrate the best-fitting synthetic spectrum that fills in the gaps, while the green line is an extrapolated spectrum. Bottom: black points are the ratios of observed fluxes from photometric databases and synthetic fluxes computed from the spectrum (the ratios are normalized to the maximal value). The red line is a polynomial fit used for the correction. Explanation of abbreviations and some parameters of the photometric bands are shown in Table 3.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Top: comparison of the synthetic Tycho colors (B − V)syn of the spectra corrected with observed colors (B − V)obs before and after spectrophotometric correction (left and right panels, respectively). Bottom: same as the top panels, but for Gaia colors (B  p  − R  p ).
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Examples of full-spectrum fitting of stellar spectra. The observed spectra are shown in black in the fitting range from 3800 to 6800 Å, the uncertainties are in orange, the best-fitting models are in red, and the fitting residuals are in blue. Pink-shaded areas denote the masked regions excluded from the fit.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption:  T eff, [Fe/H], and ﹩\mathrm{log}\,g﹩ coverage of the UVES-POP stellar library with [α/Fe] color-coded. The points in gray show the stars for which [α/Fe] was not determined.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: A screenshot of an interactive web-based spectral viewer. An R = 20,000 spectrum of HD 162305 is shown in black, the best-fitting model is in red, and the multiplicative polynomial continuum is in blue. Pink shaded areas denote the masked regions excluded from the fit.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Spectra of 12 stars from UVES-POP (red) and NGSL (black). For comparison purposes, we degraded the resolution of the UVES-POP spectra and applied the same wavelength sampling as NGSL.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Spectra of UCD 320 and UCD 330 (black) and best-fitting models (red); residuals are shown as blue points. The insets show the spectral region around the Mg b triplet. For better visibility, flux errors and residuals for UCD 320 are reduced by a factor of 4.
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Spectra of eight stars from UVES-POP (red) and X-Shooter DR3 (black).
Paper: New Generation Stellar Spectral Libraries in the Optical and Near-infrared. I. The Recalibrated UVES-POP Library for Stellar Population Synthesis 
Authors: Sviatoslav B. Borisov, Igor V. Chilingarian, Evgenii V. Rubtsov, Cédric Ledoux, Claudio Melo, Kirill A. Grishin, Ivan Yu. Katkov, Vladimir S. Goradzhanov, Anton V. Afanasiev, Anastasia V. Kasparova, Anna S. Saburova
Figure caption: Spectra of 25 stars from UVES-POP (red) and ELODIE (black).
Paper: Generating Space-based SDO/HMI-like Solar Magnetograms from Ground-based Hα Images by Deep Learning
Authors: Fei Gao, Tie Liu, WenQing Sun, Long Xu
Figure caption: The architecture of Pix2Pix model used in the paper. G is the generator, D is the discriminator. x is an Hα image, y is the corresponding HMI magnetogram of x, and the ﹩G\left(x\right)﹩ is the generated magnetogram of x.
Paper: Generating Space-based SDO/HMI-like Solar Magnetograms from Ground-based Hα Images by Deep Learning
Authors: Fei Gao, Tie Liu, WenQing Sun, Long Xu
Figure caption: Comparison between HMI magnetograms and generated ones on 2014 August 11, October 7, and December 30. The first column represents SDO/HMI magnetograms. The second and third columns represent generated ones by Model A and Model B, respectively. The last two columns represent the difference maps between SDO/HMI magnetograms and generated ones by Model A and Model B, respectively. The boxes in red color mark the ARs highlighted in Figure 4.
Paper: Generating Space-based SDO/HMI-like Solar Magnetograms from Ground-based Hα Images by Deep Learning
Authors: Fei Gao, Tie Liu, WenQing Sun, Long Xu
Figure caption: Comparisons of ARs between HMI magnetograms and generated ones by Model A and Model B on October 7 and 2014 December 30. The marked ARs in Figure 3 are zoomed in. The first column represents the ARs of SDO/HMI magnetograms, the second column represents the ARs of the generated ones by Model A, the third column represents generated ones by Model B, and the last two columns represent the difference maps of the ARs between SDO/HMI magnetograms and generated ones by Model A and Model B, respectively.
Paper: Generating Space-based SDO/HMI-like Solar Magnetograms from Ground-based Hα Images by Deep Learning
Authors: Fei Gao, Tie Liu, WenQing Sun, Long Xu
Figure caption: An example of the paired images in our data set. The left one is a GONG Hα image. The right one is the corresponding SDO/HMI magnetogram.
Paper: Extension of HOPS out to 500 pc (eHOPS). I. Identification and Modeling of Protostars in the Aquila Molecular Clouds 
Authors: Riwaj Pokhrel, S. Thomas Megeath, Robert A. Gutermuth, Elise Furlan, William J. Fischer, Samuel Federman, John J. Tobin, Amelia M. Stutz, Lee Hartmann, Mayra Osorio, Dan M. Watson, Thomas Stanke, P. Manoj, Mayank Narang, Prabhani Atnagulov, Nolan Habel, Wafa Zakri
Figure caption: Mode vs. best-fit values for envelope reference density (panel (a)), total luminosity (panel (b)), foreground extinction (panel (c)), inclination angle (panel (d)), cavity opening angle (panel (e)), and disk outer radius (panel (f)) for the closest 50 models in R ﹩\leqslant {R}_{\min }﹩ + Δ R, where ΔR = 1. Random offsets are added to the values to display superimposed markers that would otherwise be hidden.
Paper: Extension of HOPS out to 500 pc (eHOPS). I. Identification and Modeling of Protostars in the Aquila Molecular Clouds 
Authors: Riwaj Pokhrel, S. Thomas Megeath, Robert A. Gutermuth, Elise Furlan, William J. Fischer, Samuel Federman, John J. Tobin, Amelia M. Stutz, Lee Hartmann, Mayra Osorio, Dan M. Watson, Thomas Stanke, P. Manoj, Mayank Narang, Prabhani Atnagulov, Nolan Habel, Wafa Zakri
Figure caption: A few examples of morphologically identified galaxies. The top three panels and the bottom three panels are the UKIDSS J-, H-, and K-band maps of galaxies #140000 and #1521071, respectively (see Table 10).
Paper: Extension of HOPS out to 500 pc (eHOPS). I. Identification and Modeling of Protostars in the Aquila Molecular Clouds 
Authors: Riwaj Pokhrel, S. Thomas Megeath, Robert A. Gutermuth, Elise Furlan, William J. Fischer, Samuel Federman, John J. Tobin, Amelia M. Stutz, Lee Hartmann, Mayra Osorio, Dan M. Watson, Thomas Stanke, P. Manoj, Mayank Narang, Prabhani Atnagulov, Nolan Habel, Wafa Zakri
Figure caption: The ratio of the IRS flux to the corresponding IRAC or MIPS photometry for 5.8, 8.0, and 24 μm wavelengths for protostars with IRS data.
Paper: Extension of HOPS out to 500 pc (eHOPS). I. Identification and Modeling of Protostars in the Aquila Molecular Clouds 
Authors: Riwaj Pokhrel, S. Thomas Megeath, Robert A. Gutermuth, Elise Furlan, William J. Fischer, Samuel Federman, John J. Tobin, Amelia M. Stutz, Lee Hartmann, Mayra Osorio, Dan M. Watson, Thomas Stanke, P. Manoj, Mayank Narang, Prabhani Atnagulov, Nolan Habel, Wafa Zakri
Figure caption: Variation of L bol with α 4.5,24 for all of the sources in Aquila.