adsabs/AstroCLIMB huggingface dataset:
Paper: Flare Duty Cycle of Gamma-Ray Blazars and Implications for High-energy Neutrino Emission
Authors: Kenji Yoshida, Maria Petropoulou, Kohta Murase, Foteini Oikonomou
Figure caption: The total muon neutrino fluxes of gamma-ray flaring FSRQs and BL Lac objects as a function of γ. The total fluxes of muon neutrinos are estimated from our samples, multiplied by correction factors of the contribution of the 4LAC blazars and not-yet-detected blazars that are too gamma-ray faint to be included in the 4LAC catalog. The results of scenarios (1) and (2) are shown in open squares/circles and solid squares/circles, respectively, compared to a reference flux of all-sky muon neutrinos (gray dashed line; Aartsen et al. 2016). The three upper limits of Hooper et al. (2019) derived for different assumptions about the source evolution (﹩1/{D}_{L}^{2}﹩ , Flat, γ-Ray), and reduced to 1/3 for muon neutrinos, are overplotted for comparison.
Paper: Flare Duty Cycle of Gamma-Ray Blazars and Implications for High-energy Neutrino Emission
Authors: Kenji Yoshida, Maria Petropoulou, Kohta Murase, Foteini Oikonomou
Figure caption: Upper limits on the normalization parameter A  X  of the analyzed blazars in gray as a function of the indices γ based on the nondetection of neutrino events, compared to the IceCube 90% sensitivity for scenario (2). Left and right panels show the normalization parameters with 1 week binning and 10 yr binning, respectively. For sources whose flares are all less than the sensitivity flux, the normalization parameter A  X  is set to 1.0. The normalization parameter of the source is adjusted so that the maximum neutrino flux of the source is equal to the sensitivity flux if the flux of at least one flare exceeds the sensitivity. Several sources with the lowest normalization parameters are plotted with red, blue, green, and orange lines.
Paper: Flare Duty Cycle of Gamma-Ray Blazars and Implications for High-energy Neutrino Emission
Authors: Kenji Yoshida, Maria Petropoulou, Kohta Murase, Foteini Oikonomou
Figure caption: Estimated muon neutrino flare fluxes of 129 blazars (97 FSRQs, 26 BL Lac objects, and 6 BCUs) for a 1 week bin (a) and a 10 yr bin (b) of scenario (2) with A  X  = 1.0, and γ = 1.5 as a function of the ﹩\sin (\delta )﹩ with decl. δ. The 90% IceCube sensitivities are the same as in Figure 8.
Paper: Flare Duty Cycle of Gamma-Ray Blazars and Implications for High-energy Neutrino Emission
Authors: Kenji Yoshida, Maria Petropoulou, Kohta Murase, Foteini Oikonomou
Figure caption: Scatter plot between the quiescent gamma-ray and X-ray fluxes for 138 blazars of 101 FSRQs (blue squares), 31 BL Lac objects (orange circles), and 6 BCUs (green triangles). The isolated upper left FSRQ is 3C 273.
Paper: Flare Duty Cycle of Gamma-Ray Blazars and Implications for High-energy Neutrino Emission
Authors: Kenji Yoshida, Maria Petropoulou, Kohta Murase, Foteini Oikonomou
Figure caption: The upper limits of ﹩{E}_{{\nu }_{\mu }}^{2}{{\rm{\Phi }}}_{{\nu }_{\mu }}﹩ of a 10 yr binning as a function of the flare significance s above the quiescent level for FSRQs (open blue squares) and BL Lac objects (open orange circles) of scenario (1) and FSRQs (solid blue squares) and BL Lac objects (solid orange circles) of scenario (2) with γ = 1.5. The dashed horizontal line shows a reference flux of the isotropic diffuse muon neutrinos (Aartsen et al. 2016).
Paper: Flare Duty Cycle of Gamma-Ray Blazars and Implications for High-energy Neutrino Emission
Authors: Kenji Yoshida, Maria Petropoulou, Kohta Murase, Foteini Oikonomou
Figure caption: The mean gamma-ray quiescent fluxes of 106 FSRQs (blue squares), 31 BL Lac objects (orange circles), and 8 BCUs (green triangles) with the standard deviations as a function of time bins.
Paper: Flare Duty Cycle of Gamma-Ray Blazars and Implications for High-energy Neutrino Emission
Authors: Kenji Yoshida, Maria Petropoulou, Kohta Murase, Foteini Oikonomou
Figure caption: Indicative examples of weekly binned blazar gamma-ray light curves (black points) in the 1–316 GeV energy range. Gray triangles indicate 95% upper limits. See the caption of Figure 2 in details.
Paper: Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite
Authors: Lucia A. Perez, Shy Genel, Francisco Villaescusa-Navarro, Rachel S. Somerville, Austen Gabrielpillai, Daniel Anglés-Alcázar, Benjamin D. Wandelt, L. Y. Aaron Yung
Figure caption: Which clustering statistic is best at constraining cosmology and astrophysical parameters? We compare the constraints on ΩM, σ 8, ﹩{A}_{\mathrm{SN}1}﹩, ﹩{A}_{\mathrm{SN}2}﹩, and A AGN found by each of the clustering statistics that we use: (a) the 2ptCF, (b) CiC, (c) VPF, and (d) all combined. We use the respective clustering of SAM galaxies with stellar masses greater than 1 × 109 M ⊙, downsampled to a density of 0.005 h 3 cMpc−3. Detailed quantitative comparisons can be found in Tables 8 and 9.
Paper: Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite
Authors: Lucia A. Perez, Shy Genel, Francisco Villaescusa-Navarro, Rachel S. Somerville, Austen Gabrielpillai, Daniel Anglés-Alcázar, Benjamin D. Wandelt, L. Y. Aaron Yung
Figure caption: Results from neural networks that attempt to predict one SAM parameter at a time. These networks were given “all” clustering statistics at z = {0.0, 0.1, 0.5, 1.0} for all SAM galaxies with stellar masses greater than 2 × 1010 M ⊙ (a,b; magenta) or star formation rates greater than 1 M ⊙ yr−1 (c; cyan), with no downsampling to fixed number density. Predictions for each parameter when all five parameters are constrained at once are given in black with ﹩\bar{\sigma }﹩ errors for comparison. The constraints do not demonstrably improve with a focused neural network—this improves our confidence that the neural networks fitting all five parameters at once are extracting close to the full available information.
Paper: Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite
Authors: Lucia A. Perez, Shy Genel, Francisco Villaescusa-Navarro, Rachel S. Somerville, Austen Gabrielpillai, Daniel Anglés-Alcázar, Benjamin D. Wandelt, L. Y. Aaron Yung
Figure caption: Which clustering statistic is best at constraining cosmology through ΩM? We compare the constraints on ΩM found by different clustering statistics based on the clustering of dark matter halos with mass greater than 2 × 1011 M ⊙, randomly sampled to a density of 0.005 h 3 cMpc−3. We combine the clustering at z = {0.0, 0.1, 0.5, 1.0}. The 2ptCF (a) is measured between 1.1 < R < 36.1 cMpc; CiC (b) is measured at at R = 16.0, 22.4, 28.8 cMpc; and the VPF (c) is measured between 1.6 < R < 40 cMpc. We combine “all” these statistics (d) for the best constraints. Detailed quantitative comparisons for the cosmological parameters can be found in Table 8, and in Table 9 for the stellar feedback parameters.
Paper: Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite
Authors: Lucia A. Perez, Shy Genel, Francisco Villaescusa-Navarro, Rachel S. Somerville, Austen Gabrielpillai, Daniel Anglés-Alcázar, Benjamin D. Wandelt, L. Y. Aaron Yung
Figure caption: Examining redshift dependence on cosmological constraints for the clustering statistics of SAM galaxies with stellar mass greater than 2 × 1010 M ⊙, downsampled to a density of 0.001 h 3 cMpc−3. See Table 3 for details of what exact distance scales and values were used for CiC for “all” clustering measured for this training.
Paper: Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite
Authors: Lucia A. Perez, Shy Genel, Francisco Villaescusa-Navarro, Rachel S. Somerville, Austen Gabrielpillai, Daniel Anglés-Alcázar, Benjamin D. Wandelt, L. Y. Aaron Yung
Figure caption: Comparing how selections on basic galaxy properties generated by the SC-SAM affect the predictive power of a neural network trained on galaxy clustering: (a) stellar mass, (b) star formation rate, and (c) specific star formation rate. Detailed quantitative comparisons can be found in Table 4. Outliers (red stars, often out of range) are simulations in the test set whose “Z-values” (Equation (12)) are greater than 6; see Section 3.2.3 for more details.
Paper: Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite
Authors: Lucia A. Perez, Shy Genel, Francisco Villaescusa-Navarro, Rachel S. Somerville, Austen Gabrielpillai, Daniel Anglés-Alcázar, Benjamin D. Wandelt, L. Y. Aaron Yung
Figure caption: Exploring how not  downsampling to a fixed density affects constraints on cosmology and the SC-SAM feedback parameters, for “all” clustering of galaxies selected by (a) stellar mass (magenta) or (b) star formation rate (cyan). Detailed quantitative comparisons can be found in Table 5. Outliers (red stars, often out of range) are simulations in the test set whose “Z-values” are greater than 6 (Section 3.2.3).
Paper: Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite
Authors: Lucia A. Perez, Shy Genel, Francisco Villaescusa-Navarro, Rachel S. Somerville, Austen Gabrielpillai, Daniel Anglés-Alcázar, Benjamin D. Wandelt, L. Y. Aaron Yung
Figure caption: Which clustering statistic is best at constraining σ 8? The clustering and setup are the same as in Figure 19, but for σ 8 instead.
Paper: Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite
Authors: Lucia A. Perez, Shy Genel, Francisco Villaescusa-Navarro, Rachel S. Somerville, Austen Gabrielpillai, Daniel Anglés-Alcázar, Benjamin D. Wandelt, L. Y. Aaron Yung
Figure caption: Examining redshift dependence on constraints for the cosmological parameter ΩM, using the clustering of halos with masses greater than 2 × 1011 M ⊙ downsampled to a density 0.005 h 3 cMpc−3. See Table 3 for details of what exact distance scales and values were used for CiC for “all” clustering measured for this training.
Paper: Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite
Authors: Lucia A. Perez, Shy Genel, Francisco Villaescusa-Navarro, Rachel S. Somerville, Austen Gabrielpillai, Daniel Anglés-Alcázar, Benjamin D. Wandelt, L. Y. Aaron Yung
Figure caption: Similar to Figure 16, but for σ 8.
Paper: SN 2020bio: A Double-peaked, H-poor Type IIb Supernova with Evidence of Circumstellar Interaction
Authors: C. Pellegrino, D. Hiramatsu, I. Arcavi, D. A. Howell, K. A. Bostroem, P. J. Brown, J. Burke, N. Elias-Rosa, K. Itagaki, H. Kaneda, C. McCully, M. Modjaz, E. Padilla Gonzalez, T. A. Pritchard, N. Yesmin
Figure caption: The nebular spectrum of SN 2020bio (black) compared to that of SN 1993J (blue), the 12C model of (Jerkstrand et al. 2015, gray), and the transitional Type IIb/Ibn SN 2018gjx (Prentice et al. 2020). Phases are given in the legend. Fluxes have been normalized to the [O I] emission feature, and galaxy emission lines have been masked for clarity. Notable features in the spectrum of SN 2020bio have been marked with dashed lines—Hα (red), He I λ 5876, λ 7065 (blue), and Na ID (gold).
Paper: SN 2020bio: A Double-peaked, H-poor Type IIb Supernova with Evidence of Circumstellar Interaction
Authors: C. Pellegrino, D. Hiramatsu, I. Arcavi, D. A. Howell, K. A. Bostroem, P. J. Brown, J. Burke, N. Elias-Rosa, K. Itagaki, H. Kaneda, C. McCully, M. Modjaz, E. Padilla Gonzalez, T. A. Pritchard, N. Yesmin
Figure caption: Spectra of SN 2020bio compared with spectra of other SNe IIb at similar phases. Phases with respect to the estimated explosion time are given above each spectrum, and notable spectral features are identified with red (H) and blue (He) vertical lines at their rest-frame wavelengths. The dotted line shows the He I λ 5876 absorption velocity of SN 2020bio. The spectra of SN 2016gkg are unpublished spectra obtained by LCO, while the other comparison spectra were retrieved from WiseRep (Yaron & Gal-Yam 2012).
Paper: SN 2020bio: A Double-peaked, H-poor Type IIb Supernova with Evidence of Circumstellar Interaction
Authors: C. Pellegrino, D. Hiramatsu, I. Arcavi, D. A. Howell, K. A. Bostroem, P. J. Brown, J. Burke, N. Elias-Rosa, K. Itagaki, H. Kaneda, C. McCully, M. Modjaz, E. Padilla Gonzalez, T. A. Pritchard, N. Yesmin
Figure caption: The spectrum of SN 2020bio ≈26 days after explosion, compared with the mean Type Ib and Type IIb template spectra (Liu et al. 2016) at the same phase. SN 2020bio has spectral features in common with both these classes. The inset shows the region surrounding the Hα line. SN 2020bio has a weak and broad Hα emission feature that is weaker than that in the mean SN IIb spectrum. However, this feature is much different than those seen in typical SNe Ib. This may indicate that the progenitor of SN 2020bio was stripped almost entirely of its H-rich envelope.
Paper: SN 2020bio: A Double-peaked, H-poor Type IIb Supernova with Evidence of Circumstellar Interaction
Authors: C. Pellegrino, D. Hiramatsu, I. Arcavi, D. A. Howell, K. A. Bostroem, P. J. Brown, J. Burke, N. Elias-Rosa, K. Itagaki, H. Kaneda, C. McCully, M. Modjaz, E. Padilla Gonzalez, T. A. Pritchard, N. Yesmin
Figure caption: Numerical MESA and STELLA circumstellar interaction-powered model light curves of SN 2020bio at early times. Different color curves correspond to models with varying mass-loss rates and timescales. Leftward-facing arrows show the range of possible explosion epochs inferred from our SCE fits. The early-time emission excess is best reproduced with 0.001–0.01 M ⊙ of CSM.
Paper: SN 2020bio: A Double-peaked, H-poor Type IIb Supernova with Evidence of Circumstellar Interaction
Authors: C. Pellegrino, D. Hiramatsu, I. Arcavi, D. A. Howell, K. A. Bostroem, P. J. Brown, J. Burke, N. Elias-Rosa, K. Itagaki, H. Kaneda, C. McCully, M. Modjaz, E. Padilla Gonzalez, T. A. Pritchard, N. Yesmin
Figure caption: UV-optical colors of SN 2020bio compared with those of other SNe IIb with early-time Swift observations. SN 2020bio was bluer at earlier phases than the other SNe IIb. Data for these comparison SNe were obtained from the following sources: Arcavi et al. (2011) (SN 2011dh), Morales-Garoffolo et al. (2014) (SN 2013df), Arcavi et al. (2017) (SN 2016gkg), Pritchard et al. (2014) and the Open Astronomy Catalog (SN 2010jr), and this work (SN 2020bio).
Paper: SN 2020bio: A Double-peaked, H-poor Type IIb Supernova with Evidence of Circumstellar Interaction
Authors: C. Pellegrino, D. Hiramatsu, I. Arcavi, D. A. Howell, K. A. Bostroem, P. J. Brown, J. Burke, N. Elias-Rosa, K. Itagaki, H. Kaneda, C. McCully, M. Modjaz, E. Padilla Gonzalez, T. A. Pritchard, N. Yesmin
Figure caption: The evolution of the Hα pseudo-equivalent width (left) and the He I λ 5876 absorption velocity (right) of SN 2020bio (black) compared to SNe IIb (red) and Ib (blue) from Liu et al. (2016). SN 2020bio has values that are consistent with the relative extremes of both classes of objects but is more similar at later times to SNe IIb, in particular in its absorption velocities.
Paper: SN 2020bio: A Double-peaked, H-poor Type IIb Supernova with Evidence of Circumstellar Interaction
Authors: C. Pellegrino, D. Hiramatsu, I. Arcavi, D. A. Howell, K. A. Bostroem, P. J. Brown, J. Burke, N. Elias-Rosa, K. Itagaki, H. Kaneda, C. McCully, M. Modjaz, E. Padilla Gonzalez, T. A. Pritchard, N. Yesmin
Figure caption: The full spectral time series of SN 2020bio. Phases with respect to the detection epoch are given above each spectrum. Notable spectral features are identified with dashed lines. The gray dashed–dotted line shows a He I λ 5876 absorption velocity of 7500 km s−1 for reference. The first spectrum is the publicly available classification spectrum retrieved from the Transient Name Server.
Paper: SN 2020bio: A Double-peaked, H-poor Type IIb Supernova with Evidence of Circumstellar Interaction
Authors: C. Pellegrino, D. Hiramatsu, I. Arcavi, D. A. Howell, K. A. Bostroem, P. J. Brown, J. Burke, N. Elias-Rosa, K. Itagaki, H. Kaneda, C. McCully, M. Modjaz, E. Padilla Gonzalez, T. A. Pritchard, N. Yesmin
Figure caption: Shock-cooling fits to the early-time photometry of SN 2020bio using the models of (left) P15 and P21 and that of (right) SW17, assuming a constant optical opacity appropriate for solar-composition material. Photometry in each band has been offset for clarity. Itagaki discovery photometry has been included in the V-band fits.
Paper: SN 2020bio: A Double-peaked, H-poor Type IIb Supernova with Evidence of Circumstellar Interaction
Authors: C. Pellegrino, D. Hiramatsu, I. Arcavi, D. A. Howell, K. A. Bostroem, P. J. Brown, J. Burke, N. Elias-Rosa, K. Itagaki, H. Kaneda, C. McCully, M. Modjaz, E. Padilla Gonzalez, T. A. Pritchard, N. Yesmin
Figure caption: Numerical MESA and STELLA model light curves of SN 2020bio for varying M Ni. Both the secondary light-curve peak and late-time light-curve slope are best reproduced with ≈0.02 M ⊙ of 56Ni synthesized in the explosion.
Paper: Identification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
Authors: E. A. Zaborowski, A. Drlica-Wagner, F. Ashmead, J. F. Wu, R. Morgan, C. R. Bom, A. J. Shajib, S. Birrer, W. Cerny, E. J. Buckley-Geer, B. Mutlu-Pakdil, P. S. Ferguson, K. Glazebrook, S. J. Gonzalez Lozano, Y. Gordon, M. Martinez, V. Manwadkar, J. O'Donnell, J. Poh, A. Riley, J. D. Sakowska, L. Santana-Silva, B. X. Santiago, D. Sluse, C. Y. Tan, E. J. Tollerud, A. Verma, J. A. Carballo-Bello, Y. Choi, D. J. James, N. Kuropatkin, C. E. Martínez-Vázquez, D. L. Nidever, J. L. Nilo Castellon, N. E. D. Noël, K. A. G. Olsen, A. B. Pace, S. Mau, B. Yanny, A. Zenteno, T. M. C. Abbott, M. Aguena, O. Alves, F. Andrade-Oliveira, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, F. J. Castander, C. J. Conselice, M. Costanzi, M. E. S. Pereira, J. De Vicente, S. Desai, J. P. Dietrich, P. Doel, S. Everett, I. Ferrero, B. Flaugher, D. Friedel, J. Frieman, J. García-Bellido, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, K. Kuehn, H. Lin, J. L. Marshall, P. Melchior, J. Mena-Fernández, F. Menanteau, R. Miquel, A. Palmese, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, J. Prat, M. Rodriguez-Monroy, A. K. Romer, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, C. To, N. Weaverdyck
Figure caption: Thirty-six systems that were flagged as likely nonlenses based on a final examination of the candidates using larger cutouts as described in Section 3.5. In each box, the original cutout of the candidate and grade assigned are shown on the left. On the right, a larger cutout is shown, along with the data set that it was pulled from. The smaller cutouts are approximately 11.″8 × 11.″8, while the larger cutouts are approximately 30″ × 30″.
Paper: Identification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
Authors: E. A. Zaborowski, A. Drlica-Wagner, F. Ashmead, J. F. Wu, R. Morgan, C. R. Bom, A. J. Shajib, S. Birrer, W. Cerny, E. J. Buckley-Geer, B. Mutlu-Pakdil, P. S. Ferguson, K. Glazebrook, S. J. Gonzalez Lozano, Y. Gordon, M. Martinez, V. Manwadkar, J. O'Donnell, J. Poh, A. Riley, J. D. Sakowska, L. Santana-Silva, B. X. Santiago, D. Sluse, C. Y. Tan, E. J. Tollerud, A. Verma, J. A. Carballo-Bello, Y. Choi, D. J. James, N. Kuropatkin, C. E. Martínez-Vázquez, D. L. Nidever, J. L. Nilo Castellon, N. E. D. Noël, K. A. G. Olsen, A. B. Pace, S. Mau, B. Yanny, A. Zenteno, T. M. C. Abbott, M. Aguena, O. Alves, F. Andrade-Oliveira, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, F. J. Castander, C. J. Conselice, M. Costanzi, M. E. S. Pereira, J. De Vicente, S. Desai, J. P. Dietrich, P. Doel, S. Everett, I. Ferrero, B. Flaugher, D. Friedel, J. Frieman, J. García-Bellido, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, K. Kuehn, H. Lin, J. L. Marshall, P. Melchior, J. Mena-Fernández, F. Menanteau, R. Miquel, A. Palmese, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, J. Prat, M. Rodriguez-Monroy, A. K. Romer, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, C. To, N. Weaverdyck
Figure caption: Comparison of model scores for images passing our visual inspection score cutoff, conditioned on exposure time (EXPTIME) and seeing (PSF FWHM). Score distributions are overlaid for images constituting the top 50% and bottom 50% of each parameter. Plots are shown for each of the four photometric bands g, r, i, and z. No significant biases in the model scores are seen due to these image properties.
Paper: Identification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
Authors: E. A. Zaborowski, A. Drlica-Wagner, F. Ashmead, J. F. Wu, R. Morgan, C. R. Bom, A. J. Shajib, S. Birrer, W. Cerny, E. J. Buckley-Geer, B. Mutlu-Pakdil, P. S. Ferguson, K. Glazebrook, S. J. Gonzalez Lozano, Y. Gordon, M. Martinez, V. Manwadkar, J. O'Donnell, J. Poh, A. Riley, J. D. Sakowska, L. Santana-Silva, B. X. Santiago, D. Sluse, C. Y. Tan, E. J. Tollerud, A. Verma, J. A. Carballo-Bello, Y. Choi, D. J. James, N. Kuropatkin, C. E. Martínez-Vázquez, D. L. Nidever, J. L. Nilo Castellon, N. E. D. Noël, K. A. G. Olsen, A. B. Pace, S. Mau, B. Yanny, A. Zenteno, T. M. C. Abbott, M. Aguena, O. Alves, F. Andrade-Oliveira, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, F. J. Castander, C. J. Conselice, M. Costanzi, M. E. S. Pereira, J. De Vicente, S. Desai, J. P. Dietrich, P. Doel, S. Everett, I. Ferrero, B. Flaugher, D. Friedel, J. Frieman, J. García-Bellido, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, K. Kuehn, H. Lin, J. L. Marshall, P. Melchior, J. Mena-Fernández, F. Menanteau, R. Miquel, A. Palmese, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, J. Prat, M. Rodriguez-Monroy, A. K. Romer, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, C. To, N. Weaverdyck
Figure caption: Distributions of half-light radius (left) and Sérsic index (right), shown both for the simulated training source galaxies from this work and for ∼56,000 galaxies from the COSMOS survey that were used for developing the GalSim software (Rowe et al. 2015) and training the lens-finding model of Rojas et al. (2022).
Paper: Identification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
Authors: E. A. Zaborowski, A. Drlica-Wagner, F. Ashmead, J. F. Wu, R. Morgan, C. R. Bom, A. J. Shajib, S. Birrer, W. Cerny, E. J. Buckley-Geer, B. Mutlu-Pakdil, P. S. Ferguson, K. Glazebrook, S. J. Gonzalez Lozano, Y. Gordon, M. Martinez, V. Manwadkar, J. O'Donnell, J. Poh, A. Riley, J. D. Sakowska, L. Santana-Silva, B. X. Santiago, D. Sluse, C. Y. Tan, E. J. Tollerud, A. Verma, J. A. Carballo-Bello, Y. Choi, D. J. James, N. Kuropatkin, C. E. Martínez-Vázquez, D. L. Nidever, J. L. Nilo Castellon, N. E. D. Noël, K. A. G. Olsen, A. B. Pace, S. Mau, B. Yanny, A. Zenteno, T. M. C. Abbott, M. Aguena, O. Alves, F. Andrade-Oliveira, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, F. J. Castander, C. J. Conselice, M. Costanzi, M. E. S. Pereira, J. De Vicente, S. Desai, J. P. Dietrich, P. Doel, S. Everett, I. Ferrero, B. Flaugher, D. Friedel, J. Frieman, J. García-Bellido, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, K. Kuehn, H. Lin, J. L. Marshall, P. Melchior, J. Mena-Fernández, F. Menanteau, R. Miquel, A. Palmese, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, J. Prat, M. Rodriguez-Monroy, A. K. Romer, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, C. To, N. Weaverdyck
Figure caption: Distributions of exposure time (EXPTIME) and seeing (PSF FWHM) in both the target data set from this work and DECaLS DR8, which was the data set used by Huang et al. (2020, 2021). Plots are shown for each of the four photometric bands g, r, i, and z.