adsabs/AstroCLIMB huggingface dataset:
Paper: COSMOS-Web: An Overview of the JWST Cosmic Origins Survey
Authors: Caitlin M. Casey, Jeyhan S. Kartaltepe, Nicole E. Drakos, Maximilien Franco, Santosh Harish, Louise Paquereau, Olivier Ilbert, Caitlin Rose, Isabella G. Cox, James W. Nightingale, Brant E. Robertson, John D. Silverman, Anton M. Koekemoer, Richard Massey, Henry Joy McCracken, Jason Rhodes, Hollis B. Akins, Natalie Allen, Aristeidis Amvrosiadis, Rafael C. Arango-Toro, Micaela B. Bagley, Angela Bongiorno, Peter L. Capak, Jaclyn B. Champagne, Nima Chartab, Óscar A. Chávez Ortiz, Katherine Chworowsky, Kevin C. Cooke, Olivia R. Cooper, Behnam Darvish, Xuheng Ding, Andreas L. Faisst, Steven L. Finkelstein, Seiji Fujimoto, Fabrizio Gentile, Steven Gillman, Katriona M. L. Gould, Ghassem Gozaliasl, Christopher C. Hayward, Qiuhan He, Shoubaneh Hemmati, Michaela Hirschmann, Knud Jahnke, Shuowen Jin, Ali Ahmad Khostovan, Vasily Kokorev, Erini Lambrides, Clotilde Laigle, Rebecca L. Larson, Gene C. K. Leung, Daizhong Liu, Tobias Liaudat, Arianna S. Long, Georgios Magdis, Guillaume Mahler, Vincenzo Mainieri, Sinclaire M. Manning, Claudia Maraston, Crystal L. Martin, Jacqueline E. McCleary, Jed McKinney, Conor J. R. McPartland, Bahram Mobasher, Rohan Pattnaik, Alvio Renzini, R. Michael Rich, David B. Sanders, Zahra Sattari, Diana Scognamiglio, Nick Scoville, Kartik Sheth, Marko Shuntov, Martin Sparre, Tomoko L. Suzuki, Margherita Talia, Sune Toft, Benny Trakhtenbrot, C. Megan Urry, Francesco Valentino, Brittany N. Vanderhoof, Eleni Vardoulaki, John R. Weaver, Katherine E. Whitaker, Stephen M. Wilkins, Lilan Yang, Jorge A. Zavala
Figure caption: An illustration of the 4TIGHT dither pattern for NIRCam prime visits (top two panels) and MIRI parallel visits (bottom panel). The top panel shows the NIRCam SW exposure map for a single visit with coverage ranging from one (lightest) to four (darkest) exposure depth. Two of the four dither positions are outlined in color (red and blue) for clarity. The middle panel shows the NIRCam SW exposure map in the context of the larger COSMOS-Web mosaic. At the bottom, the MIRI coverage is shown. The axes are positional offsets along the V3 and V2 angles (i.e., perpendicular and parallel to the PA) relative to the reference position, given for each visit in the Appendix.
Paper: Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning
Authors: Anna Lueber, Daniel Kitzmann, Chloe E. Fisher, Brendan P. Bowler, Adam J. Burgasser, Mark Marley, Kevin Heng
Figure caption: Comparing the retrieved posterior values for effective temperature (top), surface gravity (middle), and scale factor (bottom) from our suite of brown dwarf retrievals across the L–T sequence. Shown are all permutations of the theoretical grid models for each spectral standard, as well as the posterior parameters retrieved by Helios-r2 (Lueber et al. 2022). 1σ uncertainties are shown. The sixth-order field age polynomial spectral type/effective temperature relation of Filippazzo et al. (2015), and its 1σ rms are shown in the uppermost panel as a gray bar. It should be noted that object-specific uncertainties have been considered according to Section 5.2, and are therefore data driven.
Paper: Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning
Authors: Anna Lueber, Daniel Kitzmann, Chloe E. Fisher, Brendan P. Bowler, Adam J. Burgasser, Mark Marley, Kevin Heng
Figure caption: (a) RvP comparison of the random forest training and testing approach when using a single grid. Each panel represents the RvP comparisons for effective temperature T eff, surface gravity ﹩\mathrm{log}g﹩, and scaling factor f. Each grid was interpolated individually for ﹩\mathrm{log}g﹩ (see the text for details) and binned on the spectral resolution and wavelength coverage of the ϵ Indi Ba spectrum. Model uncertainties have been considered according to Section 5.2. (b) Model uncertainties have been considered according to Section 5.2.
Paper: Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning
Authors: Anna Lueber, Daniel Kitzmann, Chloe E. Fisher, Brendan P. Bowler, Adam J. Burgasser, Mark Marley, Kevin Heng
Figure caption: Near-infrared spectra L7 (left) and T7 (right) standards (black dots), compared to median retrieved spectra from the 14 model grids. The latter are shown in shades of blue for cloud-free models and shades of orange for cloud models. For clarity, we have omitted data uncertainties in the plot, but it should be noted that object-specific uncertainties have been considered according to Section 5.2, and are therefore data driven.
Paper: Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning
Authors: Anna Lueber, Daniel Kitzmann, Chloe E. Fisher, Brendan P. Bowler, Adam J. Burgasser, Mark Marley, Kevin Heng
Figure caption:                         Mean tree depths for random forest calculations with full vs. restricted wavelength ranges, corresponding to Figure 6.
Paper: Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning
Authors: Anna Lueber, Daniel Kitzmann, Chloe E. Fisher, Brendan P. Bowler, Adam J. Burgasser, Mark Marley, Kevin Heng
Figure caption: Reduced chi-square values ﹩{\chi }_{r}^{2}﹩ associated with models fits to our curated data set, which includes three benchmark brown dwarfs (Gl 570 D, ϵ Indi Ba and Bb) as well as a sample of 19 L and T dwarfs across the L–T sequence. Each color represents one of the 14 legacy model grids.
Paper: COSMOS-Web: An Overview of the JWST Cosmic Origins Survey
Authors: Caitlin M. Casey, Jeyhan S. Kartaltepe, Nicole E. Drakos, Maximilien Franco, Santosh Harish, Louise Paquereau, Olivier Ilbert, Caitlin Rose, Isabella G. Cox, James W. Nightingale, Brant E. Robertson, John D. Silverman, Anton M. Koekemoer, Richard Massey, Henry Joy McCracken, Jason Rhodes, Hollis B. Akins, Natalie Allen, Aristeidis Amvrosiadis, Rafael C. Arango-Toro, Micaela B. Bagley, Angela Bongiorno, Peter L. Capak, Jaclyn B. Champagne, Nima Chartab, Óscar A. Chávez Ortiz, Katherine Chworowsky, Kevin C. Cooke, Olivia R. Cooper, Behnam Darvish, Xuheng Ding, Andreas L. Faisst, Steven L. Finkelstein, Seiji Fujimoto, Fabrizio Gentile, Steven Gillman, Katriona M. L. Gould, Ghassem Gozaliasl, Christopher C. Hayward, Qiuhan He, Shoubaneh Hemmati, Michaela Hirschmann, Knud Jahnke, Shuowen Jin, Ali Ahmad Khostovan, Vasily Kokorev, Erini Lambrides, Clotilde Laigle, Rebecca L. Larson, Gene C. K. Leung, Daizhong Liu, Tobias Liaudat, Arianna S. Long, Georgios Magdis, Guillaume Mahler, Vincenzo Mainieri, Sinclaire M. Manning, Claudia Maraston, Crystal L. Martin, Jacqueline E. McCleary, Jed McKinney, Conor J. R. McPartland, Bahram Mobasher, Rohan Pattnaik, Alvio Renzini, R. Michael Rich, David B. Sanders, Zahra Sattari, Diana Scognamiglio, Nick Scoville, Kartik Sheth, Marko Shuntov, Martin Sparre, Tomoko L. Suzuki, Margherita Talia, Sune Toft, Benny Trakhtenbrot, C. Megan Urry, Francesco Valentino, Brittany N. Vanderhoof, Eleni Vardoulaki, John R. Weaver, Katherine E. Whitaker, Stephen M. Wilkins, Lilan Yang, Jorge A. Zavala
Figure caption: The number density of quiescent galaxies (specific SFR, SFR/M ⋆ <10−11 yr−1) as a function of redshift for galaxies with M ⋆ > 1010 M ⊙ selected from Illustris TNG100 (blue) and extrapolated beyond z > 4 (blue dashed) where no quiescent galaxies are found in the Illustris TNG100 volume. Other simulation predictions for this population are shown from EAGLE (green), FLARES (purple), and the DREaM (red) semiempirical model and predictions from the empirical model of Long et al. (2022) in black. Overplotted is a collection of number densities of quiescent galaxies from the literature, illustrating the wide range that both the observations and simulations span. The dashed lines correspond to the number density of one object in the NIRCam volume of COSMOS-Web (black), medium-volume JWST surveys such as CEERS and PRIMER (dark gray), and deep volume surveys such as JADES-Deep and NGDEEP (light gray). Only COSMOS-Web has the volume necessary to place strong constraints on the number densities of quiescent galaxies at z > 4 if they are indeed as rare as expected.
Paper: COSMOS-Web: An Overview of the JWST Cosmic Origins Survey
Authors: Caitlin M. Casey, Jeyhan S. Kartaltepe, Nicole E. Drakos, Maximilien Franco, Santosh Harish, Louise Paquereau, Olivier Ilbert, Caitlin Rose, Isabella G. Cox, James W. Nightingale, Brant E. Robertson, John D. Silverman, Anton M. Koekemoer, Richard Massey, Henry Joy McCracken, Jason Rhodes, Hollis B. Akins, Natalie Allen, Aristeidis Amvrosiadis, Rafael C. Arango-Toro, Micaela B. Bagley, Angela Bongiorno, Peter L. Capak, Jaclyn B. Champagne, Nima Chartab, Óscar A. Chávez Ortiz, Katherine Chworowsky, Kevin C. Cooke, Olivia R. Cooper, Behnam Darvish, Xuheng Ding, Andreas L. Faisst, Steven L. Finkelstein, Seiji Fujimoto, Fabrizio Gentile, Steven Gillman, Katriona M. L. Gould, Ghassem Gozaliasl, Christopher C. Hayward, Qiuhan He, Shoubaneh Hemmati, Michaela Hirschmann, Knud Jahnke, Shuowen Jin, Ali Ahmad Khostovan, Vasily Kokorev, Erini Lambrides, Clotilde Laigle, Rebecca L. Larson, Gene C. K. Leung, Daizhong Liu, Tobias Liaudat, Arianna S. Long, Georgios Magdis, Guillaume Mahler, Vincenzo Mainieri, Sinclaire M. Manning, Claudia Maraston, Crystal L. Martin, Jacqueline E. McCleary, Jed McKinney, Conor J. R. McPartland, Bahram Mobasher, Rohan Pattnaik, Alvio Renzini, R. Michael Rich, David B. Sanders, Zahra Sattari, Diana Scognamiglio, Nick Scoville, Kartik Sheth, Marko Shuntov, Martin Sparre, Tomoko L. Suzuki, Margherita Talia, Sune Toft, Benny Trakhtenbrot, C. Megan Urry, Francesco Valentino, Brittany N. Vanderhoof, Eleni Vardoulaki, John R. Weaver, Katherine E. Whitaker, Stephen M. Wilkins, Lilan Yang, Jorge A. Zavala
Figure caption: At left, the stellar-mass-to-halo-mass ratio as a function of halo mass is shown. Curves are shown for central galaxies only, not including satellites, though both relations may be constrainable with our data set. Solid lines are measurements from COSMOS ACS weak lensing data (Leauthaud et al. 2012) at z = 0.3 (dark blue), z = 0.6 (light blue), and z = 0.9 (orange). In comparison, the SMHR from cosmological simulations is overplotted (gray dotted–dashed line from Behroozi et al. 2010, and colored dashed lines from UniverseMachine; Behroozi et al. 2019) at matched redshifts extending out to z = 2.5 (red). The existing weak lensing measurements show evolution out to z ∼ 1 (black arrow noting SMHR peak evolving); COSMOS-Web weak lensing measurements will have the power to extend this analysis to z = 2.5, where the vertical red line will represent the lower mass limit at that redshift. At right, predictions from hydrodynamic simulations, specifically Illustris TNG100 (Pillepich et al. 2018), suggest that ellipticals with blue or red cores experience different quenching mechanisms (gas stripping shown in blue, and gas exhaustion in red). These differences are reflected in how their M ⋆/M H evolves with time (z ∼ 2 in solid; z ∼ 0.7 in dashed). COSMOS-Web data will have the potential to provide powerful constraints on the evolution of the SMHR for different galaxy populations and test fundamental galaxy quenching models.
Paper: COSMOS-Web: An Overview of the JWST Cosmic Origins Survey
Authors: Caitlin M. Casey, Jeyhan S. Kartaltepe, Nicole E. Drakos, Maximilien Franco, Santosh Harish, Louise Paquereau, Olivier Ilbert, Caitlin Rose, Isabella G. Cox, James W. Nightingale, Brant E. Robertson, John D. Silverman, Anton M. Koekemoer, Richard Massey, Henry Joy McCracken, Jason Rhodes, Hollis B. Akins, Natalie Allen, Aristeidis Amvrosiadis, Rafael C. Arango-Toro, Micaela B. Bagley, Angela Bongiorno, Peter L. Capak, Jaclyn B. Champagne, Nima Chartab, Óscar A. Chávez Ortiz, Katherine Chworowsky, Kevin C. Cooke, Olivia R. Cooper, Behnam Darvish, Xuheng Ding, Andreas L. Faisst, Steven L. Finkelstein, Seiji Fujimoto, Fabrizio Gentile, Steven Gillman, Katriona M. L. Gould, Ghassem Gozaliasl, Christopher C. Hayward, Qiuhan He, Shoubaneh Hemmati, Michaela Hirschmann, Knud Jahnke, Shuowen Jin, Ali Ahmad Khostovan, Vasily Kokorev, Erini Lambrides, Clotilde Laigle, Rebecca L. Larson, Gene C. K. Leung, Daizhong Liu, Tobias Liaudat, Arianna S. Long, Georgios Magdis, Guillaume Mahler, Vincenzo Mainieri, Sinclaire M. Manning, Claudia Maraston, Crystal L. Martin, Jacqueline E. McCleary, Jed McKinney, Conor J. R. McPartland, Bahram Mobasher, Rohan Pattnaik, Alvio Renzini, R. Michael Rich, David B. Sanders, Zahra Sattari, Diana Scognamiglio, Nick Scoville, Kartik Sheth, Marko Shuntov, Martin Sparre, Tomoko L. Suzuki, Margherita Talia, Sune Toft, Benny Trakhtenbrot, C. Megan Urry, Francesco Valentino, Brittany N. Vanderhoof, Eleni Vardoulaki, John R. Weaver, Katherine E. Whitaker, Stephen M. Wilkins, Lilan Yang, Jorge A. Zavala
Figure caption: An illustration of the part of the COSMOS-Web area that will be covered by multiepoch NIRCam observations, thanks to the PRIMER survey (GO #1837). Due to the scheduling of the PRIMER program primarily in Cycle 1, and this region of the COSMOS-Web mosaic during Cycle 2, a total of 133 arcmin2 will see multiple visits; the first of these was observed in 2023 January (the PRIMER area covering both green and orange highlighted regions). The second will occur in ∼2023 April (PRIMER covering the purple and orange regions). The last epoch will occur in ∼2023 December/2024 January thanks to COSMOS-Web. Thus, the purple region will have two epochs separated by ∼9 months (this corresponds to 36.5 arcmin2), the green region will have two epochs separated by ∼1 yr (this corresponds to 53.8 arcmin2), and the orange area will have three epochs of separation ∼3 months followed by another ∼9 months, spanning a year in total (this corresponds to 42.6 arcmin2).
Paper: COSMOS-Web: An Overview of the JWST Cosmic Origins Survey
Authors: Caitlin M. Casey, Jeyhan S. Kartaltepe, Nicole E. Drakos, Maximilien Franco, Santosh Harish, Louise Paquereau, Olivier Ilbert, Caitlin Rose, Isabella G. Cox, James W. Nightingale, Brant E. Robertson, John D. Silverman, Anton M. Koekemoer, Richard Massey, Henry Joy McCracken, Jason Rhodes, Hollis B. Akins, Natalie Allen, Aristeidis Amvrosiadis, Rafael C. Arango-Toro, Micaela B. Bagley, Angela Bongiorno, Peter L. Capak, Jaclyn B. Champagne, Nima Chartab, Óscar A. Chávez Ortiz, Katherine Chworowsky, Kevin C. Cooke, Olivia R. Cooper, Behnam Darvish, Xuheng Ding, Andreas L. Faisst, Steven L. Finkelstein, Seiji Fujimoto, Fabrizio Gentile, Steven Gillman, Katriona M. L. Gould, Ghassem Gozaliasl, Christopher C. Hayward, Qiuhan He, Shoubaneh Hemmati, Michaela Hirschmann, Knud Jahnke, Shuowen Jin, Ali Ahmad Khostovan, Vasily Kokorev, Erini Lambrides, Clotilde Laigle, Rebecca L. Larson, Gene C. K. Leung, Daizhong Liu, Tobias Liaudat, Arianna S. Long, Georgios Magdis, Guillaume Mahler, Vincenzo Mainieri, Sinclaire M. Manning, Claudia Maraston, Crystal L. Martin, Jacqueline E. McCleary, Jed McKinney, Conor J. R. McPartland, Bahram Mobasher, Rohan Pattnaik, Alvio Renzini, R. Michael Rich, David B. Sanders, Zahra Sattari, Diana Scognamiglio, Nick Scoville, Kartik Sheth, Marko Shuntov, Martin Sparre, Tomoko L. Suzuki, Margherita Talia, Sune Toft, Benny Trakhtenbrot, C. Megan Urry, Francesco Valentino, Brittany N. Vanderhoof, Eleni Vardoulaki, John R. Weaver, Katherine E. Whitaker, Stephen M. Wilkins, Lilan Yang, Jorge A. Zavala
Figure caption: A selection of three color NIRCam (F115W+F150W+F277W) cutouts of galaxies at z > 3 with varied morphologies selected from IllustrisTNG mock images (Rose et al. 2023) with noise added consistent with the COSMOS-Web depth. Each cutout is 3″ on a side. These illustrate COSMOS-Web’s ability to distinguish between different morphological types and detect a diversity of morphologies thanks to JWST's sensitivity and resolution at these redshifts.
Paper: COSMOS-Web: An Overview of the JWST Cosmic Origins Survey
Authors: Caitlin M. Casey, Jeyhan S. Kartaltepe, Nicole E. Drakos, Maximilien Franco, Santosh Harish, Louise Paquereau, Olivier Ilbert, Caitlin Rose, Isabella G. Cox, James W. Nightingale, Brant E. Robertson, John D. Silverman, Anton M. Koekemoer, Richard Massey, Henry Joy McCracken, Jason Rhodes, Hollis B. Akins, Natalie Allen, Aristeidis Amvrosiadis, Rafael C. Arango-Toro, Micaela B. Bagley, Angela Bongiorno, Peter L. Capak, Jaclyn B. Champagne, Nima Chartab, Óscar A. Chávez Ortiz, Katherine Chworowsky, Kevin C. Cooke, Olivia R. Cooper, Behnam Darvish, Xuheng Ding, Andreas L. Faisst, Steven L. Finkelstein, Seiji Fujimoto, Fabrizio Gentile, Steven Gillman, Katriona M. L. Gould, Ghassem Gozaliasl, Christopher C. Hayward, Qiuhan He, Shoubaneh Hemmati, Michaela Hirschmann, Knud Jahnke, Shuowen Jin, Ali Ahmad Khostovan, Vasily Kokorev, Erini Lambrides, Clotilde Laigle, Rebecca L. Larson, Gene C. K. Leung, Daizhong Liu, Tobias Liaudat, Arianna S. Long, Georgios Magdis, Guillaume Mahler, Vincenzo Mainieri, Sinclaire M. Manning, Claudia Maraston, Crystal L. Martin, Jacqueline E. McCleary, Jed McKinney, Conor J. R. McPartland, Bahram Mobasher, Rohan Pattnaik, Alvio Renzini, R. Michael Rich, David B. Sanders, Zahra Sattari, Diana Scognamiglio, Nick Scoville, Kartik Sheth, Marko Shuntov, Martin Sparre, Tomoko L. Suzuki, Margherita Talia, Sune Toft, Benny Trakhtenbrot, C. Megan Urry, Francesco Valentino, Brittany N. Vanderhoof, Eleni Vardoulaki, John R. Weaver, Katherine E. Whitaker, Stephen M. Wilkins, Lilan Yang, Jorge A. Zavala
Figure caption: Ultracool halo subdwarf templates projected in AB magnitudes at a distance of 2 kpc. Templates are taken from Saumon & Marley (2008) spanning effective surface temperatures of 900–2500 K (purple to red). Synthetic photometry is calculated in the COSMOS-Web bands, whose depths are shown in a similar fashion as in Figure 3. Halo M-dwarfs would be detectable out to ∼10 kpc (with potential confusion with z ∼ 6–7 galaxies between 3 and 10 kpc), while L-, T-, and Y-dwarfs will be detectable to ∼2 kpc.
Paper: Investigating the Correlation of Outflow Kinematics with Radio Activity. VII. Gas Outflows in AGNs
Authors: Ashraf Ayubinia, Jong-Hak Woo, Suvendu Rakshit, Donghoon Son
Figure caption:                       ﹩\left[{\rm{O}}\,{\rm\small{III}}\right]﹩ velocity–velocity dispersion diagram, with and without normalization by stellar velocity dispersion and stellar mass, as a function of radio luminosity. AGN-dominated and SF-contaminated AGNs are denoted with red and blue dots, respectively.
Paper: Investigating the Correlation of Outflow Kinematics with Radio Activity. VII. Gas Outflows in AGNs
Authors: Ashraf Ayubinia, Jong-Hak Woo, Suvendu Rakshit, Donghoon Son
Figure caption: The observed radio luminosity at 1.4 GHz (L 1.4 GHz) as a function of the AGN dominance (the ratio of L 1.4 GHz/L 1.4 GHz,SF) (left) and radio loudness (right) for the compact (red) and extended (blue) sources. The dashed lines represent the thresholds used for radio classification (see Section 2.4.1).
Paper: Investigating the Correlation of Outflow Kinematics with Radio Activity. VII. Gas Outflows in AGNs
Authors: Ashraf Ayubinia, Jong-Hak Woo, Suvendu Rakshit, Donghoon Son
Figure caption: The normalized distribution (top) and cumulative fraction (bottom) of physical parameters for Sample A (blue), Sample B (pink), and the combined sample (black). From left to right: stellar mass, stellar velocity dispersion, black hole mass, and bolometric luminosity.
Paper: Domain Adaptation for Simulation-based Dark Matter Searches with Strong Gravitational Lensing
Authors: Stephon Alexander, Sergei Gleyzer, Hanna Parul, Pranath Reddy, Marcos Tidball, Michael W. Toomey
Figure caption: Application of ADDA for DA between two different mock simulations of strong lensing surveys: from Euclid to HST (left) and HST to Euclid (right). Dashed lines represent the naive application of a classifier trained on the source data set to the target, and solid lines represent the result after training with ADDA. Red curves represent models based on EfficientNet, and blue curves represent models based on an ENN.
Paper: Domain Adaptation for Simulation-based Dark Matter Searches with Strong Gravitational Lensing
Authors: Stephon Alexander, Sergei Gleyzer, Hanna Parul, Pranath Reddy, Marcos Tidball, Michael W. Toomey
Figure caption: Confusion matrices for EfficientNet architecture for substructure classification with and without ADDA. The top row represents the naive application on the target data set, and the bottom row shows after training with ADDA. The left (right) column represents evaluation of a Euclid- (HST-) trained model to HST (Euclid) simulations.
Paper: Domain Adaptation for Simulation-based Dark Matter Searches with Strong Gravitational Lensing
Authors: Stephon Alexander, Sergei Gleyzer, Hanna Parul, Pranath Reddy, Marcos Tidball, Michael W. Toomey
Figure caption: Confusion matrices for the ENN architecture for substructure classification with and without ADDA. The top row represents the naive application on the target data set, and the bottom row shows after training with ADDA. The left (right) column represents evaluation of a Euclid- (HST-) trained model to HST (Euclid) simulations.
Paper: Domain Adaptation for Simulation-based Dark Matter Searches with Strong Gravitational Lensing
Authors: Stephon Alexander, Sergei Gleyzer, Hanna Parul, Pranath Reddy, Marcos Tidball, Michael W. Toomey
Figure caption: Comparison of ADDA and ResNet-18 performance for classifying objects into lensed and nonlensed galaxies for unbalanced (left) and balanced (right) data sets.
Paper: Domain Adaptation for Simulation-based Dark Matter Searches with Strong Gravitational Lensing
Authors: Stephon Alexander, Sergei Gleyzer, Hanna Parul, Pranath Reddy, Marcos Tidball, Michael W. Toomey
Figure caption: Evolution of loss during training for models with ADDA aimed at substructure identification.
Paper: Domain Adaptation for Simulation-based Dark Matter Searches with Strong Gravitational Lensing
Authors: Stephon Alexander, Sergei Gleyzer, Hanna Parul, Pranath Reddy, Marcos Tidball, Michael W. Toomey
Figure caption: Example of lensing simulation of mock HST (top) and Euclid (bottom) data sets. Shown is an image of a real galaxy from the Galaxy10 DECals data set (left), the image processed with a Gaussian filter to remove unwanted background (middle), and the final lensed image (right).
Paper: Domain Adaptation for Simulation-based Dark Matter Searches with Strong Gravitational Lensing
Authors: Stephon Alexander, Sergei Gleyzer, Hanna Parul, Pranath Reddy, Marcos Tidball, Michael W. Toomey
Figure caption: Visualization of the D 2 group structure for a real gravitational lens. Lensing image credit: ESA/Hubble & NASA.
Paper: Domain Adaptation for Simulation-based Dark Matter Searches with Strong Gravitational Lensing
Authors: Stephon Alexander, Sergei Gleyzer, Hanna Parul, Pranath Reddy, Marcos Tidball, Michael W. Toomey
Figure caption: Depiction of target and source representations in the latent space of the encoder in ADDA. Images in blue are from the source domain, and images in orange are from the target domain.
Paper: Domain Adaptation for Simulation-based Dark Matter Searches with Strong Gravitational Lensing
Authors: Stephon Alexander, Sergei Gleyzer, Hanna Parul, Pranath Reddy, Marcos Tidball, Michael W. Toomey
Figure caption: Schematic of the ENN architecture.
Paper: Gravitational Wave Eigenfrequencies from Neutrino-driven Core-collapse Supernovae
Authors: Noah E. Wolfe, Carla Fröhlich, Jonah M. Miller, Alejandro Torres-Forné, Pablo Cerdá-Durán
Figure caption: Left: histogram of the low characteristic frequencies μ 1 for each EOS. Each histogram is plotted with bins 5 Hz in width. Right: surface gravity of the remnant M rem/R rem 2 (in M ⊙ km−2) plotted as a function of the high characteristic frequency μ 2 (points). The error bars on each μ 2 point come from bootstrapping procedure. The dashed black line is a linear fit of M rem/R rem 2 as a function of μ 2, with a 2σ standard error region shaded in gray; the coefficient of determination is r 2 = 0.961, indicating a good linear fit.