Galaxies: Formation, Evolution, Phenomena

Derniers papiers

• Scale-Vector Alignment: A Scale-Aware Framework for Spatially Resolved Morphological Similarity in Astronomical Images

  Mengke Zhao, Guang-Xing Li, Keping Qiu, Shanghuo Li · 22 septembre 2026

  Astronomical maps made with different tracers are not expected to have identical morphology. Excitation, optical depth, chemistry, radiation, and ISM phase alter the response of a tracer, and the resulting differences can depend on both position and spatial scale. We propose scale-vector alignment, …

• Inductive Biases in Field-Level Cosmological Inference from Galaxy Catalogs

  James O. Baldwin, Shy Genel, Francisco Villaescusa-Navarro · 10 septembre 2026

  We perform field-level likelihood-free inference of the matter density parameter $\Omega_m$ from simulated galaxy catalogs using machine learning models with differing inductive biases. Using hydrodynamic simulations from CAMELS, we examine how observable choice and architecture govern cosmological …

• Decoupling candidate dual AGN from chance superpositions in the GOTHIC survey via a deep-learning framework

  Bhavesh Mukheja, Snehanshu Saha, Anwesh Bhattacharya, Mousumi Das, Fran\c{c}oise Combes, Sudhanshu Barway · 26 août 2026

  Dual active galactic nuclei (DAGN) mark a critical phase in the evolution of merging galaxies and the pairing of supermassive black holes, yet they remain difficult to identify in large imaging surveys because of projection effects and limited spatial resolution. Compact foreground stars and unresol…

• A survey detection channel overrides the pixels in an astronomical foundation model, and biases tomographic mean redshifts

  Ihor Kendiukhov · 26 août 2026

  Foundation models for astronomy are trained on survey pixels together with the catalogue products derived from those pixels. Those catalogues are incomplete at a measurable rate, and a model trained on both inherits that incompleteness as a systematic. We audit AION-1, a 39-modality transformer trai…

• Estimating Uncertainty in Galaxy Morphology Classification

  Kai Cheng, Ruoqi Wang, Qiong Luo · 11 août 2026

  Astronomers classify galaxy morphology to investigate cosmic evolution. While deep foundation models are increasingly utilized in Galaxy Morphology Classification (GMC), little work has been done on evaluating the uncertainty of GMC results. Uncertainty evaluation is important because astronomical d…

• A General-Purpose VLM Can Teach an Astronomy Foundation Model to Better Recognize Galaxy Morphology

  Dichang Zhang, Jiaqi Deng, Yixuan Shao, Yuanpeng Liu, Jiali Cui, Zhiqiang Lao, Heather Yu, Liang Peng, Simon Birrer, Dimitris Samaras · 4 août 2026

  Existing astronomy foundation models provide strong galaxy representations, but adapting them to new survey conditions and survey-specific morphology recognition tasks still requires substantial human supervision. We show that VLM-based VQA systems contain meaningful visual-semantic priors that can …

• Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines

  Guillaume Payeur, Laurence Perreault-Levasseur, Gabriel Missael Barco, Yashar Hezaveh · 23 juillet 2026

  Modeling galaxy-galaxy strong gravitational lenses to infer the brightness of the source galaxy and the mass distribution of the foreground galaxy is computationally challenging, particularly for high-resolution, high signal-to-noise ratio observations. In this regime, high-dimensional representatio…

• Neural Posterior Estimation for Inferring Weak Lensing Shear

  Tim White, Dingrui Tao, Camille Avestruz, Jeffrey Regier, the LSST Dark Energy Science Collaboration · 14 juillet 2026

  The prevailing approach to inferring weak gravitational lensing shear from images involves detecting galaxies, estimating their ellipticities, and calibrating these estimates to correct for image noise, selection bias, and model misspecification. Characterizing the statistical model and assumptions …

• Field-level weak lensing cosmology with $<100$ simulations using multifidelity simulation-based inference

  Alex A. Saoulis, Kiyam Lin, Niall Jeffrey, Maximilian von Wietersheim-Kramsta, Davide Piras, Alessio Spurio Mancini, Ana M. G. Ferreira, Benjamin Joachimi · 23 juin 2026

  We perform a realistic KiDS-Legacy mock analysis with field-level neural compression and simulation-based inference using fewer than 100 $N$-body simulations. The weak lensing shear field encodes substantially more cosmological information than standard two-point summary statistics such as the power…

• Integral Field Unit Spectroscopy with One Fiber

  Zehao Peng, Biprateep Dey, Chris J. Maddison, Joshua S. Speagle · 10 juin 2026

  Integral field unit (IFU) spectroscopy provides spatially resolved spectra across galaxies, offering crucial insights into their evolution. However, its high observational cost limits current IFU datasets to $\sim 10^4$ objects. We present a multi-modal, probabilistic foundation model that predicts …

• Beyond Point Estimates: Benchmarking Uncertainty Quantification Methods on the AION-1 Astronomical Foundation Model

  Karla Tame-Narvaez, Aleksandra \'Ciprijanovi\'c, Shubhendu Trivedi · 9 juin 2026

  Foundation models for astronomical surveys offer powerful learned representations that can be transferred to downstream regression tasks such as galaxy property estimation. However, point predictions alone are insufficient for scientific inference; reliable uncertainty quantification (UQ) is essenti…

• Generative Diffusion Priors for 3D Mapping of the Dark Universe

  Brandon Zhao, Diana Scognamiglio, Olivier Dor\'e, Katherine L. Bouman · 2 juin 2026

  Reconstructing the three-dimensional distribution of dark matter from weak-lensing observations is a central but highly ill-posed inverse problem in cosmology. Unlike standard 3D reconstruction with multiple viewpoints, we observe the universe from a single line of sight, through noisy shape distort…

• 21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables

  Daniela Breitman, Andrei Mesinger, Steven G. Murray, Ivan Nikolic, Roberto Trotta · 2 juin 2026

  We are witnessing a surge in observations of the cosmic dawn (CD) and epoch of reionisation (EoR), driving an increasing demand for fast and robust theoretical interpretation frameworks. In response, machine learning (ML), and emulation in particular, has emerged as a powerful approach to accelerate…

• Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction

  Tilman Tr\"oster, David Mirkovic, Veronika Oehl, Arne Thomsen · 21 mai 2026

  Precise measurement of the kinematic Sunyaev-Zel'dovich (kSZ) effect - a probe of the large-scale distribution of baryonic matter, a key observable for cosmological inference - requires accurate reconstruction of galaxy velocities from spectroscopic surveys. The signal-to-noise ratio (SNR) of kSZ me…

• FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology

  Biwei Dai, Po-Wen Chang, Wahid Bhimji, Paolo Calafiura, Ragansu Chakkappai, Yuan-Tang Chou, Sascha Diefenbacher, Jordan Dudley, Ibrahim Elsharkawy, Steven Farrell, Isabelle Guyon, Chris Harris, Elham E Khoda, Benjamin Nachman, David Rousseau, Uro\v{s} Seljak, Ihsan Ullah, Yulei Zhang · 17 avril 2026

  Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribution in our universe and allows accurate constraints on the cosmological model. In recent years, high-order statistics and machine learning (ML) techn…

• Euclid Quick Data Release (Q1). AgileLens: A scalable CNN-based pipeline for strong gravitational lens identification

  Euclid Collaboration, X. Xu, R. Chen, T. Li, A. R. Cooray, S. Schuldt, J. A. Acevedo Barroso, D. Stern, D. Scott, M. Meneghetti, G. Despali, J. Chopra, Y. Cao, M. Cheng, J. Buda, J. Zhang, J. Furumizo, R. Valencia, Z. Jiang, C. Tortora, N. E. P. Lines, T. E. Collett, S. Fotopoulou, A. Galan, A. Manjón-García, R. Gavazzi, L. Iwamoto, S. Kruk, M. Millon, P. Nugent, C. Saulder, D. Sluse, J. Wilde, M. Walmsley, F. Courbin, R. B. Metcalf, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, R. Bender, A. Biviano, E. Branchini, M. Brescia, S. Camera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, C. Dolding, H. Dole, F. Dubath, X. Dupac, S. Dusini, S. Escoffier, M. Farina, R. Farinelli, S. Farrens, S. Ferriol, F. Finelli, P. Fosalba, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. J. Massey, E. Medinaceli, S. Mei, M. Melchior, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, G. Polenta, M. Poncet, L. A. Popa, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, E. Sihvola, P. Simon, C. Sirignano, G. Sirri, L. Stanco, P. Tallada-Crespí, A. N. Taylor, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, M. Ballardini, M. Bolzonella, C. Burigana, R. Cabanac, M. Calabrese, A. Cappi, T. Castro, J. A. Escartin Vigo, L. Gabarra, S. Hemmati, J. Macias-Perez, R. Maoli, J. Martín-Fleitas, N. Mauri, P. Monaco, A. A. Nucita, A. Pezzotta, M. Pöntinen, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Tucci, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, G. Angora, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, L. Bazzanini, P. Bergamini, D. Bertacca, M. Bethermin, F. Beutler, L. Blot, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, F. Cogato, S. Conseil, O. Cucciati, S. Davini, G. Desprez, A. Díaz-Sánchez, S. Di Domizio, J. M. Diego, P. -A. Duc, V. Duret, M. Y. Elkhashab, A. Enia, Y. Fang, A. Finoguenov, A. Franco, K. Ganga, T. Gasparetto, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, J. Kim, C. C. Kirkpatrick, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, T. I. Liaudat, S. J. Liu, M. Magliocchetti, E. A. Magnier, F. Mannucci, C. J. A. P. Martins, L. Maurin, M. Miluzio, C. Moretti, G. Morgante, K. Naidoo, A. Navarro-Alsina, S. Nesseris, D. Paoletti, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, G. W. Pratt, S. Quai, M. Radovich, K. Rojas, W. Roster, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, C. Scarlata, A. Schneider, M. Schultheis, D. Sciotti, E. Sellentin, L. C. Smith, K. Tanidis, C. Tao, F. Tarsitano, G. Testera, R. Teyssier, S. Tosi, A. Troja, A. Venhola, D. Vergani, G. Vernardos, G. Verza, S. Vinciguerra, N. A. Walton, A. H. Wright, H. W. Yeung · 9 avril 2026

  We present an end-to-end, iterative pipeline for efficient identification of strong galaxy--galaxy lensing systems, applied to the Euclid Q1 imaging data. Starting from VIS catalogues, we reject point sources, apply a magnitude cut (I$_E$ $\leq$ 24) on deflectors, and run a pixel-level artefact/nois…

• A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping

  Hubert Leterme, Andreas Tersenov, Jalal Fadili, Jean-Luc Starck · 24 mars 2026

  Upcoming stage-IV surveys such as Euclid and Rubin will deliver vast amounts of high-precision data, opening new opportunities to constrain cosmological models with unprecedented accuracy. A key step in this process is the reconstruction of the dark matter distribution from noisy weak lensing shear …

• Spectral Hierarchy of the Cosmic Web

  Francisco-Shu Kitaura, Francesco Sinigaglia · 18 mars 2026

  We introduce a spectral hierarchy of cosmic-web classifications obtained by applying simple scale-weighting kernels to the density field before performing a standard eigenvalue-based web classification. This unifies and extends several widely used web definitions within a single framework: the famil…

• Mapping Dark-Matter Clusters via Physics-Guided Diffusion Models

  Diego Royo, Brandon Zhao, Adolfo Muñoz, Diego Gutierrez, Katherine L. Bouman · 17 mars 2026

  Galaxy clusters are powerful probes of astrophysics and cosmology through gravitational lensing: the clusters' mass, dominated by 85% dark matter, distorts background light. Yet, mass reconstruction lacks the scalability and large-scale benchmarks to process the hundreds of thousands of clusters exp…

• AS-Bridge: A Bidirectional Generative Framework Bridging Next-Generation Astronomical Surveys

  Dichang Zhang, Yixuan Shao, Simon Birrer, Dimitris Samaras · 13 mars 2026

  The upcoming decade of observational cosmology will be shaped by large sky surveys, such as the ground-based LSST at the Vera C. Rubin Observatory and the space-based Euclid mission. While they promise an unprecedented view of the Universe across depth, resolution, and wavelength, their differences …

• Characterization of Residual Morphological Substructure Using Supervised and Unsupervised Deep Learning

  Kameswara Bharadwaj Mantha, Daniel H. McIntosh, Cody Ciaschi, Rubyet Evan, Luther Landry, Henry C. Ferguson, Camilla Pacifici, Joel Primack, Nimish Hathi, Anton Koekemoer, Yicheng Guo, The CANDELS Collaboration · 24 février 2026

  Automated characterization of galactic substructure is an essential step in understanding the transformative physical processes driving galaxy evolution. In this study, we investigate the application of deep learning (DL) frameworks to characterize different galactic substructures hosted within para…

• Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks

  Kameswara Bharadwaj Mantha, Lucy Fortson, Ramanakumar Sankar, Claudia Scarlata, Chris Lintott, Sandor Kruk, Mike Walmsley, Hugh Dickinson, Karen Masters, Brooke Simmons, Rebecca Smethurst · 23 février 2026

  Integral Field Spectroscopy (IFS) surveys offer a unique new landscape in which to learn in both spatial and spectroscopic dimensions and could help uncover previously unknown insights into galaxy evolution. In this work, we demonstrate a new unsupervised deep learning framework using Convolutional …

• Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising

  Yuduo Guo, Hao Zhang, Mingyu Li, Fujiang Yu, Yunjing Wu, Yuhan Hao, Song Huang, Yongming Liang, Xiaojing Lin, Xinyang Li, Jiamin Wu, Zheng Cai, Qionghai Dai · 20 février 2026

  The detection limit of astronomical imaging observations is limited by several noise sources. Some of that noise is correlated between neighbouring image pixels and exposures, so in principle could be learned and corrected. We present an astronomical self-supervised transformer-based denoising algor…

• Dark Energy Survey Year 3 results: Simulation-based $w$CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design

  A. Thomsen (DES Collaboration), J. Bucko (DES Collaboration), T. Kacprzak (DES Collaboration), V. Ajani (DES Collaboration), J. Fluri (DES Collaboration), A. Refregier (DES Collaboration), D. Anbajagane (DES Collaboration), F. J. Castander (DES Collaboration), A. Fert\'e (DES Collaboration), M. Gatti (DES Collaboration), N. Jeffrey (DES Collaboration), A. Alarcon (DES Collaboration), A. Amon (DES Collaboration), K. Bechtol (DES Collaboration), M. R. Becker (DES Collaboration), G. M. Bernstein (DES Collaboration), A. Campos (DES Collaboration), A. Carnero Rosell (DES Collaboration), C. Chang (DES Collaboration), R. Chen (DES Collaboration), A. Choi (DES Collaboration), M. Crocce (DES Collaboration), C. Davis (DES Collaboration), J. DeRose (DES Collaboration), S. Dodelson (DES Collaboration), C. Doux (DES Collaboration), K. Eckert (DES Collaboration), J. Elvin-Poole (DES Collaboration), S. Everett (DES Collaboration), P. Fosalba (DES Collaboration), D. Gruen (DES Collaboration), I. Harrison (DES Collaboration), K. Herner (DES Collaboration), E. M. Huff (DES Collaboration), M. Jarvis (DES Collaboration), N. Kuropatkin (DES Collaboration), P. -F. Leget (DES Collaboration), N. MacCrann (DES Collaboration), J. McCullough (DES Collaboration), J. Myles (DES Collaboration), A. Navarro-Alsina (DES Collaboration), S. Pandey (DES Collaboration), A. Porredon (DES Collaboration), J. Prat (DES Collaboration), M. Raveri (DES Collaboration), M. Rodriguez-Monroy (DES Collaboration), R. P. Rollins (DES Collaboration), A. Roodman (DES Collaboration), E. S. Rykoff (DES Collaboration), C. S\'anchez (DES Collaboration), L. F. Secco (DES Collaboration), E. Sheldon (DES Collaboration), T. Shin (DES Collaboration), M. A. Troxel (DES Collaboration), I. Tutusaus (DES Collaboration), T. N. Varga (DES Collaboration), N. Weaverdyck (DES Collaboration), R. H. Wechsler (DES Collaboration), B. Yanny (DES Collaboration), B. Yin (DES Collaboration), Y. Zhang (DES Collaboration), J. Zuntz (DES Collaboration), M. Aguena (DES Collaboration), S. Allam (DES Collaboration), F. Andrade-Oliveira (DES Collaboration), D. Bacon (DES Collaboration), J. Blazek (DES Collaboration), D. Brooks (DES Collaboration), R. Camilleri (DES Collaboration), J. Carretero (DES Collaboration), R. Cawthon (DES Collaboration), L. N. da Costa (DES Collaboration), M. E. da Silva Pereira (DES Collaboration), T. M. Davis (DES Collaboration), J. De Vicente (DES Collaboration), S. Desai (DES Collaboration), P. Doel (DES Collaboration), J. Garc\'ia-Bellido (DES Collaboration), G. Gutierrez (DES Collaboration), S. R. Hinton (DES Collaboration), D. L. Hollowood (DES Collaboration), K. Honscheid (DES Collaboration), D. J. James (DES Collaboration), K. Kuehn (DES Collaboration), O. Lahav (DES Collaboration), S. Lee (DES Collaboration), J. L. Marshall (DES Collaboration), J. Mena-Fern\'andez (DES Collaboration), F. Menanteau (DES Collaboration), R. Miquel (DES Collaboration), J. Muir (DES Collaboration), R. L. C. Ogando (DES Collaboration), A. A. Plazas Malag\'on (DES Collaboration), E. Sanchez (DES Collaboration), D. Sanchez Cid (DES Collaboration), I. Sevilla-Noarbe (DES Collaboration), M. Smith (DES Collaboration), E. Suchyta (DES Collaboration), M. E. C. Swanson (DES Collaboration), D. Thomas (DES Collaboration), C. To (DES Collaboration), D. L. Tucker (DES Collaboration) · 19 février 2026

  Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. This work presents the first simulation-based inference (SBI) pipeline that combines weak lensing and galaxy clustering maps in a realistic Dark…

• MadEvolve: Evolutionary Optimization of Cosmological Algorithms with Large Language Models

  Tianyi Li, Shihui Zang, Moritz M\"unchmeyer · 19 février 2026

  We develop a general framework to discover scientific algorithms and apply it to three problems in computational cosmology. Our code, MadEvolve, is similar to Google's AlphaEvolve, but places a stronger emphasis on free parameters and their optimization. Our code starts with a baseline human algorit…