Dr. Ghassem Gozaliasl
Astrophysicist & AI Researcher | Dual Ph.D. | Docent
Aalto University - Computer Science
University of Helsinki - Physics
Recent Breakthrough Discovery
Featured Journal Cover: Astronomy & Astrophysics
Featured Cover: The image I created for COSMOS-Web groups (from Toni, Gozaliasl et al. 2025) was selected for the cover of Astronomy & Astrophysics, Volume 697 (May 2025).
ESA Picture of the Month – September 2025: My composite image, highlighting eight spectacular gravitational lensing systems, was selected as ESA/Webb’s Picture of the Month. This recognition was based on discoveries from the COSMOS-Web Lens Survey (COWLS), which visually inspected over 42,000 galaxies and identified more than 400 strong lensing candidates. The COWLS survey revealed new Einstein rings, rare disc galaxy lenses, and unprecedented cosmic depth, enabling the study of the Universe as it was just over a billion years old.
ESA Picture of the Month – April 2025: Earlier in April, I led the COSMOS-Web galaxy group team to discover the largest sample of galaxy groups detected by JWST, also featured as ESA's Picture of the Month. This groundbreaking work pushed galaxy group detection back to when the Universe was only 1.9 billion years old, representing a major leap in our understanding of early cosmic structure formation.
ESA Picture of the Month – September 2025
September 2025: My composite image was selected as ESA/Webb’s Picture of the Month. This collage shows eight spectacular cases of gravitational lensing identified in the COSMOS-Web survey, where foreground galaxies warp spacetime and create striking arcs and circles from even more distant galaxies.
The COSMOS-Web Lens Survey (COWLS) visually inspected over 42,000 galaxies and found 400+ lensing candidates; this selection highlights some of the most remarkable. These lenses, some never seen before Webb, enable us to study the Universe as it was just over a billion years old. My work with the team revealed details including Einstein rings, rare disc galaxy lenses, and unprecedented cosmic depth.
Eight gravitationally lensed galaxies identified by COWLS in COSMOS-Web. Lensing distorts and magnifies the light from galaxies billions of light-years away. [Credit: ESA/Webb, NASA & CSA, G. Gozaliasl, A. Koekemoer, M. Franco]
Links: Pan video above | Science Paper I | Paper II | Paper III
Latest Achievement: COSMOS2025 Public Release
June 2025: We successfully created and publicly released the largest deep space catalogue ever created from the James Webb Space Telescope's COSMOS-Web survey. This unprecedented dataset from 255 hours of JWST observations is now publicly available in an easily searchable format, democratizing access to the early universe for scientists, students, educators, and the public worldwide.
"Our public release of the largest deep space catalogue ever created represents a remarkable step for science and society. We have democratized access to the early universe, enabling not only scientists but also students, educators, and the public to explore our cosmic origins interactively. It's especially exciting that we, as researchers from Finland, have contributed to this effort — showcasing how global collaboration and open science are shaping the future of discovery." - Dr. Gozaliasl
International Recognition: Our COSMOS-Web data release has been covered by major international institutions, with our team's contributions specifically highlighted by Rochester Institute of Technology in their official announcement. RIT emphasized how our research traces "how galaxies shut down star formation, undergo morphological transformation, and how these processes are shaped by their environment across cosmic time, even predicting galaxy properties using AI-driven methods."
Official Coverage: Aalto University News | RIT Official Release
ESA Picture of the Month – April 2025
Two JWST images from our COSMOS-Web survey were selected as the European Space Agency's Picture of the Month for April 2025. The April image highlights a major discovery from a project I led as the head of the COSMOS-Web Galaxy Groups Working Team. Our team detected the largest and deepest sample of galaxy groups observed to date, extending out to redshift z = 3.7, or approximately 12 billion light-years away. This was made possible by the largest General Observer program in JWST Cycle 1, and represents a significant step forward in tracing the formation of massive cosmic structures in the early universe. The featured image—created by me for this project and published as Figure 1 in Toni, Gozaliasl et al. (2025)—was selected as the ESA Picture of the Month for April 2025. On the left is the original deep JWST image, and on the right, a processed overlay map reveals the hot gas distribution (in purple), tracing the presence of massive dark matter halos.
Official ESA Links: composite JWST+HST images | Extended X-ray Emission Overlay Article: Toni, Gozaliasl et al. 2025
Credit: ESA/Webb, NASA & CSA, G. Gozaliasl, A. Koekemoer, M. Franco, and the COSMOS-Web team
Science Papers for ESA Picture of the Month – April 2025:
- Science Paper I: COSMOS X-ray galaxy groups: G. Gozaliasl, A. Finoguenov, et al. (2019)
- Science Paper II: AMICO: Cluster Detection Algorithm: M. Maturi et al. (2019)
- Science Paper III: The COSMOS-Web Survey: C. Casey, J. Kartaltepe et al. (2023)
- Science Paper IV: COSMOS-Web Deep Galaxy Group: G. Toni, G. Gozaliasl et al. (2025)
See also the official ESA page for April 2025 Picture of the Month for more details.
Brightest Group Galaxies Catalog (0.08 < z < 3.7)
Comprehensive study of Brightest Group Galaxies (BGGs) spanning cosmic time from the local universe to early epochs. This research catalog covers an unprecedented redshift range of 0.08 < z < 3.7, tracing the evolution of the most massive galaxies in group environments across over 11 billion years of cosmic history.
The composite image below showcases the diversity of brightest group galaxies identified through our COSMOS-Web survey, revealing how these massive central galaxies have evolved through mergers, accretion, and environmental interactions across cosmic time. The catalog represents a cornerstone dataset for understanding galaxy formation and the growth of massive structures in the universe.
Composite image of Brightest Group Galaxies from the COSMOS-Web catalog spanning redshifts 0.08 < z < 3.7, demonstrating the morphological diversity and evolution of massive central galaxies across cosmic time.
Article: Gozaliasl et al. 2025
Credit: ESA/Webb, NASA & CSA, G. Gozaliasl, A. Koekemoer, M. Franco, and the COSMOS-Web team
AI-Driven Galaxy Parameter Estimation: Self-Organizing Maps
Led groundbreaking research (Abedini, Gozaliasl et al. 2025): "COSMOS-Web: Estimating Physical Parameters of Galaxies Using Self-Organizing Maps" - A revolutionary application of unsupervised machine learning to extract physical properties of galaxies directly from JWST photometric data.
Using Self-Organizing Maps (SOMs), we developed a novel method to estimate key galaxy parameters including redshift, stellar mass, star formation rate (SFR), specific SFR (sSFR), and age out to z=3.5. The approach efficiently projects high-dimensional galaxy color information onto interpretable 2D maps, revealing how physical properties vary among galaxies with similar spectral energy distributions.
Validation & Performance: First validated using HORIZON-AGN simulation mock catalogs, then successfully applied to real COSMOS-Web observations. Performance metrics (NMAD and RMSE typically 0.1-0.3) demonstrate exceptional accuracy despite the complexity of real galaxy populations.
Complete workflow of the Self-Organizing Maps methodology: from input HORIZON-AGN and COSMOS-Web data through training phases to final galaxy parameter predictions. The SOM effectively maps high-dimensional photometric data to physical properties across different redshift bins.
Research Impact & Metrics
Global Media Coverage & Impact
Worldwide Recognition: Dr. Gozaliasl's research achievements have generated unprecedented global media coverage across multiple breakthroughs:
🏆 ESA Picture of the Month (April 2025): Galaxy group discovery covered by 134 articles across 34 countries, reaching over 770 million people worldwide with $7.1+ million advertising value equivalent.
📊 COSMOS2025 Catalog Release (June 2025): Featured prominently by Rochester Institute of Technology as a major international achievement, highlighting Dr. Gozaliasl's pioneering work in galaxy evolution and AI-driven astronomical methods. Expected to generate significant additional media coverage globally.
Coverage Languages: English, Indonesian, Vietnamese, Finnish, Portuguese, Spanish, German, Italian, and more
🔬 PRISM Project
PRISM (Physically Realistic Infrared Strong-lensing Models) is an empirically calibrated simulation framework addressing the limitations of current strong-lensing simulations in the JWST era. PRISM introduces three key innovations:
- Ultra-realistic galaxy morphologies, including ultra-low Sérsic indices and irregular structures that dominate high-redshift populations, calibrated to 436 confirmed lens systems from the COSMOS-Web Strong Lensing Survey (COWLS), with empirically derived mass--size relations capturing redshift evolution.
- Integration of real, spatially-varying JWST point spread function (PSF) models and empirical noise distributions from hundreds of observations, producing realistic multi-band imaging across four NIRCam filters (F115W, F150W, F277W, F444W) used in COSMOS-Web.
- Machine learning models that predict realistic environmental context for each lens system.
PRISM generates a balanced training dataset spanning lens redshifts $z_{\rm l} = 0.2$--6.0 and source redshifts $z_{\rm s} = 0.8$--15.0. Validation shows excellent agreement with COWLS observations: Einstein radius and Sérsic index distributions are statistically indistinguishable from observed values, mass--Einstein radius correlations closely match empirical measurements, and magnitude accuracy reaches $\pm 0.08$ mag across all four COSMOS-Web NIRCam bands. Completeness analysis reveals redshift-dependent selection effects, with empirical noise modeling providing more realistic detection estimates than idealized approximations. This framework enables robust machine learning algorithm development and systematic testing under realistic JWST observational conditions.
Publications & Research Output
Prolific Research Portfolio: With 220+ peer-reviewed publications, Dr. Gozaliasl has established himself as a leading figure in modern astrophysics. His work spans high-impact journals including Astronomy & Astrophysics, Monthly Notices of the Royal Astronomical Society, Astrophysical Journal, and Nature Machine Intelligence.
Recent High-Impact Publications:
- 📄 COSMOS Web: Morphological quenching and size-mass evolution of brightest group galaxies (Gozaliasl et al. 2025) - Astronomy & Astrophysics (2025)
- 📄 Self-Organizing Maps for Galaxy Parameter Estimation (Abedini, Gozaliasl et al. 2025) - MNRAS (2025)
- 📄 COSMOS-Web Galaxy Groups Catalog (Toni, Gozaliasl et al. 2025) - Astronomy & Astrophysics (2025)
- 📄 COSMOS2025: COSMOS Web catalog paper series - see Shuntov et al. (2025), Franco et al. (2025), Harish et al. (2025)
- 📄 COSMOS Web Strong Lens Survey - Hogg et al. (2025), Nightingale et al.(2025), Mahler et al. , (2025)
- 📄 Brightest Group Galaxies -III: evolution of stellar age (Gozaliasl et al. 2024) - Astronomy & Astrophysics (2024)
- 📄 Euclid Preparation Series - Multiple papers on cosmological surveys and data analysis
- 📄 Machine Learning for COVID-19 Diagnostics - Nature Machine Intelligence (2021)
- 📄 Galaxy Evolution in COSMOS Field - (2019-2024)
Professional Overview
Leading Interdisciplinary Scientist bridging astrophysics, artificial intelligence, and computational physics. Currently directing the "USA-pilot" (FARIA) project of PlasmaAI while serving as Docent at the University of Helsinki. Dual Ph.D. holder with extensive expertise in galaxy evolution, machine learning applications in astronomy, and space mission data analysis. Team Leader for groundbreaking COSMOS-Web discoveries featured as ESA's Picture of the Month and creator of the largest publicly available deep space catalog. Active member of major international collaborations including the Euclid Consortium and multiple ESA missions.
Research Areas
🌌 Astrophysics & Cosmology
- Galaxy formation and evolution
- Galaxy groups and clusters
- Large-scale structure
- Dark matter and dark energy
- Exoplanetary systems
- Brightest Group Galaxies (BGGs)
🤖 AI & Machine Learning
- Deep learning for astronomy
- Computer vision applications
- Predictive modeling
- Time series analysis
- Big Data interpretation
- MCMC sampling methods
🔬 Computational Physics
- Numerical simulations
- Data quality algorithms
- Pipeline development
- Sub-grid modeling
- Turbulent transport physics
- High-performance computing
🛰️ Space Missions & Surveys
- ESA Euclid mission
- JWST COSMOS-Web
- X-ray astronomy (XMM, Chandra)
- Multi-wavelength analysis
- Data calibration systems
- Survey design and planning
Current & Recent Projects
COSMOS-Web Survey Leadership
Team Leader for galaxy group discovery in the largest JWST imaging survey. Led the team that discovered the most extensive and deepest sample of galaxy groups using the Webb telescope—extending out to z = 3.7—featured as ESA's Picture of the Month (April 2025).
Our work used the AMICO algorithm to trace group-scale dark matter halos as early as 1.9 billion years after the Big Bang.
This builds on a decade of foundational work I led in X-ray galaxy group detection, including:
- COSMOS X-ray Galaxy Groups (Gozaliasl et al. 2019)
- XMM-LSS Survey (Gozaliasl et al. 2014a)
🔗 View Related Publications
ML/AI in Universe Science
A series of research projects led by my graduate and undergraduate students, applying advanced machine learning techniques to major challenges in astronomy. These include:
- Exoplanet detection using TESS and Kepler data
- Galaxy morphology classification at high redshift
- Detection of strong gravitational lenses
- Stellar and galaxy property estimation using Self-Organizing Maps (SOMs)
BGG-X: Brightest Group Galaxies Legacy Project
A decade-long research program dedicated to understanding the evolution of Brightest Group Galaxies (BGGs) across cosmic time.
This series (BGG-I to BGG-III and beyond) investigates their stellar populations, dynamical properties, stellar-to-halo mass relation, and multiwavelength signals, including radio and X-ray.
The project serves as a cornerstone of my scientific work and is closely tied to key extragalactic surveys such as COSMOS-Web, HST, and ALMA.
🔗
View BGG Publications on ADS
PlasmaAI - USA-pilot (FARIA)
Leading innovative research combining machine learning with physical ansatzes to address finite resolution limitations in numerical simulations. Developing sustainable computational tools for turbulent transport modeling.
Euclid Mission
Long-term contributor to ESA's flagship cosmology mission since 2014. Developed data quality tools, algorithms, and pipelines for understanding dark energy and dark matter.
COVID-19 AI Diagnostics
Developed AI-assisted tools for COVID-19 diagnosis combining chest imaging, clinical, and laboratory data. Contributed to open-source solutions for medical diagnostics during the pandemic.
Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.
Exoplanet Prediction Systems
Successfully developed machine learning models to predict additional exoplanets in known planetary systems and analyzed mass-radius relationships. Advanced understanding of planetary system architecture through innovative AI approaches.
Education & Qualifications
Recognition of research achievements and teaching excellence in astrophysics
Notable Achievements
Scientific Leadership: Successfully led international collaborations across three continents, published 220+ peer-reviewed papers, and contributed to major space missions. Recognized as Docent at the University of Helsinki, demonstrating excellence in both research and teaching.
Technical Innovation: Developed cutting-edge algorithms for space mission data quality, created novel AI applications for medical diagnostics during COVID-19, and pioneered machine learning approaches for astronomical big data challenges.
Recent Breakthrough:
ESA Picture of the Month – September 2025: My composite image, highlighting eight spectacular gravitational lensing systems, was selected as ESA/Webb’s Picture of the Month. This recognition was based on discoveries from the COSMOS-Web Lens Survey (COWLS), which visually inspected over 42,000 galaxies and identified more than 400 strong lensing candidates. The COWLS survey revealed new Einstein rings, rare disc galaxy lenses, and unprecedented cosmic depth, enabling the study of the Universe as it was just over a billion years old.
ESA Picture of the Month – April 2025: Earlier in April, I led the COSMOS-Web galaxy group team to discover the largest sample of galaxy groups detected by JWST, also featured as ESA's Picture of the Month. These discoveries represent major leaps in our understanding of early cosmic structure formation and the power of JWST for unveiling the distant Universe.
Languages & Communication
Professional Contact
Available for academic collaborations, research partnerships,
and consultations in astrophysics and AI applications.
Phone: +358 50 412 3209
Location: Helsinki, Finland