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INAF SCHOOL OF AI

14–17 Apr 2026
INAF Astronomical Observatory of Capodimonte (Naples)
Europe/Rome timezone

Contribution List

26 out of 26 displayed
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  1. 3. Welcome
    Pietro Schipani (Istituto Nazionale di Astrofisica (INAF))
    14/04/2026, 14:00
  2. 15. USC-C
    Andrea Possenti (Istituto Nazionale di Astrofisica (INAF))
    14/04/2026, 14:05
  3. 16. AI Thematic Group
    Dr Simone Riggi (INAF - Osservatorio Astrofisico di Catania)
    14/04/2026, 14:15
  4. 14. Info & Logistics
    Giuseppe Riccio (Istituto Nazionale di Astrofisica (INAF))
    14/04/2026, 14:25
  5. 25. Introduction to Machine Learning
    Dr Simone Riggi (INAF - Osservatorio Astrofisico di Catania)
    14/04/2026, 14:30
    • Introduction
    • Big data projects in astronomy
    • What is ML and how do we use it in astronomy?
    • Regression & highlights
    • Classification & highlights
    • Anomaly detection & highlights
    • Object detection & highlights
    • Forecasting & highlights
    • Data preprocessing & generation & highlights
    • Outlier detection
    • Simple methods (e.g. IQR-based)
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  6. 26. Data basics & formats
    Farida Farsian (Istituto Nazionale di Astrofisica (INAF))
    14/04/2026, 15:00
    • Data properties (5Vs, format, modality, dimensionality, etc)
    • Tabular data formats most used in Astrophysics
    • Ascii/csv
    • ROOT
    • FITS
    • HDF5/NetCDF
    • VO tables
    • Parquet
    • Relational DB
    • Data I/O considerations
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  7. 27. Data Cleaning
    Dr Simone Riggi (INAF - Osservatorio Astrofisico di Catania)
    14/04/2026, 16:10
    • Data consistency checks
    • Data visualization
    • Examples of 1D data visualization: pie/bar/graph/histogram
    • Examples of 2D visualization: scatter plots, 2D histograms (lego, contour, color maps)
    • Examples of 3D visualization: volume renderings, iso surface, slicing planes
    • Examples of ND data visualization: correlation, scatter plots
    • Dimensionality reduction: curse of...
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  8. 28. Data Transformation
    Farida Farsian (Istituto Nazionale di Astrofisica (INAF))
    14/04/2026, 17:10
    • Tabular data pre-processing
    • Linear transforms: minmax normalization, standardization, scaling
    • Non-linear transforms: power/log, Box-Cox, Yeo-Johnson, Quantile
    • Transforming categorical data
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  9. 31. Tutorial - Data Cleaning & Transformation
    Farida Farsian (Istituto Nazionale di Astrofisica (INAF)), Dr Simone Riggi (INAF - Osservatorio Astrofisico di Catania)
    14/04/2026, 17:40
  10. 4. Quantum Markov Chain Monte Carlo for Cosmological Functions
    Giuseppe Sarracino (Istituto Nazionale di Astrofisica (INAF))
    14/04/2026, 18:00

    In this talk, an hybrid quantum algorithm for Bayesian inference will be presented, QMCMC, following the Markow Chain Monte Carlo method. This algorithm proposes the new steps via the evaluation of the statevector of a quantum circuit, then evaluates the objective function and the acceptance rate classically using the Metropolis-Hastings criterium. The QMCMC algorithm has been tested with real...

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  11. 5. DeepLandforms: A Deep Learning Computer Vision Toolset Applied to a Prime Use Case for Mapping Planetary Skylights
    Giacomo Nodjoumi (Istituto Nazionale di Astrofisica (INAF))
    14/04/2026, 18:10

    This work presents DeepLandforms, an open-source toolkit for landform mapping using validated Deep Learning methods. It provides a complete workflow, covering data preparation, model training, and inference, and gives users full control and customization over the entire process. To validate the tool, it was applied to a science case mapping sinkhole-like landforms on Mars, demonstrating its...

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  12. 7. The GalRSG project: Red Supergiants on the brink of core-collapse
    Mario Lauriano (INAF - Osservatorio Astronomico di Palermo, Università di Padova)
    14/04/2026, 18:20

    The GalRSG project is a long-term, high-cadence, multi-band photometric monitoring campaign designed to detect pre-supernova variability and intense mass-loss events in Red Supergiants (RSGs), which are key progenitors of Type II supernovae. By applying advanced machine learning techniques to high-precision photometric data, the project aims to identify anomalies and mass-loss signatures in...

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  13. 12. Supervised Learning Part I - Theory
    Dr Stefano Cavuoti (INAF - Astronomical Observatory of Capodimonte Napoli)
    15/04/2026, 09:30

    theoretical introduction to supervised learning key concepts:

    • scaling
    • hyper parameter tuning
    • train-validation-test split
    • cross validation
    • classification and regression metrics
    • feature importance

    the following models will be introduced as well:

    • k-nn
    • Decision Trees (DT)
    • Random Forests (RF)
    • Support Vector Machines (SVM)
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  14. 13. Supervised Learning Part I - hands-on session
    Dr Stefano Cavuoti (INAF - Astronomical Observatory of Capodimonte Napoli)
    15/04/2026, 11:30
  15. 19. Supervised Learning Part II - Theory and Hands-on session
    Giuseppe Angora (Istituto Nazionale di Astrofisica (INAF))
    15/04/2026, 14:15

    1) Theoretical introduction to Supervised Learning key concepts:

    • Perceptron
    • Multi Layer Perceptron
    • Activation Functions
    • Cost Functions
    • Optmizers
    • Regularization techniques
    • Convolutional Neural Network
      • Convolution
      • Pooling
      • building CNNs
      • CNN examples: VGG, ResNet, Inception
    • Convolutional Autoencoder
      The lesson includes code...
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  16. 30. Supervised Learning Part II - Hands-on
    Giuseppe Angora (Istituto Nazionale di Astrofisica (INAF))
    15/04/2026, 16:00

    Please refer to 'Supervised Learning Part II - Theory' in order to download materials as well as to the contribution description

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  17. 8. Machine Learning and Super resolution for radio astronomical data analysis
    Giorgia Vitanza (INAF - Osservatorio Astrofisico di Catania)
    15/04/2026, 17:30

    The main objective of the project is to improve the quality and usability of data from next-generation radio telescopes, such as those involved in the SKA project, by developing advanced methodologies to increase resolution, automatically remove artifacts, and intelligently compress datasets to optimize storage. These datasets, extremely large and multidimensional, require innovative solutions...

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  18. 9. The first extragalactic Mg-K anticorrelation
    Deimer Antonio Alvarez Garay (Istituto Nazionale di Astrofisica (INAF))
    15/04/2026, 17:40

    It is well established that Globular Clusters (GCs) are formed by multiple populations (MPs) with different light element abundances (C, N, O, Na, Mg, Al, Si, K), that are structured in well-defined patterns, such as the Na-O and Mg-Al anticorrelations. This evidence is interpreted as the result of a self-enrichment process, where a second population of stars was formed from material...

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  19. 11. Multimodal Foundation Models for Galaxy Classification in Abell S1063
    Lorenzo Santo (Istituto Nazionale di Astrofisica (INAF))
    15/04/2026, 17:50

    In the era of big-data astronomy, automated and scalable galaxy classification is essential to handle the output of next-generation surveys. This talk presents an application of AstroCLIP, a state-of-the-art cross-modal foundation model, to classify 423 spectroscopically confirmed member galaxies of the cluster Abell S1063 from the CLASH-VLT survey. Leveraging the Transformer architecture and...

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  20. 21. Unsupervised Learning Part I - Theory and Hands-on Session
    Ylenia Maruccia (INAF - Astronomical Observatory of Capodimonte)
    16/04/2026, 09:30
    • Introduction to Unsupervised Learning
    • Paradigm and algorithms
    • Clustering
      • Overview, workflow, and evaluation metrics
      • K-Means (mechanism, pros and cons, choice of k)
    • Hands-on session
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  21. 22. Unsupervised Learning Part II
    Francesco Schilliro' (Istituto Nazionale di Astrofisica (INAF))
    16/04/2026, 11:30

    Self Organizing Maps

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  22. 23. Unsupervised Learning Part III - Theory
    Ylenia Maruccia (INAF - Astronomical Observatory of Capodimonte)
    16/04/2026, 14:00
    • Clustering algorithms:
      • DBSCAN
      • Hierarchical Clustering
      • HDBSCAN
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  23. 24. Unsupervised Learning Part III - Hands-on Session
    Ylenia Maruccia (INAF - Astronomical Observatory of Capodimonte)
    16/04/2026, 16:00

    This session includes a practical hands-on component.

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  24. 1. Anomaly Detection theory
    Dr Stefano Cavuoti (INAF - Astronomical Observatory of Capodimonte Napoli)
    17/04/2026, 09:30

    We will analyze four fundamental paradigms for anomaly detection and one model for each of them:

    • Distance (k-NN);
    • Relative Density (LOF);
    • Isolation (Isolation Forest);
    • Reconstruction (Autoencoders).
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  25. 2. Anomaly Detection Hands-on
    Dr Stefano Cavuoti (INAF - Astronomical Observatory of Capodimonte Napoli)
    17/04/2026, 11:30

    Five models

    • k-NN
    • LOF
    • Isolation Forest
    • One-Class SVM
    • Autoencoders
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  26. 10. An ubiquitous population of dynamically-cold galaxy disks at z=4-5
    Lingrui Lin (Istituto Nazionale di Astrofisica (INAF))