Name: International Conference on Machine Learning for Astrophysics 2nd Ed. - ML4ASTRO2
Start: 2024-07-08T08:00:00+02:00
End: 2024-07-12T23:55:00+02:00
Location: Catania

Monday 8 July 2024

08:00

08:00 - 09:00
Room: Conference Room

09:00

09:00 - 09:20
Room: Aula Magna
09:20 I
I
09:20 - 10:40
Room: Aula Magna
Contributions

09:20 [INVITED] Expediting the discovery process: Application of unsupervised machine learning algorithms to multi-wavelength astronomical datasets - Dalya Baron

10:00 Deblending and photometry of faint sources using VAEs and multi-wavelength data - Fernando Caro

10:20 Measuring the intracluster light fraction using machine learning - Louisa Canepa
10:40 Coffee Break
Coffee Break
10:40 - 11:10
Room: Terrace (Conference Room)
11:10 II
II
11:10 - 13:00
Room: Aula Magna
Contributions

11:10 Machine Learning-Driven Determination of 3D Structures of Molecular Clouds in High-Resolution mm Images - Efe Öztaban

11:30 A domain-adaptation approach to classify ionised nebulae in nearby galaxies - Francesco Belfiore

11:50 A semi-supervised "cluster-then-label" scheme for photometric classification of evolved stars - Cristobal Bordiu

12:10 Machine learning for star parametrization - Alessio Turchi

12:30 LEMON: Lens modeling with Bayesian neural networks - Valerio Busillo
13:00 Lunch
Lunch
13:00 - 14:30
Room: Terrace (Conference Room)
14:30 III
III
14:30 - 16:10
Room: Aula Magna
Contributions

14:30 Conditional Invertible Neural Networks for enhanced analysis of young, low-mass stars in Trumpler 14: Classical vs. Deep learning methodologies - Da Eun Kang

14:50 Exploring the effects of external photoevaporation in Trumpler 14, with the use of neural networks - Katia Gkimisi

15:10 [INVITED] From Data to Discovery: AI in Radio Astronomy - Tao An

15:50 Applying Self-Organising Maps to identify and classify complex radio morphologies in next-generation radio surveys - Afrida Alam
16:10 Coffee Break
Coffee Break
16:10 - 16:30
Room: Terrace (Conference Room)
16:30 IV
IV
16:30 - 17:30
Room: Aula Magna
Contributions

16:30 Radio Galaxy Zoo: EMU - paving the way for EMU cataloguing using AI and citizen science - Hongming Tang

16:50 WALLABY: HI source-finding with a machine learning framework - Li Wang

17:10 A fully convolutional neural network to detect diffuse sources in radio surveys - Chiara Stuardi
Tuesday 9 July 2024
09:00 V
V
09:00 - 10:40
Room: Aula Magna
Contributions

09:00 [INVITED] Building Robust and Impactful Machine Learning Models in Astronomy - Daniela Huppenkothen

09:40 Neural network for improving the flux density estimations in polarization of compact sources. - Laura Bonavera

10:00 Deep learning tools for detecting and segmenting galactic structures and contaminants in low surface brightness images - Adeline Paiement

10:20 Detection of Galaxy Tidal Features using Self-Supervised Machine Learning - Alice Desmons
10:40 Coffee
Coffee
10:40 - 11:10
Room: Terrace (Conference Room)
11:10 VI
VI
11:10 - 13:00
Room: Aula Magna
Contributions

11:10 [INVITED] The Italian National Centre on HPC, Big Data and Quantum Computing NRRP From Research to Industry - Ugo Becciani

11:20 The GaXNet tools for Stage-IV Surveys and Cosmology with Galaxies and Galaxy Clusters - Nicola Napolitano

11:40 Exploring Multi-Band Imaging for Identifying z > 6.5 Quasars: A Contrastive Learning Approach Using HSC Data - Laura Natalia Martinez Ramirez

12:00 Predictive Modeling of Galaxy Spectra: A Comprehensive Framework Using Transformer Architecture - Ginés Martínez Solaeche

12:20 Multi-Task Neural Nets with Monte Carlo Dropout for Spectral Analysis of Galaxies - Michele Ginolfi

12:40 Galaxy Morphological Classification via Unsupervised Machine Learning in the Big Data Era led by JWST, EUCLID, LSST and SKA - Ilin Lazar
13:00 Lunch
Lunch
13:00 - 14:30
Room: Terrace (Conference Room)
14:30 I
I
14:30 - 16:10
Room: Aula Magna
Contributions

14:30 [INVITED] The ALeRCE broker: a community broker for the Vera C. Rubin Observatory - Francisco Forster

15:10 Using bowties to catch radio emitting transients - Sergio Belmonte Díaz

15:30 Bridging the Gap between Spectra and Light-Curves: Exploring Multimodal Learning for the Identification of Broad Absorption Line Quasars - Nicolás Guerra-Varas

15:50 Unsupervised Machine Learning Techniques for Young Stellar Object Light Curve Characterization - Máté Madarász
16:10 Coffee Break
Coffee Break
16:10 - 16:30
Room: Terrace (Conference Room)
16:30 II
II
16:30 - 17:30
Room: Aula Magna
Contributions

16:30 Improving ML training sets with Active Learning in Fink - Anais Möller

16:50 A new Deep Learning Model for Gamma-Ray Bursts’ light curves simulation - Riccardo Falco

17:10 Signatures to help interpretability of anomalies in astronomical data - Emmanuel Gangler
20:00 Social Dinner
Social Dinner
20:00 - 00:00
Wednesday 10 July 2024
09:00 I
I
09:00 - 10:40
Room: Aula Magna
Contributions

09:00 [INVITED] Cosmology in the machine learning era - Francisco Villaescusa

09:40 Inferring Galaxy Baryonic Properties from IllustrisTNG Dark Matter Merger Trees with Graph Neural Networks - Nick Andreadis (Astronomical Observatory, Ghent University)

10:00 Recovering the CMB signal with neural networks - Jose Manuel Casas

10:20 EMBER: emulating baryons from dark matter-only simulations over cosmic time - Mauro Bernardini (University of Zurich)
09:40
09:40 - 10:40
Room: Conference Room
Contributions

09:40 Unsupervised learning for GRBs classification - Nicolò Cibrario

10:00 A worthwhile detour via artificial spectra: galaxy properties from photometric images with diffusion models - Lars Doorenbos

10:20 The TransformerPayne: Toward a Foundation Model for Stellar Spectra - Tomasz Różański
10:40 Coffee break
Coffee break
10:40 - 11:10
Room: Terrace (Conference Room)
11:10 II
II
11:10 - 12:30
Room: Aula Magna
Contributions

11:10 Calibrating Bayesian Tension Statistics with Neural Ratio Estimation - Harry Bevins (University of Cambridge)

11:30 Finding analogues of observed galaxy groups and clusters in cosmological simulations with contrastive learning - Urmila Chadayammuri (MPIA)

11:50 Application of physics-informed neural networks to neutron star magnetospheres - Petros Stefanou (Universidad de Alicante / Universidad de Valencia)

12:10 Evaluating Summary Statistics with Mutual Information for Cosmological Inference - Ce Sui (Tsinghua University)
Time Domain / Multi-wavelength All-Sky Surveys
Time Domain / Multi-wavelength All-Sky Surveys
11:10 - 12:45
Room: Conference Room
Contributions

11:10 Enhancing X-ray Binary Analysis through Deep Learning - Antonio Tutone

11:15 LightPred - a deep learning model for stellar properties predictions - Ilay Kamai

11:20 Inferring stellar parameters and their uncertainties from high-resolution spectroscopy using conditional Invertible Neural Networks - Nils Candebat

11:25 GWEEP: A Deep Learning Toolkit for Gravitational Waves Analysis - Daniel Tonoiu

11:35 Deep Learning-Based Visual-Binary Source Detection in Stamp Images from Multiple Surveys - Ilknur Gezer

11:40 Early-stopping SOMs as a tool to classify SSOs in space surveys - Simone Sacquegna

11:45 Inter-annotator consensus: optimizing machine learning for astrophysical feature segmentation and classification - Renaud Vancoellie

11:50 Automatic Modelling and Object Identification in Radio Astronomy - Richard Fuchs

11:55 Morphological classification of radio sources in star-forming regions - Elena Díaz-Márquez

12:00 Target Selection for a redshift limited survey with Machine Learning - Gursharanjit Kaur

12:10 Spatially Variant Point Spread Functions for Bayesian Imaging - Vincent Eberle

12:15 Predicting AGN Obscuration with Machine Learning - Ross Silver

12:20 Galaxy stellar and total mass estimation using machine learning - Jiani Chu

12:25 ULISSE: A tool for one-shot sky exploration and its application for detection of AGN and galaxy properties. - Lars Doorenbos

12:30 Bayesian Generative Strong Lensing with LensCharm - Matteo Guardiani

12:35 Unmasking the Hidden: Anomaly Detection in ASKAP’s Monitoring - Zhuowei Wang
12:30 Lunch
Lunch
12:30 - 14:00
Room: Terrace (Conference Room)

14:00 Social Event
Social Event
14:00 - 18:00
Thursday 11 July 2024
09:00 III
III
09:00 - 10:40
Room: Aula Magna
Contributions

09:00 [INVITED] Expediting Astronomical Discovery with Large Language Models: Progress, Challenges, and Future Directions - Yuan Sen-Ting

09:40 Extracting optimal information from cosmological surveys with field-level inference and joint analyses - Adrian Bayer

10:00 Dynamical Modelling of Galactic Kinematics using Neural Networks - David Simon

10:20 Surrogate Modeling for Supernova Feedback toward Star-by-Star Simulations of Milky-Way-sized Galaxies - Keiya Hirashima
09:40 I
I
09:40 - 10:40
Room: Conference Room
Contributions

09:40 Bayesian Imaging of the Spatio-Spectral X-Ray Sky - Margret Westerkamp (Max Planck Institute for Astrophysics; Ludwig-Maximilians-Universität München A)

10:00 Conditional Variational Autoencoders for the analysis of the Gaia spectrophotometric data of pre-main sequence stars - Cristina Paola Marcellino (INAF - Osservatorio Astrosico di Catania)

10:20 Recurrent Neural Networks for Adaptive Optics parameters estimation - Fabio Rossi (INAF)
10:40

10:40 - 11:10
Room: Terrace (Conference Room)
11:10 IV
IV
11:10 - 13:00
Contributions

11:10 Self-supervised learning for component separation for the extragalactic submillimetre sky - Victor Bonjean

11:30 Simulation-based Supernova Cosmology - Konstantin Karchev

11:50 Machine-Learnt Star-formation laws: Symbolic Regression with FIRE-2 galaxies - Diane Salim

12:10 Toward a deep learning approach for fast galaxy generation - Tanner Sether

12:30 ForSE+: Simulating non-Gaussian CMB foregrounds at 3 arcminutes in a stochastic way basing on GAN - Jian Yao

12:50 Flash Talk: Morphology and spatial distribution of high-redshift dust emission using component separation and deep learning - Arnab Lahiry

12:55 Flash Talk: Machine learning for radiometer calibration in global 21cm cosmology - Samuel Leeney
II
II
11:10 - 11:50
Room: Conference Room
Contributions

11:10 Tracing ongoing quenching in jellyfish galaxies with machine learning techniques: Challenges in full spectral classification - Ariel Werle

11:30 Using Deep Learning for spectral classification, redshift predictions and anomaly detection. - Fucheng Zhong
11:50
11:50 - 12:35
Room: Conference Room
Contributions

11:50 Predicting the Ages of Galaxies with an Artificial Neural Network - Laura Hunt (University of Hull)

11:55 Machine learning-based photometric classification of galaxies, quasars and stars - Donato Cascio (University of Palermo)

12:00 Analysis of Velocity Distribution Functions using the Gaussian Mixture Model - Beniamino Sanò (Università di Trento)

12:05 Probing AGN dynamics through a natural language processor lens - Ben Rouse (Pontificia Universidad Catolica de Chile)
13:00 Lunch
Lunch
13:00 - 14:30
Room: Terrace (Conference Room)
14:30 V
V
14:30 - 15:50
Room: Aula Magna
Contributions

14:30 [INVITED] Symbolic regression and interpretable ML - Deaglan Bartlett

15:10 Accelerated inference with neural networks for CMB and LSS analyses - Boris Bolliet

15:30 Globular clusters' dynamical age determination based on machine learning performed on large number of detailed MOCCA simulations - Arkadiusz Hypki
15:50 I
I
15:50 - 16:30
Room: Aula Magna
Contributions

15:50 The SPACE Centre of Excellence - Giuliano Taffoni

16:10 Interpreting the largest cosmological simulations using Representation Learning - Sebastian Trujillo
16:30 Coffee break
Coffee break
16:30 - 16:50
Room: Terrace (Conference Room)
16:50 II
II
16:50 - 17:30
Room: Aula Magna
Contributions

16:50 Deep learning for scientific data compression - Lubomir Riha Petr Strakos

17:10 High-Performance Unsupervised Machine-Learning in Numerical Simulations and Satellite imaging - Francesco Tomba
Friday 12 July 2024
09:00
09:00 - 10:40
Room: Aula Magna
Contributions

09:00 [INVITED] Astroparticle Physics and Deep Learning - Jonas Glombitza (Erlangen Centre for Astroparticle Physics)

09:40 Machine Learning Techniques for Space Calorimeter Experiments - Maria Bossa (INFN Napoli)

10:00 Graph neural networks for track reconstruction in space experiments - Federica Cuna (INFN-Bari)

10:20 Machine learning enhancements for Cherenkov telescopes data analysis - Ambra Di Piano (Università di Modena e Reggio Emiglia; INAF/OAS Bologna)
10:40 Coffe Break
Coffe Break
10:40 - 11:10
Room: Terrace (Conference Room)
11:10
11:10 - 13:00
Room: Aula Magna
Contributions

11:10 CHEREN-ZOO: an educational project for automatic routines - Angelo Adamo (Istituto Nazionale di Astrofisica (INAF))

11:30 Extending Cosmic Ray Background in the LiteBIRD Experiment using Generative Adversarial Networks - Giovanni Cavallotto (INFN Milano Bicocca)

11:50 The application of the Stack-CNN algorithm for the analysis of Mini-EUSO data - Antonio Giulio Coretti (INFN / University of Turin)

12:10 ML Methods For Space Debris Detection in Bistatic Radar Data - Miguel Andrea Zammit (University of Malta, Institute of Space Sciences & Astronomy)

12:30 Convolutional Neural Networks for Detecting Moving Objects in Wide-Field Surveys - Belén Yu Irureta-Goyena (École Polytechnique Fédérale de Lausanne)
13:00 Lunch
Lunch
13:00 - 14:30
Room: Terrace (Conference Room)
14:30
14:30 - 15:50
Room: Aula Magna
Contributions

14:30 Advancing Solar Flare Forecasting: Predicting Solar Flares via a Deep Learning Approach Using Time Series of SDO/HMI Line-of-Sight Magnetograms - Elizabeth Doria Rosales (Università di Trento / Università della Calabria)

14:50 Bayes in Space: A Bayesian Deep Learning approach for coronal temperature estimation - Nikita Balodhi (Northumbria University, Newcastle)

15:10 Reducing stellar noise in exoplanet observables using machine learning - Manuel Perger (Institut de Ciències de l’Espai (ICE, CSIC) & Institut d’Estudis Espacials de Catalunya (IEEC))

15:30 Applications of autoencoders to exoplanetary transit light curves - Lucy Smith (University of Hull)
14:50

14:50 - 15:50
15:50
15:50 - 16:10
Room: Aula Magna
Contributions

15:50 Machine Learning Based Parametrization of Solar Active Regions Using Disentangled Variational Autoencoders - Ekaterina Dineva

15:55 GraphNeT: A Deep Learning Library for Neutrino Telescopes - Rasmus Ørsøe

16:00 Real-Time Detection of Cosmic Ray Systematic Glitches for the LiteBIRD satellite - Emile Carinos

16:05 Poster Q&A
16:10

16:10 - 16:20
Room: Aula Magna

16:20 Coffe Break
Coffe Break
16:20 - 16:50
Room: Terrace (Conference Room)