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

International Conference on Machine Learning for Astrophysics 2nd Ed. - ML4ASTRO2

from Monday, July 8, 2024 (8:00 AM) to Friday, July 12, 2024 (11:55 PM)

Monday, July 8, 2024

8:00 AM

8:00 AM - 9:00 AM
Room: Conference Room

9:00 AM

9:00 AM - 9:20 AM
Room: Aula Magna
9:20 AM I
I
9:20 AM - 10:40 AM
Room: Aula Magna
Contributions

9:20 AM [INVITED] Expediting the discovery process: Application of unsupervised machine learning algorithms to multi-wavelength astronomical datasets - Dalya Baron

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

10:20 AM Measuring the intracluster light fraction using machine learning - Louisa Canepa
10:40 AM Coffee Break
Coffee Break
10:40 AM - 11:10 AM
Room: Terrace (Conference Room)
11:10 AM II
II
11:10 AM - 1:00 PM
Room: Aula Magna
Contributions

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

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

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

12:10 PM Machine learning for star parametrization - Alessio Turchi

12:30 PM LEMON: Lens modeling with Bayesian neural networks - Valerio Busillo
1:00 PM Lunch
Lunch
1:00 PM - 2:30 PM
Room: Terrace (Conference Room)
2:30 PM III
III
2:30 PM - 4:10 PM
Room: Aula Magna
Contributions

2:30 PM Conditional Invertible Neural Networks for enhanced analysis of young, low-mass stars in Trumpler 14: Classical vs. Deep learning methodologies - Da Eun Kang

2:50 PM Exploring the effects of external photoevaporation in Trumpler 14, with the use of neural networks - Katia Gkimisi

3:10 PM [INVITED] From Data to Discovery: AI in Radio Astronomy - Tao An

3:50 PM Applying Self-Organising Maps to identify and classify complex radio morphologies in next-generation radio surveys - Afrida Alam
4:10 PM Coffee Break
Coffee Break
4:10 PM - 4:30 PM
Room: Terrace (Conference Room)
4:30 PM IV
IV
4:30 PM - 5:30 PM
Room: Aula Magna
Contributions

4:30 PM Radio Galaxy Zoo: EMU - paving the way for EMU cataloguing using AI and citizen science - Hongming Tang

4:50 PM WALLABY: HI source-finding with a machine learning framework - Li Wang

5:10 PM A fully convolutional neural network to detect diffuse sources in radio surveys - Chiara Stuardi
Tuesday, July 9, 2024
9:00 AM V
V
9:00 AM - 10:40 AM
Room: Aula Magna
Contributions

9:00 AM [INVITED] Building Robust and Impactful Machine Learning Models in Astronomy - Daniela Huppenkothen

9:40 AM Neural network for improving the flux density estimations in polarization of compact sources. - Laura Bonavera

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

10:20 AM Detection of Galaxy Tidal Features using Self-Supervised Machine Learning - Alice Desmons
10:40 AM Coffee
Coffee
10:40 AM - 11:10 AM
Room: Terrace (Conference Room)
11:10 AM VI
VI
11:10 AM - 1:00 PM
Room: Aula Magna
Contributions

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

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

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

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

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

12:40 PM Galaxy Morphological Classification via Unsupervised Machine Learning in the Big Data Era led by JWST, EUCLID, LSST and SKA - Ilin Lazar
1:00 PM Lunch
Lunch
1:00 PM - 2:30 PM
Room: Terrace (Conference Room)
2:30 PM I
I
2:30 PM - 4:10 PM
Room: Aula Magna
Contributions

2:30 PM [INVITED] The ALeRCE broker: a community broker for the Vera C. Rubin Observatory - Francisco Forster

3:10 PM Using bowties to catch radio emitting transients - Sergio Belmonte Díaz

3:30 PM Bridging the Gap between Spectra and Light-Curves: Exploring Multimodal Learning for the Identification of Broad Absorption Line Quasars - Nicolás Guerra-Varas

3:50 PM Unsupervised Machine Learning Techniques for Young Stellar Object Light Curve Characterization - Máté Madarász
4:10 PM Coffee Break
Coffee Break
4:10 PM - 4:30 PM
Room: Terrace (Conference Room)
4:30 PM II
II
4:30 PM - 5:30 PM
Room: Aula Magna
Contributions

4:30 PM Improving ML training sets with Active Learning in Fink - Anais Möller

4:50 PM A new Deep Learning Model for Gamma-Ray Bursts’ light curves simulation - Riccardo Falco

5:10 PM Signatures to help interpretability of anomalies in astronomical data - Emmanuel Gangler
8:00 PM Social Dinner
Social Dinner
8:00 PM - 12:00 AM
Wednesday, July 10, 2024
9:00 AM I
I
9:00 AM - 10:40 AM
Room: Aula Magna
Contributions

9:00 AM [INVITED] Cosmology in the machine learning era - Francisco Villaescusa

9:40 AM Inferring Galaxy Baryonic Properties from IllustrisTNG Dark Matter Merger Trees with Graph Neural Networks - Nick Andreadis (Astronomical Observatory, Ghent University)

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

10:20 AM EMBER: emulating baryons from dark matter-only simulations over cosmic time - Mauro Bernardini (University of Zurich)
9:40 AM
9:40 AM - 10:40 AM
Room: Conference Room
Contributions

9:40 AM Unsupervised learning for GRBs classification - Nicolò Cibrario

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

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

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

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

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

12:10 PM 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 AM - 12:45 PM
Room: Conference Room
Contributions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

12:35 PM Unmasking the Hidden: Anomaly Detection in ASKAP’s Monitoring - Zhuowei Wang
12:30 PM Lunch
Lunch
12:30 PM - 2:00 PM
Room: Terrace (Conference Room)

2:00 PM Social Event
Social Event
2:00 PM - 6:00 PM
Thursday, July 11, 2024
9:00 AM III
III
9:00 AM - 10:40 AM
Room: Aula Magna
Contributions

9:00 AM [INVITED] Expediting Astronomical Discovery with Large Language Models: Progress, Challenges, and Future Directions - Yuan Sen-Ting

9:40 AM Extracting optimal information from cosmological surveys with field-level inference and joint analyses - Adrian Bayer

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

10:20 AM Surrogate Modeling for Supernova Feedback toward Star-by-Star Simulations of Milky-Way-sized Galaxies - Keiya Hirashima
9:40 AM I
I
9:40 AM - 10:40 AM
Room: Conference Room
Contributions

9:40 AM Bayesian Imaging of the Spatio-Spectral X-Ray Sky - Margret Westerkamp (Max Planck Institute for Astrophysics; Ludwig-Maximilians-Universität München A)

10:00 AM 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 AM Recurrent Neural Networks for Adaptive Optics parameters estimation - Fabio Rossi (INAF)
10:40 AM

10:40 AM - 11:10 AM
Room: Terrace (Conference Room)
11:10 AM IV
IV
11:10 AM - 1:00 PM
Contributions

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

11:30 AM Simulation-based Supernova Cosmology - Konstantin Karchev

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

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

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

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

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

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

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

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

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

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

12:05 PM Probing AGN dynamics through a natural language processor lens - Ben Rouse (Pontificia Universidad Catolica de Chile)
1:00 PM Lunch
Lunch
1:00 PM - 2:30 PM
Room: Terrace (Conference Room)
2:30 PM V
V
2:30 PM - 3:50 PM
Room: Aula Magna
Contributions

2:30 PM [INVITED] Symbolic regression and interpretable ML - Deaglan Bartlett

3:10 PM Accelerated inference with neural networks for CMB and LSS analyses - Boris Bolliet

3:30 PM Globular clusters' dynamical age determination based on machine learning performed on large number of detailed MOCCA simulations - Arkadiusz Hypki
3:50 PM I
I
3:50 PM - 4:30 PM
Room: Aula Magna
Contributions

3:50 PM The SPACE Centre of Excellence - Giuliano Taffoni

4:10 PM Interpreting the largest cosmological simulations using Representation Learning - Sebastian Trujillo
4:30 PM Coffee break
Coffee break
4:30 PM - 4:50 PM
Room: Terrace (Conference Room)
4:50 PM II
II
4:50 PM - 5:30 PM
Room: Aula Magna
Contributions

4:50 PM Deep learning for scientific data compression - Lubomir Riha Petr Strakos

5:10 PM High-Performance Unsupervised Machine-Learning in Numerical Simulations and Satellite imaging - Francesco Tomba
Friday, July 12, 2024
9:00 AM
9:00 AM - 10:40 AM
Room: Aula Magna
Contributions

9:00 AM [INVITED] Astroparticle Physics and Deep Learning - Jonas Glombitza (Erlangen Centre for Astroparticle Physics)

9:40 AM Machine Learning Techniques for Space Calorimeter Experiments - Maria Bossa (INFN Napoli)

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

10:20 AM Machine learning enhancements for Cherenkov telescopes data analysis - Ambra Di Piano (Università di Modena e Reggio Emiglia; INAF/OAS Bologna)
10:40 AM Coffe Break
Coffe Break
10:40 AM - 11:10 AM
Room: Terrace (Conference Room)
11:10 AM
11:10 AM - 1:00 PM
Room: Aula Magna
Contributions

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

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

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

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

12:30 PM Convolutional Neural Networks for Detecting Moving Objects in Wide-Field Surveys - Belén Yu Irureta-Goyena (École Polytechnique Fédérale de Lausanne)
1:00 PM Lunch
Lunch
1:00 PM - 2:30 PM
Room: Terrace (Conference Room)
2:30 PM
2:30 PM - 3:50 PM
Room: Aula Magna
Contributions

2:30 PM 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)

2:50 PM Bayes in Space: A Bayesian Deep Learning approach for coronal temperature estimation - Nikita Balodhi (Northumbria University, Newcastle)

3:10 PM 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))

3:30 PM Applications of autoencoders to exoplanetary transit light curves - Lucy Smith (University of Hull)
2:50 PM

2:50 PM - 3:50 PM
3:50 PM
3:50 PM - 4:10 PM
Room: Aula Magna
Contributions

3:50 PM Machine Learning Based Parametrization of Solar Active Regions Using Disentangled Variational Autoencoders - Ekaterina Dineva

3:55 PM GraphNeT: A Deep Learning Library for Neutrino Telescopes - Rasmus Ørsøe

4:00 PM Real-Time Detection of Cosmic Ray Systematic Glitches for the LiteBIRD satellite - Emile Carinos

4:05 PM Poster Q&A
4:10 PM

4:10 PM - 4:20 PM
Room: Aula Magna

4:20 PM Coffe Break
Coffe Break
4:20 PM - 4:50 PM
Room: Terrace (Conference Room)

Choose timezone