Event Information

• Hashtag: #AstroML1D
• Date/Time: 10:30-15:30 Friday 10 March 2023
• Format: Hybrid. Please join our slack channel for further discussion.
• Venue: The Geological Society, Piccadilly, LONDON, W1J 0BD
• RAS Event Website
• Registration Links:
  • Book a RAS Fellows In Person or Online SDM ticket (free)
  • Book a Non-Fellows In Person SDM Ticket (£15)
  • Book a Non-Fellows Online SDM Ticket (£5)

Timetable

Invited Talk:  25 mins talk + 5 mins Q&A – 30 mins
Long Talk:     12 mins talk + 3 mins Q&A – 15 mins
Short Talk:     3 mins talk + 2 mins Q&A – 5 mins

Morning Session

• 10:30 – 10:35   Introduction
• 10:35 – 11:05   Invited Talk  Yuan-Sen Ting        Australian National University, Australia
  • Time series and spectroscopy of stars -- opportunities, caveats and challenges
• 11:05 – 11:20   Long Talk     Parth Nayak          LMU Munich, Germany
  • Deep learning with Lyman-α forest for inference in IGM astrophysics and cosmology
• 11:20 – 11:35   Long Talk     Alessia Longobardi   Università Milano-Bicocca, Italy
  • Towards an automatic approach to modelling the circumgalactic medium: new tools for mock making and fitting of metal profiles in large surveys
• 11:35 – 11:50   Long Talk     Ming-Feng Ho         UC Riverside, USA
  • Automated detection of damped Lyman alpha absorbers using Bayesian models
• 11:50 – 12:05   Long Talk     Sinan Deger          Stockholm University, Sweden
  • Inspecting Galaxy Spectra Information Content Through Representation Learning
• 12:05 – 12:20   Long Talk     Andrew Saydjari      Harvard CFA, USA
  • Measuring the 8623 Å Diffuse Interstellar Band in Gaia DR3 RVS Spectra: Obtaining a Clean Catalog by Marginalizing over Stellar Types
• 12:20 – 12:35   Long Talk     William Davison      KASI, Korea
  • Hunting for Transients using the Dark Energy Spectroscopic Instrument

Afternoon Session

• 13:30 – 13:35   Starting of Afternoon Session
• 13:35 – 14:05   Invited Talk  Emille Ishida        Laboratoire de Physique de Clermont, France
  • Enabling scientific discovery in the era of big data
• 14:05 – 14:10   Short Talk    Matthew Grayling     University of Cambridge, UK
  • Augmenting supernova training sets using Generative Adversarial Networks
• 14:10 – 14:15   Short Talk    Robbie Webbe         University of Bristol, UK
  • Detection of Quasi-Periodic Eruptions in Extragalactic X-Ray Sources with Machine Learning
• 14:15 – 14:30   Long Talk     Daniel Mata Sanchez  IAC, Spain
  • Systematic detection of outflow features in the optical and near infrared spectra of accreting stellar-mass black holes
• 14:30 – 14:35   Short Talk    John F.Suárez-Pérez  Universidad de Los Andes, Colombia
  • Assessing the quality of massive spectroscopic surveys with unsupervised machine learning
• 14:35 – 14:40   Short Talk    Guillaume Guiglion   Max-Planck-Institut für Astronomie, Germany
  • Extracting the most from stellar spectra with Convolutional Neural-Networks
• 14:40 – 14:55   Long Talk     Yan Yan              Chinese Academy of Sciences, China
  • Explosions on other Suns
• 14:55 – 15:00   Short Talk    Mikhail Denissenya   Nazarbayev University, Kazakhstan
  • Resolving the unresolved: the deep learning way
• 15:00 – 15:05   Short Talk    Ignacio Ferreras     UCL, UK & IAC, Spain
  • The entropy of galaxy spectra
• 15:05 – 15:20   Long Talk     Collin Politsch      University of Cambridge, UK
  • Trend Filtering: A Modern Statistical Tool for Time-Domain Astronomy and Astronomical Spectroscopy
• 15:20 – 15:30   Summary