Machine Learning at the ICCUB

Contribution List

14. Registration

23/10/2023, 09:30

9. A four philosophers' introduction to machine learning (Invited talk)

Oriol Pujol (U Barcelona (Math & CS))

23/10/2023, 10:00

AI is everywhere and suddenly everybody is an expert on machine learning. In this talk, I will try to give a gentle introduction the main topics in the design of machine learning algorithms and what guarantees we have they will work. The relationship between ML and empirical science method will be highlighted and some applications to the Physics domain discussed.

2. Neural Network classifier for the generation of clean Magellanic Cloud samples

Óscar Jiménez Arranz

23/10/2023, 11:30

The proximity of the Magellanic Clouds (MCs) to the Milky Way (MW) makes them a perfect laboratory for testing methodologies and models designed for the study of external galaxies using Gaia (ESA) data. To do so, we need to separate in the Gaia data the MCs stars from the foreground MW stars, in order to obtain “clean” MC samples. This is achieved through the design and training of a neural...

8. Anomaly detection for non-gaussian transient noise characterization in LIGO data using auxiliary channel information

Paloma Laguarta

23/10/2023, 12:00

Gravitational-wave interferometers are able to detect a change in distance of ∼ 1/10,000th the size of a proton. Such sensitivity leads to large rates of appearance of non-gaussian transient noise bursts in the main output of the detectors (the strain), also known as glitches. These glitches come in a wide range of frequency- amplitude-time morphologies and have unknown environmental and...

10. Machine Learning Techniques in Strong Gravity (Invited talk)

Raimon Luna (U Valencia)

23/10/2023, 14:00

In this talk we will review some recent uses of machine learning techniques to perform calculations in strong gravity. These will include physics-informed neural networks (PINNs) for the solution of differential equations, and generative models such as GANs.

5. Machine learning in the context of large stellar surveys

Friedrich Anders (Universitat de Barcelona)

23/10/2023, 15:00

The Gaia mission as well as large-scale ground-based spectroscopic surveys are collecting complex data for millions (even billions) of stars. Within the Gaia group we are therefore been using more and more machine-learning methods to cope with the amount of data. In this talk I will present some examples from recent publications in which we have successfully used supervised (typically...

7. Symmetries in Neural Quantum States

Javi Rozalén Sarmiento (Universitat de Barcelona)

23/10/2023, 15:30

Neural Quantum States are at the basis of a new ab-initio method especially designed to tackle the quantum many-body problem. These combine the variational method with neural networks, a flagship tool of modern Machine Learning. Neural Quantum States have been successfully used in spin, electronic and nuclear many-body systems. Neural networks can provide an unbiased approximation of complex...

12. Transforming Healthcare: Machine Learning and Computer Vision at the Forefront (Invited Talk)

Prof. Piella Fenoy (Universitat Pompeu Fabra)

24/10/2023, 10:00

6. Machine learning in LHCb

Lukas Calefice (Universitat de Barcelona/ICCUB)

24/10/2023, 11:30

Machine learning techniques have a variety of use cases within the LHCb experiment. They are an essential ingredient to achieve the ultimate performance in event reconstruction and high precision in physics output.
 This talk will give an insight to the use of ML algorithms in online event selections performed by the LHCb trigger system, offline data analyses of physics measurements
, as well...

1. Quasar classification and resdhift estimation

Ignasi Pérez (ICCUB)

24/10/2023, 12:00

I will present SQUEzE, a machine-learning-based quasar classifier that assigns redshifts to the classified objects. SQUEzE was originally designed to work with SDSS (DESI and WEAVE too) spectra but is highly flexible and can also cope with photometric data from multi-narrow-band photometric surveys (e.g. J-PAS). It follows the human visual inspection procedure and classifies the objects using...

13. Data Science Approaches to Physics and Complex Systems (Invited talk)

Dimitri Marinelli (UBICS)

24/10/2023, 14:00

As physicists, we know that experiments can produce massive amounts of data. However, nowadays, we collect information from many different sources. We store different kinds of data points when we run simulations. Experiments do save data beyond what they are designed to provide (for example, metadata or instrument data). And, in complex system science, data comes from everywhere, from social...

4. Real vs. bogus classifier in time-domain surveys

Dr Nadia Blagorodnova (Univeristat de Barcelona)

24/10/2023, 15:00

Time-domain surveys are designed to study astrophysical transient phenomena appearing in the night sky. The improvements in instrumentation and data analysis are allowing the new generation of surveys to discover several thousand (and soon to be millions) of events per night. However, some of such discoveries are associated with spurious detections related to spikes from bright stars,...

3. Search for lensed gravitational-wave events with machine learning

Mr Roberto Bada Nerín (UPC, Barcelona)

24/10/2023, 15:30

The growing significance of Gravitational Wave Astrophysics puts in evidence the need of techniques capable of effectively and reliably analyzing all the collected data. Furthermore, the search for lensing signatures within gravitational-wave signals is a challenging task that holds the potential to uncover fresh insights into fundamental physics, astrophysics, and cosmology. In this context,...