Conveners
4.1 Pattern recognition, Image analysis & Uncertainty quantification
- Julián García Pardiñas (CERN)
Strong gravitational lensing has become one of the most important tools for investigating the nature of dark matter (DM). With a technique called gravitational imaging, the number and mass of dark matter subhaloes can be measured in strong lenses, constraining the underlying DM model.
Gravitational imaging however is an expensive method and requires adaptation in astronomy's current "big...
Methods for training jet taggers directly on real data are well motivated due to both the ambiguity of parton labels and the potential for mismodelled jet substructure in Monte Carlo. This talk presents a study of weakly-supervised learning applied to Z+jet and dijet events in CMS Open Data. In order to measure the discrimination power in real data, we consider three different estimates of the...
Timepix4 is a hybrid pixel detector readout ASIC developed by the Medipix4 Collaboration at CERN. It consists of a matrix of about 230\,k pixels, each equipped with amplifier, discriminator and time-to-digital converter with 195 ps bin size that allows to measure both time-of-arrival and time-over-threshold of the hits. Due to its characteristics, it can be exploited in a wide range of fields,...
In the field of nuclear physics, multi-neutron detection plays a critical role in revealing specific nuclear properties(e.g. the structure of light exotic nuclei or four-neutron resonance states). However, one neutron can interact several times in different bars of neutron detector array, since it will likely pass through the detectors without losing all its energy. The phenomenon commonly...
The application of modern Machine Learning (ML) techniques for anomaly detection in collider physics is a very active and prolific field, with use cases that include the exploration of physics beyond the Standard Model and the detection of faults in the experimental setup. Our primary focus is on data-quality monitoring. Within large experimental collaborations, this anomaly detection task...
The Hubble function entirely characterizes a given Friedmann-Robertson-Walker spacetime as a consequence of homogeneity and isotropy on cosmological scales. In conjunction with the gravitational field equation, it can be related to the densities of the cosmological fluids and their respective equation of state. The type Ia supernovae allow to constrain the evolution of the luminosity distance...
Accelerator-based experiments in particle physics and medical experiments in neuroscience generate petabytes of data, where well-defined questions could be answered by intense computing analysis, however, new correlations may remain hidden in the huge data-sea. On the other hand, physics/neuroscience-informed AI/ML can help to discover new connections, integrating seamlessly data and...
Weakly supervised methods have emerged as a powerful tool for anomaly detection at the LHC. While these methods have shown remarkable performance on specific signatures, their application in an even more model-agnostic manner requires using higher dimensional feature spaces compared to the first publications on this topic. We present two directions towards more model agnosticity, either by...
Dark energy has ushered in a golden age of astronomical galaxy surveys, allowing for the meticulous mapping of galaxy distributions to constrain models of dark energy and dark matter. The majority of these surveys rely on measuring galaxy redshifts through a limited set of observations in broad optical bands. While determining redshift is theoretically a straightforward machine learning...
Physics-Informed Neural Networks (PINNs) have gained significant attention in the field of deep learning for their ability to tackle physical scenarios, gaining significant interest since its inception in scientific literature. These networks optimize neural architectures by incorporating inductive biases derived from knowledge of physics. To embed the underlying physics, a suitable loss...
