Conveners
1.1 Pattern recognition & Image analysis
- Stefano Forte (Universita' di Milano and INFN)
Recent years have shown that more and more tasks can be effectively aided by AI. Often supervised learning methods, which are based on labelled data, lead to excellent results. Artificial neural networks, that were trained on this data, allow to make accurate predictions, also for cases, that were not explicitly covered by the training data potentially leading to a more optimal solution for a...
The lack of new physics discoveries at the LHC calls for an effort to to go beyond model-driven analyses. In this talk I will present the New Physics Learning Machine, a methodology powered by machine learning to perform a signal-agnostic and multivariate likelihood ratio test (arXiv:2305.14137). I will focus on an implementation based on kernel methods, which is efficient and scalable while...
The early inspiral from stellar-mass black hole binaries can emit milli-Hertz gravitational wave signals, making them detectable sources for space-borne gravitational wave missions like TianQin. However, the traditional matched filtering technique poses a significant challenge for analyzing these kinds of signals, as it requires an impractically high number of templates ranging from 10^31 to...
Due to poor observational constraints on the low-mass end of the subhalo mass function, the detection of dark matter (DM) subhalos lacking a visible counterpart on sub-galactic scales would provide valuable information about the nature of DM. Novel indirect probes for DM substructure within the Milky Way (MW) are stellar wakes, which are perturbations of the stellar medium induced by DM...
As a new era of gravitational wave detections rapidly unfolds, the importance of having accurate models for their signals becomes increasingly important.
The best model for gravitational waves are the fully-fledged simulations of General Relativity, although their daunting cost make it prohibitive to perform data analysis. To alleviate this, the community has developed a variety of...
The formation mechanism of supermassive black holes is yet unknown, despite their presence in nearly every galaxy, including the Milky Way. As stellar evolution predicts that stars cannot collapse to black holes $\gtrsim 50 - 130\, \text{M}_{\odot}$ due to pair-instability, plausible formation mechanisms include the hierarchical mergers of intermediate-mass black holes (IMBHs). The direct...
We introduce an innovative approach to combinatorial optimization problems through Physics-Informed Graph Neural Networks (GNNs). We combine the structural advantages of GNNs with physics-based algorithms, enhancing solution accuracy and computational efficiency. With respect to available literature we were able to design and train a deep graph neural network model able to solve the graph...
The ongoing search for physics beyond the Standard Model imposes a growing demand for highly sensitive anomaly detection methods. Various approaches to anomaly detection exist, and prominent techniques include semi-supervised and unsupervised training of neural networks. While semi-supervised approaches often require sophisticated methods for precise background estimation, unsupervised methods...
The High Luminosity upgrade for the Large Hadron Collider (HL-LHC) is due to come online in 2029. This will result in an unprecedented throughput of collision event data. Identifying and analysing meaningful signals within this information poses a formidable challenge in the search for new physics. The demand for automatic tools capable of physically-aware and data-driven inference, which can...
The Data-Directed paradigm (DDP) represents an innovative approach to efficiently investigate new physics across diverse spectra, which are in the presence of smoothly falling Standard Model (SM) backgrounds. Diverging from the conventional analysis employed in collider particle physics, DDP eliminates the necessity for a simulated or functionally derived background estimate. Instead, it...
