Fellows

Clusters of galaxies are the largest, most massive virialized structures in the Universe, and highly dominated by dark matter (DM). According to the most accepted scenario, they should shine brightly in gamma-rays. This gamma-ray diffuse emission is expected from the decay of the neutral pions created from the hadronic interactions of cosmic ray protons; but also, from the annihilation and/or decay of DM particles. Despite the efforts in the last decades searching for this diffuse gamma-ray emission, such signal remained elusive. However, recent works strongly suggest that we may finally be on the verge of the first conclusive detection. My SMASH research project aims to investigate the challenges associated with the detection of diffuse gamma-rays from galaxy clusters and expand our understanding on the different processes that originate this emission. To achieve this ambitious goal, I will exploit at its maximum the data from existing gamma-ray telescopes (Fermi-LAT) and explore the capabilities of the future Cherenkov Telescope Array Observatory, using a Convolutional Neural Network based pipeline especially trained for the identification of faint sources (as clusters of galaxies) in complex environments, involving extended and overlapping sources. The use of this novel pipeline based on Machine Learning techniques will serve as benchmark for future CTA analyses and due to its inherent adaptability, will also aid solving other gamma-ray puzzles related to faint sources.

The need for highly accurate large-scale simulation of particle collisions at the Large Hadron Collider motivates the development of new simulation techniques. Generative deep learning algorithms have been successfully used in various scientific fields, including High Energy Physics. Generative adversarial networks are already applied in the simulation of ATLAS calorimeters. The aim of the project is to translate the idea on silicon tracking detectors in order to cope with the increasing number of sensitive detectors in the new ATLAS Inner Tracker built for High-Luminosity LHC.

The Galactic Center Excess (GCE), a GeV gamma-ray excess observed by the Fermi Large Area Telescope (Fermi LAT), has attracted significant interest as a potential dark matter (DM) signature. However, determining the GCE's true origin is hindered by complex astrophysical backgrounds and uncertainties. In this research project, I propose an innovative approach to deciphering the GCE by utilizing advanced statistical methods and machine learning techniques. My strategy encompasses modeling DM signals, investigating astrophysical backgrounds that impact DM searches, and analyzing gamma-ray data from both the Cherenkov Telescope Array (CTA) and Fermi LAT. I will employ simulation-based inference (SBI) to achieve the necessary flexibility in gamma-ray emission models and to address observational systematic uncertainties often neglected in current literature. A key component of my project is the development of an SBI gamma-ray analysis pipeline also suited for CTA datasets, maximizing the potential of current and next-generation instruments in gamma-ray astronomy. By elucidating the GCE and exploring non-thermal astrophysical processes in the Galactic center at GeV and TeV energies, this project aims to advance our understanding of DM and high-energy astrophysics. Simultaneously, it offers a novel approach to gamma-ray data analysis through the application of SBI.

The MEGLICA project aims to tackle a series of problems in the current state of the art of the aerosol science, and more in particular in the topic of the warming effect of absorbing aerosols on climate. In fact, the contribution of light absorbing aerosols (LAA), and in particular BC, the highest aerosol contributor to aerosol absorption, and one of the highest contributors overall, has presented 4-fold decrease on its effective radiative forcing on climate in the newest IPCC report (2021) in comparison with the previous one (2013). The large uncertainties still present prevent from correctly constraining their effect on climate. The MEGLICA proposal aims to reduce these uncertainties by accomplishing several steps for which it will use Machine learning as a tool to help with the analysis: first, we propose a novel calibration technique using the potential reference methodology PTI for the measurement of the aerosol absorption and BC concentration, then we intend to improve the LAA global spatial heterogeneity in 3-dimensions and determine the atmospheric heating rate due to their presence on the atmosphere, and finally, to use these improvements to model the LAA radiative effect on the climate.

Aerosolized Particulate Matter effects on human health and climate depend on its composition, hence proper classification and determination of sources are necessary for effective abatement. This is performed by source apportionment (SA), the process of identification of the sources of air pollution, usually conducted through receptor modelling (RM). The usual approach is decomposing the input measurements into the product of time evolution (time series) and chemical composition patterns (profiles) of pollutant sources. However, RMs struggle to provide well-separated profiles, leading in turn, to mixed time series contributions. To address this issue, we propose the integration of data science tools, including sparsity approaches, into novel and state-of-the-art RMs to aim for a better source differentiation. Performance assessment of RMs is usually conducted through synthetic datasets, but current approaches contain design biases or are of limited environmental reproducibility. In order to evaluate more accurately the RM performance and validate the RMs, our approach consists on creating new methodologies for synthetic dataset creation through machine learning from real-world measurements and modelling outcomes. In such way, we will reach the ultimate objective of this project, which is to upgrade SA techniques guaranteeing further source differentiation to improve the monitoring of atmospheric pollution composition and focused abatement of air pollution and climate change.

Exposure to ambient particulate matter is responsible for adverse health effects, while aerosol and climate feedback mechanisms are still obscured by large uncertainties, related to the vast temporal and spatial variability of ambient concentrations and aerosol sources. Source apportionment (SA) is an important research field, providing valuable information on the presence and variability of sources of particulate matter, acting as an essential tool for informed decision making aiming at abatement of atmospheric pollution through regulating specific polluting activities. SA of organic aerosol (OA) is commonly performed via positive matrix factorization (PMF) on OA mass spectra, obtained with aerosol mass spectrometric techniques. PMF suffers from certain caveats, i.e., non-unique solutions, significant level of subjectivity and modeler bias, bias in the source mass spectrum characterization, inability to follow varying number of sources present and demands significant computational time. In this project a novel method implementing a two-step machine learning OA SA technique will be developed, tackling the problems listed above. The technique is trained on an extensive library of deconvolved and source specific OA mass spectra and is evaluated on four distinct long-term ambient OA datasets, characteristic of both urban background and traffic conditions, and three datasets obtained in cities heavily polluted by sources like traffic, biomass burning and trash incineration.

Weather forecasts are subject to uncertainties originating from various factors, including initial condition uncertainties, model imperfections, and numerical errors. Data assimilation (DA) is a critical process in Numerical Weather Prediction (NWP) that fuses observations with model simulations to refine the initial atmospheric state for accurate forecasts. DA is computationally intensive, and traditional methods require reducing the computational space using linear methods. Neural Networks (NNs) could solve this challenge by enabling nonlinear reductions. Nevertheless, their application in NWP DA has remained limited so far. This proposal builds upon recent Neural Network DA (NNDA) advancements, particularly those using autoencoder NNs for dimension reduction. A novel approach leverages a Variational Autoencoder (VAE) convolutional NN, applying the 3D-Var cost function in the latent space, drastically reducing the background error covariance matrix dimensions. This innovation simplifies the inversion process, maintaining Gaussian properties in the latent space. This project focuses on constructing an encoder-decoder observation operator that models radar data from atmospheric model fields, allowing comparisons with observed data. The proposed approach aims to advance operational weather prediction by enabling more efficient DA and providing near-continuous estimates of atmospheric states, benefiting not only meteorology but also a wide range of scientific disciplines.