arXiv subjects

Carlo Schiavi

Publications and source records attributed to Carlo Schiavi.

5 recordsLinked to original sources

arXiv2026

Filtering hits for speeding up online track reconstruction at hadron colliders

Collider experiments are equipped with trigger systems that rapidly inspect the physics content emerging from collisions to decide whether the resulting products are worth saving for later analysis. One crucial aspect for analyzing the final states originating from the collisions is to process the information produced by charged particles in the innermost detectors to reconstruct the corresponding trajectories. This task is a challenge for the experiments running at the Large Hadron Collider (LHC) at CERN because of the large number of secondary collisions per bunch crossing, the so-called pile-up vertices, giving rise to extremely high hit occupancies in the detector layers close to the beam line. Reconstructing tracks is a combinatorial problem and its processing time strongly depends on the average pile-up per event. The future accelerator-complex upgrade to the High-Luminosity LHC, implying even higher detector occupancies, will result in a considerable growth of the computational cost of the current trigger strategies. To face this issue, a new technique for assisting track reconstruction by filtering out unnecessary detector information is presented and characterized in this work. The algorithm is based on a convolutional-neural-network architecture which can be easily deployed on accelerator cards. The impact of this approach is assessed and future prospects are also discussed.

hep-ex↗

arXiv2026

Learning to Reconstruct: A Differentiable Approach to Muon Tracking at the LHC

Reconstructing the trajectories of charged particles in high-energy collisions requires high precision to ensure reliable event reconstruction and accurate downstream physics analyses. In particular, both precise hit selection and transverse momentum estimation are essential to improve the overall resolution of reconstructed physics observables. Enhanced momentum resolution also enables more efficient trigger threshold settings, leading to more effective data selection within the given data acquisition constraints. In this paper, we introduce a novel end-to-end tracking approach that employs the differentiable programming paradigm to incorporate physics priors directly into a machine learning model. This results in an optimized pipeline capable of simultaneously reconstructing tracks and accurately determining their transverse momenta. The model combines a graph attention network with differentiable clustering and fitting routines, and is trained using a composite loss that, due to its differentiable design, allows physical constraints to be back-propagated effectively through both the neural network and the fitting procedures. This proof of concept shows that introducing differentiable connections within the reconstruction process improves overall performance compared to an equivalent factorized and more standard-like approach, highlighting the potential of integrating physics information through differentiable programming.

hep-ex↗

arXiv2024

Accelerating Graph-based Tracking Tasks with Symbolic Regression

The reconstruction of particle tracks from hits in tracking detectors is a computationally intensive task due to the large combinatorics of detector signals. Recent efforts have proven that ML techniques can be successfully applied to the tracking problem, extending and improving the conventional methods based on feature engineering. However, complex models can be challenging to implement on heterogeneous trigger systems, integrating architectures such as FPGAs. Deploying the network on an FPGA is feasible but challenging and limited by its resources. An efficient alternative can employ symbolic regression (SR). We propose a novel approach that uses SR to replace a graph-based neural network. Substituting each network block with a symbolic function preserves the graph structure of the data and enables message passing. The technique is perfectly suitable for heterogeneous hardware, as it can be implemented more easily on FPGAs and grants faster execution times on CPU with respect to conventional methods. While the tracking problem is the target for this work, it also provides a proof-of-principle for the method that can be applied to many use cases.

hep-ex↗

arXiv2021

Commissioning and improvements of the instrumentation and launch of the scientific exploitation of OARPAF, the Regional Astronomical Observatory of the Antola Park

The \oarpaf telescope is an $80\rm cm$-diameter optical telescope installed in the Antola Mount Regional Reserve, in Northern Italy. This work presents the results of the characterization of the site, as well as developments and interventions that have been implemented, with the goal of exploiting the facility for scientific and educational purposes. During the characterization of the site, an average background brightness of $22.40 \, m_{AB}$ ($B$ filter) -- $21.14 \, m_{AB}$ ($I$) per arcsecond squared, and a $1.5$--$3.0"$ seeing, have been measured. An estimate of the magnitude zero points for photometry is also reported. The material under commissioning includes 3 CCD detectors for which we provide the linearity range, gain, and dark current; a 31 orders échelle Northern Italy. This work presents the results of the characterization of the site, as well as developments andspectrograph with $R\sim 8500$--$15000$, and a dispersion of $n = 1.39\times 10^{-6}\rm px^{-1}λ+ 1.45\times 10^{-4}\rm nm/px$, where $λ$ is expressed in $\rm nm$. The scientific and outreach potential of the facility is proven in different science cases, such as exoplanetary transits and active galactic nuclei variability. The determination of time delays of gravitationally lensed quasars, the microlensing phenomenon and the tracking and the study of asteroids are also discussed as prospective science cases.

astro-ph.IM↗

arXiv2014

Standard Model updates and new physics analysis with the Unitarity Triangle fit

We present here the update of the Unitarity Triangle (UT) analysis performed by the UTfit Collaboration within the Standard Model (SM) and beyond. Continuously updated flavour results contribute to improving the precision of several constraints and through the global fit of the CKM parameters and the SM predictions. We also extend the UT analysis to investigate new physics (NP) effects on $ΔF=2$ processes. Finally, based on the NP constraints, we derive upper bounds on the coefficients of the most general $ΔF=2$ effective Hamiltonian. These upper bounds can be translated into lower bounds on the scale of NP that contributes to these low-energy effective interactions.

hep-ph↗