|
|
ATLAS Collaboration(Aad, G. et al), Aikot, A., Amos, K. R., Bouchhar, N., Cabrera Urban, S., Cantero, J., et al. (2026). Observation of t(t)over-barγγ production at √s=13 TeV with the ATLAS detector. Phys. Lett. B, 874, 140195–19pp.
Abstract: This paper presents the first observation of top-quark pair production in association with two photons (t (t) over bar gamma gamma). The measurement is performed in the single-lepton decay channel using proton-proton collision data collected by the ATLAS detector at the Large Hadron Collider. The data correspond to an integrated luminosity of 140 fb(-1) recorded during Run 2 at a centre-of-mass energy of 13 TeV. The t (t) over bar gamma gamma production cross section, measured in a fiducial phase space based on particle-level kinematic criteria for the lepton, photons, and jets, is found to be 2.42(-0.53)(+0.58) fb, corresponding to an observed significance of 5.2 standard deviations. Additionally, the ratio of the production cross section of t (t) over bar gamma gamma. to top-quark pair production in association with one photon is determined, yielding (3.30(-0.65)(+0.70)) x 10(-3).
|
|
|
|
Hervas Alvarez, F., Valero, A., Fiorini, L., Gutierrez Arance, H., Carrio, F., Ahuja, S., et al. (2025). Versal Adaptive Compute Acceleration Platform Processing for ATLAS-TileCal Signal Reconstruction. Particles, 8(2), 49–9pp.
Abstract: Particle detectors at accelerators generate large amounts of data, requiring analysis to derive insights. Collisions lead to signal pile-up, where multiple particles produce signals in the same detector sensors, complicating individual signal identification. This contribution describes the implementation of a deep-learning algorithm on a Versal Adaptive Compute Acceleration Platform (ACAP) device for improved processing via parallelization and concurrency. Connected to a host computer via Peripheral Component Interconnect express (PCIe), this system aims for enhanced speed and energy efficiency over Central Processing Units (CPUs) and Graphics Processing Units (GPUs). In the contribution, we will describe in detail the data processing and the hardware, firmware and software components of the system. The contribution presents the implementation of the deep-learning algorithm on a Versal ACAP device, as well as the system for transferring data in an efficient way.
|
|
