ATLAS Collaboration(Aad, G. et al), Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Bouchhar, N., Cabrera Urban, S., et al. (2024). Deep Generative Models for Fast Photon Shower Simulation in ATLAS. Comput. Softw. Big Sci., 8, 7–40pp.
Abstract: The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using GEANT4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.
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ATLAS Collaboration(Aad, G. et al), Aikot, A., Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Bouchhar, N., et al. (2024). Software Performance of the ATLAS Track Reconstruction for LHC Run 3. Comput. Softw. Big Sci., 8, 9–32pp.
Abstract: Charged particle reconstruction in the presence of many simultaneous proton–proton (pp) collisions in the LHC is a challenging task for the ATLAS experiment’s reconstruction software due to the combinatorial complexity. This paper describes the major changes made to adapt the software to reconstruct high-activity collisions with an average of 50 or more simultaneous pp interactions per bunch crossing (pile-up) promptly using the available computing resources. The performance of the key components of the track reconstruction chain and its dependence on pile-up are evaluated, and the improvement achieved compared to the previous software version is quantified. For events with an average of 60 pp collisions per bunch crossing, the updated track reconstruction is twice as fast as the previous version, without significant reduction in reconstruction efficiency and while reducing the rate of combinatorial fake tracks by more than a factor two.
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Montani, G., De Angelis, M., Bombacigno, F., & Carlevaro, N. (2024). Metric f(R) gravity with dynamical dark energy as a scenario for the Hubble tension. Mon. Not. Roy. Astron. Soc., 527, L156–L161.
Abstract: We introduce a theoretical framework to interpret the Hubble tension, based on the combination of a metric f(R) gravity with a dynamical dark energy contribution. The modified gravity provides the non-minimally coupled scalar field responsible for the proper scaling of the Hubble constant, in order to accommodate for the local SNIa pantheon+ data and Planck measurements. The dynamical dark energy source, which exhibits a phantom divide line separating the low redshift quintessence regime (−1 < w < −1/3) from the phantom contribution (w < −1) in the early Universe, guarantees the absence of tachyonic instabilities at low redshift. The resulting H0(z) profile rapidly approaches the Planck value, with a plateau behaviour for z ≳ 5. In this scenario, the Hubble tension emerges as a low redshift effect, which can be in principle tested by comparing SNIa predictions with far sources, like QUASARS and gamma ray bursts.
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Barenboim, G., Del Debbio, L., Hirn, J., & Sanz, V. (2024). Exploring how a generative AI interprets music. Neural Comput. Appl., 36, 17007–17022.
Abstract: We aim to investigate how closely neural networks (NNs) mimic human thinking. As a step in this direction, we study the behavior of artificial neuron(s) that fire most when the input data score high on some specific emergent concepts. In this paper, we focus on music, where the emergent concepts are those of rhythm, pitch and melody as commonly used by humans. As a black box to pry open, we focus on Google’s MusicVAE, a pre-trained NN that handles music tracks by encoding them in terms of 512 latent variables. We show that several hundreds of these latent variables are “irrelevant” in the sense that can be set to zero with minimal impact on the reconstruction accuracy. The remaining few dozens of latent variables can be sorted by order of relevance by comparing their variance. We show that the first few most relevant variables, and only those, correlate highly with dozens of human-defined measures that describe rhythm and pitch in music pieces, thereby efficiently encapsulating many of these human-understandable concepts in a few nonlinear variables.
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Martin-Luna, P., Bonatto, A., Bontoiu, C., Xia, G., & Resta-Lopez, J. (2024). Plasmonic excitations in double-walled carbon nanotubes. Results Phys., 60, 107698–11pp.
Abstract: The interactions of charged particles moving paraxially in multi-walled carbon nanotubes (MWCNTs) may excite electromagnetic modes. This wake effect has recently been proposed as a potential novel method of short-wavelength high-gradient particle acceleration. In this work, the excitation of wakefields in double-walled carbon nanotubes (DWCNTs) is studied by means of the linearized hydrodynamic theory. General expressions have been derived for the excited longitudinal and transverse wakefields and related to the resonant wavenumbers which can be obtained from the dispersion relation. In the absence of friction, the stopping power of the wakefield driver, modelled here as a charged macroparticle, can be written solely as a function of these resonant wavenumbers. The dependencies of the wakefields on the radii of the DWCNT and the driving velocity have been studied. DWCNTs with inter-wall distances much smaller than the internal radius may be a potential option to obtain higher wakefields for particle acceleration compared to single-walled carbon nanotubes (SWCNTs).
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