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The Super Proton Synchrotron (SPS) is the last stage in the injector chain for CERN's Large Hadron Collider, and it also provides proton and ion beams for several fixed-target experiments. The SPS has been in operation since 1976, and it has been upgraded over the years. For the SPS to operate safely, its internal beam dump must be able to repeatedly absorb the energy of the circulating beams without sustaining damage that would affect its function. The latest upgrades of the SPS led to the requirement for its beam dump to absorb proton beams with a momentum spectrum from 14 to 450~GeV/$c$ and an average beam power up to $\sim$270~kW. This paper presents the technical details of a new design of SPS beam dump that was installed in one of the long straight sections of the SPS during the 2019--2020 shutdown of CERN's accelerator complex. This new beam dump has been in operation since May 2021, and it is foreseen that it will operate with a lifetime of 20~years. The key challenges in the design of the beam dump were linked to the high levels of thermal energy to be dissipated -- to avoid overheating and damage to the beam dump itself -- and high induced levels of radiation, which have implications for personnel access to monitor the beam dump and repair any problems occurring during operation. The design process therefore included extensive thermomechanical finite-element simulations of the beam-dump core and its cooling system's response to normal operation and worst-case scenarios for beam dumping. To ensure high thermal conductivity between the beam-dump core and its water-cooling system, hot isostatic pressing techniques were used in its manufacturing process. A comprehensive set of instrumentation was installed in the beam dump to monitor it during operation and to cross-check the numerical models with operational feedback., Comment: 20 pages, 36 figures, submitted to Phys. Rev. Accel. Beams

Stars can be ripped apart by tidal forces in the vicinity of a massive black hole (MBH), causing luminous flares known as tidal disruption events (TDEs). These events could be contributing to the mass growth of intermediate-mass MBHs, and new samples from transient surveys can provide useful information on this growth channel. This work aims to study the demographics of TDEs by modeling the co-evolution of MBHs and their galactic environments in a cosmological framework. We use the semi-analytic galaxy formation model \emph{L-Galaxies}BH, which follows the evolution of galaxies as well as of MBHs, including multiple scenarios for MBH seeds and growth, spin evolution, and binary MBH dynamics. Time-dependent TDE rates are associated with each MBH depending on the stellar environment, following the solutions to the 1-D Fokker Planck equation solved with \textsc{PhaseFlow}. Our model produces volumetric rates that are in agreement with the latest optical and previous X-ray samples. This agreement requires a high occupation fraction of nuclear star clusters with MBHs since these star reservoirs host the majority of TDEs at all mass regimes. We predict that TDE rates are an increasing function of MBH mass up to $\sim\, 10^{5.5}$M$_\odot$, beyond which the distribution flattens and eventually drops for $>\,10^{7}$M$_\odot$. In general, volumetric rates are predicted to be redshift-independent at $z\,{<}\,1$. We discuss how the spin distribution of MBHs around the event horizon suppression can be constrained via TDE rates and what is the average contribution of TDEs to the MBH growth. In our work, the majority of low-mass galaxies host nuclear star clusters that have their loss-cone depleted by $z\,=\,0$, explaining why TDEs are rare in these systems. This highlights that time-dependent TDE rates are essential for any model to be in good agreement with observations at all mass regimes., Comment: 26 pages, 11 figures

LiMn2O4 (LMO), cathodes present large stability when cycled in aqueous electrolytes, contrasting its behavior in conventional organic electrolytes in Lithium-ion batteries (LIBs). To elucidate the mechanisms underlying this distinctive behavior, we employ unconventional characterization techniques, including Variable Energy Positron Annihilation Lifetime Spectroscopy (VEPALS), Tip-Enhanced Raman Spectroscopy (TERS) and macro-Raman Spectroscopy (with mm-size laser spot). These still rather unexplored techniques in the battery field provide complementary information across different length scales, revealing previously hidden features. VEPALS offers atomic-scale insights, uncovering cationic defects and sub-nanometer pores that tend to collapse with cycling. TERS, operating at the nanometric range at the surface, captured the presence of Mn3O4 and its dissolution with cycling, elucidating dynamic changes during operation. Additionally, TERS highlights SO42- accumulation at grain boundaries. Macro-Raman Spectroscopy focuses on the micrometer scale, depicting small changes in the cathode's long-range order, suggesting a slow but progressive loss of crystalline quality under operation. Integrating these techniques provides a comprehensive assessment of LMO cathode stability in aqueous electrolytes, offering multifaceted insights into phase and defect evolution that can help to rationalize the origin of such stability when compared to conventional organic electrolytes. Our findings advance the understanding of LMO behavior in aqueous environments and provide guidelines for its development for next-generation LIBs.

We present the new Guided Moments ($\texttt{GM}$) formalism for neutrino modeling in astrophysical scenarios like core-collapse supernovae and neutron star mergers. The truncated moments approximation ($\texttt{M1}$) and Monte-Carlo ($\texttt{MC}$) schemes have been proven to be robust and accurate in solving the Boltzmann's equation for neutrino transport. However, it is well-known that each method exhibits specific strengths and weaknesses in various physical scenarios. The $\texttt{GM}$ formalism effectively solves these problems, providing a comprehensive scheme capable of accurately capturing the optically thick limit through the exact $\texttt{M1}$ closure and the optically thin limit through a $\texttt{MC}$ based approach. In addition, the $\texttt{GM}$ method also approximates the neutrino distribution function with a reasonable computational cost, which is crucial for the correct estimation of the different neutrino-fluid interactions. Our work provides a comprehensive discussion of the formulation and application of the $\texttt{GM}$ method, concluding with a thorough comparison across several test problems involving the three schemes ($\texttt{M1}$, $\texttt{MC}$, $\texttt{GM}$) under consideration., Comment: 17+3 pages, 14 figures. Accepted in PRD

This study presents the outcomes of empirical investigations pertaining to human-vehicle interactions involving an autonomous vehicle equipped with both internal and external Human Machine Interfaces (HMIs) within a crosswalk scenario. The internal and external HMIs were integrated with implicit communication techniques, incorporating a combination of gentle and aggressive braking maneuvers within the crosswalk. Data were collected through a combination of questionnaires and quantifiable metrics, including pedestrian decision to cross related to the vehicle distance and speed. The questionnaire responses reveal that pedestrians experience enhanced safety perceptions when the external HMI and gentle braking maneuvers are used in tandem. In contrast, the measured variables demonstrate that the external HMI proves effective when complemented by the gentle braking maneuver. Furthermore, the questionnaire results highlight that the internal HMI enhances passenger confidence only when paired with the aggressive braking maneuver., Comment: Submitted to the IEEE TIV; 13 pages, 13 figures, 7 tables. arXiv admin note: text overlap with arXiv:2307.12708

Prediction of vehicle lane change maneuvers has gained a lot of momentum in the last few years. Some recent works focus on predicting a vehicle's intention by predicting its trajectory first. This is not enough, as it ignores the context of the scene and the state of the surrounding vehicles (as they might be risky to the target vehicle). Other works assessed the risk made by the surrounding vehicles only by considering their existence around the target vehicle, or by considering the distance and relative velocities between them and the target vehicle as two separate numerical features. In this work, we propose a solution that leverages Knowledge Graphs (KGs) to anticipate lane changes based on linguistic contextual information in a way that goes well beyond the capabilities of current perception systems. Our solution takes the Time To Collision (TTC) with surrounding vehicles as input to assess the risk on the target vehicle. Moreover, our KG is trained on the HighD dataset using the TransE model to obtain the Knowledge Graph Embeddings (KGE). Then, we apply Bayesian inference on top of the KG using the embeddings learned during training. Finally, the model can predict lane changes two seconds ahead with 97.95% f1-score, which surpassed the state of the art, and three seconds before changing lanes with 93.60% f1-score.

This paper addresses the often overlooked issue of fairness in the autonomous driving domain, particularly in vision-based perception and prediction systems, which play a pivotal role in the overall functioning of Autonomous Vehicles (AVs). We focus our analysis on biases present in some of the most commonly used visual datasets for training person and vehicle detection systems. We introduce an annotation methodology and a specialised annotation tool, both designed to annotate protected attributes of agents in visual datasets. We validate our methodology through an inter-rater agreement analysis and provide the distribution of attributes across all datasets. These include annotations for the attributes age, sex, skin tone, group, and means of transport for more than 90K people, as well as vehicle type, colour, and car type for over 50K vehicles. Generally, diversity is very low for most attributes, with some groups, such as children, wheelchair users, or personal mobility vehicle users, being extremely underrepresented in the analysed datasets. The study contributes significantly to efforts to consider fairness in the evaluation of perception and prediction systems for AVs. This paper follows reproducibility principles. The annotation tool, scripts and the annotated attributes can be accessed publicly at https://github.com/ec-jrc/humaint_annotator., Comment: Paper submitted to the IEEE TIV journal; 17 pages, 16 figures, 10 tables

Constraining turbulence in disks is key towards understanding their evolution through the transport of angular momentum. Until now measurements of high turbulence have remained elusive and methods for estimating turbulence relay mostly on complex radiative transfer models of the data. Using the disk emission from IM Lup, a source proposed to be undergoing Magneto-Rotational Instabilities (MRI) and possibly have high turbulence values in the upper disk layers, we present a new way of directly measuring turbulence without need of radiative transfer or thermochemical models. Through the characterization of the CN and C$_2$H emission in IM Lup, we aim to connect the information on the vertical and thermal structure of a particular disk region to derive turbulence at that location. By using an optically thin tracer it is possible to directly measure turbulence from the non-thermal broadening of the line. The vertical layers of the CN and C$_2$H emission are traced directly from the channel maps using ALFAHOR. By comparing their position to that of optically thick CO observations we are able to characterize the kinetic temperature of the emitting region. Using a simple parametric model of the line intensity with DISCMINER we accurately measure the emission linewidth and separate the thermal and non-thermal components. Assuming that the non-thermal component is fully turbulent, we are able to directly estimate the turbulent motions at the studied radial and vertical location of CN emission. IM Lup shows high turbulence of Mach 0.4-0.6 at $z/r \sim$ 0.25. Considering previous estimates of low turbulence near the midplane, this may indicate a vertical gradient in the disk turbulence, which is a key prediction of MRI studies. CN and C$_2$H are both emitting from a localized upper disk region at $z/r =$0.2-0.3, in agreement with thermochemical models., Comment: Accepted for publication in A&A

Ensuring the reliability of power electronic converters is a matter of great importance, and data-driven condition monitoring techniques are cementing themselves as an important tool for this purpose. However, translating methods that work well in controlled lab environments to field applications presents significant challenges, notably because of the limited diversity and accuracy of the lab training data. By enabling the use of field data, online machine learning can be a powerful tool to overcome this problem, but it introduces additional challenges in ensuring the stability and predictability of the training processes. This work presents an edge computing method that mitigates these shortcomings with minimal additional memory usage, by employing an autonomous algorithm that prioritizes the storage of training samples with larger prediction errors. The method is demonstrated on the use case of a self-commissioning condition monitoring system, in the form of a thermal anomaly detection scheme for a variable frequency motor drive, where the algorithm self-learned to distinguish normal and anomalous operation with minimal prior knowledge. The obtained results, based on experimental data, show a significant improvement in prediction accuracy and training speed, when compared to equivalent models trained online without the proposed data selection process.

The task of Visual Place Recognition (VPR) aims to match a query image against references from an extensive database of images from different places, relying solely on visual cues. State-of-the-art pipelines focus on the aggregation of features extracted from a deep backbone, in order to form a global descriptor for each image. In this context, we introduce SALAD (Sinkhorn Algorithm for Locally Aggregated Descriptors), which reformulates NetVLAD's soft-assignment of local features to clusters as an optimal transport problem. In SALAD, we consider both feature-to-cluster and cluster-to-feature relations and we also introduce a 'dustbin' cluster, designed to selectively discard features deemed non-informative, enhancing the overall descriptor quality. Additionally, we leverage and fine-tune DINOv2 as a backbone, which provides enhanced description power for the local features, and dramatically reduces the required training time. As a result, our single-stage method not only surpasses single-stage baselines in public VPR datasets, but also surpasses two-stage methods that add a re-ranking with significantly higher cost. Code and models are available at https://github.com/serizba/salad.

Non-linear Sigma models involving U(1) symmetry group are studied using a geometrical formalism. In this type of models, Q-balls and Q-Kinks solutions are found. The geometrical framework described in this article allows the identification of the necessary conditions on the metric and the potential to guarantee the existence of these Q-balls and Q-Kinks. Using this procedure, Sigma models where both types of solutions coexist, have been identified. Only the internal rotational frequency distinguishes which one of these defects will arise., Comment: 15 pages, 9 figures

Massive black holes are key inhabitants of the nuclei of galaxies. Moreover, their astrophysical relevance has gained significant traction in recent years, thanks especially to the amazing results that are being (or will be) delivered by instruments such as the James Webb Space Telescope, Pulsar Timing Array projects and LISA. In this Chapter, we aim to detail a broad set of aspects related to the astrophysical nature of massive black holes embedded in galactic nuclei, with a particular focus on recent and upcoming advances in the field. In particular, we will address questions such as: What shapes the relations connecting the mass of massive black holes with the properties of their host galaxies? How do massive black holes form in the early Universe? What mechanisms keep on feeding them so that they can attain very large masses at z = 0? How do binaries composed of two massive black holes form and coalesce into a single, larger black hole? Here we present these topics from a mainly theoretical viewpoint and discuss how present and upcoming facilities may enhance our understanding of massive black holes in the near future., Comment: All authors equally contributed to the Chapter writing. To appear as Chapter 3 in the book "Black Holes in the Era of Gravitational Wave Astronomy", ed. Arca Sedda, Bortolas, Spera, pub. Elsevier

The flow of shape eigenmodes of the small fluctuation operator around BPS 2-vortex solutions is calculated, as a function of the intervortex separation $2d$. For the rotationally-invariant 2-vortex, with $d = 0$, there are three discrete modes; the lowest is non-degenerate and the upper two are degenerate. As $d$ increases, the degeneracy splits, with one eigenvalue increasing and entering the continuous spectrum, and the other decreasing and asymptotically coalescing with the lowest eigenvalue, where they jointly become the eigenvalue of the 1-vortex radial shape mode. The behaviour of the eigenvalues near $d=0$ is clarified using a perturbative analysis, and also in light of the 2-vortex moduli space geometry., Comment: 12 pages, 8 figures

The dynamics of a wobbling kink in a two-component coupled $\phi^4$ scalar field theory (with an excited orthogonal shape mode) is addressed. For this purpose, the vibration spectrum of the second order small kink fluctuation is studied in order to find the corresponding vibration modes associated to the first (longitudinal) and second (orthogonal) field components. By means of this analysis, it was found that the number of possible shape modes depends on the value of the coupling constant. It is notable that when one of the orthogonal field shape modes is initially triggered, the unique shape mode of the longitudinal field is also activated. This coupling causes the kink to emit radiation with twice the frequency of excited mode in the first field component. Meanwhile, in the orthogonal channel we find radiation with two different frequencies: one is three times the frequency of the orthogonal wobbling mode and another is the sum of the frequencies of the longitudinal shape mode and the triggered mode. All the analytical results obtained in this study have been successfully contrasted with those obtained through numerical simulations., Comment: 20 pages, 12 figures

Cobimaximal mixing predicts $\pi/4$ and $3\pi/2$ for the atmospheric angle and the Dirac CP-violating phase, respectively. These values are in tension with the neutrino globals fits. If this pattern was behind the lepton mixings, then it would have to be broken. In that case, in this paper, we explore the $\mathbf{S}_{3}$ flavor symmetry within the $B-L$ gauge model where the aforementioned scheme comes from the neutrino sector but the charged lepton contribution breaks the well known predictions so the mixing observables as well as the $m_{ee}$ mass can be accommodated quite well according to the available data. Notably, the predicted regions for the Dirac CP-violating phase would allow us to test the model in future experiments., Comment: 16 pages, 7 figures. Minor corrections, references added and the conclusions are unchanged. Version to be published in Revista Mexicana de F\'isica