arXiv Physics @arxiv_physics@qoto.org

Over the next decade, increases in instantaneous luminosity and detector granularity will amplify the amount of data that has to be analysed by high-energy physics experiments, whether in real time or offline, by an order of magnitude. The reconstruction of charged particle tracks, which has always been a crucial element of offline data processing pipelines, must increasingly be deployed from the very first stages of the real time processing to enable experiments to achieve their physics goals. Graph Neural Networks (GNNs) have received a great deal of attention in the community because their computational complexity scales nearly linearly with the number of hits in the detector, unlike conventional algorithms which often scale quadratically or worse. This paper presents ETX4VELO, a GNN-based track-finding pipeline tailored for the Run 3 LHCb experiment's Vertex Locator, in the context of LHCb's fully GPU-based first-level trigger system, Allen. Currently implemented in Python, ETX4VELO offers the ability to reconstruct tracks with shared hits using a novel triplet-based method. When benchmarked against the traditional track-finding algorithm in Allen, this GNN-based approach not only matches but occasionally surpasses its physics performance. In particular, the fraction of fake tracks is reduced from over 2\% to below 1\% and the efficiency to reconstruct electrons is improved. While achieving comparable physics performance is a milestone, the immediate priority remains implementing ETX4VELO in Allen in order to determine and optimise its throughput, to meet the demands of this high-rate environment.

COSINUS is a dark matter (DM) direct search experiment that uses sodium iodide (NaI) crystals as cryogenic calorimeters. Thanks to the low nuclear recoil energy threshold and event-by-event discrimination capability, COSINUS will address the long-standing DM claim made by the DAMA/LIBRA collaboration. The experiment is currently under construction at the Laboratori Nazionali del Gran Sasso, Italy, and employs a large cylindrical water tank as a passive shield to meet the required background rate. However, muon-induced neutrons can mimic a DM signal therefore requiring an active veto system, which is achieved by instrumenting the water tank with an array of photomultiplier tubes (PMTs). This study optimizes the number, arrangement, and trigger conditions of the PMTs as well as the size of an optically invisible region. The objective was to maximize the muon veto efficiency while minimizing the accidental trigger rate due to the ambient and instrumental background. The final configuration predicts a veto efficiency of 99.63 $\pm$ 0.16 $\%$ and 44.4 $\pm$ $5.6\%$ in the tagging of muon events and showers of secondary particles, respectively. The active veto will reduce the cosmogenic neutron background rate to 0.11 $\pm$ 0.02 cts$\cdot$kg$^{-1}$$\cdot$year$^{-1}$, corresponding to less than one background event in the region of interest for the whole COSINUS-1$π$ exposure of 1000 kg$\cdot$days.

Research on aging processes of Gas Electron Multipliers (GEMs) is important to obtain insights on how to increase detector's longevity, stability and performance, as highlighted in the latest developments roadmap by the European Council of Future Accelerators (ECFA). In this work, through the analysis of the molecular content on the surface of a used GEM, we provide evidences for polyimide redeposition as a source of organic material contributing to the formation of insulating layers on the electrodes, which eventually lead to sparks and detector failure. Furthermore, we show that chromium, used to promote adhesion between copper and polyimide, in the device undergoes a diffusion process, effectively blurring the layered structure. We demonstrate the significance of surface-sensitive chemical analysis to investigate the aging process of gaseous detectors and our results reveal the necessity of standardization and more stringent study protocols.

LUX-ZEPLIN (LZ) is a tonne-scale experiment searching for direct dark matter interactions and other rare events. It is located at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, USA. The core of the LZ detector is a dual-phase xenon time projection chamber (TPC), designed with the primary goal of detecting Weakly Interacting Massive Particles (WIMPs) via their induced low energy nuclear recoils. Surrounding the TPC, two veto detectors immersed in an ultra-pure water tank enable reducing background events to enhance the discovery potential. Intricate calibration systems are purposely designed to precisely understand the responses of these three detector volumes to various types of particle interactions and to demonstrate LZ's ability to discriminate between signals and backgrounds. In this paper, we present a comprehensive discussion of the key features, requirements, and performance of the LZ calibration systems, which play a crucial role in enabling LZ's WIMP-search and its broad science program. The thorough description of these calibration systems, with an emphasis on their novel aspects, is valuable for future calibration efforts in direct dark matter and other rare-event search experiments.

The Global Event Processor (GEP) FPGA is an area-constrained, performance-critical element of the Large Hadron Collider's (LHC) ATLAS experiment. It needs to very quickly determine which small fraction of detected events should be retained for further processing, and which other events will be discarded. This system involves a large number of individual processing tasks, brought together within the overall Algorithm Processing Platform (APP), to make filtering decisions at an overall latency of no more than 8ms. Currently, such filtering tasks are hand-coded implementations of standard deterministic signal processing tasks. In this paper we present methods to automatically create machine learning based algorithms for use within the APP framework, and demonstrate several successful such deployments. We leverage existing machine learning to FPGA flows such as hls4ml and fwX to significantly reduce the complexity of algorithm design. These have resulted in implementations of various machine learning algorithms with latencies of 1.2us and less than 5% resource utilization on an Xilinx XCVU9P FPGA. Finally, we implement these algorithms into the GEP system and present their actual performance. Our work shows the potential of using machine learning in the GEP for high-energy physics applications. This can significantly improve the performance of the trigger system and enable the ATLAS experiment to collect more data and make more discoveries. The architecture and approach presented in this paper can also be applied to other applications that require real-time processing of large volumes of data.

We present a design for a high-granularity zero-degree calorimeter (ZDC) for the upcoming Electron-Ion Collider (EIC). The design uses SiPM-on-tile technology and features a novel staggered-layer arrangement that improves spatial resolution. To fully leverage the design's high granularity and non-trivial geometry, we employ graph neural networks (GNNs) for energy and angle regression as well as signal classification. The GNN-boosted performance metrics meet, and in some cases, significantly surpass the requirements set in the EIC Yellow Report, laying the groundwork for enhanced measurements that will facilitate a wide physics program. Our studies show that GNNs can significantly enhance the performance of high-granularity CALICE-style calorimeters by automating and optimizing the software compensation algorithms required for these systems. This improvement holds true even in the case of complicated geometries that pose challenges for image-based AI/ML methods.

The half-size prototype of the multi wire drift chamber (MWDC) for the cooling storage ring (CSR) external-target experiment (CEE) was assembled and tested in 350 MeV/u Kr+Fe reactions on the heavy ion research facility in Lanzhou (HIRFL). The prototype consists of 6 sense layers, where the sense wires are stretched in three directions X, U and V, meeting $0^\circ$, $30^\circ$ and $-30^\circ$ with respect to the vertical axis, respectively. The sensitive area of the prototype is $76 {\rm cm} \times 76 {\rm cm}$. The amplified and shaped signals from the anode wires are digitized in a serial capacity array. Being operated with 1500 V high voltage on the anode wires, the efficiency for each layer is beyond 95\%. The tracking residual is about $301 \pm 2 \rm μm$. The performance meets the requirements of CEE.

Identification of particles generated by ion collisions in the NICA collider is one of the basic functions of the Multipurpose Detector (MPD). The main means of identification in MPD are the time-of-flight system (TOF) and the time-projection chamber (TPC). The article considers the optimization of the algorithms of particles identification by these systems. Under certain conditions, the use of the statistical Bayesian approach has made it possible to achieve an optimal ratio of the efficiency of particle identification and contamination by incorrectly defined particles.

The CODEX-$β$ apparatus is a demonstrator for the proposed future CODEX-b experiment, a long-lived-particle detector foreseen for operation at IP8 during HL-LHC data-taking. The demonstrator project, intended to collect data in 2025, is described, with a particular focus on the design, construction, and installation of the new apparatus.

Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of logbook entries can hinder their usability and automation. As natural language processing (NLP) continues advancing, it offers opportunities to address various challenges that logbooks present. This work explores jointly testing a tailored Retrieval Augmented Generation (RAG) model for enhancing the usability of particle accelerator logbooks at institutes like DESY, BESSY, Fermilab, BNL, SLAC, LBNL, and CERN. The RAG model uses a corpus built on logbook contributions and aims to unlock insights from these logbooks by leveraging retrieval over facility datasets, including discussion about potential multimodal sources. Our goals are to increase the FAIR-ness (findability, accessibility, interoperability, and reusability) of logbooks by exploiting their information content to streamline everyday use, enable macro-analysis for root cause analysis, and facilitate problem-solving automation.

Earth global and regional effective thermal conductance G(eff) (in (W/m^2)/C and often labeled lambda in climate research) and the related Equilibrium Climate Sensitivity (ECS) are evaluated by applying a modified version of the Energy Budget method, and using data only after 1970. By removing Periodic Interfering temperature components (using a novel PIR process) and applying high frequency filtering, an extraordinarily near linear temperature response is revealed, enhancing accurate G(eff) calculation and avoiding the pre-1970 aerosol forcing and ocean energy per area (E*) absorption uncertainties. A formal/empirical method is used to determine more reliable values of Q(t)=d[E*(Ocean.energy)]/dt . Using NOAA data, and after PIR, it is shown that: 1) The Energy Budget Method can be realistically applied to the Ocean and Land regions independently, 2) the effective volcanic forcing is <= 1/5 the IPCC AR5 estimate, 3) the "historical" 1980-2020 ECS(eff) values for Global, global Ocean, and global Land regions values are <= 2.16, 1.69, 2.96 C/2xCO2 respectively; where the updated IPCC AR5 orthodox independent global Forcing value of 0.4 (W/m^2)/Decade and F/2xCO2= 3.7 W/m^2 were used. The Global average ECS(true) value of <= 2.10 C is 70% of the IPCC AR6 ECS estimate of 3.0 C, but 127% of the ECS(eff) value reported by Lewis (1.66 C) . The estimated oceans average TCR/ECS ratio = 0.71 and the global average TCR/ECS ratio = 0.83, and ECS(land)/ECS(Ocean) = 1.78 . [Results using HADCRUT temperature data instead are similar, but 6% "cooler" over land, and 8% "warmer" over oceans.] A simplified physically realistic formal/empirical Coarse 2-D Global Climate Model is derived wherein variation of Geff(t) until equilibrium (i.e. "pattern effects") are proven to be negligible or "cooling", using these Methods. And so it is likely ECS(true) <= ECS(eff).

Due to the unavailability of solar irradiance data for many potential sites of Nepal, the paper proposes predicting solar irradiance based on alternative meteorological parameters. The study focuses on five distinct regions in Nepal and utilizes a dataset spanning almost ten years, obtained from CERES SYN1deg and MERRA-2. Machine learning models such as Random Forest, XGBoost, K-Nearest Neighbors, and deep learning models like LSTM and ANN-MLP are employed and evaluated for their performance. The results indicate high accuracy in predicting solar irradiance, with R-squared(R2) scores close to unity for both train and test datasets. The impact of parameter integration on model performance is analyzed, revealing the significance of various parameters in enhancing predictive accuracy. Each model demonstrates strong performance across all parameters, consistently achieving MAE values below 6, RMSE values under 10, MBE within |2|, and nearly unity R2 values. Upon removal of various solar parameters such as "Solar_Irradiance_Clear_Sky", "UVA", etc. from the datasets, the model's performance is significantly affected. This exclusion leads to considerable increases in MAE, reaching up to 82, RMSE up to 135, and MBE up to |7|. Among the models, KNN displays the weakest performance, with an R2 of 0.7582546. Conversely, ANN exhibits the strongest performance, boasting an R2 value of 0.9245877. Hence, the study concludes that Artificial Neural Network (ANN) performs exceptionally well, showcasing its versatility even under sparse data parameter conditions.

The optimal fingerprint method serves as a potent approach for detecting and attributing climate change. However, its experimental validation encounters challenges due to the intricate nature of climate systems. Here, we experimentally examine the optimal fingerprint method simulated by a precisely controlled magnetic resonance system of spins. The spin dynamic under an applied deterministic driving field and a noise field is utilized to emulate the complex climate system with external forcing and internal variability. Our experimental results affirm the theoretical prediction regarding the existence of an optimal detection direction which maximizes the signal-to-noise ratio, thereby validating the optimal fingerprint method. This work offers direct empirical verification of the optimal fingerprint method, crucial for comprehending climate change and its societal impacts.

We develop a three-timescale framework for modelling climate change and introduce a space-heterogeneous one-dimensional energy balance model. This model, addressing temperature fluctuations from rising carbon dioxide levels and the super-greenhouse effect in tropical regions, fits within the setting of stochastic reaction-diffusion equations. Our results show how both mean and variance of temperature increase, without the system going through a bifurcation point. This study aims to advance the conceptual understanding of the extreme weather events frequency increase due to climate change.

Numerical model forecasts of near-surface temperatures are prone to error. This is because terrain can exert a strong influence on temperature that is not captured in numerical weather models due to spatial resolution limitations. To account for the terrain height difference between the forecast model and reality, temperatures are commonly corrected using a vertical adjustment based on a fixed lapse rate. This, however, ignores the fact that true lapse rates vary from 1.2 K temperature drop per 100 m of ascent to more than 10 K temperature rise over the same vertical distance. In this work, we develop topographic visualization techniques to assess the resulting uncertainties in near-surface temperatures and reveal relationships between those uncertainties, features in the resolved and unresolved topography, and the temperature distribution in the near-surface atmosphere. Our techniques highlight common limitations of the current lapse rate scheme and hint at their topographic dependencies in the context of the prevailing weather conditions. Together with scientists working in postprocessing and downscaling of numerical model output, we use these findings to develop an improved lapse rate scheme. This model adapts to both the topography and the current weather situation. We examine the quality and physical consistency of the new estimates by comparing them with station observations around the world and by including visual representations of radiation-slope interactions.

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.

Gamification of quantum theory can provide new inroads into the subject: by allowing users to experience simulated worlds that manifest obvious quantum behaviors they can potentially build intuition for quantum phenomena. The Qubit Factory is an engineering-style puzzle game based on a gamified quantum circuit simulator that is designed to provide an introduction to qubits and quantum computing, while being approachable to those with no prior background in the area. It introduces an intuitive visual language for representing quantum states, gates and circuits, further enhanced by animations to aid in visualization. The Qubit Factory presents a hierarchy of increasingly difficult tasks for the user to solve, where each task requires the user to construct and run an appropriate classical/quantum circuit built from a small selection of components. Earlier tasks cover the fundamentals of qubits, quantum gates, superpositions and entanglement. Later tasks cover important quantum algorithms and protocols including superdense coding, quantum teleportation, entanglement distillation, classical and quantum error correction, state tomography, the Bernstein-Vazirani algorithm, quantum repeaters and more.

Coarse-graining is a molecular modeling technique in which an atomistic system is represented in a simplified fashion that retains the most significant system features that contribute to a target output, while removing the degrees of freedom that are less relevant. This reduction in model complexity allows coarse-grained molecular simulations to reach increased spatial and temporal scales compared to corresponding all-atom models. A core challenge in coarse-graining is to construct a force field that represents the interactions in the new representation in a way that preserves the atomistic-level properties. Many approaches to building coarse-grained force fields have limited transferability between different thermodynamic conditions as a result of averaging over internal fluctuations at a specific thermodynamic state point. Here, we use a graph-convolutional neural network architecture, the Hierarchically Interacting Particle Neural Network with Tensor Sensitivity (HIP-NN-TS), to develop a highly automated training pipeline for coarse grained force fields which allows for studying the transferability of coarse-grained models based on the force-matching approach. We show that this approach not only yields highly accurate force fields, but also that these force fields are more transferable through a variety of thermodynamic conditions. These results illustrate the potential of machine learning techniques such as graph neural networks to improve the construction of transferable coarse-grained force fields.

We investigate the morphodynamics of an ice layer over a turbulent stream of warm water using numerical simulations. At low water speeds, characteristic streamwise undulations appear, which can be explained by the Reynolds analogy between heat and momentum transfer. As the water speed increases, these undulations combine with spanwise ripples of a much greater length scale. These ripples are generated by a melting mechanism controlled by the instability originating from the ice-water interactions, and, through a melting/freezing process, they evolve downstream with a migration velocity much slower than the turbulence characteristic velocity.

These notes are an introduction to fluctuating hydrodynamics (FHD) and the formulation of numerical schemes for the resulting stochastic partial differential equations (PDEs). Fluctuating hydrodynamics was originally introduced by Landau and Lifshitz as a way to put thermal fluctuations into a continuum framework by including a stochastic forcing to each dissipative transport process (e.g., heat flux). While FHD has been useful in modeling transport and fluid dynamics at the mesoscopic scale, theoretical calculations have been feasible only with simplifying assumptions. As such there is great interest in numerical schemes for Computational Fluctuating Hydrodynamics (CFHD). There are a variety of algorithms (e.g., spectral, finite element, lattice Boltzmann) but in this introduction we focus on finite volume schemes. Accompanying these notes is a demonstration program in Python available on GitHub.