bluuebunny/crossref_metadata_2025 huggingface dataset:
Accelerating the Inference of the Exa.TrkX Pipeline
Authors: Lazar Alina , Ju Xiangyang , Murnane Daniel , Calafiura Paolo , Farrell Steven , Xu Yaoyuan , Spiropulu Maria , Vlimant Jean-Roch , Cerati Giuseppe , Gray Lindsey , Klijnsma Thomas , Kowalkowski Jim , Atkinson Markus , Neubauer Mark , DeZoort Gage , Thais Savannah , Hsu Shih-Chieh , Aurisano Adam , Hewes Jeremy , Ballow Alexandra , Acharya Nirajan , Wang Chun-yi , Liu Emma , Lucas Alberto
Published: February 2023

Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.TrkX pipeline based on GNNs demonstrated promising performance in reconstructing particle tracks in dense environments. It includes five discrete steps: data encoding, graph building, edge filtering, GNN, and track labeling. All steps were written in Python and run on both GPUs and CPUs. In this work, we accelerate the Python implementation of the pipeline through customized and commercial GPU-enabled software libraries, and develop a C++ implementation for inferencing the pipeline. The implementation features an improved, CUDA-enabled fixed-radius nearest neighbor search for graph building and a weakly connected component graph algorithm for track labeling. GNNs and other trained deep learning models are converted to ONNX and inferenced via the ONNX Runtime C++ API. The complete C++ implementation of the pipeline allows integration with existing tracking software. We report the memory usage and average event latency tracking performance of our implementation applied to the TrackML benchmark dataset.
Adding multi-core support to the ALICE Grid Middleware
Authors: Weisz Sergiu , Ferrer Marta Bertran
Published: February 2023

The major upgrade of the ALICE experiment for the LHC Run3 poses unique challenges and opportunities for new software development. In particular, the entirely new data taking and processing software of ALICE relies on process parallelism and large amounts of shared objects in memory. Thus from a single-core single thread workload in the past, the new workloads are exclusively multithreaded. This requires a profound change in the ALICE Grid midleware job handling, from scheduling to execution, and thus the entire middleware has been rewritten during the past 3 years to support the new multithreaded reality.

This paper presents the ALICE middlewre development for multi-core job management and the tools used to achieve an efficient and secure environment. In particular, it covers job isolation and scheduling and how they can be implemented in different site configurations, such as sites shared with other experiments or High Performance Computing resources.
Data transfer for STAR grid jobs
Authors: Chakaberia Irakli , Lauret Jerome , Poat Michael , Porter Jefferson
Published: February 2023

The Solenoidal Tracker at RHIC (STAR) is a multipurpose experiment at the Relativistic Heavy Ion Collider (RHIC) with the primary goal to study the formation and properties of the quark-gluon plasma. STAR is an international collaboration of member institutions and laboratories from around the world. Yearly data-taking period produces PBytes of raw data collected by the experiment. STAR primarily uses its dedicated facility at BNL to process this data, but has routinely leveraged distributed systems, both high throughput (HTC) and high performance (HPC) computing clusters, to significantly augment the processing capacity available to the experiment.

The ability to automate the efficient transfer of large data sets on reliable, scalable, and secure infrastructure is critical for any large-scale distributed processing campaign. For more than a decade, STAR computing has relied upon GridFTP with its x509-based authentication to build such data transfer systems and integrate them into its larger production workflow. The end of support by the community for both GridFTP and the x509 standard requires STAR to investigate other approaches to meet its distributed processing needs.

In this study we investigate two multi-purpose data distribution systems, Globus.org and XRootD, as alternatives to GridFTP. We compare both their performance and the ease by which each service is integrated into the type of secure and automated data transfer systems STAR has previously built using GridFTP. The presented approach and study may be applicable to other distributed data processing use cases beyond STAR.
Design of a request/response buffering application for I/O intensive workloads
Authors: Grötschla F , Lehmann Miotto G , Sipos R
Published: February 2023

The performance of I/O intensive applications is largely determined by the organization of data and the associated insertion/extraction techniques. In this paper we present the design and implementation of an application that is targeted at managing data received (upto ~ 150 Gb/s payload throughput) into host DRAM, buffering data for several seconds, matched with the DRAM size, before being dropped. All data are validated, processed and indexed. The features extracted from the processing are streamed out to subscribers over the network; in addition, while data resides in the buffer, about 0.1 ‰ of them are served to remote clients upon request. Last but not least, the application must be able to locally persist data at full input speed when instructed to do so. The characteristics of the incoming data stream (fixed or variable rate, fixed or variable payload size) heavily influences the choice of implementation of the buffer management system. The application design promotes the separation of interfaces (concepts) and application oriented specializations (models) that makes it possible to generalize most of the workflows and only requires minimal effort to integrate new data sources. After the description of the application design, we will present the hardware platform used for validation and benchmarking of the software, and the performance results obtained.
Experience in dynamic tape drive allocation to manage scientific data
Authors: Cavalli A. , Cesini D. , Falabella A. , Fattibene E. , Fornari F. , Morganti L. , Prosperini A. , Sapunenko V.
Published: February 2023

The main computing and storage facility of INFN (Italian Institute for Nuclear Physics) running at CNAF hosts and manages tens of Petabytes of data produced by the LHC (Large Hadron Collider) experiments at CERN and other scientific collaborations in which INFN is involved. Most of these data are stored on tape resources of different technologies. All the tape drives can be used for administrative tasks (as repack, audit, space reclamation), as well to write and read data of all the experiments. Moreover, the usage of tape resources by scientific communities will become considerably more intense in the next years and the amount of data on tape will double by 2025. For these reasons, the issue of the concurrent access to tape drives is significant. We designed a software solution to optimize the efficiency of the shared usage of tape drives in our environment and put it in production in January 2020. In this paper we present the experience with such dynamic tape resources allocation in production. Comparing it with the previous static allocation method, we observed an improvement in reading throughput up to 85%. Moreover, we describe the new features added to our solution to optimize the efficiency of the shared usage of tape drives of different technologies.
Parallel processing in data analysis of the JUNO experiment
Authors: Yang Yixiang
Published: February 2023

The JUNO experiment is being built mainly to determine the neutrino mass hierarchy by detecting neutrinos generated in the Yangjiang and Taishan nuclear plants in southern China. The detector will record 5.6 TB raw data every day for offline analysis, but each day it can only collect about 60 neutrino events scattered among huge background events. Selection of extremely sparse neutrino events brings a big challenge to offline data analysis. A typical neutrino physics event normally spans across a number of consecutive readout events, flagged by a fast positron signal followed by a slow neutron signal within a varying-size time window. To facilitate this analysis, a two-step data processing scheme has been proposed. In the first step (called data preparation), the event index data is produced and skimmed, which only contains information of minimum physics quantities of events as well as their addresses in the original reconstructed data file. In the second step (called time correlation analysis), event index data is further selected with stricter criteria. And then, for each selected event, the time correlation analysis is performed by reading all associated events within a pre-defined time window from the original data file according to the selected event’s address and timestamp.

This contribution will start to introduce the design of the above data processing scheme and then focus on the multi-threaded implementation of time correlation analysis based on the Intel Threading Building Block (TBB) in the SNiPER framework. Afterwards, this contribution will describe the implementation of distributed analysis using MPI in which the time correlation analysis task is divided into sub-tasks running on multiple computing nodes. At last, this contribution will present the detailed performance measurements made on a multiple-node test bed. By using both skimming and indexing techniques, the total amount of data finally used for neutrino signal time correlation analysis is significantly reduced, and the processing time could be reduced by two orders of magnitude.
Acceleration with GPUs and other RooFit news
Authors: Michalainas Emmanouil , Rembser Jonas , Hageboeck Stephan , Moneta Lorenzo
Published: February 2023

RooFit is a toolkit for statistical modeling and fitting, and together with RooStats it is used for measurements and statistical tests by most experiments in particle physics, particularly the LHC experiments. As the LHC program progresses, physics analysis becomes more computationally demanding. Therefore, RooFit development in recent years is focused on modernizing RooFit, improving its ease of use, and on performance optimization. This paper presents the new RooFit vectorized computation mode, which supports calculations on the GPU. Additionally, we discuss new features in the upcoming ROOT 6.26 release, highlighting the new pythonizations in particular.
Application of the Quantum Kernel Algorithm on the Particle Identification at the BESIII Experiment
Authors: Li Teng , Yao Zhipeng , Huang Xingtao , Zou Jiaheng , Lin Tao , Li Weidong
Published: February 2023

Particle identification is one of most fundamental tools in various particle physics experiments. For the BESIII experiment on the BEPCII, the realization of numerous physical goals heavily relies on advanced particle identification algorithms. In recent years, the emerging of quantum machine learning could potentially arm particle physics experiments with a powerful new toolbox. In this work, targeting at the muon/pion discrimination problem at BESIII, we have developed a quantum SVM classifier for the Noisy Intermediate-Scale Quantum (NISQ) device. By studying and optimizing various encoding circuits, the quantum SVM trained with the BESIII MC data shows comparable discrimination power than other traditional machine learning models. This has demonstrated the potential of using quantum machine learning techniques to form a new approach for particle identification in particle physics experiments. In this talk, we present the application of the quantum SVM for particle identification at BESIII, and demonstrate how to construct and optimize the quantum kernel. Furthermore, we present results obtained from the noisy simulator as well as the small-scale hardware to show the potential advantage of the quantum SVM algorithm.
Autoencoder-extended Conditional Invertible Neural Networks for Unfolding Signal Traces
Authors: Erdmann M , Hafner K , Schulte J , Straub M
Published: February 2023

The reconstruction of cosmic ray-induced air showers from measurements of radio waves constitutes a major challenge. In this work, we focus on recovering the full three-dimensional electromagnetic field from two recorded signal traces of an antenna station covering two horizontal polarization directions. The simulated field is folded by a direction and frequency-dependent characteristic antenna response pattern, resulting in voltage signal traces as a function of time. Both signal traces are contaminated by simulated background noise. We use conditional Invertible Neural Networks (cINNs) to learn posterior distributions, from which the most likely electromagnetic field given a measured signal trace can be inferred. To improve robustness, we extend the method with an autoencoder by reducing the parameter phase space and decoupling the cINN from specific data shapes. Thereby, each signal trace is condensed into a small number of abstract parameters in the latent space on which the cINN operates. The presented method shows promising results and can be transferred to other unfolding problems where the recovery of the pre-measurement state is of interest.
Declarative interfaces for HEP data analysis: FuncADL and ADL/CutLang
Authors: Huh C , Proffitt M , Prosper H B , Sekmen S , Sen B , Unel G , Watts G
Published: February 2023

Analysis description languages are declarative interfaces for HEP data analysis that allow users to avoid writing event loops, simplify code, and enable performance improvements to be decoupled from analysis development. One example is FuncADL, inspired by functional programming and developed using Python as a host language. FuncADL borrows concepts from database query languages to isolate the interface from the underlying physical and logical schemas. The same query can be used to select data from different sources and formats and with different execution mechanisms. FuncADL is one of the tools being developed by IRIS-HEP for highly scalable physics analysis for the LHC and HL-LHC. FuncADL is demonstrated by implementing example analysis tasks designed by HSF and IRIS-HEP. Another language example is ADL, which expresses the physics content of an analysis in a standard and unambiguous way, independent of computing frameworks. In ADL, analyses are described in human-readable text files composed of blocks with a keyword-expression structure. Two infrastructures are available to render ADL executable: CutLang, a runtime interpreter written in C++; and adl2tnm, a transpiler converting ADL into C++ or Python code. ADL/CutLang are already used in several physics studies and educational projects, and are adapted for use with LHC Open Data.
Deferred Optical Photon simulation for the JUNO experiment
Authors: Lin Tao
Published: February 2023

The Jiangmen Underground Neutrino Observatory (JUNO) is designed to determine the neutrino mass ordering and precisely measure oscillation parameters. It is being built in South China at a depth of 700 m underground and comprises a central detector, water Cerenkov detector and top tracker. The central detector is designed to detect anti-neutrinos with an energy resolution of 3% at 1 MeV, using a 20 kt liquid scintillator target with 17,612 20-inch PMTs and 25,600 3-inch PMTs. The scintillator provides a light yield of approximately 10,000 photons per MeV. Monte Carlo simulation is a crucial tool for developing an understanding of detector performance, requiring the production of large samples of background processes with optical photons. Simulation of large numbers of optical photons with Geant4 is computationally challenging for both processing time and memory resources. In order to optimize resource usage, a deferred optical photon simulation workflow is proposed and implemented using Geant4 classes. The key idea is to simulate events initially without optical photons, only performing the optical photon simulation when user specified criteria are met.

In this contribution, the design and the implementation of the deferred optical photon simulation will be presented. Optical simulation comprises generation of photons and propagation through the detector implementing optical physics processes including reflection, refraction, scattering and absorption. Instead of generating the optical photons at each step by Geant4 immediately, the necessary data to generate optical photons at each step are collected, which is termed GenStep. At the end of each event, user specified criteria determines if the optical photon simulation is performed using a class called G4OPSimulator. The class G4OPSimulator implements a customized simulation workflow, based on Geant4 internal classes including G4TrackingManager and G4StackManager. The simulator passes references to the collected GenStep objects to customized Scintillation and Cerenkov processes which generate the optical photons in G4Track objects. As a track could be absorbed and re-emitted, the secondaries will be retrieved from the G4TrackingManager and pushed to the G4StackManager. The performance of the simulator will be presented. The technique of deferred optical photon simulation can be applied to all event types and it is expected to be particularly beneficial with rare processes. Especially the events must be selected during the detector simulation at runtime, instead of the event generation. An application of the technique to the simulation of such events will be shown.
Impact of quantum noise on the training of quantum Generative Adversarial Networks
Authors: Borras Kerstin , Chang Su Yeon , Funcke Lena , Grossi Michele , Hartung Tobias , Jansen Karl , Kruecker Dirk , Kühn Stefan , Rehm Florian , Tüysüz Cenk , Vallecorsa Sofia
Published: February 2023

Current noisy intermediate-scale quantum devices suffer from various sources of intrinsic quantum noise. Overcoming the effects of noise is a major challenge, for which different error mitigation and error correction techniques have been proposed. In this paper, we conduct a first study of the performance of quantum Generative Adversarial Networks (qGANs) in the presence of different types of quantum noise, focusing on a simplified use case in high-energy physics. In particular, we explore the effects of readout and two-qubit gate errors on the qGAN training process. Simulating a noisy quantum device classically with IBM’s Qiskit framework, we examine the threshold of error rates up to which a reliable training is possible. In addition, we investigate the importance of various hyperparameters for the training process in the presence of different error rates, and we explore the impact of readout error mitigation on the results.
The Iterative Clustering framework for the CMS HGCAL Reconstruction
Authors: Pantaleo Felice , Rovere Marco
Published: February 2023

To sustain the harsher conditions of the high-luminosity LHC [1], the CMS Collaboration [2] is designing a novel endcap calorimeter system [3]. The new calorimeter will predominantly use silicon sensors to achieve sufficient radiation tolerance and will maintain highly granular information in the readout to help mitigate the effects of the pile up. In regions characterized by lower radiation levels, small scintillator tiles with individual SiPM on-tile readout are employed. A unique reconstruction framework (TICL: The Iterative CLustering) is being developed within the CMS Software CMSSW to fully exploit the granularity and other significant detector features, such as particle identification and precision timing, with a view to mitigating pile up in the very dense environment of HL-LHC. The TICL framework has been thought of with heterogeneous computing in mind: the algorithms and their data structures are designed to be executed on GPUs. In addition, geometry agnostic data structures have been designed to provide fast navigation and searching capabilities. Seeding capabilities (also exploiting information coming from other detectors), dynamic cluster masking, energy calibration, and particle identification are the main components of the framework. To allow for maximal flexibility, TICL allows the composition of different combinations of modules that can be chained together in an iterative fashion.
Machine learning based background rejection for Baikal-GVD neutrino telescope
Authors: Kalashev O. , Kharuk I. , Rubtsov G.
Published: February 2023

Baikal-GVD is a gigaton-scale underwater neutrino telescope currently under construction in Lake Baikal. Its principal components are optical modules, registering photons propagating through the telescope’s working volume. Part of the activations of the optical modules are due to the natural luminescence of the water, and thus appear as noise in the data. We present a neural network, which efficiently rejects this background and reaches 97% signal purity (precision) and 99% survival efficiency (recall) on the Monte-Carlo data. The neural network has a U-net like architecture based on the temporal structure of optical modules activations.
Multi-Jet Event classification with Convolutional neural network at Large Scale
Authors: Kim Jiwoong , Moon Chang-Seong , Nam Hokyeong , Goh Junghwan , Bae Dongsung , Yoo Changhyun , Kim Sungwon , Kim Tongil , Yoo Hwidong , Hwang Soonwook , Cho Kihyeon , Hahm Jaegyoon , Myung Hunjoo , Kim Minsik , Hong Taeyoung
Published: February 2023

We present an application of Scalable Deep Learning to analyze simulation data of the LHC proton-proton collisions at 13 TeV. We built a Deep Learning model based on the Convolutional Neural Network (CNN) which utilizes detector responses as two-dimensional images reflecting the geometry of the Compact Muon Solenoid (CMS) detector. The model discriminates signal events of the R-parity violating Supersymmetry (RPV SUSY) from the background events with multiple jets due to the inelastic QCD scattering (QCD multi-jets). With the CNN model, we obtained x1.85 efficiency and x1.2 expected significance with respect to the traditional cut-based method. We demonstrated the scalability of the model at a Large Scale with the High-Performance Computing (HPC) resources at the Korea Institute of Science and Technology Information (KISTI) up to 1024 nodes.
NA61/SHINE online noise filtering using machine learning methods
Authors: Kawęcka Anna , Bryliński Wojciech , Omana Kuttan Manjunath , Linnyk Olena , Pawlowski Janik , Schmidt Katarzyna , Słodkowski Marcin , Wyszyński Oskar , Zieliński Jakub
Published: February 2023

The NA61/SHINE is a high-energy physics experiment operating at the SPS accelerator at CERN. The physics program of the experiment was recently extended, requiring a significant upgrade of the detector setup. The main goal of the upgrade is to increase the event flow rate from 80Hz to 1kHz by exchanging the read-out electronics of the NA61/SHINE main tracking detectors (Time-Projection-Chambers - TPCs). As the amount of collected data will increase significantly, a tool for online noise filtering is needed. The standard method is based on the reconstruction of tracks and removal of clusters which do not belong to any particle trajectory. However, this method takes a substantial amount of time and resources. A novel approach based on machine learning methods is presented in this proceedings.
Neural Network-Based Primary Vertex Reconstruction with FPGAs for the Upgrade of the CMS Level-1 Trigger System
Authors: Brown C. , Bundock A. , Komm M. , Loncar V. , Pierini M. , Radburn-Smith B. , Shtipliyski A. , Summers S. , Dancu J.-S. , Tapper A.
Published: February 2023

The CMS experiment will be upgraded to maintain physics sensitivity and exploit the improved performance of the High Luminosity LHC. Part of this upgrade will see the first level (Level-1) trigger use charged particle tracks reconstructed within the full outer silicon tracker volume as an input for the first time and new algorithms are being designed to make use of these tracks. One such algorithm is primary vertex finding which is used to identify the hard scatter in an event and separate the primary interaction from additional simultaneous interactions. This work presents a novel approach to regress the primary vertex position and to reject tracks from additional soft interactions, which uses an end-to-end neural network. This neural network possesses simultaneous knowledge of all stages in the reconstruction chain, which allows for end-to-end optimisation. The improved performance of this network versus a baseline approach in the primary vertex regression and track-to-vertex classification is shown. A quantised and pruned version of the neural network is deployed on an FPGA to match the stringent timing and computing requirements of the Level-1 Trigger.
Quantum machine learning in the latent space of high energy physics events
Authors: Anna Woźniak Kinga , Belis Vasilis , Pierini Maurizio , Vallecorsa Sofia , Dissertori Günther , Barkoutsos Panagiotis , Tavernelli Ivano
Published: February 2023

We investigate supervised and unsupervised quantum machine learning algorithms in the context of typical data analyses at the LHC. To accommodate the constraints on the problem size, dictated by limitations on the quantum hardware, we concatenate the quantum algorithms to the encoder of a classical convolutional autoencoder, used for dimensionality reduction. We present results for a quantum classifier and a quantum anomaly detection algorithm, comparing performance to corresponding classical algorithms.
Study of dynamic geometry in simulation and reconstruction of liquid-based detector
Authors: Li Jingshu , Zhang Shu , Zhang Yumei , You Zhengyun
Published: February 2023

The non-uniformity of liquid in a detector is difficult to be described with the traditional detector description method used in Geant4. To solve the simulation and reconstruction problem with dynamic and non-uniform liquid-based detector, a method of combining GDML and tessellated 3D modeling is put forward to export the detector geometry information from computational fluid dynamics simulation software and import it into the detector simulation software. This method can be used to study the effects of non-uniformity on detector performance. It can be further used to solve other dynamic geometry-related problems in particle and nuclear physics experiments.
Symmetry aware generation of two-staged particle decays in high-energy physics
Authors: Eich Niclas , Erdmann Martin , Fischer Benjamin , Zimmermann Niklas
Published: February 2023

We present a specialised layer for generative modeling of LHC events with generative adversarial networks. We use Lorentz boosts, rotations, momentum and energy conservation to build a network cell generating a 2-body particle decay. This cell is stacked consecutively in order to model two staged decays, respecting the symmetries across the decay chain. We allow for modifications of the resulting four-vectors in order to model higher order and detector effects. We give an evaluation of the generator quality in a Higgs decay into two Z bosons, further decaying into a muon pair each.
Vectorised Neutrino Reconstruction by Computing Graphs
Authors: Noll Dennis , Erdmann Martin , Fackeldey Peter , Fischer Benjamin
Published: February 2023

Many particle physics analyses are adopting the concept of vectorised computing, often making them increasingly performant and resource-efficient. While a variety of computing steps can be vectorised directly, some calculations are challenging to implement. One of these is the analytical neutrino reconstruction which involves fitting that naturally varies between events. We show a vectorised implementation of the analytical neutrino reconstruction using a graph computing model. It uses established deep learning software libraries and is natively portable to local and external hardware accelerators such as GPUs. Using the example of ttH events with a semi-leptonic final state, we present performance studies for our implementation.
A vendor-agnostic, single code-based GPU tracking for the Inner Tracking System of the ALICE experiment
Authors: Concas M
Published: February 2023

During the LHC Run 3 the ALICE online computing farm will process up to 50 times more Pb-Pb events per second than in Run 2. The implied computing resource scaling requires a shift in the approach that comprises the extensive usage of Graphics Processing Units (GPU) for the processing. We will give an overview of the state of the art for the data reconstruction on GPUs in ALICE, with additional focus on the Inner Tracking System detector. A detailed teardown of adopted techniques, implemented algorithms and approaches and performance report will be shown. Additionally, we will show how we support different GPUs brands (NVIDIA and AMD) with a single code-base using an automatic code translation and generation for different target architectures. Strengths and possible weaknesses of this approach will be discussed. Finally, an overview of the next steps towards an even more comprehensive usage of GPUs in ALICE software will be illustrated.
A factorisation-aware matrix element emulator
Authors: Truong H
Published: February 2023

In this contribution I present a neural network based model to emulate matrix elements. This model improves on existing methods by taking advantage of the known factorisation properties of matrix elements to drastically improve per-point accuracy. By building in the factorisation properties into the model we can control the behaviour of simulated matrix elements when extrapolating into more singular regions than the ones used for training the neural network. I apply this model to the case of leading-order jet production in e
+
e
−collisions with up to five jets where this model can reproduce the matrix elements with errors below the one-percent level on the phase-space covered during fitting and testing, with a robust extrapolation to the parts of the phase-space where the matrix elements are more singular than seen at the fitting stage. Finally I discuss the usage and performance of the model on a GPU system.
Loop integral evaluation and asymptotic expansion with pySecDec
Authors: Magerya Vitaly
Published: February 2023

The evaluation of higher-loop Feynman integrals is at the core of the quest to reduce the uncertainty of theoretical predictions and match experimental data from the LHC and future colliders. pySecDec is a program to evaluate such integrals numerically based on the sector decomposition approach; its new release version 1.5 introduces features significantly improving its performance: automatic adaptive evaluation of weighted sums of integrals (e.g. amplitudes) and asymptotic expansion in kinematic ratios. Here we briefly review both, illustrating the expected performance benefits.
Development of hard X-ray spectrometer with full digital data acquisition for runaway electron studies at HL-2M
Authors: Cheng Shikui , Zhang Jie , Zhang Yipo , He Li , Xu Hongbing , Zhu Yuxuan , Zhan Xuwen , Shi Zhongbing , Zhong Wulv
Published: February 2023

The development of hard X-ray (HXR) spectrometers with full digital data acquisition for runaway electron studies on the HL-2M tokamak is presented. Consisting of large-sized NaI:Tl detectors and high-performance multichannel analyzers, this system can realize the measurement of HXR with energies ranging from 0.5 to 10 MeV, and spectrum temporal resolution of 1 ms. An average energy resolution of 56 keV at 662 keV has been achieved at counts rates of about 100 kcps. The spectra data is acquired by a digital-based data acquisition system with fast digitization and software signal processing technology. The energy calibrations are performed by using several radioisotope gamma-ray sources, and the detection efficiencies are simulated by MCNP code. First experimental results from HL-2M Ohmic discharges are presented in this paper.
Persistent anticyclonic conditions and climate change exacerbated the exceptional 2022 European-Mediterranean drought
Authors: Faranda Davide , Pascale Salvatore , Bulut Burak
Published: February 2023

A prolonged drought affected Western Europe and the Mediterranean region in 2022 producing large socio-ecological impacts. The role of anthropogenic climate change (ACC) in exacerbating this drought has been often invoked in the public debate, but the link between atmospheric circulation and ACC has not received much attention so far. Here we address this question by applying the method of circulation analogs, which allows us to identify atmospheric patterns in the period 1836-2021 very similar to those occurred in 2022. By comparing the circulation analogs when global warming was absent (1836-1915) with those occurred recently (1942-2021), and by excluding interannual and interdecadal variability as possible drivers, we identify the contribution of ACC. The 2022 drought was associated with a persistent anticyclonic anomaly over Western Europe. Circulation analogs of this atmospheric pattern in 1941-2021 feature 500 hPa geopotential height anomalies larger in both extent and magnitude, and higher temperatures at the surface, relative to those in 1836-1915. Both factors exacerbated the drought, by increasing the area affected and enhancing soil drying through evapotranspiration. While the occurrence of the atmospheric circulation associated with the 2022 drought has not become more frequent in recent decades, the influence of the Atlantic Multidecadal oscillation cannot be ruled-out.
The hot deformation behaviors and constitutive modeling of Hastelloy C276
Authors: Wu Jia-Fu , Jiang Yu-Qiang , Deng Wei , Yao Gu-Wen
Published: February 2023

Hastelloy C276 is widely used in the new generation of nuclear power plants, and hot deformation is the optimum way to form the C276 part. In this investigation, the hot deformation and constitutive modeling of Hastelloy C276 alloy are researched, and the processing maps are drawn. The results show that strain rate and hot deformation temperature have remarkable impacts on the deformation behaviors of the Hastelloy C276 alloy. The yield behavior and the flow stress are predicted based on the Arrhenius constitutive equation, and the correlation coefficients are 0.9613 and 0.9837, indicating the high prediction ability of the established constitutive equation. Rising the deformation temperature can decrease the unstable deformation area, and the studied alloy can be deformed at low strain rates. With the increased strain rate, flow localization occurs, which is not suitable for the hot deformation.
A fully passive transmitter for decoy-state quantum key distribution
Authors: Zapatero Víctor , Wang Wenyuan , Curty Marcos
Published: February 2023

A passive quantum key distribution (QKD) transmitter generates the quantum states prescribed by a QKD protocol at random, combining a fixed quantum mechanism and a post-selection step. By circumventing the use of active optical modulators externally driven by random number generators, passive QKD transmitters offer immunity to modulator side channels and potentially enable higher frequencies of operation. Recently, the first linear optics setup suitable for passive decoy-state QKD has been proposed. In this work, we simplify the prototype and adopt sharply different approaches for BB84 polarization encoding and decoy-state parameter estimation. In particular, our scheme avoids a probabilistic post-selection step that is central to the former proposal. On top of it, we elaborate a simple and tight custom-made security analysis.
Interactions of deposited Ca with TiSe2 and TiTe2 surfaces
Authors: Starnberg H I
Published: February 2023

The interaction of evaporated Ca with TiSe2 and TiTe2 surfaces was studied using photoelectron spectroscopy. The surfaces were held at room temperature, and the measured spectra clearly show that the deposited Ca reacted with the substrates, without formation of any Ca overlayers. For depositions on TiSe2 corresponding to less than 2 nm of Ca there were indications of an intercalation reaction taking place, with charge transfer to the TiSe2 layers, but as more Ca was added a layer-breaking reaction dominated. For the case of TiTe2 only a large Ca deposition was studied, resulting in a layer-breaking reaction very similar to that found on the TiSe2 surface for similar amounts of deposited Ca.
Floquet formulation of the dynamical Berry-phase approach to nonlinear optics in extended systems
Authors: Alliati Ignacio M , Grüning Myrta
Published: February 2023

We present a Floquet scheme for the ab-initio calculation of nonlinear optical properties in extended systems. This entails a reformulation of the real-time approach based on the dynamical Berry-phase polarisation (Attaccalite and Grüning 2013 Phys. Rev. B 88 1–9) and retains the advantage of being non-perturbative in the electric field. The proposed method applies to periodically-driven Hamiltonians and makes use of this symmetry to turn a time-dependent problem into a self-consistent time-independent eigenvalue problem. We implemented this Floquet scheme at the independent particle level and compared it with the real-time approach. Our reformulation reproduces real-time-calculated 2nd and 3rd order susceptibilities for a number of bulk and two-dimensional materials, while reducing the associated computational cost by one or two orders of magnitude.
Bioinspired composites: nature’s guidance for advanced materials future
Authors: Pereira Anna Cecília do Nascimento , Titotto Silvia
Published: February 2023

Biomimetics enables the use of nature as a source of inspiration for the elaboration of high-performance materials. In this scenario, the development of bioinspired composites emerges as a promising proposal, capable of generating technological innovation in numerous areas of engineering, considering the exceptional mechanical performance of materials of this kind. That said, this review article characterizes the design principles and fundamental parameters for bioinspired composites design. In addition, the main challenges to be overcome in the development of bioinspired materials are discussed, with the presentation of some experimental studies that lead to the practical application of such principles. Future applications for this class of materials are also highlighted.
Varieties of general type with doubly exponential asymptotics
Authors: Esser Louis , Totaro Burt , Wang Chengxi
Published: February 2023

We construct smooth projective varieties of general type with the smallest known volume and others with the most known vanishing plurigenera in high dimensions. The optimal volume bound is expected to decay doubly exponentially with dimension, and our examples achieve this decay rate. We also consider the analogous questions for other types of varieties. For example, in every dimension we conjecture the terminal Fano variety of minimal volume, and the canonical Calabi-Yau variety of minimal volume. In each case, our examples exhibit doubly exponential behavior.
Restriction estimates for hyperbolic cone in high dimensions
Authors: Gao Zhong , Zheng Jiqiang
Published: February 2023

In this paper, we obtain the

L
p

L^p

restriction estimates for the truncated conic surface
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="normal upper Sigma equals StartSet left-parenthesis xi prime comma xi Subscript n Baseline comma minus xi Subscript n Superscript negative 1 Baseline mathematical left-angle xi prime comma upper N xi Superscript prime Baseline mathematical right-angle right-parenthesis colon left-parenthesis xi prime comma xi Subscript n Baseline right-parenthesis element-of upper B Superscript n minus 1 Baseline left-parenthesis 0 comma 1 right-parenthesis times left-bracket 1 comma 2 right-bracket EndSet">

Σ
=

{

(

ξ
′

,

ξ
n

,
−

ξ
n

−
1

⟨

ξ
′

,
N

ξ
′

⟩
)
:
(

ξ
′

,

ξ
n

)
∈

B

n
−
1

(
0
,
1
)
×
[
1
,
2
]

}

\begin{equation*} \Sigma =\big \{(\xi ’,\xi _n,-\xi _n^{-1}\langle \xi ’,N\xi ’\rangle ): (\xi ’,\xi _n)\in B^{n-1}(0,1)\times [1,2]\big \} \end{equation*}

with

N
=

I

n
−
1
−
m

⊕
(
−

I
m

)

N=I_{n-1-m}\oplus (-I_m)

for

m
≤
⌊

n
−
3

2

⌋

m\leq \lfloor \tfrac {n-3}2\rfloor

provided

p
>

2
(
n
+
3
)

n
+
1

p>\tfrac {2(n+3)}{n+1}

. The main ingredients of the proof are the bilinear estimates of strongly separated property and a geometric distribution about caps.
Symmetry and monotonicity results for solutions of vectorial 𝑝-Stokes systems
Authors: López-Soriano Rafael , Montoro Luigi , Sciunzi Berardino
Published: February 2023

In this paper we shall study qualitative properties of a

p
p

-Stokes type system, namely
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="minus bold upper Delta Subscript p Baseline bold-italic u equals minus bold d times bold i times bold v left-parenthesis StartAbsoluteValue upper D bold-italic u EndAbsoluteValue Superscript p minus 2 Baseline upper D bold-italic u right-parenthesis equals bold-italic f left-parenthesis x comma bold-italic u right-parenthesis in normal upper Omega comma">

−

Δ

p

u

=
−

d
i
v

⁡
(

|

D

u

|

p
−
2

D

u

)
=

f

(
x
,

u

)

 in 

Ω

,

\begin{equation*} -{\boldsymbol \Delta }_p{\boldsymbol u}=-\operatorname {\mathbf {div}}(|D{\boldsymbol u}|^{p-2}D{\boldsymbol u}) = {\boldsymbol f}(x,{\boldsymbol u}) \text { in $\Omega $}, \end{equation*}

where

Δ

p

{\boldsymbol \Delta }_p

is the

p
p

-Laplacian vectorial operator. More precisely, under suitable assumptions on the domain

Ω
\Omega

and the function

f
\boldsymbol { f}

, it is deduced that system solutions are symmetric and monotone. Our main results are derived from a vectorial version of the weak and strong comparison principles, which enable to proceed with the moving-planes technique for systems. As far as we know, these are the first qualitative kind results involving vectorial operators.
Convex-compact subgroups of the Goeritz group
Authors: Tshishiku Bena
Published: February 2023

Let

G
>

Mod
2

G>\operatorname {Mod}_2

be the Goeritz subgroup of the genus-2 mapping class group. We show that finitely-generated, purely pseudo-Anosov subgroups of

G
G

are convex cocompact in

Mod
2

\operatorname {Mod}_2

, addressing a case of a general question of Farb–Mosher. We also give a simple criterion to determine if a Goeritz mapping class is pseudo-Anosov, which we use to give very explicit convex-cocompact subgroups. In our analysis, a central role is played by the primitive disk complex

P

\mathcal {P}

. In particular, we (1) establish a version of the Masur–Minksy distance-formula for

P

\mathcal {P}

, (2) classify subsurfaces

X
⊂
S

X\subset S

that are infinite-diameter holes of

P

\mathcal {P}

, and (3) show that

P

\mathcal {P}

is quasi-isometric to a coned-off Cayley graph for

G
G

.
Monodromie unipotente maximale, congruences “à la Lucas” et indépendance algébrique
Authors: Vargas Montoya Daniel
Published: February 2023

Let

f
(
z
)

f(z)

be in

1
+
z

Q

[
[
z
]
]

1+z\mathbb {Q}[[z]]

and

S

\mathcal {S}

be an infinite set of prime numbers such that, for all

p
∈

S

p\in \mathcal {S}

, we can reduce

f
(
z
)

f(z)

modulo

p
p

. We let

f
(
z

)

∣
p

f(z)_{\mid p}

denote the reduction of

f
(
z
)

f(z)

modulo

p
p

. Generally, when

f
(
z
)

f(z)

is D-finite,

f

∣
p

(
z
)

f_{\mid p}(z)

is algebraic over

F

p

(
z
)

\mathbb {F}_p(z)

. It turns out that if

f

∣
p

(
z
)

f_{\mid p}(z)

is a solution of a polynomial of the form

X
−

A
p

(
z
)

X

p
l

X-A_p(z)X^{p^l}

, we can use this type of equations to obtain results of transcendence and algebraic independence over

Q

(
z
)

\mathbb {Q}(z)

. In the present paper, we look for conditions on the differential operators annihilating

f
(
z
)

f(z)

to guarantee the existence of these particular equations. Suppose that

f
(
z
)

f(z)

is solution of a differential operator

H

∈

Q

(
z
)
[
d

/

d
z
]

\mathcal {H}\in \mathbb {Q}(z)[d/dz]

having a strong Frobenius structure for all

p
∈

S

p\in \mathcal {S}

and we also suppose that

f
(
z
)

f(z)

annihilates a Fuchsian differential operator

D

∈

Q

(
z
)
[
d

/

d
z
]

\mathcal {D}\in \mathbb {Q}(z)[d/dz]

such that zero is a regular singular point of

D

\mathcal {D}

and the exponents of

D

\mathcal {D}

at zero are equal to zero. Our main result states that, for almost every prime

p
∈

S

p\in \mathcal {S}

,

f

∣
p

(
z
)

f_{\mid p}(z)

is solution of a polynomial of the form

X
−

A
p

(
z
)

X

p
l

X-A_p(z)X^{p^l}

, where

A
p

(
z
)

A_p(z)

is a rational function with coefficients in

F

p

\mathbb {F}_p

of height less than or equal to

C

p

2
l

Cp^{2l}

with

C
C

a positive constant that does not depend on

p
p

. We also study the algebraic independence of these power series over

Q

(
z
)

\mathbb {Q}(z)

.
Bernstein-Sato theory for singular rings in positive characteristic
Authors: Jeffries Jack , Núñez-Betancourt Luis , Quinlan-Gallego Eamon
Published: February 2023

The Bernstein-Sato polynomial is an important invariant of an element or an ideal in a polynomial ring or power series ring of characteristic zero, with interesting connections to various algebraic and topological aspects of the singularities of the vanishing locus. Work of Mustaţă, later extended by Bitoun and the third author, provides an analogous Bernstein-Sato theory for regular rings of positive characteristic.

In this paper, we extend this theory to singular ambient rings in positive characteristic. We establish finiteness and rationality results for Bernstein-Sato roots for large classes of singular rings, and relate these roots to other classes of numerical invariants defined via the Frobenius map. We also obtain a number of new results and simplified arguments in the regular case.
Corrigendum to “The Class Number one Problem for the Normal CM-fields of degree 32”
Authors: Kwon Soun-Hi
Published: February 2023

We correct some errors in The class number one problem for the normal CM-fields of degree

32
32

in Trans. Amer. Math. Soc., vol. 359 no. 10 (2007).
Rigidity and continuous extension for conformal maps of circle domains
Authors: Ntalampekos Dimitrios
Published: February 2023

We present sufficient conditions so that a conformal map between planar domains whose boundary components are Jordan curves or points has a continuous or homeomorphic extension to the closures of the domains. Our conditions involve the notions of cofat domains and

C
N
E
D

CNED

sets, i.e., countably negligible for extremal distances, recently introduced by the author. We use this result towards establishing conformal rigidity of a class of circle domains. A circle domain is conformally rigid if every conformal map onto another circle domain is the restriction of a Möbius transformation. We show that circle domains whose point boundary components are

C
N
E
D

CNED

are conformally rigid. This result is the strongest among all earlier works and provides substantial evidence towards the rigidity conjecture of He–Schramm [Invent. Math. 115 (1994), no. 2, 297–310], relating the problems of conformal rigidity and removability.
Interrelated effects of chromosome size, mechanics, number, location-orientation and polar ejection force on the spindle accuracy: a 3D computational study
Authors: Kliuchnikov Evgenii , Marx Kenneth A. , Mogilner Alexander , Barsegov Valeri
Published: February 2023

The extension of the 3D computational model presented sheds light on some questions about spindle assembly’s speed and accuracy, such as the effect of chromosome size, number, geometry, and polar ejection force.