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DOE OSTI2025

The Analysis Description Language Ecosystem: Latest developments and physics applications

We present latest developments in Analysis Description Language (ADL), a declarative domain-specific language describing the physics algorithm of a HEP data analysis decoupled from software frameworks. Analyses written in ADL can be integrated into any framework for various tasks. ADL is a multipurpose construct with uses ranging from analysis design to preservation, reinterpretation, queries, visualisation, combination, etc. The most advanced infrastructure to execute ADL on events is the CutLang runtime interpreter. Recent technical developments include an automated interface with different data types, generation of the abstract syntax tree, a visualization tool that that auto-converts analysis flows to graphs, incorporation of trained machine learning models and a Jupyter-based plotting tool. We also report physics implications including a large scale LHC analysis implementation and validation effort for beyond the standard model reinterpretation purposes and studies with ATLAS and CMS open data.

Sekmen, Sezen [Kyungpook National Univ., Daegu (Ko↗

DOE OSTI2026

Diquark scalar production of a vectorlike quark pair at the LHC

We study the discovery potential of LHC experiments for resonantly produced vectorlike quarks ($\chi$) when the $s$-channel resonance is an ultraheavy scalar diquark ($S_{uu}$) with a mass in the 7–8.5 TeV range. Focusing on the process $pp \rightarrow S_{uu} \rightarrow \chi \chi \rightarrow (W^+b)(W^+b)$ we target the fully hadronic decay mode of both $W^+$ bosons, resulting in a six-jet final state. Signal–background separation is performed using Machine Learning algorithms trained to construct a multidimensional classifier. Our results show that ATLAS or CMS searches in this channel, with an integrated luminosity of 3000 fb$^{-1}$, could discover or exclude a scalar diquark with a mass near 8 TeV even for a relatively small Yukawa coupling to up quarks, $y_{uu} \simeq 0.2$. We also present preliminary studies of the four-jet final state from $pp \rightarrow S_{uu} \rightarrow u \chi \rightarrow u (W^+b)$.

Duminica, Ioana [Bucharest, IFIN-HH; Bucharest U.]↗

DOE OSTI2021

Impact of radiation on electronics and opto-electronics

In this chapter we will present the results of the impact of radiation on electronics and opto-electronics for two of the LHC experiments during Run 1 and Run 2. ATLAS results are presented in Section 6.1; CMS in Section 6.2. In Section 6.3 we will present a comparison between the two experiments, highlighting operational guidelines and proposing solutions to build the electronics and opto-electronics of the future LHC experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Porting HEP Parameterized Calorimeter Simulation Code to GPUs

The High Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), traditionally consume large amounts of CPU cycles for detector simulations and data analysis, but rarely use compute accelerators such as GPUs. As the LHC is upgraded to allow for higher luminosity, resulting in much higher data rates, purely relying on CPUs may not provide enough computing power to support the simulation and data analysis needs. As a proof of concept, we investigate the feasibility of porting a HEP parameterized calorimeter simulation code to GPUs. We have chosen to use FastCaloSim, the ATLAS fast parametrized calorimeter simulation. While FastCaloSim is sufficiently fast such that it does not impose a bottleneck in detector simulations overall, significant speed-ups in the processing of large samples can be achieved from GPU parallelization at both the particle (intra-event) and event levels; this is especially beneficial in conditions expected at the high-luminosity LHC, where extremely high per-event particle multiplicities will result from the many simultaneous proton-proton collisions. We report our experience with porting FastCaloSim to NVIDIA GPUs using CUDA. A preliminary Kokkos implementation of FastCaloSim for portability to other parallel architectures is also described.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Training and onboarding initiatives in high energy physics experiments

In this article we document the current analysis software training and onboarding activities in several High Energy Physics (HEP) experiments: ATLAS, CMS, LHCb, Belle II and DUNE. Fast and efficient onboarding of new collaboration members is increasingly important for HEP experiments. With rapidly increasing data volumes and larger collaborations the analyses and consequently, the related software, become ever more complex. This necessitates structured onboarding and training. Recognizing this, a meeting series was held by the HEP Software Foundation (HSF) in 2022 for experiments to showcase their initiatives. Here we document and analyze these in an attempt to determine a set of key considerations for future HEP experiments.

analysis software↗

DOE OSTI2023

Oncogenic Ras and ΔNp63α cooperate to recruit immunosuppressive polymorphonuclear myeloid-derived suppressor cells in a mouse model of squamous cancer pathogenesis

Introduction Amplification of human chromosome 3q26-29, which encodes oncoprotein ΔNp63 among other isoforms of the p63 family, is a feature common to squamous cell carcinomas (SCCs) of multiple tissue origins. Along with overexpression of ΔNp63, activation of the protooncogene, RAS , whether by overexpression or oncogenic mutation, is frequently observed in many cancers. In this study, analysis of transcriptome data from The Cancer Genome Atlas (TCGA) demonstrated that expression of TP63 mRNA , particularly ΔNp63 isoforms, and HRAS are significantly elevated in advanced squamous cell carcinomas of the head and neck (HNSCCs), suggesting pathological significance. However, how co-overexpressed ΔNp63 and HRAS affect the immunosuppressive tumor microenvironment (TME) is incompletely understood. Methods Here, we established and characterized an immune competent mouse model using primary keratinocytes with retroviral-mediated overexpression of ΔNp63α and constitutively activated HRAS (v-ras Ha G12R) to evaluate the role of these oncogenes in the immune TME. Results In this model, orthotopic grafting of wildtype syngeneic keratinocytes expressing both v-ras Ha and elevated levels of ΔNp63α consistently yield carcinomas in syngeneic hosts, while cells expressing v-ras Ha alone yield predominantly papillomas. We found that polymorphonuclear (PMN) myeloid cells, experimentally validated to be immunosuppressive and thus representing myeloid-derived suppressor cells (PMN-MDSCs), were significantly recruited into the TME of carcinomas arising early following orthotopic grafting of ΔNp63α/v-ras Ha -expressing keratinocytes. ΔNp63α/v-ras Ha -driven carcinomas expressed higher levels of chemokines implicated in recruitment of MDSCs compared to v-ras Ha -initiated tumors, providing a heretofore undescribed link between ΔNp63α/HRAS-driven carcinomas and the development of an immunosuppressive TME. Conclusion These results support the utilization of a genetic carcinogenesis model harboring specific genomic drivers of malignancy to study mechanisms underlying the development of local immunosuppression.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

In-vivo neuronal dysfunction by Aβ and tau overlaps with brain-wide inflammatory mechanisms in Alzheimer’s disease

The molecular mechanisms underlying neuronal dysfunction in Alzheimer’s disease (AD) remain uncharacterized. Here, we identify genes, molecular pathways and cellular components associated with whole-brain dysregulation caused by amyloid-beta (Aβ) and tau deposits in the living human brain. We obtained in-vivo resting-state functional MRI (rs-fMRI), Aβ- and tau-PET for 47 cognitively unimpaired and 16 AD participants from the Translational Biomarkers in Aging and Dementia cohort. Adverse neuronal activity impacts by Aβ and tau were quantified with personalized dynamical models by fitting pathology-mediated computational signals to the participant’s real rs-fMRIs. Then, we detected robust brain-wide associations between the spatial profiles of Aβ-tau impacts and gene expression in the neurotypical transcriptome (Allen Human Brain Atlas). Within the obtained distinctive signature of in-vivo neuronal dysfunction, several genes have prominent roles in microglial activation and in interactions with Aβ and tau. Moreover, cellular vulnerability estimations revealed strong association of microglial expression patterns with Aβ and tau’s synergistic impact on neuronal activity (q < 0.001). These results further support the central role of the immune system and neuroinflammatory pathways in AD pathogenesis. Neuronal dysregulation by AD pathologies also associated with neurotypical synaptic and developmental processes. In addition, we identified drug candidates from the vast LINCS library to halt or reduce the observed Aβ-tau effects on neuronal activity. Top-ranked pharmacological interventions target inflammatory, cancer and cardiovascular pathways, including specific medications undergoing clinical evaluation in AD. Our findings, based on the examination of molecular-pathological-functional interactions in humans, may accelerate the process of bringing effective therapies into clinical practice.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis

Introduction Recent studies in human brain connectomics with multimodal magnetic resonance imaging (MRI) data have widely reported abnormalities in brain structure, function and connectivity associated with schizophrenia (SZ). However, most previous discriminative studies of SZ patients were based on MRI features of brain regions, ignoring the complex relationships within brain networks. Methods We applied a graph convolutional network (GCN) to discriminating SZ patients using the features of brain region and connectivity derived from a combined multimodal MRI and connectomics analysis. Structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 140 SZ patients and 205 normal controls. Eighteen types of brain graphs were constructed for each subject using 3 types of node features, 3 types of edge features, and 2 brain atlases. We investigated the performance of 18 brain graphs and used the TopK pooling layers to highlight salient brain regions (nodes in the graph). Results The GCN model, which used functional connectivity as edge features and multimodal features (sMRI + fMRI) of brain regions as node features, obtained the highest average accuracy of 95.8%, and outperformed other existing classification studies in SZ patients. In the explainability analysis, we reported that the top 10 salient brain regions, predominantly distributed in the prefrontal and occipital cortices, were mainly involved in the systems of emotion and visual processing. Discussion Our findings demonstrated that GCN with a combined multimodal MRI and connectomics analysis can effectively improve the classification of SZ at an individual level, indicating a promising direction for the diagnosis of SZ patients. The code is available at https://github.com/CXY-scut/GCN-SZ.git .

Chen, Xiaoyi↗

DOE OSTI2022

From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Fabrication of Silicon Sensors Based on Low-Gain Avalanche Diodes

Low-Gain Avalanche Diodes are a recently-developed class of silicon sensors. Characterized by an internal moderate gain that enhances the signal amplitude and if built on thin silicon substrates of a few tens of microns, they feature fast signals and exhibit excellent timing performance. Thanks to their fast timing they are planned to be exploited in timing detectors in High-Energy Physics experiments, for example for the upgrades of the ATLAS and CMS detectors at the High Luminosity Large Hadron Collider (HL-LHC) at CERN. However, to achieve a spatially uniform multiplication a large pixel pitch is needed, preventing a fine spatial resolution. To overcome this limitation, the AC-coupled LGAD approach was introduced. In this type of device, metal electrodes are placed over an insulator at a fine pitch, and signals are capacitively induced on these electrodes. The fabrication technology is similar for the two LGAD families, although a fine tuning of a few process parameters needs to be carefully studied. Other R&D efforts towards detectors that can simultaneously provide good time and spatial resolution, based on the LGAD concept, are under way. These efforts aim also to mitigate the loss of performance at high irradiation fluences due to the acceptor removal within the gain layer. In this paper we describe the main points in the fabrication of LGADs and AC-LGADs in a clean-room. We also discuss novel efforts carried on related topics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

MaizeMine: A Data Mining Warehouse for the Maize Genetics and Genomics Database

MaizeMine is the data mining resource of the Maize Genetics and Genome Database (MaizeGDB; http://maizemine.maizegdb.org). It enables researchers to create and export customized annotation datasets that can be merged with their own research data for use in downstream analyses. MaizeMine uses the InterMine data warehousing system to integrate genomic sequences and gene annotations from the Zea mays B73 RefGen_v3 and B73 RefGen_v4 genome assemblies, Gene Ontology annotations, single nucleotide polymorphisms, protein annotations, homologs, pathways, and precomputed gene expression levels based on RNA-seq data from the Z. mays B73 Gene Expression Atlas. MaizeMine also provides database cross references between genes of alternative gene sets from Gramene and NCBI RefSeq. MaizeMine includes several search tools, including a keyword search, built-in template queries with intuitive search menus, and a QueryBuilder tool for creating custom queries. The Genomic Regions search tool executes queries based on lists of genome coordinates, and supports both the B73 RefGen_v3 and B73 RefGen_v4 assemblies. The List tool allows you to upload identifiers to create custom lists, perform set operations such as unions and intersections, and execute template queries with lists. When used with gene identifiers, the List tool automatically provides gene set enrichment for Gene Ontology (GO) and pathways, with a choice of statistical parameters and background gene sets. With the ability to save query outputs as lists that can be input to new queries, MaizeMine provides limitless possibilities for data integration and meta-analysis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Searching for Pairs of Higgs Bosons in the LHC Run 2 Dataset

The discovery of the Higgs boson confirms the existence of a scalar sector of the standard model, responsible for electroweak symmetry breaking, but the nature and properties of the potential at the origin of this mechanism are still unknown. By studying the production of pairs of Higgs bosons (HH), physicists can directly measure the coupling of the Higgs boson to itself and thus determine the shape of this potential, which has far-reaching implications on the origin and evolution of our Universe. Because of this deep connection to the foundations of electroweak symmetry breaking, HH production is also an ideal place to search for manifestations of yet-unknown physics, such as modifications of the strength of the self-coupling and of the interaction between pairs of vector bosons and Higgs bosons. In this review article, we summarize the current searches for HH production at ATLAS and CMS, using the LHC Run 2 dataset, discuss the implications of our current constraints on physics beyond the standard model, and briefly review prospect for future HH searches.