Searches for supersymmetry in the vector boson fusion topology with the ATLAS detector

The Standard Model of particle physics attempts to describe the most fundamental aspects of our world. It has generated a vast number of successful predictions and offers immense explanatory power as to why and how the fundamental interactions observed in nature proceed the way they do. At the same time, there exist deep mysteries about our world and inconsistencies within the Standard Model itself that cannot be ignored. Supersymmetry is an extension to the Standard Model proposed to resolve some of these problems. A search for supersymmetric particles called electroweakinos within the vector boson fusion topology is presented using the full Run-2 dataset collected by the ATLAS experiment at CERN. Generation of the signal model hypothesis, modeling and estimation of backgrounds, development of powerful discriminating variables, and statistical interpretation of the results are detailed. No excess above the Standard Model prediction is found. Model independent limits on generic new physics scenarios are performed, with the most stringent limit having a visible cross-section of 0.017 fb. Model dependent limits placed on electroweakino production exclude neutralino masses below 60 GeV. This constitutes the first signature based search of its kind at ATLAS and lays the groundwork for future work, which may include searches for light scalar states, which solve the strong-CP problem and bulk gravitons, which incorporate gravity into the Standard Model

Persistent Homology Based Characterization of the Breast Cancer Immune Microenvironment: A Feasibility Study

Persistent homology is a common tool of topological data analysis, whose main descriptor, the persistence diagram, aims at computing and encoding the geometry and topology of given datasets. In this article, we present a novel application of persistent homology to characterize the spatial arrangement of immune and epithelial (tumor) cells within the breast cancer immune microenvironment. More specifically, quantitative and robust characterizations are built by computing persistence diagrams out of a staining technique (quantitative multiplex immunofluorescence) which allows us to obtain spatial coordinates and stain intensities on individual cells. The resulting persistence diagrams are evaluated as characteristic biomarkers of cancer subtype and prognostic biomarker of overall survival. For a cohort of approximately 700 breast cancer patients with median 8.5-year clinical follow-up, we show that these persistence diagrams outperform and complement the usual descriptors which capture spatial relationships with nearest neighbor analysis. This provides new insights and possibilities on the general problem of building (topology-based) biomarkers that are characteristic and predictive of cancer subtype, overall survival and response to therapy

Persistent homology based characterization of the breast cancer immune microenvironment: a feasibility study

International audiencePersistent homology is a powerful tool in topological data analysis. The main output, persistence diagrams, encode the geometry and topology of given datasets. We present a novel application of persistent homology to characterize the biological environment surrounding breast cancers, known as the tumor microenvironment. Specifically, we will characterize the spatial arrangement of immune and malignant epithelial (tumor) cells within the breast cancer immune microenvironment. Quantitative and robust characterizations are built by computing persistence diagrams from quantitative multiplex immunofluorescence, which is a technology which allows us to obtain spatial coordinates and protein intensities on individual cells. The resulting persistence diagrams are evaluated as characteristic biomarkers predictive of cancer subtype and prognostic of overall survival. For a cohort of approximately 700 breast cancer patients with median 8.5-year clinical follow-up, we show that these persistence diagrams outperform and complement the usual descriptors which capture spatial relationships with nearest neighbor analysis. Our results thus suggest new methods which can be used to build topology-based biomarkers which are characteristic and predictive of cancer subtype and response to therapy as well as prognostic of overall survival

Kaiso (ZBTB33) subcellular partitioning functionally links LC3A/B, the tumor microenvironment, and breast cancer survival

The use of digital pathology for the histomorphologic profiling of pathological specimens is expanding the precision and specificity of quantitative tissue analysis at an unprecedented scale; thus, enabling the discovery of new and functionally relevant histological features of both predictive and prognostic significance. In this study, we apply quantitative automated image processing and computational methods to profile the subcellular distribution of the multi-functional transcriptional regulator, Kaiso (ZBTB33), in the tumors of a large racially diverse breast cancer cohort from a designated health disparities region in the United States. Multiplex multivariate analysis of the association of Kaiso’s subcellular distribution with other breast cancer biomarkers reveals novel functional and predictive linkages between Kaiso and the autophagy-related proteins, LC3A/B, that are associated with features of the tumor immune microenvironment, survival, and race. These findings identify effective modalities of Kaiso biomarker assessment and uncover unanticipated insights into Kaiso’s role in breast cancer progression.Fil: Singhal, Sandeep K.. North Dakota State University; Estados UnidosFil: Byun, Jung S.. National Institutes of Health; Estados UnidosFil: Park, Samson. National Institutes of Health; Estados UnidosFil: Yan, Tingfen. National Institutes of Health; Estados UnidosFil: Yancey, Ryan. Columbia University; Estados UnidosFil: Caban, Ambar. Columbia University; Estados UnidosFil: Hernandez, Sara Gil. National Institutes of Health; Estados UnidosFil: Hewitt, Stephen M.. U.S. Department of Health & Human Services. National Institute of Health. National Cancer Institute; Estados UnidosFil: Boisvert, Heike. Ultivue, Inc; Reino UnidoFil: Hennek, Stephanie. Ultivue Inc.; Reino UnidoFil: Bobrow, Mark. Ultivue Inc.; Reino UnidoFil: Ahmed, Md Shakir Uddin. Tuskegee University; Estados UnidosFil: White, Jason. Tuskegee University; Estados UnidosFil: Yates, Clayton. Tuskegee University; Estados UnidosFil: Aukerman, Andrew. Columbia University; Estados UnidosFil: Vanguri, Rami. Columbia University; Estados UnidosFil: Bareja, Rohan. Columbia University; Estados UnidosFil: Lenci, Romina. Columbia University; Estados UnidosFil: Farré, Paula Lucía. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: de Siervi, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Nápoles, Anna María. National Institutes of Health; Estados UnidosFil: Vohra, Nasreen. East Carolina University; Estados UnidosFil: Gardner, Kevin. Columbia University; Estados Unido

ATLAS trigger operations: Monitoring with “Xmon” rate prediction system

We present the operations and online monitoring with the “Xmon” rate prediction system for the trigger system at the ATLAS Experiment. A two-level trigger system reduces the LHC’s bunch-crossing rate, 40 MHz at design capacity, to an average recording rate of about 1 kHz, while maintaining a high efficiency of selecting events of interest. The Xmon system uses the luminosity value to predict trigger rates that are, in turn, compared with incoming rates. The predictions rely on past runs to parameterize the luminosity dependency of the event rate for a trigger algorithm. Some examples are given to illustrate the performance of the tool during recent operations

ATLAS Level-1 Topological Trigger : Commissioning and Validation in Run 2

The ATLAS experiment has recently commissioned a new hardware component of its first-level trigger: the topological processor (L1Topo). This innovative system, using state-of-the-art FPGA processors, selects events by applying kinematic and topological requirements on candidate objects (energy clusters, jets, and muons) measured by calorimeters and muon sub-detectors. Since the first-level trigger is a synchronous pipelined system, such requirements are applied within a latency of 200ns. We will present the first results from data recorded using the L1Topo trigger; these demonstrate a significantly improved background event rejection, thus allowing for a rate reduction without efficiency loss. This improvement has been shown for several physics processes leading to low-$P_{T}$ leptons, including $H\to{}\tau{}\tau{}$ and $J/\Psi\to{}\mu{}\mu{}$. In addition, we will discuss the use of an accurate L1Topo simulation as a powerful tool to validate and optimize the performance of this new trigger system. To reach the required accuracy, the simulation must take into account the limited precision that can be achieved with kinematic calculations implemented in firmware

Commissioning and validation of the ATLAS Level-1 topological trigger

The ATLAS experiment has recently commissioned a new hardware component of its first-level trigger: the topological processor (L1Topo). This innovative system, using state-of-the-art FPGA processors, selects events by applying kinematic and topological requirements on candidate objects (energy clusters, jets, and muons) measured by calorimeters and muon sub-detectors. Since the first-level trigger is a synchronous pipelined system, such requirements are applied within a latency of 200ns. We will present the first results from data recorded using the L1Topo trigger; these demonstrate a significantly improved background event rejection, thus allowing for a rate reduction without efficiency loss. This improvement has been shown for several physics processes leading to low-$P_{T}$ leptons, including $H\to{}\tau{}\tau{}$ and $J/\Psi\to{}\mu{}\mu{}$. In addition, we will discuss the use of an accurate L1Topo simulation as a powerful tool to validate and optimize the performance of this new trigger system. To reach the required accuracy, the simulation must take into account the limited precision that can be achieved with kinematic calculations implemented in firmware

Commissioning and Validation of the ATLAS Level-1 Topological Trigger in Run 2

The ATLAS experiment has introduced and recently commissioned a completely new hardware sub-system of its first-level trigger: the topological processor (L1Topo). L1Topo consist of two AdvancedTCA blades mounting state-of-the-art FPGA processors, providing high input bandwidth (up to 4 Gb/s) and low latency data processing (200 ns). L1Topo is able to select collision events by applying kinematic and topological requirements on candidate objects (energy clusters, jets, and muons) measured by calorimeters and muon sub-detectors. Results from data recorded using the L1Topo trigger will be presented. These results demonstrate a significantly improved background event rejection, thus allowing for rate reduction with minimal efficiency loss. This improvement has been shown for several physics processes leading to low-$p_T$ leptons, including $H\rightarrow\tau \tau$ and $J/\psi \rightarrow \mu \mu$. In addition to describing the L1Topo trigger system, we will discuss the use of an accurate L1Topo simulation as a powerful tool to validate and optimize the performance of this new system. To reach the required accuracy, the simulation must mimic the approximations applied in firmware to execute the kinematic calculations