We report on a search for Standard Model (SM) production of Higgs to WW* in the two charged lepton (e, [mu]) and two neutrino final state in p̄p collisions at a center of mass energy [sqrt]s = 1.96 TeV. The data were collected with the CDF II detector at the Fermilab Tevatron and correspond to an integrated luminosity of $1.9fb⁻¹. The Matrix Element method is developed to calculate the event probability and to construct a likelihood ratio discriminator. There are 522 candidates observed with an expectation of 513±41 background events and 7.8±0.6 signal events for Higgs mass 160GeV/c² at next-to-next-to-leading logarithmic level calculation. The observed 95% C.L. upper limit is $0.8 pb which is 2.0 times the SM prediction while the median expected limit is 3.1}+1.3}_}-0.9} with systematics included. Results for 9 other Higgs mass hypotheses ranging from 110GeV/c² to 200GeVc² are also presented. The same dilepton plus large transverse energy imbalance (E/T) final state is used in the SM ZZ production search and the WW production study. The observed significance of ZZ[right arrow] ll[nu][nu] channel is 1.2[sigma]. It adds extra significance to the ZZ[right arrow] 4l channel and leads to a strong evidence of ZZ production with $4.4̃ [sigma] significance. The potential improvement of the anomalous triple gauge coupling measurement by using the Matrix Element method in WW production is also studied

Abstract: The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on different PSMCs learn nearly the same function. This means that when these classifiers are applied to the same PSMC for testing, they result in nearly the same performance. This classifier universality indicates that a machine learning model trained on one simulation and tested on another simulation (or data) will likely be optimal. Our observations are based on detailed studies of shallow and deep neural networks applied to simulated Lorentz boosted Higgs jet tagging at the LHC.

Increased anthropogenic-induced aerosol concentrations over the Himalayas and Tibetan Plateau have affected regional climate, accelerated snow/glacier melting, and influenced water supply and quality in Asia. Although sulfate is a predominant chemical component in aerosols and the hydrosphere, the contributions from different sources remain contentious. Here, we report multiple sulfur isotope composition of sedimentary sulfates from a remote freshwater alpine lake near Mount Everest to reconstruct a two-century record of the atmospheric sulfur cycle. The sulfur isotopic anomaly is utilized as a probe for sulfur source apportionment and chemical transformation history. The nineteenth-century record displays a distinct sulfur isotopic signature compared with the twentieth-century record when sulfate concentrations increased. Along with other elemental measurements, the isotopic proxy suggests that the increased trend of sulfate is mainly attributed to enhancements of dust-associated sulfate aerosols and climate-induced weathering/erosion, which overprinted sulfur isotopic anomalies originating from other sources (e.g., sulfates produced in the stratosphere by photolytic oxidation processes and/or emitted from combustion) as observed in most modern tropospheric aerosols. The changes in sulfur cycling reported in this study have implications for better quantification of radiative forcing and snow/glacier melting at this climatically sensitive region and potentially other temperate glacial hydrological systems. Additionally, the unique Δ³³S-δ³⁴S pattern in the nineteenth century, a period with extensive global biomass burning, is similar to the Paleoarchean (3.6-3.2 Ga) barite record, potentially providing a deeper insight into sulfur photochemical/thermal reactions and possible volcanic influences on the Earth's earliest sulfur cycle.

The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph-nodes represent hits, while edges represent possible track segments-and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called hls4ml, for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.

FASER is a proposed small and inexpensive experiment designed to search for light, weakly-interacting particles at the LHC. Such particles are dominantly produced along the beam collision axis and may be long-lived, traveling hundreds of meters before decaying. To exploit both of these properties, FASER is to be located along the beam collision axis, 480 m downstream from the ATLAS interaction point, in the unused service tunnel TI18. We propose that FASER be installed in TI18 in Long Shutdown 2 in time to collect data from 2021-23 during Run 3 of the 14 TeV LHC. FASER will detect new particles that decay within a cylindrical volume with radius R= 10 cm and length L = 1.5 m. With these small dimensions, FASER will complement the LHC's existing physics program, extending its discovery potential to a host of new particles, including dark photons, axion-like particles, and other CP-odd scalars. A FLUKA simulation and analytical estimates have confirmed that numerous potential backgrounds are highly suppressed at the FASER location, and the first in situ measurements are currently underway. We describe FASER's location and discovery potential, its target signals and backgrounds, the detector's layout and components, and the experiment's preliminary cost estimate, funding, and timeline.

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.

The ForwArd Search ExpeRiment (FASER) is an approved experiment dedicated to searching for light, extremely weakly interacting particles at the LHC. Such particles may be produced in the LHC's high-energy collisions and travel long distances through concrete and rock without interacting. They may then decay to visible particles in FASER, which is placed 480 m downstream of the ATLAS interaction point. In this work we briefly describe the FASER detector layout and the status of potential backgrounds. We then present the sensitivity reach for FASER for a large number of long-lived particle models, updating previous results to a uniform set of detector assumptions, and analyzing new models. In particular, we consider all of the renormalizable portal interactions, leading to dark photons, dark Higgs bosons, and heavy neutral leptons; light B-L and Li-Lj gauge bosons; axionlike particles that are coupled dominantly to photons, fermions, and gluons through nonrenormalizable operators; and pseudoscalars with Yukawa-like couplings. We find that FASER and its follow-up, FASER 2, have a full physics program, with discovery sensitivity in all of these models and potentially far-reaching implications for particle physics and cosmology.

FASER, the ForwArd Search ExpeRiment, is a proposed experiment dedicated to searching for light, extremely weakly-interacting particles at the LHC. Such particles may be produced in the LHC's high-energy collisions in large numbers in the far-forward region and then travel long distances through concrete and rock without interacting. They may then decay to visible particles in FASER, which is placed 480 m downstream of the ATLAS interaction point. In this work, we describe the FASER program. In its first stage, FASER is an extremely compact and inexpensive detector, sensitive to decays in a cylindrical region of radius R = 10 cm and length L = 1.5 m. FASER is planned to be constructed and installed in Long Shutdown 2 and will collect data during Run 3 of the 14 TeV LHC from 2021-23. If FASER is successful, FASER 2, a much larger successor with roughly R ~ 1 m and L ~ 5 m, could be constructed in Long Shutdown 3 and collect data during the HL-LHC era from 2026-35. FASER and FASER 2 have the potential to discover dark photons, dark Higgs bosons, heavy neutral leptons, axion-like particles, and many other long-lived particles, as well as provide new information about neutrinos, with potentially far-ranging implications for particle physics and cosmology. We describe the current status, anticipated challenges, and discovery prospects of the FASER program.

FASER,the ForwArd Search ExpeRiment,is a proposed experiment dedicated to searching for light, extremely weakly-interacting particles at the LHC. Such particles may be produced in the LHC's high-energy collisions and travel long distances through concrete and rock without interacting. They may then decay to visible particles in FASER, which is placed 480 m downstream of the ATLAS interaction point. In this work we briefly describe the FASER detector layout and the status of potential backgrounds. We then present the sensitivity reach for FASER for a large number of long-lived particle models, updating previous results to a uniform set of detector assumptions, and analyzing new models. In particular, we consider all of the renormalizable portal interactions, leading to dark photons, dark Higgs bosons, and heavy neutral leptons (HNLs); light B-L and $L_i - L_j$ gauge bosons; axion-like particles (ALPs) that are coupled dominantly to photons, fermions, and gluons through non-renormalizable operators; and pseudoscalars with Yukawa-like couplings. We find that FASER and its follow-up, FASER 2, have a full physics program, with discovery sensitivity in all of these models and potentially far-reaching implications for particle physics and cosmology.