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Playing pool with neutrinosHard to believe you can play pool with neutrinos, but certain neutrino events are closer to the game than you think. These special interactions involve a neutrino — famously elusive — striking a particle inside a nucleus like a billiard ball. MINERvA scientists study the dynamics of this subatomic ricochet to learn about the neutrino that triggered the collision. Now they have measured the probability of these quasielastic interactions using Fermilab's medium-energy neutrino beam. Such measurements are important for current and future neutrino experiments.

High-Statistics Measurement of Antineutrino Quasielastic-like scattering at $E_ν\sim$ 6~GeV on a Hydrocarbon TargetWe present measurements of the cross section for anti-neutrino charged-current quasielastic-like scattering on hydrocarbon using the medium energy (ME) \numi wide-band neutrino beam peaking at $<E_ν>\sim 6$ GeV. The cross section measurements are presented as a function of the longitudinal momentum ($p_{||}$) and transverse momentum ($p_{T}$) of the final state muon. This work complements our previously reported high statistics measurement in the neutrino channel and extends the previous anti-neutrino measurement made in the low energy (LE) beam at neutrino energy($<E_ν>$) $\sim$ 3.5 GeV to $p_{T}$ of 2.5 GeV/c. Current theoretical models do not completely describe the data in this previously unexplored high $p_{T}$ region. The single differential cross section as a function of four momentum transfer ($Q^{2}_{QE}$) now extends to 4 GeV$^2$ with high statistics. The cross section as a function of $Q^{2}_{QE}$ shows that the tuned simulations developed by the \minerva collaboration that agreed well with the low energy beam measurements do not agree as well with the medium energy beam measurements. Newer neutrino interaction models such as the GENIE 3 tunes are better able to simulate the high $Q^{2}_{QE}$.

Neutrino Structure Functions from GeV to EeV EnergiesThe interpretation of present and future neutrino experiments requires accurate theoretical predictions for neutrino-nucleus scattering rates. Neutrino structure functions can be reliably evaluated in the deep-inelastic scattering regime within the perturbative QCD (pQCD) framework. At low momentum transfers ($Q^2 \le {\rm few}$ GeV$^2$), inelastic structure functions are however affected by large uncertainties which distort event rate predictions for neutrino energies $E_ν$ up to the TeV scale. Here we present a determination of neutrino inelastic structure functions valid for the complete range of energies relevant for phenomenology, from the GeV region entering oscillation analyses to the multi-EeV region accessible at neutrino telescopes. Our NNSF$ν$ approach combines a machine-learning parametrisation of experimental data with pQCD calculations based on state-of-the-art analyses of proton and nuclear parton distributions (PDFs). We compare our determination to other calculations, in particular to the popular Bodek-Yang model. We provide updated predictions for inclusive cross sections for a range of energies and target nuclei, including those relevant for LHC far-forward neutrino experiments such as FASER$ν$, SND@LHC, and the Forward Physics Facility. The NNSF$ν$ determination is made available as fast interpolation LHAPDF grids, and can be accessed both through an independent driver code and directly interfaced to neutrino event generators such as GENIE.

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GPU-friendly data structures for real time simulation - Advanced Modeling and Simulation in Engineering SciencesSimulators for virtual surgery training need to perform complex calculations very quickly to provide realistic haptic and visual interactions with a user. The complexity is further increased by the addition of cuts to virtual organs, such as would be needed for performing tumor resection. A common method for achieving large performance improvements is to make use of the graphics hardware (GPU) available on most general-use computers. Programming GPUs requires data structures that are more rigid than on conventional processors (CPU), making that data more difficult to update. We propose a new method for structuring graph data, which is commonly used for physically based simulation of soft tissue during surgery, and deformable objects in general. Our method aligns all nodes of the graph in memory, independently from the number of edges they contain, allowing for local modifications that do not affect the rest of the structure. Our method also groups memory transfers so as to avoid updating the entire graph every time a small cut is introduced in a simulated organ. We implemented our data structure as part of a simulator based on a meshless method. Our tests show that the new GPU implementation, making use of the new graph structure, achieves a 10 times improvement in computation times compared to the previous CPU implementation. The grouping of data transfers into batches allows for a 80–90% reduction in the amount of data transferred for each graph update, but accounts only for a small improvement in performance. The data structure itself is simple to implement and allows simulating increasingly complex models that can be cut at interactive rates.

Amit Bashyal - Fermilab Paper Submission Guideline You have finally completed writing the paper, gone through committee review and collaboration review (or whatever the guidelines are) and the paper is signed off by the collaboration. Congratulations! However, the submission to the targeted journal paper as a collaborator of a Fermilab hosted

A GPU-friendly Geometric Data Model and Algebra for Spatial Queries: Extended VersionThe availability of low cost sensors has led to an unprecedented growth in the volume of spatial data. However, the time required to evaluate even simple spatial queries over large data sets greatly hampers our ability to interactively explore these data sets and extract actionable insights. Graphics Processing Units~(GPUs) are increasingly being used to speedup spatial queries. However, existing GPU-based solutions have two important drawbacks: they are often tightly coupled to the specific query types they target, making it hard to adapt them for other queries; and since their design is based on CPU-based approaches, it can be difficult to effectively utilize all the benefits provided by the GPU. As a first step towards making GPU spatial query processing mainstream, we propose a new model that represents spatial data as geometric objects and define an algebra consisting of GPU-friendly composable operators that operate over these objects. We demonstrate the expressiveness of the proposed algebra by formulating standard spatial queries as algebraic expressions. We also present a proof-of-concept prototype that supports a subset of the operators and show that it is at least two orders of magnitude faster than a CPU-based implementation. This performance gain is obtained both using a discrete Nvidia mobile GPU and the less powerful integrated GPUs common in commodity laptops.

You Can't See Me: Physical Removal Attacks on LiDAR-based Autonomous Vehicles Driving FrameworksAutonomous Vehicles (AVs) increasingly use LiDAR-based object detection systems to perceive other vehicles and pedestrians on the road. While existing attacks on LiDAR-based autonomous driving architectures focus on lowering the confidence score of AV object detection models to induce obstacle misdetection, our research discovers how to leverage laser-based spoofing techniques to selectively remove the LiDAR point cloud data of genuine obstacles at the sensor level before being used as input to the AV perception. The ablation of this critical LiDAR information causes autonomous driving obstacle detectors to fail to identify and locate obstacles and, consequently, induces AVs to make dangerous automatic driving decisions. In this paper, we present a method invisible to the human eye that hides objects and deceives autonomous vehicles' obstacle detectors by exploiting inherent automatic transformation and filtering processes of LiDAR sensor data integrated with autonomous driving frameworks. We call such attacks Physical Removal Attacks (PRA), and we demonstrate their effectiveness against three popular AV obstacle detectors (Apollo, Autoware, PointPillars), and we achieve 45° attack capability. We evaluate the attack impact on three fusion models (Frustum-ConvNet, AVOD, and Integrated-Semantic Level Fusion) and the consequences on the driving decision using LGSVL, an industry-grade simulator. In our moving vehicle scenarios, we achieve a 92.7% success rate removing 90\% of a target obstacle's cloud points. Finally, we demonstrate the attack's success against two popular defenses against spoofing and object hiding attacks and discuss two enhanced defense strategies to mitigate our attack.

Intel Shows How A CPU Is MadeIntel released a press kit that visually explains how a CPU is made and reveals all the major steps in a process that normally takes hundreds of stages to complete. Check out the engineering magic!

Theory of QED radiative corrections to neutrino scattering at accelerator energiesNeutrino beam experiments to measure neutrino cross sections on nuclear targets are part of a major experimental program. In this paper, the authors compute radiative QED corrections to neutrino-nucleus scattering using effective field theory methods that separate perturbative QED soft and collinear contributions from hard nuclear ones. Applications of this framework are promising because theoretical control of neutrino-nucleus scattering is crucial to making sense of experimental data.

What’s the best design for splash-free urinal? Physics now has the answerThe optimal splash-reducing angle for the average human is approximately 30 degrees.

CUDA TutorialsCUDA is an extension of C, and designed to let you do general purpose computation on a graphics processor. GPUs often far surpass the computational speed of even the fastest modern CPU today. If yo…

First measurement of quasi-elastic $Λ$ baryon production in muon anti-neutrino interactions in the MicroBooNE detectorWe present the first measurement of the cross section of Cabibbo-suppressed $Λ$ baryon production, using data collected with the MicroBooNE detector when exposed to the neutrinos from the Main Injector beam at the Fermi National Accelerator Laboratory. The data analyzed correspond to $2.2 \times 10^{20}$ protons on target of neutrino mode running and $4.9 \times 10^{20}$ protons on target of anti-neutrino mode running. An automated selection is combined with hand scanning, with the former identifying five candidate $Λ$ production events when the signal was unblinded, consistent with the GENIE prediction of $5.3 \pm 0.8$ events. Several scanners were employed, selecting between three and five events, compared with a prediction from a blinded Monte Carlo simulation study of $3.7 \pm 1.0$ events. Restricting the phase space to only include $Λ$ baryons that decay above MicroBooNE's detection thresholds, we obtain a flux averaged cross section of $2.0^{+2.1}_{-1.6} \times 10^{-40}$ cm$^2/$Ar, where statistical and systematic uncertainties are combined.

Ancient grammatical puzzle solved after 2,500 yearsA grammatical problem that has defeated Sanskrit scholars since the 5th century BC has finally been solved by an Indian Ph.D. student at the University of Cambridge. Rishi Rajpopat made the breakthrough by decoding a rule taught by "the father of linguistics," Pāṇini.

How has the Universe changed since last year?In the grand scheme of the cosmic story, a single year isn’t all that significant. But over time, the annual changes really add up!

A Sun-like star orbiting a black holeWe report discovery of a bright, nearby ($G = 13.8;\,\,d = 480\,\rm pc$) Sun-like star orbiting a dark object. We identified the system as a black hole candidate via its astrometric orbital solution from the Gaia mission. Radial velocities validated and refined the Gaia solution, and spectroscopy ruled out significant light contributions from another star. Joint modeling of radial velocities and astrometry constrains the companion mass to $M_2 = 9.62\pm 0.18\,M_{\odot}$. The spectroscopic orbit alone sets a minimum companion mass of $M_2>5\,M_{\odot}$; if the companion were a $5\,M_{\odot}$ star, it would be $500$ times more luminous than the entire system. These constraints are insensitive to the mass of the luminous star, which appears as a slowly-rotating G dwarf ($T_{\rm eff}=5850\,\rm K$, $\log g = 4.5$, $M=0.93\,M_{\odot}$), with near-solar metallicity ($\rm [Fe/H] = -0.2$) and an unremarkable abundance pattern. We find no plausible astrophysical scenario that can explain the orbit and does not involve a black hole. The orbital period, $P_{\rm orb}=185.6$ days, is longer than that of any known stellar-mass black hole binary. The system's modest eccentricity ($e=0.45$), high metallicity, and thin-disk Galactic orbit suggest that it was born in the Milky Way disk with at most a weak natal kick. How the system formed is uncertain. Common envelope evolution can only produce the system's wide orbit under extreme and likely unphysical assumptions. Formation models involving triples or dynamical assembly in an open cluster may be more promising. This is the nearest known black hole by a factor of 3, and its discovery suggests the existence of a sizable population of dormant black holes in binaries. Future Gaia releases will likely facilitate the discovery of dozens more.

Measurement of the axial vector form factor from antineutrino–proton scattering - NatureThe authors measure the nucleon axial vector form factor, which encodes information on the distribution of the nucleon weak charge, through antineutrino–proton scattering.

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https://openjicareport.jica.go.jp/pdf/11202181_03.pdf [JiCA Report on the Rivers of Nepal]

JWST Might Have Spotted the First Dark Matter StarsStars fueled by the self-annihilation of dark matter might have been spotted for the first time by JWST

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