arXiv subjects

John Graham

Publications and source records attributed to John Graham.

14 recordsLinked to original sources

arXiv2026

Deep-Ocean Application-Specific Neutrino Experiment

This report introduces the concept, prototype design, projected costs, and scientific goals of a mobile experiment for detecting geoneutrinos originating from uranium and thorium decay chains in the Earth's mantle. This will constrain the planet's radiogenic heat production and unearth its geochemical makeup. This design of a deep-ocean mobile neutrino experiment, which is not mirrored by any active or planned experiments, supports physics and geoscience's goal of multi-modal data on the Earth's internal composition and structure. Based on geoscientific studies, this design is expected to achieve a 50--100-fold reduction in crustal background compared to similarly sized continental detectors, thereby enabling direct measurements of mantle geoneutrinos. The multiple stereoscopic projections enabled by the detector's unique mobility can map spatial variations in heat-producing elements within the mantle. Beyond discussing the design, we report on our collaboration's most recent hardware developments in the active prototyping of this detector. We briefly highlight the potential multiuse and interdisciplinary nature of this detector.

physics.ins-det↗

arXiv2026

Towards imaging Earth's large-scale structures by directional geoneutrino detection with Ocean Bottom Detector

Geoneutrinos, electron antineutrinos produced by radioactive decays of heat-producing elements (HPEs) within the Earth, provide unique insights into Earth's interior and heat budget since their first detection in 2005 by KamLAND. Conventional geoneutrino detectors currently provide integrated global information and lack the capability to spatially resolve structures deep within the Earth. Here, we evaluate the ability of angular-sensitive geoneutrino detectors to distinguish between homogeneous and heterogeneous mantle models, focusing on Large Low Shear Velocity Provinces (LLSVPs). Our results show that LLSVPs enriched in Th and U yield a distinct flux of geoneutrinos with distinctive angular patterns. An oceanic site above the Pacific LLSVP is considered a particularly favorable detector location. The Ocean Bottom Detector (OBD) project aims to leverage this spatial resolving advantage by deploying a kiloton-scale liquid scintillator detector directly on the ocean floor, enabling unprecedented sensitivity for mantle geoneutrino detection. These findings demonstrate the critical role of combining geophysical and geochemical data to guide detector site selection, ultimately improving constraints on Earth's internal heat and the HPE distribution.

physics.geo-ph↗

arXiv2025

Benchmarking XRootD-HTTPS on 400Gbps Links with Variable Latencies

In anticipation of the High Luminosity-LHC era, there is a critical need to oversee software readiness for upcoming growth in network traffic for production and user data analysis access. This paper looks into software and hardware required improvements in US-CMS Tier-2 sites to be able to sustain and meet the projected 400 Gbps bandwidth demands while tackling the challenge posed by varying latencies between sites. Specifically, our study focuses on identifying the performance of XRootD HTTP third-party copies across multiple 400 Gbps links and exploring different host and transfer configurations. Our approach involves systematic testing with variations in the number of origins per cluster and CPU allocations for each origin. By replicating real network conditions and creating network "loops" that traverse multiple switches across the wide area network, we are able to replicate authentic network conditions

cs.NI↗

arXiv2025

The National Research Platform: Stretched, Multi-Tenant, Scientific Kubernetes Cluster

The National Research Platform (NRP) represents a distributed, multi-tenant Kubernetes-based cyberinfrastructure designed to facilitate collaborative scientific computing. Spanning over 75 locations in the U.S. and internationally, the NRP uniquely integrates varied computational resources, ranging from single nodes to extensive GPU and CPU clusters, to support diverse research workloads including advanced AI and machine learning tasks. It emphasizes flexibility through user-friendly interfaces such as JupyterHub and low level control of resources through direct Kubernetes interaction. Critical operational insights are discussed, including security enhancements using Kubernetes-integrated threat detection, extensive monitoring, and comprehensive accounting systems. This paper highlights the NRP's growing importance and scalability in addressing the increasing demands for distributed scientific computational resources.

cs.DC↗

arXiv2022

Towards a Dynamic Composability Approach for using Heterogeneous Systems in Remote Sensing

Influenced by the advances in data and computing, the scientific practice increasingly involves machine learning and artificial intelligence driven methods which requires specialized capabilities at the system-, science- and service-level in addition to the conventional large-capacity supercomputing approaches. The latest distributed architectures built around the composability of data-centric applications led to the emergence of a new ecosystem for container coordination and integration. However, there is still a divide between the application development pipelines of existing supercomputing environments, and these new dynamic environments that disaggregate fluid resource pools through accessible, portable and re-programmable interfaces. New approaches for dynamic composability of heterogeneous systems are needed to further advance the data-driven scientific practice for the purpose of more efficient computing and usable tools for specific scientific domains. In this paper, we present a novel approach for using composable systems in the intersection between scientific computing, artificial intelligence (AI), and remote sensing domain. We describe the architecture of a first working example of a composable infrastructure that federates Expanse, an NSF-funded supercomputer, with Nautilus, a Kubernetes-based GPU geo-distributed cluster. We also summarize a case study in wildfire modeling, that demonstrates the application of this new infrastructure in scientific workflows: a composed system that bridges the insights from edge sensing, AI and computing capabilities with a physics-driven simulation.

cs.DC↗

arXiv2022

Managed Network Services for Exascale Data Movement Across Large Global Scientific Collaborations

Unique scientific instruments designed and operated by large global collaborations are expected to produce Exabyte-scale data volumes per year by 2030. These collaborations depend on globally distributed storage and compute to turn raw data into science. While all of these infrastructures have batch scheduling capabilities to share compute, Research and Education networks lack those capabilities. There is thus uncontrolled competition for bandwidth between and within collaborations. As a result, data "hogs" disk space at processing facilities for much longer than it takes to process, leading to vastly over-provisioned storage infrastructures. Integrated co-scheduling of networks as part of high-level managed workflows might reduce these storage needs by more than an order of magnitude. This paper describes such a solution, demonstrates its functionality in the context of the Large Hadron Collider (LHC) at CERN, and presents the next-steps towards its use in production.

cs.NI↗

arXiv2022

Data Transfer and Network Services management for Domain Science Workflows

This paper describes a vision and work in progress to elevate network resources and data transfer management to the same level as compute and storage in the context of services access, scheduling, life cycle management, and orchestration. While domain science workflows often include active compute resource allocation and management, the data transfers and associated network resource coordination is not handled in a similar manner. As a result data transfers can introduce a degree of uncertainty in workflow operations, and the associated lack of network information does not allow for either the workflow operations or the network use to be optimized. The net result is that domain science workflow processes are forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. Developing mechanisms to allow an application workflow to obtain information regarding the network services, capabilities, and options, to a degree similar to what is possible for compute resources is the primary motivation for this work. The initial focus is on the Open Science Grid (OSG)/Compact Muon Solenoid (CMS) Large Hadron Collider (LHC) workflows with Rucio/FTS/XRootD based data transfers and the interoperation with the ESnet SENSE (Software-Defined Network for End-to-end Networked Science at the Exascale) system.

cs.NI↗

arXiv2021

HTCondor data movement at 100 Gbps

HTCondor is a major workload management system used in distributed high throughput computing (dHTC) environments, e.g., the Open Science Grid. One of the distinguishing features of HTCondor is the native support for data movement, allowing it to operate without a shared filesystem. Coupling data handling and compute scheduling is both convenient for users and allows for significant infrastructure flexibility but does introduce some limitations. The default HTCondor data transfer mechanism routes both the input and output data through the submission node, making it a potential bottleneck. In this document we show that by using a node equipped with a 100 Gbps network interface (NIC) HTCondor can serve data at up to 90 Gbps, which is sufficient for most current use cases, as it would saturate the border network links of most research universities at the time of writing.

cs.DC↗

arXiv2020

Characterizing network paths in and out of the clouds

Commercial Cloud computing is becoming mainstream, with funding agencies moving beyond prototyping and starting to fund production campaigns, too. An important aspect of any scientific computing production campaign is data movement, both incoming and outgoing. And while the performance and cost of VMs is relatively well understood, the network performance and cost is not. This paper provides a characterization of networking in various regions of Amazon Web Services, Microsoft Azure and Google Cloud Platform, both between Cloud resources and major DTNs in the Pacific Research Platform, including OSG data federation caches in the network backbone, and inside the clouds themselves. The paper contains both a qualitative analysis of the results as well as latency and throughput measurements. It also includes an analysis of the costs involved with Cloud-based networking.

cs.NI↗

arXiv2019

The late-time afterglow evolution of long gamma-ray bursts GRB 160625B and GRB 160509A

We present post-jet-break \textit{HST}, VLA and \textit{Chandra} observations of the afterglow of the long $\gamma$-ray bursts GRB 160625B (between 69 and 209 days) and GRB 160509A (between 35 and 80 days). We calculate the post-jet-break decline rates of the light curves, and find the afterglow of GRB 160625B inconsistent with a simple $t^{-3/4}$ steepening over the break, expected from the geometric effect of the jet edge entering our line of sight. However, the favored optical post-break decline ($f_{\nu} \propto t^{-1.96 \pm 0.07}$) is also inconsistent with the $f_{\nu} \propto t^{-p}$ decline (where $p \approx 2.3$ from the pre-break light curve), which is expected from exponential lateral expansion of the jet; perhaps suggesting lateral expansion that only affects a fraction of the jet. The post-break decline of GRB 160509A is consistent with both the $t^{-3/4}$ steepening and with $f_{\nu} \propto t^{-p}$. We also use {\sc boxfit} to fit afterglow models to both light curves and find both to be energetically consistent with a millisecond magnetar central engine, although the magnetar parameters need to be extreme (i.e. $E \sim 3 \times 10^{52}$ erg). Finally, the late-time radio light curves of both afterglows are not reproduced well by {\sc boxfit} and are inconsistent with predictions from the standard jet model; instead both are well represented by a single power law decline (roughly $f_{\nu} \propto t^{-1}$) with no breaks. This requires a highly chromatic jet break ($t_{j,\mathrm{radio}} > 10 \times t_{j,\mathrm{optical}}$) and possibly a two-component jet for both bursts.

astro-ph.HE↗

arXiv2019

Workflow-Driven Distributed Machine Learning in CHASE-CI: A Cognitive Hardware and Software Ecosystem Community Infrastructure

The advances in data, computing and networking over the last two decades led to a shift in many application domains that includes machine learning on big data as a part of the scientific process, requiring new capabilities for integrated and distributed hardware and software infrastructure. This paper contributes a workflow-driven approach for dynamic data-driven application development on top of a new kind of networked Cyberinfrastructure called CHASE-CI. In particular, we present: 1) The architecture for CHASE-CI, a network of distributed fast GPU appliances for machine learning and storage managed through Kubernetes on the high-speed (10-100Gbps) Pacific Research Platform (PRP); 2) A machine learning software containerization approach and libraries required for turning such a network into a distributed computer for big data analysis; 3) An atmospheric science case study that can only be made scalable with an infrastructure like CHASE-CI; 4) Capabilities for virtual cluster management for data communication and analysis in a dynamically scalable fashion, and visualization across the network in specialized visualization facilities in near real-time; and, 5) A step-by-step workflow and performance measurement approach that enables taking advantage of the dynamic architecture of the CHASE-CI network and container management infrastructure.

cs.DC↗

arXiv2007

GRB051022: physical parameters and extinction of a prototype dark burst

GRB051022 was undetected to deep limits in early optical observations, but precise astrometry from radio and X-ray showed that it most likely originated in a galaxy at z~0.8. We report radio, optical, near infra-red and X-ray observations of GRB051022. Using the available X-ray and radio data, we model the afterglow and calculate the energetics of the afterglow, finding it to be an order of magnitude lower than that of the prompt emission. The broad-band modeling also allows us to precisely define various other physical parameters and the minimum required amount of extinction, to explain the absence of an optical afterglow. Our observations suggest a high extinction, at least 2.3 magnitudes in the infrared (J) and at least 5.4 magnitudes in the optical (U) in the host-galaxy restframe. Such high extinctions are unusual for GRBs, and likely indicate a geometry where our line of sight to the burst passes through a dusty region in the host that is not directly co-located with the burst itself.

astro-ph↗

arXiv1999

The Hubble Constant from the HST Key Project on the Extragalactic Distance Scale

The final efforts of the HST Key Project on the Extragalactic Distance Scale are presented. Four distance indicators, the Surface Brightness Fluctuation method, the Fundamental Plane for early-type galaxies, the Tully-Fisher relation and the Type Ia Supernovae, are calibrated using Cepheid distances to galaxies within 25 Mpc. The calibration is then applied to distant samples reaching cz~10000 km/s and (in the case of SNIa) beyond. By combining the constraints imposed on the Hubble constant by the four distance indicators, we obtain H0 = 71+/-6 km/s/Mpc.

astro-ph↗

arXiv1998

The HST Key Project on the Extragalactic Distance Scale. XV. A Cepheid Distance to the Fornax Cluster and Its Implications

Using the Hubble Space Telescope (HST) 37 long-period Cepheid variables have been discovered in the Fornax Cluster spiral galaxy NGC 1365. The resulting V and I period-luminosity relations yield a true distance modulus of 31.35 +/- 0.07 mag, which corresponds to a distance of 18.6 +/- 0.6 Mpc. This measurement provides several routes for estimating the Hubble Constant. (1) Assuming this distance for the Fornax Cluster as a whole yields a local Hubble Constant of 70 +/-18_{random} [+/-7]_{systematic} km/s/Mpc. (2) Nine Cepheid-based distances to groups of galaxies out to and including the Fornax and Virgo clusters yield Ho = 73 (+/-16)_r [+/-7]_s km/s/Mpc. (3) Recalibrating the I-band Tully-Fisher relation using NGC 1365 and six nearby spiral galaxies, and applying it to 15 galaxy clusters out to 100 Mpc gives Ho = 76 (+/-3)_r [+/-8]_s km/s/Mpc. (4) Using a broad-based set of differential cluster distance moduli ranging from Fornax to Abell 2147 gives Ho = 72 (+/-)_r [+/-6]_s km/s/Mpc. And finally, (5) Assuming the NGC 1365 distance for the two additional Type Ia supernovae in Fornax and adding them to the SnIa calibration (correcting for light curve shape) gives Ho = 67 (+/-6)_r [+/-7]_s km/s/Mpc out to a distance in excess of 500 Mpc. All five of these Ho determinations agree to within their statistical errors. The resulting estimate of the Hubble Constant combining all these determinations is Ho = 72 (+/-5)_r [+/-12]_s km/s/Mpc.

astro-ph↗