Engineering Papers⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1922522

First machine learning gravitational-wave search mock data challenge

Schäfer, Marlin B. (ORCID:0000000269900627)·Zelenka, Ondřej (ORCID:0000000336391587)·Nitz, Alexander H. (ORCID:0000000218504587)·Wang, He (ORCID:000000021353391X)·Wu, Shichao·Guo, Zong-Kuan (ORCID:0000000271502855)·Cao, Zhoujian (ORCID:0000000219327295)·Ren, Zhixiang (ORCID:0000000241043790)·Nousi, Paraskevi (ORCID:0000000230873174)·Stergioulas, Nikolaos (ORCID:0000000254905302)·Iosif, Panagiotis (ORCID:0000000316217709)·Koloniari, Alexandra E. (ORCID:0000000205465638)·Tefas, Anastasios·Passalis, Nikolaos·Salemi, Francesco (ORCID:0000000295113846)·Vedovato, Gabriele (ORCID:0000000172261320)·Klimenko, Sergey·Mishra, Tanmaya (ORCID:0000000278811677)·Brügmann, Bernd (ORCID:0000000346230525)·Cuoco, Elena (ORCID:0000000265283449)·Huerta, E. A. (ORCID:0000000296823604)·Messenger, Chris·Ohme, Frank (ORCID:0000000304935607)
View original source ↗Export metadata ↓

Abstract

Not Available

Explore related subjects

79 ASTRONOMY AND ASTROPHYSICSgravitational wave detectiongravitational wavesmachine learning

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Schäfer, Marlin B. (ORCID:0000000269900627), Zelenka, Ondřej (ORCID:0000000336391587), Nitz, Alexander H. (ORCID:0000000218504587), Wang, He (ORCID:000000021353391X), Wu, Shichao, Guo, Zong-Kuan (ORCID:0000000271502855), Cao, Zhoujian (ORCID:0000000219327295), Ren, Zhixiang (ORCID:0000000241043790), Nousi, Paraskevi (ORCID:0000000230873174), Stergioulas, Nikolaos (ORCID:0000000254905302), Iosif, Panagiotis (ORCID:0000000316217709), Koloniari, Alexandra E. (ORCID:0000000205465638), Tefas, Anastasios, Passalis, Nikolaos, Salemi, Francesco (ORCID:0000000295113846), Vedovato, Gabriele (ORCID:0000000172261320), Klimenko, Sergey, Mishra, Tanmaya (ORCID:0000000278811677), Brügmann, Bernd (ORCID:0000000346230525), Cuoco, Elena (ORCID:0000000265283449), Huerta, E. A. (ORCID:0000000296823604), Messenger, Chris, Ohme, Frank (ORCID:0000000304935607). 2023-01-27. First machine learning gravitational-wave search mock data challenge. https://doi.org/10.1103/physrevd.107.023021

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Dust Survival in Galactic Winds

This repository contains three-dimensional volumetric data from an Eulerian hydrodynamical simulation (conducted on a uniform Cartesian grid) generated by the Cholla hydrodynamics code. The datasets contain snapshots (full-grid, projections, and slices) in the HDF5 format of a multi-phase medium in which a hot, diffuse, dust-free background wind accelerates a cool, dense cloud of gas and dust. This scenario is intended to represent a supernova-driven galactic outflow, in which hot supernova winds are thought to accelerate cool interstellar medium material out of the galactic disk into the surrounding circumgalactic medium. There are three separate datasets for simulations corresponding to three cloud evolutionary scenarios: long-term cloud survival (surv), marginal cloud survival (disr), and cloud destruction (dest). Projection and slice images of the simulations are also included in this repository.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Unraveling TeV halos with the Cherenkov Telescope Array

Pulsars are observed to emit bright and spatially extended gamma-ray emission at multi-TeV energies. These so-called "TeV halos" are now understood to be a nearly universal feature of middle-aged pulsars. However, many of the key physical processes that govern these systems, particularly those affecting particle diffusion, remain poorly constrained. We aim to evaluate the ability of the Cherenkov Telescope Array (CTA) to probe the physical properties of TeV halos, with a focus on the nearby and well-studied case of the Geminga pulsar. We simulate gamma-ray emission from various TeV halo models, incorporating different assumptions for the injected electron spectrum, spin-down evolution, and energy-dependent diffusion. These models are then used to forecast CTA's sensitivity to spectral and spatial differences, based on realistic mock observations and instrument response simulations. We find that CTA will be able to distinguish between a wide range of TeV halo models that are currently consistent with existing data. In particular, CTA observations can constrain the normalization, energy dependence, and spatial extent of the diffusion coefficient surrounding Geminga, as well as the spectral shape of the injected electron population.

79 ASTRONOMY AND ASTROPHYSICS↗