arXiv subjects

J. P. Naiman

Publications and source records attributed to J. P. Naiman.

12 recordsLinked to original sources

arXiv2024

Audience Reach of Scientific Data Visualizations in Planetarium-Screened Films

Quantifying the global reach of planetarium dome shows presents significant challenges due to the lack of standardized viewership tracking mechanisms across diverse planetarium venues. We present an analysis of the global impact of dome shows, presenting data regarding four documentary films from a single visualization lab. Specifically, we designed and administered a viewership survey of four long-running shows that contained cinematic scientific visualizations. Reported survey data shows that between 1.2 - 2.6 million people have viewed these four films across the 68 responding planetariums (mean: 1.9 million). When we include estimates and extrapolate for the 315 planetariums that licensed these shows, we arrive at an estimate of 16.5 - 24.1 million people having seen these films (mean: 20.3 million).

physics.pop-ph↗

arXiv2024

pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy

The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with keywords. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), Pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use keywords or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool's versatility through case studies, showcasing its application in various research scenarios. The system's performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, Pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature.

astro-ph.IM↗

arXiv2022

Figure and Figure Caption Extraction for Mixed Raster and Vector PDFs: Digitization of Astronomical Literature with OCR Features

Scientific articles published prior to the "age of digitization" in the late 1990s contain figures which are "trapped" within their scanned pages. While progress to extract figures and their captions has been made, there is currently no robust method for this process. We present a YOLO-based method for use on scanned pages, post-Optical Character Recognition (OCR), which uses both grayscale and OCR-features. When applied to the astrophysics literature holdings of the Astrophysics Data System (ADS), we find F1 scores of 90.9% (92.2%) for figures (figure captions) with the intersection-over-union (IOU) cut-off of 0.9 which is a significant improvement over other state-of-the-art methods.

astro-ph.IM↗

arXiv2021

CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data

Artifact removal is an integral component of cinematic scientific visualization, and is especially challenging with big datasets in which artifacts are difficult to define. In this paper, we describe a method for creating cloud artifact masks which can be used to remove artifacts from satellite imagery using a combination of traditional image processing together with deep learning based on U-Net. Compared to previous methods, our approach does not require multi-channel spectral imagery but performs successfully on single-channel Digital Elevation Models (DEMs). DEMs are a representation of the topography of the Earth and have a variety applications including planetary science, geology, flood modeling, and city planning.

cs.CV↗

arXiv2020

Clustering-informed Cinematic Astrophysical Data Visualization with Application to the Moon-forming Terrestrial Synestia

Scientific visualization tools are currently not optimized to create cinematic, production-quality representations of numerical data for the purpose of science communication. In our pipeline \texttt{Estra}, we outline a step-by-step process from a raw simulation into a finished render as a way to teach non-experts in the field of visualization how to achieve production-quality outputs on their own. We demonstrate feasibility of using the visual effects software Houdini for cinematic astrophysical data visualization, informed by machine learning clustering algorithms. To demonstrate the capabilities of this pipeline, we used a post-impact, thermally-equilibrated Moon-forming synestia from \cite{Lock18}. Our approach aims to identify "physically interpretable" clusters, where clusters identified in an appropriate phase space (e.g. here we use a temperature-entropy phase-space) correspond to physically meaningful structures within the simulation data. Clustering results can then be used to highlight these structures by informing the color-mapping process in a simplified Houdini software shading network, where dissimilar phase-space clusters are mapped to different color values for easier visual identification. Cluster information can also be used in 3D position space, via Houdini's Scene View, to aid in physical cluster finding, simulation prototyping, and data exploration. Our clustering-based renders are compared to those created by the Advanced Visualization Lab (AVL) team for the full dome show "Imagine the Moon" as proof of concept. With \texttt{Estra}, scientists have a tool to create their own production-quality, data-driven visualizations.

astro-ph.IM↗

arXiv2018

Cinematic Visualization of Multiresolution Data: Ytini for Adaptive Mesh Refinement in Houdini

We have entered the era of large multidimensional datasets represented by increasingly complex data structures. Current tools for scientific visualization are not optimized to efficiently and intuitively create cinematic production quality, time-evolving representations of numerical data for broad impact science communication via film, media, or journalism. To present such data in a cinematic environment, it is advantageous to develop methods that integrate these complex data structures into industry standard visual effects software packages, which provide a myriad of control features otherwise unavailable in traditional scientific visualization software. In this paper, we present the general methodology for the import and visualization of nested multiresolution datasets into commercially available visual effects software. We further provide a specific example of importing Adaptive Mesh Refinement data into the software Houdini. This paper builds on our previous work, which describes a method for using Houdini to visualize uniform Cartesian datasets. We summarize a tutorial available on the website www.ytini.com, which includes sample data downloads, Python code, and various other resources to simplify the process of importing and rendering multiresolution data.

cs.GR↗

arXiv2018

Should I Stay or Should I Go: Stellar Wind Retention and Expulsion in Massive Star Clusters

Mass and energy injection throughout the lifetime of a star cluster contributes to the gas reservoir available for subsequent episodes of star formation and the feedback energy budget responsible for ejecting material from the cluster. In addition, mass processed in stellar interiors and ejected as winds has the potential to augment the abundance ratios of currently forming stars, or stars which form at a later time from a retained gas reservoir. Here we present hydrodynamical simulations that explore a wide range of cluster masses, compactnesses, metallicities and stellar population age combinations in order to determine the range of parameter space conducive to stellar wind retention or wind powered gas expulsion in star clusters. We discuss the effects of the stellar wind prescription on retention and expulsion effectiveness, using MESA stellar evolutionary models as a test bed for exploring how the amounts of wind retention/expulsion depend upon the amount of mixing between the winds from stars of different masses and ages. We conclude by summarizing some implications for gas retention and expulsion in a variety of compact ($σ_v \gtrsim 20 \, {\rm km s^{-1}}$) star clusters including young massive star clusters ($10^5 \lesssim M/M_\odot \lesssim 10^7$, $age \lesssim 500$~Myrs), intermediate age clusters ($10^5 \lesssim M/M_\odot \lesssim 10^7$, $age \approx 1-4$~Gyrs), and globular clusters ($10^5 \lesssim M/M_\odot \lesssim 10^7$, $age \gtrsim 10$~Gyrs).

astro-ph.GA↗

arXiv2017

Houdini for Astrophysical Visualization

The rapid growth in scale and complexity of both computational and observational astrophysics over the past decade necessitates efficient and intuitive methods for examining and visualizing large datasets. Here we discuss some newly developed tools to import and manipulate astrophysical data into the three dimensional visual effects software, {\it Houdini}. This software is widely used by visual effects artists, but a recently implemented Python API now allows astronomers to more easily use Houdini as a visualization tool. This paper includes a description of features, work flow, and various example visualizations. The project website, www.ytini.com, contains Houdini tutorials and links to the Python script Bitbucket repository aimed at a scientific audience to simplify the process of importing and rendering astrophysical data.

astro-ph.IM↗

arXiv2016

AstroBlend: An Astrophysical Visualization Package for Blender

The rapid growth in scale and complexity of both computational and observational astrophysics over the past decade necessitates efficient and intuitive methods for examining and visualizing large datasets. Here, I present {\it AstroBlend}, an open-source Python library for use within the three dimensional modeling software, {\it Blender}. While {\it Blender} has been a popular open-source software among animators and visual effects artists, in recent years it has also become a tool for visualizing astrophysical datasets. {\it AstroBlend} combines the three dimensional capabilities of {\it Blender} with the analysis tools of the widely used astrophysical toolset, {\it yt}, to afford both computational and observational astrophysicists the ability to simultaneously analyze their data and create informative and appealing visualizations. The introduction of this package includes a description of features, work flow, and various example visualizations. A website - www.astroblend.com - has been developed which includes tutorials, and a gallery of example images and movies, along with links to downloadable data, three dimensional artistic models, and various other resources.

astro-ph.IM↗

arXiv2015

The Role of Nuclear Star Clusters in Enhancing Supermassive Black Hole Feeding Rates During Galaxy Mergers

During galaxy mergers the gas falls to the center, triggers star formation, and feeds the rapid growth of supermassive black holes (SMBHs). SMBHs respond to this fueling by supplying energy back to the ambient gas. Numerical studies suggest that this feedback is necessary to explain why the properties of SMBHs and the formation of bulges are closely related. This intimate link between the SMBH's mass and the large scale dynamics and luminosity of the host has proven to be a difficult issue to tackle with simulations due to the inability to resolve all the relevant length scales simultaneously. In this paper we simulate SMBH growth at high-resolution with {\it FLASH}, accounting for the gravitational focusing effects of nuclear star clusters (NSCs), which appear to be ubiquitous in galactic nuclei. In the simulations, the NSC core is resolved by a minimum cell size of about 0.001 pc or approximately $10^{-3}$ of the cluster's radius. We discuss the conditions required for effective gas funneling to occur, which are mainly dominated by a relationship between NSC velocity dispersion and the local sound speed, and provide a sub-grid prescription for the augmentation of central SMBH accretion rates in the presence of NSCs. For the conditions expected to persist in the centers of merging galaxies, the resultant large central gas densities in NSCs should produce drastically enhanced embedded SMBH accretion rates - up to an order of magnitude increase can be achieved for gas properties resembling those in large-scale galaxy merger simulations. This will naturally result in faster black hole growth rates and higher luminosities than predicted by the commonly used Bondi-Hoyle-Lyttleton accretion formalism.

astro-ph.GA↗

arXiv2011

External Mass Accumulation onto Core Potentials: Implications for Star Clusters, Galaxies and Galaxy Clusters

Accretion studies have been focused on the flow around bodies with point mass gravitational potentials, but few general results are available for non-point mass distributions. Here, we study the accretion flow onto non-divergent, core potentials moving through a background medium. We use Plummer and Hernquist potentials as examples to study gas accretion onto star clusters, dwarf and large galaxy halos and galaxy clusters in a variety of astrophysical environments. The general conditions required for a core potential to collectively accrete large quantities of gas from the external medium are derived using both simulations and analytic results. The consequences of large mass accumulation in galaxy nuclei, dwarf galaxies and star clusters are twofold. First, if the gas cools effectively star formation can be triggered, generating new stellar members in the system. Second, if the collective potential of the system is able to alter the ambient gas properties before the gas is accreted onto the individual core members, the augmented mass supply rates could significantly alter the state of the various accreting stellar populations and result in an enhanced central black hole accretion luminosity.

astro-ph.CO↗

arXiv2009

Gas Accretion by Star Clusters and the Formation of Ultraluminous X-ray Sources from Cusps of Compact Remnants

Here we show that the overabundance of ultra-luminous, compact X-ray sources (ULXs) associated with moderately young clusters in interacting galaxies such as the Antennae and Cartwheel can be given an alternative explanation that does not involve the presence of intermediate mass black holes (IMBHs). We argue that gas density within these systems is enhanced by the collective potential of the cluster prior to being accreted onto the individual cluster members and, as a result, the aggregate X-ray luminosity arising from the neutron star cluster members can exceed $>10^{39} {\rm erg s^{-1}}$. Various observational tests to distinguish between IMBHs and accreting neutron star cusps are discussed.

astro-ph.HE↗