Joshua S. Speagle

5.5k total citations · 4 hit papers
60 papers, 2.7k citations indexed

About

Joshua S. Speagle is a scholar working on Astronomy and Astrophysics, Instrumentation and Ecology. According to data from OpenAlex, Joshua S. Speagle has authored 60 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Astronomy and Astrophysics, 25 papers in Instrumentation and 6 papers in Ecology. Recurrent topics in Joshua S. Speagle's work include Stellar, planetary, and galactic studies (40 papers), Galaxies: Formation, Evolution, Phenomena (31 papers) and Astronomy and Astrophysical Research (25 papers). Joshua S. Speagle is often cited by papers focused on Stellar, planetary, and galactic studies (40 papers), Galaxies: Formation, Evolution, Phenomena (31 papers) and Astronomy and Astrophysical Research (25 papers). Joshua S. Speagle collaborates with scholars based in United States, Canada and Australia. Joshua S. Speagle's co-authors include Charlie Conroy, Benjamin D. Johnson, Joel Leja, Catherine Zucker, Douglas P. Finkbeiner, Adam C. Carnall, Alyssa Goodman, J. Alves, Gregory Green and Edward F. Schlafly and has published in prestigious journals such as Nature, The Astrophysical Journal and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

Joshua S. Speagle

54 papers receiving 2.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences

2020 1.1k citations Joshua S. Speagle Monthly Notices of the Royal Astronomical Society profile →
How to Measure Galaxy Star Formation Histories. II. Nonparametric Models

2019 253 citations Joel Leja, Benjamin D. Johnson et al. The Astrophysical Journal profile →
Star formation near the Sun is driven by expansion of the Local Bubble

2022 132 citations Catherine Zucker, Alyssa Goodman et al. Nature profile →
A parsec-scale Galactic 3D dust map out to 1.25 kpc from the Sun

2024 75 citations Catherine Zucker, Philipp Frank et al. Astronomy and Astrophysics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Joshua S. Speagle United States	17	2.5k	786	300	121	109	60	2.7k
Daniel Foreman-Mackey United States	27	3.2k 1.3×	1.1k 1.4×	316 1.1×	79 0.7×	84 0.8×	84	3.6k
Chris Flynn Australia	31	2.5k 1.0×	916 1.2×	260 0.9×	62 0.5×	56 0.5×	92	2.6k
Johannes Büchner Germany	22	2.3k 0.9×	459 0.6×	679 2.3×	63 0.5×	97 0.9×	89	2.6k
Dale D. Kocevski United States	30	3.1k 1.3×	1.3k 1.7×	673 2.2×	54 0.4×	48 0.4×	73	3.3k
Frederick B. Davies Germany	24	2.0k 0.8×	542 0.7×	639 2.1×	45 0.4×	58 0.5×	84	2.3k
J. L. Donley United States	17	2.5k 1.0×	781 1.0×	559 1.9×	43 0.4×	49 0.4×	22	2.6k
M. Zechmeister Germany	18	1.9k 0.8×	743 0.9×	107 0.4×	46 0.4×	43 0.4×	45	2.0k
H. M. Antia India	29	2.4k 1.0×	221 0.3×	365 1.2×	153 1.3×	102 0.9×	132	2.7k
A. Georgakakis Greece	31	3.9k 1.6×	1.3k 1.7×	1.1k 3.6×	52 0.4×	52 0.5×	113	4.1k
D. Mortlock United Kingdom	30	2.6k 1.1×	595 0.8×	731 2.4×	71 0.6×	23 0.2×	80	2.9k

Countries citing papers authored by Joshua S. Speagle

Since Specialization

Citations

This map shows the geographic impact of Joshua S. Speagle's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Joshua S. Speagle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joshua S. Speagle more than expected).

Fields of papers citing papers by Joshua S. Speagle

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joshua S. Speagle. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Joshua S. Speagle. The network helps show where Joshua S. Speagle may publish in the future.

Co-authorship network of co-authors of Joshua S. Speagle

This figure shows the co-authorship network connecting the top 25 collaborators of Joshua S. Speagle. A scholar is included among the top collaborators of Joshua S. Speagle based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Joshua S. Speagle. Joshua S. Speagle is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Prato, L., et al.. (2025). SPYGLASS. VI. Feedback-driven Star Formation in the Circinus Complex. The Astrophysical Journal. 985(1). 111–111.

Patil, Aarya A., et al.. (2025). Improving Harmonic Analysis Using Multitapering: Precise Frequency Estimation of Stellar Oscillations Using the Harmonic F-test. The Astronomical Journal. 170(1). 7–7.

Li, Dayi, Gwendolyn M. Eadie, Patrick Brown, et al.. (2025). Discovery of Two Ultra-diffuse Galaxies with Unusually Bright Globular Cluster Luminosity Functions via a Mark-dependently Thinned Point Process (MATHPOP). The Astrophysical Journal. 984(2). 147–147. 3 indexed citations

Leos‐Barajas, Vianey, et al.. (2025). Detecting Stellar Flares in Photometric Data Using Hidden Markov Models. The Astrophysical Journal. 979(2). 141–141.

Hwang, Hsiang-Chih, Yuan-Sen Ting, Sihao Cheng, & Joshua S. Speagle. (2024). Dynamical masses across the Hertzsprung–Russell diagram. Monthly Notices of the Royal Astronomical Society. 528(3). 4272–4288. 4 indexed citations

Patil, Aarya A., et al.. (2024). Improving Power Spectrum Estimation Using Multitapering: Efficient Asteroseismic Analyses for Understanding Stars, the Milky Way, and Beyond. The Astronomical Journal. 168(5). 193–193. 1 indexed citations

Zucker, Catherine, Philipp Frank, Andrew K. Saydjari, et al.. (2024). A parsec-scale Galactic 3D dust map out to 1.25 kpc from the Sun. Astronomy and Astrophysics. 685. A82–A82. 75 indexed citations breakdown →

Li, Ting S., Joshua S. Speagle, G. E. Medina, et al.. (2024). The Power of High-precision Broadband Photometry: Tracing the Milky Way Density Profile with Blue Horizontal Branch Stars in the Dark Energy Survey. The Astrophysical Journal. 975(1). 81–81. 3 indexed citations

Iyer, Kartheik G., Masayuki Akiyama, Greg L. Bryan, et al.. (2024). Katachi (形): Decoding the Imprints of Past Star Formation on Present-day Morphology in Galaxies with Interpretable CNNs*. The Astrophysical Journal. 967(2). 152–152. 2 indexed citations

10.

Drout, M. R., B. M. Gaensler, C. S. Kochanek, et al.. (2023). Cool, Luminous, and Highly Variable Stars in the Magellanic Clouds. II. Spectroscopic and Environmental Analysis of Thorne–Żytkow Object and Super-AGB Star Candidates. The Astrophysical Journal. 943(1). 18–18. 9 indexed citations

11.

Saydjari, Andrew K., Edward F. Schlafly, Dustin Lang, et al.. (2023). The Dark Energy Camera Plane Survey 2 (DECaPS2): More Sky, Less Bias, and Better Uncertainties. The Astrophysical Journal Supplement Series. 264(2). 28–28. 28 indexed citations

12.

Eadie, Gwendolyn M., et al.. (2023). The HERBAL Model: A Hierarchical Errors-in-variables Bayesian Lognormal Hurdle Model for Galactic Globular Cluster Populations. The Astrophysical Journal. 955(1). 22–22. 5 indexed citations

13.

Hwang, Hsiang-Chih, Yuan-Sen Ting, Charlie Conroy, et al.. (2022). Wide binaries from the H3 survey: the thick disc and halo have similar wide binary fractions. Monthly Notices of the Royal Astronomical Society. 513(1). 754–767. 8 indexed citations

14.

Leja, Joel, Joshua S. Speagle, Yuan-Sen Ting, et al.. (2022). A New Census of the 0.2 < z < 3.0 Universe. II. The Star-forming Sequence. The Astrophysical Journal. 936(2). 165–165. 84 indexed citations

15.

Zucker, Catherine, Alyssa Goodman, J. Alves, et al.. (2022). Star formation near the Sun is driven by expansion of the Local Bubble. Nature. 601(7893). 334–337. 132 indexed citations breakdown →

16.

Zucker, Catherine, Joshua S. Speagle, Alyssa Goodman, et al.. (2022). Characterizing the 3D Kinematics of Young Stars in the Radcliffe Wave. The Astrophysical Journal. 936(1). 57–57. 7 indexed citations

17.

Johnson, Benjamin D., Joel Leja, Charlie Conroy, & Joshua S. Speagle. (2019). Prospector: Stellar population inference from spectra and SEDs. ascl. 6 indexed citations

18.

Forbes, John C., Mark R. Krumholz, & Joshua S. Speagle. (2019). Towards a radially resolved semi-analytic model for the evolution of disc galaxies tuned with machine learning. Monthly Notices of the Royal Astronomical Society. 487(3). 3581–3606. 30 indexed citations

19.

Speagle, Joshua S., P. Capak, Daniel J. Eisenstein, Daniel Masters, & Charles L. Steinhardt. (2016). Exploring photometric redshifts as an optimization problem: an ensemble MCMC and simulated annealing-driven template-fitting approach. Monthly Notices of the Royal Astronomical Society. 461(4). 3432–3442. 12 indexed citations

20.

Masters, Daniel, P. Capak, Daniel Stern, et al.. (2015). MAPPING THE GALAXY COLOR–REDSHIFT RELATION: OPTIMAL PHOTOMETRIC REDSHIFT CALIBRATION STRATEGIES FOR COSMOLOGY SURVEYS. The Astrophysical Journal. 813(1). 53–53. 91 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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