Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Creating pull request for 10.21105.joss.06589 #5388

Closed
wants to merge 3 commits into from
Closed
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
356 changes: 356 additions & 0 deletions joss.06589/10.21105.joss.06589.crossref.xml
Original file line number Diff line number Diff line change
@@ -0,0 +1,356 @@
<?xml version="1.0" encoding="UTF-8"?>
<doi_batch xmlns="http://www.crossref.org/schema/5.3.1"
xmlns:ai="http://www.crossref.org/AccessIndicators.xsd"
xmlns:rel="http://www.crossref.org/relations.xsd"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
version="5.3.1"
xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schemas/crossref5.3.1.xsd">
<head>
<doi_batch_id>20240525T202750-3d988b9aa0374022e2393e1fd730461138f72fbf</doi_batch_id>
<timestamp>20240525202750</timestamp>
<depositor>
<depositor_name>JOSS Admin</depositor_name>
<email_address>admin@theoj.org</email_address>
</depositor>
<registrant>The Open Journal</registrant>
</head>
<body>
<journal>
<journal_metadata>
<full_title>Journal of Open Source Software</full_title>
<abbrev_title>JOSS</abbrev_title>
<issn media_type="electronic">2475-9066</issn>
<doi_data>
<doi>10.21105/joss</doi>
<resource>https://joss.theoj.org</resource>
</doi_data>
</journal_metadata>
<journal_issue>
<publication_date media_type="online">
<month>05</month>
<year>2024</year>
</publication_date>
<journal_volume>
<volume>9</volume>
</journal_volume>
<issue>97</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>FIGARO: hierarchical non-parametric inference for
population studies</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Stefano</given_name>
<surname>Rinaldi</surname>
<ORCID>https://orcid.org/0000-0001-5799-4155</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Walter</given_name>
<surname>Del Pozzo</surname>
<ORCID>https://orcid.org/0000-0003-3978-2030</ORCID>
</person_name>
</contributors>
<publication_date>
<month>05</month>
<day>25</day>
<year>2024</year>
</publication_date>
<pages>
<first_page>6589</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.06589</identifier>
</publisher_item>
<ai:program name="AccessIndicators">
<ai:license_ref applies_to="vor">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="am">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="tdm">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
</ai:program>
<rel:program>
<rel:related_item>
<rel:description>Software archive</rel:description>
<rel:inter_work_relation relationship-type="references" identifier-type="doi">10.5281/zenodo.11302325</rel:inter_work_relation>
</rel:related_item>
<rel:related_item>
<rel:description>GitHub review issue</rel:description>
<rel:inter_work_relation relationship-type="hasReview" identifier-type="uri">https://github.com/openjournals/joss-reviews/issues/6589</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.06589</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.06589</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.06589.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="rinaldi:2022:hdpgmm">
<article_title>(H)DPGMM: a hierarchy of Dirichlet process
Gaussian mixture models for the inference of the black hole mass
function</article_title>
<author>Rinaldi</author>
<journal_title>Monthly Notices of the Royal Astronomical
Society</journal_title>
<issue>4</issue>
<volume>509</volume>
<doi>10.1093/mnras/stab3224</doi>
<cYear>2022</cYear>
<unstructured_citation>Rinaldi, S., &amp; Del Pozzo, W.
(2022). (H)DPGMM: a hierarchy of Dirichlet process Gaussian mixture
models for the inference of the black hole mass function. Monthly
Notices of the Royal Astronomical Society, 509(4), 5454–5466.
https://doi.org/10.1093/mnras/stab3224</unstructured_citation>
</citation>
<citation key="rinaldi:2022:figaro">
<article_title>Rapid localization of gravitational wave
hosts with FIGARO</article_title>
<author>Rinaldi</author>
<journal_title>Monthly Notices of the Royal Astronomical
Society</journal_title>
<issue>1</issue>
<volume>517</volume>
<doi>10.1093/mnrasl/slac101</doi>
<cYear>2022</cYear>
<unstructured_citation>Rinaldi, S., &amp; Del Pozzo, W.
(2022). Rapid localization of gravitational wave hosts with FIGARO.
Monthly Notices of the Royal Astronomical Society, 517(1), L5–L10.
https://doi.org/10.1093/mnrasl/slac101</unstructured_citation>
</citation>
<citation key="rinaldi:2024:m1qz">
<article_title>Evidence of evolution of the black hole mass
function with redshift</article_title>
<author>Rinaldi</author>
<journal_title>Astronomy &amp; Astrophysics</journal_title>
<volume>684</volume>
<doi>10.1051/0004-6361/202348161</doi>
<cYear>2024</cYear>
<unstructured_citation>Rinaldi, S., Del Pozzo, W., Mapelli,
M., Lorenzo-Medina, A., &amp; Dent, T. (2024). Evidence of evolution of
the black hole mass function with redshift. Astronomy &amp;
Astrophysics, 684, A204.
https://doi.org/10.1051/0004-6361/202348161</unstructured_citation>
</citation>
<citation key="cheung:2023">
<article_title>Mitigating the effect of population model
uncertainty on strong lensing Bayes factor using nonparametric
methods</article_title>
<author>Cheung</author>
<journal_title>arXiv e-prints</journal_title>
<doi>10.48550/arXiv.2308.12182</doi>
<cYear>2023</cYear>
<unstructured_citation>Cheung, D. H. T., Rinaldi, S.,
Toscani, M., &amp; Hannuksela, O. A. (2023). Mitigating the effect of
population model uncertainty on strong lensing Bayes factor using
nonparametric methods. arXiv e-Prints, arXiv:2308.12182.
https://doi.org/10.48550/arXiv.2308.12182</unstructured_citation>
</citation>
<citation key="rinaldi:2023:bigG">
<article_title>Bayesian analysis of systematic errors in the
determination of the constant of gravitation</article_title>
<author>Rinaldi</author>
<journal_title>European Physical Journal C</journal_title>
<issue>10</issue>
<volume>83</volume>
<doi>10.1140/epjc/s10052-023-12078-6</doi>
<cYear>2023</cYear>
<unstructured_citation>Rinaldi, S., Middleton, H., Del
Pozzo, W., &amp; Gair, J. (2023). Bayesian analysis of systematic errors
in the determination of the constant of gravitation. European Physical
Journal C, 83(10), 891.
https://doi.org/10.1140/epjc/s10052-023-12078-6</unstructured_citation>
</citation>
<citation key="sgalletta:2023">
<article_title>Binary neutron star populations in the Milky
Way</article_title>
<author>Sgalletta</author>
<journal_title>Monthly Notices of the Royal Astronomical
Society</journal_title>
<issue>2</issue>
<volume>526</volume>
<doi>10.1093/mnras/stad2768</doi>
<cYear>2023</cYear>
<unstructured_citation>Sgalletta, C., Iorio, G., Mapelli,
M., Artale, M. C., Boco, L., Chattopadhyay, D., Lapi, A., Possenti, A.,
Rinaldi, S., &amp; Spera, M. (2023). Binary neutron star populations in
the Milky Way. Monthly Notices of the Royal Astronomical Society,
526(2), 2210–2229.
https://doi.org/10.1093/mnras/stad2768</unstructured_citation>
</citation>
<citation key="morton:2023">
<article_title>GW190521: A binary black hole merger inside
an active galactic nucleus?</article_title>
<author>Morton</author>
<journal_title>Physical Review D</journal_title>
<issue>12</issue>
<volume>108</volume>
<doi>10.1103/PhysRevD.108.123039</doi>
<cYear>2023</cYear>
<unstructured_citation>Morton, S. L., Rinaldi, S.,
Torres-Orjuela, A., Derdzinski, A., Vaccaro, M. P., &amp; Del Pozzo, W.
(2023). GW190521: A binary black hole merger inside an active galactic
nucleus? Physical Review D, 108(12), 123039.
https://doi.org/10.1103/PhysRevD.108.123039</unstructured_citation>
</citation>
<citation key="rallapalli:2023">
<article_title>Bayesian inference of W-boson
mass</article_title>
<author>Rallapalli</author>
<journal_title>European Physical Journal C</journal_title>
<issue>7</issue>
<volume>83</volume>
<doi>10.1140/epjc/s10052-023-11754-x</doi>
<cYear>2023</cYear>
<unstructured_citation>Rallapalli, A., &amp; Desai, S.
(2023). Bayesian inference of W-boson mass. European Physical Journal C,
83(7), 580.
https://doi.org/10.1140/epjc/s10052-023-11754-x</unstructured_citation>
</citation>
<citation key="escobar:1995">
<article_title>Bayesian density estimation and inference
using mixtures</article_title>
<author>Escobar</author>
<journal_title>Journal of the American Statistical
Association</journal_title>
<issue>430</issue>
<volume>90</volume>
<doi>10.1080/01621459.1995.10476550</doi>
<cYear>1995</cYear>
<unstructured_citation>Escobar, M. D., &amp; West, M.
(1995). Bayesian density estimation and inference using mixtures.
Journal of the American Statistical Association, 90(430), 577–588.
https://doi.org/10.1080/01621459.1995.10476550</unstructured_citation>
</citation>
<citation key="pedregosa:2011">
<article_title>Scikit-learn: Machine Learning in
Python</article_title>
<author>Pedregosa</author>
<journal_title>Journal of Machine Learning
Research</journal_title>
<volume>12</volume>
<doi>10.48550/arXiv.1201.0490</doi>
<cYear>2011</cYear>
<unstructured_citation>Pedregosa, F., Varoquaux, G.,
Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller,
A., Nothman, J., Louppe, G., Prettenhofer, P., Weiss, R., Dubourg, V.,
Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M.,
&amp; Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python.
Journal of Machine Learning Research, 12, 2825–2830.
https://doi.org/10.48550/arXiv.1201.0490</unstructured_citation>
</citation>
<citation key="virtanen:2020">
<article_title>SciPy 1.0: Fundamental Algorithms for
Scientific Computing in Python</article_title>
<author>Virtanen</author>
<journal_title>Nature Methods</journal_title>
<volume>17</volume>
<doi>10.1038/s41592-019-0686-2</doi>
<cYear>2020</cYear>
<unstructured_citation>Virtanen, P., Gommers, R., Oliphant,
T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson,
P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson,
J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R.,
Larson, E., … SciPy 1.0 Contributors. (2020). SciPy 1.0: Fundamental
Algorithms for Scientific Computing in Python. Nature Methods, 17,
261–272.
https://doi.org/10.1038/s41592-019-0686-2</unstructured_citation>
</citation>
<citation key="astrodistGWTC3:2023">
<article_title>Population of Merging Compact Binaries
Inferred Using Gravitational Waves through GWTC-3</article_title>
<author>Abbott</author>
<journal_title>Physical Review X</journal_title>
<issue>1</issue>
<volume>13</volume>
<doi>10.1103/PhysRevX.13.011048</doi>
<cYear>2023</cYear>
<unstructured_citation>Abbott, R., Abbott, T. D., Acernese,
F., Ackley, K., Adams, C., Adhikari, N., Adhikari, R. X., Adya, V. B.,
Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N.,
Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., de Alarcón,
P. F., … others. (2023). Population of Merging Compact Binaries Inferred
Using Gravitational Waves through GWTC-3. Physical Review X, 13(1),
011048.
https://doi.org/10.1103/PhysRevX.13.011048</unstructured_citation>
</citation>
<citation key="tiwari:2021:vamana">
<article_title>VAMANA: modeling binary black hole population
with minimal assumptions</article_title>
<author>Tiwari</author>
<journal_title>Classical and Quantum Gravity</journal_title>
<issue>15</issue>
<volume>38</volume>
<doi>10.1088/1361-6382/ac0b54</doi>
<cYear>2021</cYear>
<unstructured_citation>Tiwari, V. (2021). VAMANA: modeling
binary black hole population with minimal assumptions. Classical and
Quantum Gravity, 38(15), 155007.
https://doi.org/10.1088/1361-6382/ac0b54</unstructured_citation>
</citation>
<citation key="edelman:2023">
<article_title>Cover Your Basis: Comprehensive Data-driven
Characterization of the Binary Black Hole Population</article_title>
<author>Edelman</author>
<journal_title>Astrophysical Journal</journal_title>
<issue>1</issue>
<volume>946</volume>
<doi>10.3847/1538-4357/acb5ed</doi>
<cYear>2023</cYear>
<unstructured_citation>Edelman, B., Farr, B., &amp; Doctor,
Z. (2023). Cover Your Basis: Comprehensive Data-driven Characterization
of the Binary Black Hole Population. Astrophysical Journal, 946(1), 16.
https://doi.org/10.3847/1538-4357/acb5ed</unstructured_citation>
</citation>
<citation key="toubiana:2023">
<article_title>Is there an excess of black holes around 20
M\odot? Optimizing the complexity of population models with the use of
reversible jump MCMC.</article_title>
<author>Toubiana</author>
<journal_title>Monthly Notices of the Royal Astronomical
Society</journal_title>
<issue>4</issue>
<volume>524</volume>
<doi>10.1093/mnras/stad2215</doi>
<cYear>2023</cYear>
<unstructured_citation>Toubiana, A., Katz, M. L., &amp;
Gair, J. R. (2023). Is there an excess of black holes around 20 M\odot?
Optimizing the complexity of population models with the use of
reversible jump MCMC. Monthly Notices of the Royal Astronomical Society,
524(4), 5844–5853.
https://doi.org/10.1093/mnras/stad2215</unstructured_citation>
</citation>
<citation key="callister:2024">
<article_title>Parameter-Free Tour of the Binary Black Hole
Population</article_title>
<author>Callister</author>
<journal_title>Physical Review X</journal_title>
<issue>2</issue>
<volume>14</volume>
<doi>10.1103/PhysRevX.14.021005</doi>
<cYear>2024</cYear>
<unstructured_citation>Callister, T. A., &amp; Farr, W. M.
(2024). Parameter-Free Tour of the Binary Black Hole Population.
Physical Review X, 14(2), 021005.
https://doi.org/10.1103/PhysRevX.14.021005</unstructured_citation>
</citation>
<citation key="teh:2010">
<article_title>Dirichlet process</article_title>
<author>Teh</author>
<journal_title>Encyclopedia of machine
learning</journal_title>
<doi>10.1007/978-0-387-30164-8_219</doi>
<isbn>978-0-387-30164-8</isbn>
<cYear>2010</cYear>
<unstructured_citation>Teh, Y. W. (2010). Dirichlet process.
In Encyclopedia of machine learning (pp. 280–287). Springer US.
https://doi.org/10.1007/978-0-387-30164-8_219</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Binary file added joss.06589/10.21105.joss.06589.pdf
Binary file not shown.
Loading