diff --git a/joss.07201/10.21105.joss.07201.crossref.xml b/joss.07201/10.21105.joss.07201.crossref.xml new file mode 100644 index 0000000000..797455707a --- /dev/null +++ b/joss.07201/10.21105.joss.07201.crossref.xml @@ -0,0 +1,201 @@ + + + + 20241101212721-ec363f6ca359a42cc3866e78fe9c18bc26ebbbd0 + 20241101212721 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 11 + 2024 + + + 9 + + 103 + + + + bayes_spec: A Bayesian Spectral Line Modeling Framework +for Astrophysics + + + + Trey V. + Wenger + + NSF Astronomy & Astrophysics Postdoctoral Fellow, University of Wisconsin-Madison, USA + + https://orcid.org/0000-0003-0640-7787 + + + + 11 + 01 + 2024 + + + 7201 + + + 10.21105/joss.07201 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.13947167 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/7201 + + + + 10.21105/joss.07201 + https://joss.theoj.org/papers/10.21105/joss.07201 + + + https://joss.theoj.org/papers/10.21105/joss.07201.pdf + + + + + + Autonomous Gaussian +decomposition + Lindner + The Astronomical Journal + 4 + 149 + 10.1088/0004-6256/149/4/138 + 2015 + Lindner, R. R., Vera-Ciro, C., +Murray, C. E., Stanimirović, S., Babler, B., Heiles, C., Hennebelle, P., +Goss, W. M., & Dickey, J. (2015). Autonomous Gaussian decomposition. +The Astronomical Journal, 149(4), 138. +https://doi.org/10.1088/0004-6256/149/4/138 + + + ROHSA: Regularized Optimization for +Hyper-Spectral Analysis. Application to phase separation of 21 cm +data + Marchal + Astronomy & Astrophysics + 626 + 10.1051/0004-6361/201935335 + 2019 + Marchal, A., Miville-Deschênes, +M.-A., Orieux, F., Gac, N., Soussen, C., Lesot, M.-J., d’Allonnes, A. +R., & Salomé, Q. (2019). ROHSA: Regularized Optimization for +Hyper-Spectral Analysis. Application to phase separation of 21 cm data. +Astronomy & Astrophysics, 626, A101. +https://doi.org/10.1051/0004-6361/201935335 + + + Amoeba: Automated Molecular Excitation +Bayesian Line-fitting Algorithm + Petzler + The Astrophysical Journal + 2 + 923 + 10.3847/1538-4357/ac2f42 + 2021 + Petzler, A., Dawson, J. R., & +Wardle, M. (2021). Amoeba: Automated Molecular Excitation Bayesian +Line-fitting Algorithm. The Astrophysical Journal, 923(2), 261. +https://doi.org/10.3847/1538-4357/ac2f42 + + + Pyspeckit: A spectroscopic analysis and +plotting package + Ginsburg + The Astronomical Journal + 6 + 163 + 10.3847/1538-3881/ac695a + 2022 + Ginsburg, A., Sokolov, V., de +Val-Borro, M., Rosolowsky, E., Pineda, J. E., Sipőcz, B. M., & +Henshaw, J. D. (2022). Pyspeckit: A spectroscopic analysis and plotting +package. The Astronomical Journal, 163(6), 291. +https://doi.org/10.3847/1538-3881/ac695a + + + PyMC: A modern and comprehensive +probabilistic programming framework in Python + Oriol + PeerJ Computer Science + 9 + 10.7717/peerj-cs.1516 + 2023 + Oriol, A.-P., Virgile, A., Colin, C., +Larry, D., J., F. C., Maxim, K., Ravin, K., Jupeng, L., C., L. C., A., +M. O., Michael, O., Ricardo, V., Thomas, W., & Robert, Z. (2023). +PyMC: A modern and comprehensive probabilistic programming framework in +Python. PeerJ Computer Science, 9, e1516. +https://doi.org/10.7717/peerj-cs.1516 + + + The dynamic centres of infrared-dark clouds +and the formation of cores + Rigby + Monthly Notices of the Royal Astronomical +Society + 2 + 528 + 10.1093/mnras/stae030 + 2024 + Rigby, A. J., Peretto, N., Anderson, +M., Ragan, S. E., Priestley, F. D., Fuller, G. A., Thompson, M. A., +Traficante, A., Watkins, E. J., & Williams, G. M. (2024). The +dynamic centres of infrared-dark clouds and the formation of cores. +Monthly Notices of the Royal Astronomical Society, 528(2), 1172–1197. +https://doi.org/10.1093/mnras/stae030 + + + McFine: PYTHON-based Monte Carlo +multicomponent hyperfine structure fitting + Williams + Monthly Notices of the Royal Astronomical +Society + 2 + 534 + 10.1093/mnras/stae2130 + 2024 + Williams, T. G., & Watkins, E. J. +(2024). McFine: PYTHON-based Monte Carlo multicomponent hyperfine +structure fitting. Monthly Notices of the Royal Astronomical Society, +534(2), 1150–1165. +https://doi.org/10.1093/mnras/stae2130 + + + + + + diff --git a/joss.07201/10.21105.joss.07201.pdf b/joss.07201/10.21105.joss.07201.pdf new file mode 100644 index 0000000000..f8f594899c Binary files /dev/null and b/joss.07201/10.21105.joss.07201.pdf differ diff --git a/joss.07201/paper.jats/10.21105.joss.07201.jats b/joss.07201/paper.jats/10.21105.joss.07201.jats new file mode 100644 index 0000000000..d8da9c24c8 --- /dev/null +++ b/joss.07201/paper.jats/10.21105.joss.07201.jats @@ -0,0 +1,395 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +7201 +10.21105/joss.07201 + +bayes_spec: A Bayesian Spectral Line Modeling Framework +for Astrophysics + + + +https://orcid.org/0000-0003-0640-7787 + +Wenger +Trey V. + + + + + +NSF Astronomy & Astrophysics Postdoctoral Fellow, +University of Wisconsin-Madison, USA + + + + +15 +8 +2024 + +9 +103 +7201 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +astronomy +astrophysics +spectroscopy +Bayesian models + + + + + + Summary +

The study of the interstellar medium (ISM)—the matter between the + stars—relies heavily on the tools of spectroscopy. Spectral line + observations of atoms, ions, and molecules in the ISM reveal the + physical conditions and kinematics of the emitting gas. Robust and + efficient numerical techniques are thus necessary for inferring the + physical conditions of the ISM from observed spectral line data.

+

bayes_spec is a Bayesian spectral line + modeling framework for astrophysics. Given a user-defined model and a + spectral line dataset, bayes_spec enables + inference of the model parameters through different numerical + techniques, such as Monte Carlo Markov Chain (MCMC) methods, + implemented in the PyMC probabilistic programming library + (Oriol + et al., 2023). The API for bayes_spec is + designed to support astrophysical researchers who wish to “fit” + arbitrary, user-defined models, such as simple spectral line profile + models or complicated physical models that include a full physical + treatment of radiative transfer. These models are “cloud-based”, + meaning that the spectral line data are decomposed into a series of + discrete clouds with parameters defined by the user’s model. + Importantly, bayes_spec provides algorithms to + determine the optimal number of clouds for a given model and + dataset.

+ + + Statement of need +

Bayesian models of spectral line observations are rare in + astrophysics. Physical inference is typically achieved through inverse + modeling: the spectral line data are decomposed into Gaussian + components, and then the physical parameters are inferred from the + fitted Gaussian parameters under numerous assumptions. For example, + this is the strategy of gausspy + (Lindner + et al., 2015), ROHSA + (Marchal + et al., 2019), pyspeckit + (Ginsburg + et al., 2022), and MWYDYN + (Rigby + et al., 2024). This strategy suffers from two problems: (1) the + degeneracies of Gaussian decomposition and (2) the assumptions of the + inverse model. Bayesian forward models, like those enabled by + bayes_spec, can overcome both of these + limitations because (1) prior knowledge about the physical conditions + can reduce the space of possible solutions, and (2) all assumptions + are explicitly built into the model rather than being applied + a priori.

+

bayes_spec is inspired by + AMOEBA + (Petzler + et al., 2021), an MCMC-based Bayesian model for interstellar + hydroxide observations. McFine + (Williams + & Watkins, 2024) is a new MCMC-based Bayesian model for + hyperfine spectroscopy similar in spirit to + bayes_spec. With + bayes_spec, we aim to provide a user-friendly, + general-purpose Bayesian modeling framework for any + astrophysical spectral line observation.

+ + + Usage +

Here we demonstrate how to use bayes_spec to + fit a simple Gaussian line profile model to a synthetic spectrum. For + more details, see the + documentation + and tutorials.

+ # Generate data structure +import numpy as np +from bayes_spec import SpecData + +velocity_axis = np.linspace(-250.0, 250.0, 501) # km s-1 +noise = 1.0 # K +brightness_data = noise * np.random.randn(len(velocity_axis)) # K +observation = SpecData( + velocity_axis, brightness_data, noise, + ylabel=r"Brightness Temperature $T_B$ (K)", + xlabel=r"LSR Velocity $V_{\rm LSR}$ (km s$^{-1})$", +) +data = {"observation": observation} + +# Prepare a three cloud GaussLine model with polynomial baseline degree = 2 +from bayes_spec.models import GaussModel + +model = GaussModel(data, n_clouds=3, baseline_degree=2) +model.add_priors() +model.add_likelihood() + +# Evaluate the model for a given set of parameters to generate a +# synthetic "observation" +sim_brightness = model.model.observation.eval({ + "fwhm": [25.0, 40.0, 35.0], # FWHM line width (km/s) + "line_area": [250.0, 125.0, 175.0], # line area (K km/s) + "velocity": [-35.0, 10.0, 55.0], # velocity (km/s) + # normalized baseline coefficients + "baseline_observation_norm": [-0.5, -2.0, 3.0], +}) + +# Pack data structure with synthetic "observation" +observation = SpecData( + velocity_axis, sim_brightness, noise, + ylabel=r"Brightness Temperature $T_B$ (K)", + xlabel=r"LSR Velocity $V_{\rm LSR}$ (km s$^{-1})$", +) +data = {"observation": observation} + +# Initialize the model with the synthetic observation +model = GaussModel(data, n_clouds=3, baseline_degree=2) +model.add_priors() +model.add_likelihood() + +# Draw posterior samples via MCMC +model.sample() + +# Solve labeling degeneracy +model.solve() + +# Draw posterior predictive samples +from bayes_spec.plots import plot_predictive + +posterior = model.sample_posterior_predictive(thin=100) +axes = plot_predictive(model.data, posterior.posterior_predictive) +axes.ravel()[0].figure.show() + +# visualize posterior distribution +from bayes_spec.plots import plot_pair + +axes = plot_pair( + model.trace.solution_0, + model.cloud_deterministics, + labeller=model.labeller, +) +axes.ravel()[0].figure.show() + +

Posterior predictive samples for a three-cloud + GaussLine model fit to a synthetic spectrum. + The black line represents the synthetic spectrum, and each colored + line is one posterior predictive sample.

+ + + +

Projections of the posterior distribution for a + three-cloud GaussLine model fit to a + synthetic spectrum. The free model parameters are the integrated + line area, + + ∫TBdV, + the full-width at half-maximum line width, + + + ΔV, + and the line-center velocity, + . + The line amplitude, + + TB, + is a derived quantity. The three posterior modes correspond to the + three clouds in this model.

+ + + + + + + + + + + LindnerRobert R. + Vera-CiroCarlos + MurrayClaire E. + StanimirovićSnežana + BablerBrian + HeilesCarl + HennebellePatrick + GossW. M. + DickeyJohn + + Autonomous Gaussian decomposition + The Astronomical Journal + 201504 + 149 + 4 + https://arxiv.org/abs/1409.2840 + 10.1088/0004-6256/149/4/138 + 138 + + + + + + + MarchalAntoine + Miville-DeschênesMarc-Antoine + OrieuxFrançois + GacNicolas + SoussenCharles + LesotMarie-Jeanne + d’ AllonnesAdrien Revault + SaloméQuentin + + ROHSA: Regularized Optimization for Hyper-Spectral Analysis. Application to phase separation of 21 cm data + Astronomy & Astrophysics + 201906 + 626 + https://arxiv.org/abs/1905.00658 + 10.1051/0004-6361/201935335 + A101 + + + + + + + PetzlerAnita + DawsonJ. R. + WardleMark + + Amoeba: Automated Molecular Excitation Bayesian Line-fitting Algorithm + The Astrophysical Journal + 202112 + 923 + 2 + https://arxiv.org/abs/2107.11922 + 10.3847/1538-4357/ac2f42 + 261 + + + + + + + GinsburgAdam + SokolovVlas + de Val-BorroMiguel + RosolowskyErik + PinedaJaime E. + SipőczBrigitta M. + HenshawJonathan D. + + Pyspeckit: A spectroscopic analysis and plotting package + The Astronomical Journal + 202206 + 163 + 6 + https://arxiv.org/abs/2205.04987 + 10.3847/1538-3881/ac695a + 291 + + + + + + + OriolAbril-Pla + VirgileAndreani + ColinCarroll + LarryDong + J.Fonnesbeck Christopher + MaximKochurov + RavinKumar + JupengLao + C.Luhmann Christian + A.Martin Osvaldo + MichaelOsthege + RicardoVieira + ThomasWiecki + RobertZinkov + + PyMC: A modern and comprehensive probabilistic programming framework in Python + PeerJ Computer Science + PeerJ + 2023 + 9 + 10.7717/peerj-cs.1516 + e1516 + + + + + + + RigbyAndrew J. + PerettoNicolas + AndersonMichael + RaganSarah E. + PriestleyFelix D. + FullerGary A. + ThompsonMark A. + TraficanteAlessio + WatkinsElizabeth J. + WilliamsGwenllian M. + + The dynamic centres of infrared-dark clouds and the formation of cores + Monthly Notices of the Royal Astronomical Society + 202402 + 528 + 2 + https://arxiv.org/abs/2401.04238 + 10.1093/mnras/stae030 + 1172 + 1197 + + + + + + WilliamsThomas G. + WatkinsElizabeth J. + + McFine: PYTHON-based Monte Carlo multicomponent hyperfine structure fitting + Monthly Notices of the Royal Astronomical Society + 202410 + 534 + 2 + https://arxiv.org/abs/2409.03835 + 10.1093/mnras/stae2130 + 1150 + 1165 + + + + +
diff --git a/joss.07201/paper.jats/posterior_pair.pdf b/joss.07201/paper.jats/posterior_pair.pdf new file mode 100644 index 0000000000..46f75ef370 Binary files /dev/null and b/joss.07201/paper.jats/posterior_pair.pdf differ diff --git a/joss.07201/paper.jats/posterior_predictive.pdf b/joss.07201/paper.jats/posterior_predictive.pdf new file mode 100644 index 0000000000..644f8e9ade Binary files /dev/null and b/joss.07201/paper.jats/posterior_predictive.pdf differ