diff --git a/joss.05703/10.21105.joss.05703.crossref.xml b/joss.05703/10.21105.joss.05703.crossref.xml new file mode 100644 index 0000000000..be1b0b0132 --- /dev/null +++ b/joss.05703/10.21105.joss.05703.crossref.xml @@ -0,0 +1,550 @@ + + + + 20230915T221355-ed1b2372159ece9b8ccfb7d165267614ffbf1d12 + 20230915221355 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 09 + 2023 + + + 8 + + 89 + + + + pysersic: A Python package for determining galaxy +structural properties via Bayesian inference, accelerated with +jax + + + + Imad + Pasha + https://orcid.org/0000-0002-7075-9931 + + + Tim B. + Miller + https://orcid.org/0000-0001-8367-6265 + + + + 09 + 15 + 2023 + + + 5703 + + + 10.21105/joss.05703 + + + 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.8335352 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5703 + + + + 10.21105/joss.05703 + https://joss.theoj.org/papers/10.21105/joss.05703 + + + https://joss.theoj.org/papers/10.21105/joss.05703.pdf + + + + + + A hybrid Fourier–Real Gaussian Mixture method +for fast galaxy–PSF convolution + Lang + arXiv e-prints + 10.48550/arXiv.2012.15797 + 2020 + Lang, D. (2020). A hybrid +Fourier–Real Gaussian Mixture method for fast galaxy–PSF convolution. +arXiv e-Prints, arXiv:2012.15797. +https://doi.org/10.48550/arXiv.2012.15797 + + + Atlas de Galaxias Australes + Sersic + 1968 + Sersic, J. L. (1968). Atlas de +Galaxias Australes. + + + JAX: Composable transformations of +Python+NumPy programs + Bradbury + 2018 + Bradbury, J., Frostig, R., Hawkins, +P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., +VanderPlas, J., Wanderman-Milne, S., & Zhang, Q. (2018). JAX: +Composable transformations of Python+NumPy programs (Version 0.3.13). +http://github.com/google/jax + + + Pyro: Deep universal probabilistic +programming + Bingham + J. Mach. Learn. Res. + 20 + 2019 + Bingham, E., Chen, J. P., Jankowiak, +M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P. +A., Horsfall, P., & Goodman, N. D. (2019). Pyro: Deep universal +probabilistic programming. J. Mach. Learn. 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Corner.py: +Scatterplot matrices in python. The Journal of Open Source Software, +1(2), 24. https://doi.org/10.21105/joss.00024 + + + Astropy: A community Python package for +astronomy + Astropy Collaboration + Astronomy and Astrophysics + 558 + 10.1051/0004-6361/201322068 + 2013 + Astropy Collaboration, Robitaille, T. +P., Tollerud, E. J., Greenfield, P., Droettboom, M., Bray, E., Aldcroft, +T., Davis, M., Ginsburg, A., Price-Whelan, A. M., Kerzendorf, W. E., +Conley, A., Crighton, N., Barbary, K., Muna, D., Ferguson, H., Grollier, +F., Parikh, M. M., Nair, P. H., … Streicher, O. (2013). Astropy: A +community Python package for astronomy. Astronomy and Astrophysics, 558, +A33. https://doi.org/10.1051/0004-6361/201322068 + + + The Astropy Project: Building an Open-science +Project and Status of the v2.0 Core Package + Astropy Collaboration + Astronomical Journal + 3 + 156 + 10.3847/1538-3881/aabc4f + 2018 + Astropy Collaboration, Price-Whelan, +A. M., Sipőcz, B. M., Günther, H. 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(2022). The +Astropy Project: Sustaining and Growing a Community-oriented Open-source +Project and the Latest Major Release (v5.0) of the Core Package. +Astrophysical Journal, 935(2), 167. +https://doi.org/10.3847/1538-4357/ac7c74 + + + Asdf-format/asdf: 2.15.0 + D’Avella + 10.5281/zenodo.7799772 + 2023 + D’Avella, D., Jamieson, W., +Droettboom, M., Slavich, E., Graham, B., Robitaille, T., Dencheva, N., +perrygreenfield, Simon, B., MacDonald, K., Bray, E. M., Burnett, Z., +Davies, J., Mumford, S., Markovtsev, V., Tollerud, E., Sipőcz, B., +Bradley, L., Fabry, Ç., … Ginsburg, A. (2023). Asdf-format/asdf: 2.15.0 +(Version 2.15.0). Zenodo. +https://doi.org/10.5281/zenodo.7799772 + + + ArviZ a unified library for exploratory +analysis of Bayesian models in Python + Kumar + Journal of Open Source +Software + 33 + 4 + 10.21105/joss.01143 + 2019 + Kumar, R., Carroll, C., Hartikainen, +A., & Martin, O. (2019). ArviZ a unified library for exploratory +analysis of Bayesian models in Python. Journal of Open Source Software, +4(33), 1143. https://doi.org/10.21105/joss.01143 + + + + + + diff --git a/joss.05703/10.21105.joss.05703.jats b/joss.05703/10.21105.joss.05703.jats new file mode 100644 index 0000000000..4374a41783 --- /dev/null +++ b/joss.05703/10.21105.joss.05703.jats @@ -0,0 +1,1414 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5703 +10.21105/joss.05703 + +pysersic: A Python package for determining galaxy +structural properties via Bayesian inference, accelerated with +jax + + + +https://orcid.org/0000-0002-7075-9931 + +Pasha +Imad + + + + + +https://orcid.org/0000-0001-8367-6265 + +Miller +Tim B. + + + + + +Department of Astronomy, Yale University, USA + + + + +National Science Foundation Graduate Research +Fellow + + + + +5 +6 +2023 + +8 +89 +5703 + +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 +galaxies +model fitting + + + + + + Summary +

The modern standard for measuring structural parameters of galaxies + involves a forward-modeling procedure in which parametric models are + fit directly to images while accounting for the effect of the + point-spread function (PSF). This is an integral step in many + extragalactic studies. The most common parametric form is a Sérsic + profile + (Sersic, + 1968) which is described by a radial surface brightness + profiles following,

+

+ + I(R)∝Ftotalexp[(RRe)1/n−1],

+

where the total flux, + + Ftotal, + half-light radius, + + Re + and Sérsic index, + + n + are the parameters of interest to be fit and subsequently used to + characterize a galaxy’s morphology.

+

Here we present pysersic, a Bayesian + framework created to facilitate the inference of structural parameters + from galaxy images. It is written in pure + Python, and built using the + jax framework + (Bradbury + et al., 2018) allowing for just-in-time (JIT) compilation, + auto-differentiation and seamless execution on CPUs, GPUs or TPUs. + Inference is performed with the numpyro + (Bingham + et al., 2019; + Phan + et al., 2019) package utilizing gradient based methods, e.g., + No U-Turn Sampling (NUTS) + (Hoffman + et al., 2014), for efficient and robust posterior estimation. + pysersic was designed to have a user-friendly + interface, allowing users to fit single or multiple sources in a few + lines of code. It was also designed to scale to many images, such that + it can be seamlessly integrated into current and future analysis + pipelines.

+ + + Statement of need +

Parametric profile fitting has become a ubiquitous and essential + tool for numerous applications including measuring the photometry — or + total flux — of galaxies, as well as the investigation of the + structural evolution of galaxies over cosmic time + (Kawinwanichakij + et al., 2021; + Lange + et al., 2015; + Mowla + et al., 2019). This approach allows one to both extrapolate + galaxy surface brightness profiles beyond the noise limit of images, + as well as account for the PSF to accurately measure the structure of + galaxies near the resolution limit of those images. The empirically + derived Sérsic profile is the most common parametric form for the + surface-brightness profile as it provides a reasonable approximation + to nearly all galaxies, given the additional freedom of the Sérsic + index, + + n, + over fixed-index profiles.

+

Given the long history of Sérsic fitting codes with many available + tools, the development of pysersic was largely + motivated by two related factors, first and foremost of which was the + desire to implement Sérsic fitting in a fully Bayesian context + at speed. The ability to place the + typical Sérsic fitting problem into a Bayesian context with runtimes + that are not prohibitive (the traditional drawback of MCMC methods) + has recently been unlocked by the second motivation: to leverage the + jax library. jax + utilizes JIT compilation to decrease computational runtimes, provides + seamless integration with hardware accelerators such as GPUs and TPUs + for further improvements in performance, and enables automatic + differentiation, facilitating gradient based optimization and sampling + methods. Together, these features greatly increase speed and + efficiency, especially when sampling or optimizing a large number of + parameters.

+

Inference in pysersic is implemented using + the numpyro probabilistic programming language + (PPL). This allows for total control over the priors and methods used + for inference. The numpyro package utilizes + jax’s auto-differentiation capabilities for + gradient based samplers such as Hamiltonian Monte Carlo (HMC) and + No-U-Turn-Sampling (NUTS). In addition, there are recently-developed + techniques for posterior estimation, including variational inference + (Ranganath + et al., 2014) utilizing normalizing flows + (De + Cao et al., 2020). These techniques dramatically reduce the + number of likelihood calls required to provide accurate estimates of + the posterior relative to gradient-free methods. Combined with the + jax’s JIT compilation, posteriors can now be + generated in a few minutes or less on modern laptops.

+ + + Code Description +

pysersic was designed to have a + user-friendly API with sensible defaults. Tools are provided to + automatically generate priors for all free parameters based on an + initial characterization of a given image — but can also easily be set + manually. We provide default inference routines for NUTS MCMC and + variational inference using neural flows. Users can access the + underlying numpyro model if desired, to perform + inference using any tools available within the + numpyro ecosystem. The goal for + pysersic is to provide reasonable defaults for + new users interested in a handful of galaxies, yet maintain the + ability for advanced users to tweak options as necessary to perform + inference for entire surveys.

+

A crucial component of any Sérsic fitting code is an efficient and + accurate rendering algorithm. Sérsic profiles with high index, + + + n≳3 + are notoriously difficult to render accurately given the steep + increase in brightness as + + r→0. + In pysersic, the + rendering module is kept separate from the + frontend API and inference modules, such that different algorithms can + be interchanged and therefore easily tested (and hopefully encourage + innovation as well). In this initial release, we provide three + algorithms. The first is a traditional rendering algorithm in which + the intrinsic profile is rendered in real space, with oversampling in + the center to ensure accurate results for high index profiles. The + second and third methods render the profiles in Fourier space, + providing accurate results even for strongly peaked profiles and + avoiding artifacts due to pixelization. In + pysersic, this is achieved by representing the + profiles using a series of Gaussian following the algorithm presented + in Shajib + (2019). + We include one algorithm that is fully based in Fourier space, along + with a version of the hybrid real-Fourier algorithm introduced in Lang + (2020) + which helps avoid some of the aliasing present when rendering solely + in Fourier space.

+ + + Related Software +

There is a long history and many software tools designed for Sérsic + profile fitting. Some of the most popular libraries are listed + below.

+ + +

galfit + (Peng + et al., 2002)

+ + +

imfit + (Erwin, + 2015)

+ + +

profit + (Robotham + et al., 2017)

+ + +

galight + (Ding + et al., 2021), which is built on top of + lenstronomy + (Birrer + et al., 2021)

+ + +

PetroFit + (Geda + et al., 2022)

+ + +

PyAutoGalaxy + (Nightingale + et al., 2023)

+ + + + + Software Citations +

pysersic makes use of the following + packages:

+ + +

arviz + (Kumar + et al., 2019)

+ + +

asdf + (D’Avella + et al., 2023)

+ + +

astropy + (Astropy + Collaboration et al., 2013, + 2018, + 2022)

+ + +

corner + (Foreman-Mackey, + 2016)

+ + +

jax + (Bradbury + et al., 2018)

+ + +

matplotlib + (Hunter, + 2007)

+ + +

numpy + (Harris + et al., 2020)

+ + +

numpyro + (Bingham + et al., 2019; + Phan + et al., 2019)

+ + +

pandas + (team, + 2020)

+ + +

photutils + (Bradley + et al., 2022)

+ + +

pytest + (Krekel + et al., 2004)

+ + +

scipy + (Virtanen + et al., 2020)

+ + +

tqdm + (Costa-Luis + et al., 2023)

+ + + + + Acknowledgements +

We acknowledge Pieter van Dokkum for useful conversations + surrounding the design and implementation of + pysersic.

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