RECORD
jaxspec : a fast and robust Python library for X-ray spectral fitting
Instrumentation and Methods for Astrophysics · Submitted 9 Sep 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We obtain identical results when compared to other softwares and approaches, meaning that jaxspec provides reliable results while being $\sim 10$ times faster than existing alternatives.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
Context. Inferring spectral parameters from X-ray data is one of the cornerstones of high-energy astrophysics, and is achieved using software stacks that have been developed over the last twenty years and more. However, as models get more complex and spectra reach higher resolutions, these established software solutions become more feature-heavy, difficult to maintain and less efficient. Aims. We present jaxspec, a Python package for performing this task quickly and robustly in a fully Bayesian framework. Based on the JAX ecosystem, jaxspec allows the generation of differentiable likelihood functions compilable on core or graphical process units (resp. CPU and GPU), enabling the use of robust algorithms for Bayesian inference. Methods. We demonstrate the effectiveness of jaxspec samplers, in particular the No U-Turn Sampler, using a composite model and comparing what we obtain with the existing frameworks. We also demonstrate its ability to process high-resolution spectroscopy data and using original methods, by reproducing the results of the Hitomi collaboration on the Perseus cluster, while solving the inference problem using variational inference on a GPU. Results. We obtain identical results when compared to other softwares and approaches, meaning that jaxspec provides reliable results while being $\sim 10$ times faster than existing alternatives. In addition, we show that variational inference can produce convincing results even on high-resolution data in less than 10 minutes on a GPU. Conclusions. With this package, we aim to pursue the goal of opening up X-ray spectroscopy to the existing ecosystem of machine learning and Bayesian inference, enabling researchers to apply new methods to solve increasingly complex problems in the best possible way. Our long-term ambition is the scientific exploitation of the data from the newAthena X-ray Integral Field Unit (X-IFU).
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Context. | background 1.00 | 0.02 | 0.01 | unclear 1.00 |
| s1 | Inferring spectral parameters from X-ray data is one of the cornerstones of high-energy astrophysics, and is achieved using software stacks that have been developed over the last twenty years and more. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | However, as models get more complex and spectra reach higher resolutions, these established software solutions become more feature-heavy, difficult to maintain and less efficient. Aims. | gap 0.65 | 0.06 | 0.02 | asserted 1.00 |
| s3 | We present jaxspec, a Python package for performing this task quickly and robustly in a fully Bayesian framework. | contribution 1.00 | 0.86 | 0.08 | asserted 0.98 |
| s4 | Based on the JAX ecosystem, jaxspec allows the generation of differentiable likelihood functions compilable on core or graphical process units (resp. CPU and GPU), enabling the use of robust algorithms for Bayesian inference. Methods. | method 0.85 | 0.69 | 0.03 | asserted 0.99 |
| s5 | We demonstrate the effectiveness of jaxspec samplers, in particular the No U-Turn Sampler, using a composite model and comparing what we obtain with the existing frameworks. | method 0.98 | 0.95 | 0.03 | asserted 1.00 |
| s6 | We also demonstrate its ability to process high-resolution spectroscopy data and using original methods, by reproducing the results of the Hitomi collaboration on the Perseus cluster, while solving the inference problem using variational inference on a GPU. Results. | method 0.78 | 0.94 | 0.05 | asserted 1.00 |
| s7 | We obtain identical results when compared to other softwares and approaches, meaning that jaxspec provides reliable results while being $\sim 10$ times faster than existing alternatives. | result 0.99 | 0.96 | 0.45 | asserted 1.00 |
| s8 | In addition, we show that variational inference can produce convincing results even on high-resolution data in less than 10 minutes on a GPU. Conclusions. | result 0.99 | 0.95 | 0.16 | asserted 0.98 |
| s9 | With this package, we aim to pursue the goal of opening up X-ray spectroscopy to the existing ecosystem of machine learning and Bayesian inference, enabling researchers to apply new methods to solve increasingly complex problems in the best possible way. | future work 0.95 | 0.13 | 0.12 | asserted 0.37 |
| s10 | Our long-term ambition is the scientific exploitation of the data from the newAthena X-ray Integral Field Unit (X-IFU). | future work 1.00 | 0.09 | 0.04 | asserted 0.65 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
Context. Inferring spectral parameters from X-ray data is one of the cornerstones of high-energy astrophysics, and is achieved using software stacks that have been developed over the last twenty years and more. However, as models get more complex and spectra reach higher resolutions, these established software solutions become more feature-heavy, difficult to maintain and less efficient. Aims. We present jaxspec, a Python package for performing this task quickly and robustly in a fully Bayesian framework. Based on the JAX ecosystem, jaxspec allows the generation of differentiable likelihood functions compilable on core or graphical process units (resp. CPU and GPU), enabling the use of robust algorithms for Bayesian inference. Methods. We demonstrate the effectiveness of jaxspec samplers, in particular the No U-Turn Sampler, using a composite model and comparing what we obtain with the existing frameworks. We also demonstrate its ability to process high-resolution spectroscopy data and using original methods, by reproducing the results of the Hitomi collaboration on the Perseus cluster, while solving the inference problem using variational inference on a GPU. Results. We obtain identical results when compared to other softwares and approaches, meaning that jaxspec provides reliable results while being $\sim 10$ times faster than existing alternatives. In addition, we show that variational inference can produce convincing results even on high-resolution data in less than 10 minutes on a GPU. Conclusions. With this package, we aim to pursue the goal of opening up X-ray spectroscopy to the existing ecosystem of machine learning and Bayesian inference, enabling researchers to apply new methods to solve increasingly complex problems in the best possible way. Our long-term ambition is the scientific exploitation of the data from the newAthena X-ray Integral Field Unit (X-IFU).
Human review
References
How the literature uses it
1 arXiv paper cite this in 1 sentence
Papers citing it
Uses its method or tool 1 paper
-
For the spectral analysis, we use [ref] , a Python library for X-ray spectral analysis with Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling using the No U-Turn Sampler [ref] .
4U 1538-52 in a Heartbeat: Broadband X-ray Spectral Properties from XMM-Newton and NuSTAR · Observation and data reduction · 1.00
What it cites · 11 sentences without a confident label
Uses the method or tool of 5 papers
-
A popular implementation of NS used in X-ray spectral fitting can be found in [ref] , which interfaces the NS algorithm of [ref] with or .
UltraNest -- a robust, general purpose Bayesian inference engine · Comparison of inference time for various methods · 0.83
-
A popular implementation of NS used in X-ray spectral fitting can be found in [ref] , which interfaces the NS algorithm of [ref] with or .
X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue · Comparison of inference time for various methods · 0.83
-
[ref] , a probabilistic programming library that implements the construction of hierarchical Bayesian models and the tools needed to perform the associated inference.
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro · Description of · 0.95
Cites as background 2 papers
-
Constraining the plausible values of these parameters given an X-ray spectrum is usually achieved using publicly available software, such as [ref] , [ref] , [ref] , or [ref] .
Sherpa: a Mission-Independent Data Analysis Application · Introduction · 0.87
-
For further insight, we direct the interested reader to the review by [ref] .
Variational Inference: A Review for Statisticians · High-resolution spectroscopy with · 0.99
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.