An official website of the United States government
High resolution x-ray emission spectrometer for multiple hard x-ray emission lines: Demonstration for Cu Kα and Kβ emissions
- Award ID(s):
- 2003910
- PAR ID:
- 10277220
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- Review of Scientific Instruments
- Volume:
- 92
- Issue:
- 7
- ISSN:
- 0034-6748
- Page Range / eLocation ID:
- Article No. 073105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
We report a comprehensive computational study of unsupervised machine learning for extraction of chemically relevant information in X-ray absorption near edge structure (XANES) and in valence-to-core X-ray emission spectra (VtC-XES) for classification of a broad ensemble of sulphorganic molecules. By progressively decreasing the constraining assumptions of the unsupervised machine learning algorithm, moving from principal component analysis (PCA) to a variational autoencoder (VAE) to t-distributed stochastic neighbour embedding (t-SNE), we find improved sensitivity to steadily more refined chemical information. Surprisingly, when embedding the ensemble of spectra in merely two dimensions, t-SNE distinguishes not just oxidation state and general sulphur bonding environment but also the aromaticity of the bonding radical group with 87% accuracy as well as identifying even finer details in electronic structure within aromatic or aliphatic sub-classes. We find that the chemical information in XANES and VtC-XES is very similar in character and content, although they unexpectedly have different sensitivity within a given molecular class. We also discuss likely benefits from further effort with unsupervised machine learning and from the interplay between supervised and unsupervised machine learning for X-ray spectroscopies. Our overall results, i.e. , the ability to reliably classify without user bias and to discover unexpected chemical signatures for XANES and VtC-XES, likely generalize to other systems as well as to other one-dimensional chemical spectroscopies.more » « less
-
Abstract While gamma-ray bursts (GRBs) have the potential to shed light on the astrophysics of jets, compact objects, and cosmology, a major set back in their use as probes of these phenomena stems from our incomplete knowledge surrounding their prompt emission. There are numerous models that can account for various observations of GRBs in the gamma-ray and X-ray energy ranges, due to the flexibility in the number of parameters that can be tuned to increase agreement with data. Furthermore, these models lack predictive power that can test future spectropolarimetric observations of GRBs across the electromagnetic spectrum. In this work, we use the MCRaT radiative transfer code to calculate the X-ray spectropolarimetric signatures expected from the photospheric model for two unique hydrodynamic simulations of long GRBs. We make time-resolved and time-integrated comparisons between the X-ray and gamma-ray mock observations, shedding light on the information that can be obtained from X-ray prompt emission signatures. Our results show that theT90derived from the X-ray light curve is the best diagnostic for the time that the central engine is active. We also find that our simulations reproduce the observed characteristics of the Einstein Probe–detected GRB 240315C. Based on our simulations, we are also able to make predictions for future X-ray spectropolarimetric measurements. Our results show the importance of conducting global radiative transfer calculations of GRB jets to better contextualize the prompt emission observations and constrain the mechanisms that produce the prompt emission.more » « less
