Fast Identification of Transients: Applying Expectation Maximization to Neutrino Data
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914884335173632 |
|---|---|
| author | Karl, Martina Eller, Philipp |
| author_facet | Karl, Martina Eller, Philipp |
| contents | We present a novel method for identifying transients suitable for both strong signal-dominated and background-dominated objects. By employing the unsupervised machine learning algorithm known as Expectation Maximization, we achieve computing time reductions of over $10^4$ on a single CPU compared to conventional brute-force methods. Furthermore, this approach can be readily extended to analyze multiple flares. We illustrate the algorithm's application by fitting the IceCube neutrino flare of TXS 0506+056. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_15196 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fast Identification of Transients: Applying Expectation Maximization to Neutrino Data Karl, Martina Eller, Philipp Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Computational Physics Data Analysis, Statistics and Probability We present a novel method for identifying transients suitable for both strong signal-dominated and background-dominated objects. By employing the unsupervised machine learning algorithm known as Expectation Maximization, we achieve computing time reductions of over $10^4$ on a single CPU compared to conventional brute-force methods. Furthermore, this approach can be readily extended to analyze multiple flares. We illustrate the algorithm's application by fitting the IceCube neutrino flare of TXS 0506+056. |
| title | Fast Identification of Transients: Applying Expectation Maximization to Neutrino Data |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2312.15196 |