Human vs. machine -- 1:3. Joint analysis of classical and ML-based summary statistics of the Lyman-$α$ forest
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915428361568256 |
|---|---|
| author | Chang, S. Nayak, P. Walther, M. Gruen, D. |
| author_facet | Chang, S. Nayak, P. Walther, M. Gruen, D. |
| contents | In order to compress and more easily interpret Lyman-$α$ forest (Ly$α$F) datasets, summary statistics, e.g. the power spectrum, are commonly used. However, such summaries unavoidably lose some information, weakening the constraining power on parameters of interest. Recently, machine learning (ML)-based summary approaches have been proposed as an alternative to human-defined statistical measures. This raises a question: can ML-based summaries contain the full information captured by traditional statistics, and vice versa? In this study, we apply three human-defined techniques and one ML-based approach to summarize mock Ly$α$F data from hydrodynamical simulations and infer two thermal parameters of the intergalactic medium, assuming a power-law temperature-density relation. We introduce a metric for measuring the improvement in the figure of merit when combining two summaries. Consequently, we demonstrate that the ML-based summary approach not only contains almost all of the information from the human-defined statistics, but also that it provides significantly stronger constraints by a ratio of better than 1:3 in terms of the posterior volume on the temperature-density relation parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03264 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Human vs. machine -- 1:3. Joint analysis of classical and ML-based summary statistics of the Lyman-$α$ forest Chang, S. Nayak, P. Walther, M. Gruen, D. Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies In order to compress and more easily interpret Lyman-$α$ forest (Ly$α$F) datasets, summary statistics, e.g. the power spectrum, are commonly used. However, such summaries unavoidably lose some information, weakening the constraining power on parameters of interest. Recently, machine learning (ML)-based summary approaches have been proposed as an alternative to human-defined statistical measures. This raises a question: can ML-based summaries contain the full information captured by traditional statistics, and vice versa? In this study, we apply three human-defined techniques and one ML-based approach to summarize mock Ly$α$F data from hydrodynamical simulations and infer two thermal parameters of the intergalactic medium, assuming a power-law temperature-density relation. We introduce a metric for measuring the improvement in the figure of merit when combining two summaries. Consequently, we demonstrate that the ML-based summary approach not only contains almost all of the information from the human-defined statistics, but also that it provides significantly stronger constraints by a ratio of better than 1:3 in terms of the posterior volume on the temperature-density relation parameters. |
| title | Human vs. machine -- 1:3. Joint analysis of classical and ML-based summary statistics of the Lyman-$α$ forest |
| topic | Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2508.03264 |