Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.18824 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912500180582400 |
|---|---|
| author | Sadasivan, Daniel Cordero, Isaac Graham, Andrew Marsh, Cecilia Kupcho, Daniel Mourad, Melana Mai, Maxim |
| author_facet | Sadasivan, Daniel Cordero, Isaac Graham, Andrew Marsh, Cecilia Kupcho, Daniel Mourad, Melana Mai, Maxim |
| contents | Simulation Based Inference (SBI) is shown to yield more accurate resonance parameter estimates than traditional chi-squared minimization in certain cases of model misspecification, demonstrated through a case study of pi-pi scattering and the rho(770) resonance. Models fit to some data sets using chi-squared minimization can predict inaccurate pole positions for the rho(770), while SBI provides more robust predictions across the same models and data. This result is significant both as a proof of concept that SBI can handle model misspecification, and because accurate modeling of pi-pi scattering is essential in the study of many contemporary physical systems (e.g., a1(1260), omega(782)). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18824 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification Sadasivan, Daniel Cordero, Isaac Graham, Andrew Marsh, Cecilia Kupcho, Daniel Mourad, Melana Mai, Maxim High Energy Physics - Phenomenology Nuclear Theory Applications Machine Learning Simulation Based Inference (SBI) is shown to yield more accurate resonance parameter estimates than traditional chi-squared minimization in certain cases of model misspecification, demonstrated through a case study of pi-pi scattering and the rho(770) resonance. Models fit to some data sets using chi-squared minimization can predict inaccurate pole positions for the rho(770), while SBI provides more robust predictions across the same models and data. This result is significant both as a proof of concept that SBI can handle model misspecification, and because accurate modeling of pi-pi scattering is essential in the study of many contemporary physical systems (e.g., a1(1260), omega(782)). |
| title | Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification |
| topic | High Energy Physics - Phenomenology Nuclear Theory Applications Machine Learning |
| url | https://arxiv.org/abs/2507.18824 |