Parameter estimation from Ly$α$ forest in Fourier space using Information Maximising Neural Network
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| Format: | Preprint |
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2024
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| _version_ | 1866917796369137664 |
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| author | Maitra, Soumak Cristiani, Stefano Viel, Matteo Trotta, Roberto Cupani, Guido |
| author_facet | Maitra, Soumak Cristiani, Stefano Viel, Matteo Trotta, Roberto Cupani, Guido |
| contents | We aim to present a robust parameter estimation with simulated Lya forest spectra from Sherwood-Relics simulations suite using Information Maximizing Neural Network(IMNN) to extract maximal information from Lya 1D-transmitted flux in Fourier space. We perform 1D estimations using IMNN for IGM thermal parameters $T_0$ & $γ$ at z=2-4 and cosmological parameters $σ_8$ & $n_s$ at z=3-4. We compare our results with estimates from power spectrum using posterior distribution from Markov Chain Monte Carlo(MCMC). We then check robustness of IMNN estimates against deviation in spectral noise levels,continuum uncertainties & instrumental smoothing effects. Using mock Lya forest sightlines from publicly available CAMELS project we also check the robustness of the trained IMNN on a different simulation. We also perform a 2D-parameter estimation for $T_0$ & HI photoionization rates $Γ_{HI}$. We obtain improved estimates of $T_0$ & $γ$ using IMNN over standard MCMC approach. These estimates are also more robust against SNR deviations at z=2 & 3. At z=4 the sensitivity to noise deviations is on par with MCMC estimates. The IMNN also provides $T_0$ and $γ$ estimates which are robust against continuum uncertainties by extracting continuum-independent small-scale information from Fourier domain. In case of $σ_8$ & $n_s$ IMNN performs on par with MCMC but still offers a significant speed boost in estimating parameters from a new dataset. The improved estimates with IMNN are seen for high instrumental-resolution(FWHM=6km/s). At medium or low resolutions IMNN performs similar to MCMC suggesting an improved extraction of small-scale information with IMNN. We also find that IMNN estimates are robust against the choice of simulation. By performing a 2D-parameter estimation for $T_0$ & $Γ_{HI}$ we also demonstrate how to take forward this approach observationally in the future. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_04327 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Parameter estimation from Ly$α$ forest in Fourier space using Information Maximising Neural Network Maitra, Soumak Cristiani, Stefano Viel, Matteo Trotta, Roberto Cupani, Guido Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies We aim to present a robust parameter estimation with simulated Lya forest spectra from Sherwood-Relics simulations suite using Information Maximizing Neural Network(IMNN) to extract maximal information from Lya 1D-transmitted flux in Fourier space. We perform 1D estimations using IMNN for IGM thermal parameters $T_0$ & $γ$ at z=2-4 and cosmological parameters $σ_8$ & $n_s$ at z=3-4. We compare our results with estimates from power spectrum using posterior distribution from Markov Chain Monte Carlo(MCMC). We then check robustness of IMNN estimates against deviation in spectral noise levels,continuum uncertainties & instrumental smoothing effects. Using mock Lya forest sightlines from publicly available CAMELS project we also check the robustness of the trained IMNN on a different simulation. We also perform a 2D-parameter estimation for $T_0$ & HI photoionization rates $Γ_{HI}$. We obtain improved estimates of $T_0$ & $γ$ using IMNN over standard MCMC approach. These estimates are also more robust against SNR deviations at z=2 & 3. At z=4 the sensitivity to noise deviations is on par with MCMC estimates. The IMNN also provides $T_0$ and $γ$ estimates which are robust against continuum uncertainties by extracting continuum-independent small-scale information from Fourier domain. In case of $σ_8$ & $n_s$ IMNN performs on par with MCMC but still offers a significant speed boost in estimating parameters from a new dataset. The improved estimates with IMNN are seen for high instrumental-resolution(FWHM=6km/s). At medium or low resolutions IMNN performs similar to MCMC suggesting an improved extraction of small-scale information with IMNN. We also find that IMNN estimates are robust against the choice of simulation. By performing a 2D-parameter estimation for $T_0$ & $Γ_{HI}$ we also demonstrate how to take forward this approach observationally in the future. |
| title | Parameter estimation from Ly$α$ forest in Fourier space using Information Maximising Neural Network |
| topic | Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2404.04327 |