Parameter estimation from Ly$\alpha$ forest in Fourier space using Information Maximising Neural Network

Abstract

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 $T_0$ & $\gamma$ at z=2-4 and cosmological parameters ${\sigma }_8$ & $n_s$ 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 ${\Gamma }_{HI}$. We obtain improved estimates of $T_0$ & $\gamma$ 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 $\gamma$ estimates which are robust against continuum uncertainties by extracting continuum-independent small-scale information from Fourier domain. In case of ${\sigma }_8$ & $n_s$ 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 $T_0$ & ${\Gamma }_{HI}$ we also demonstrate how to take forward this approach observationally in the future.

17 pages, 7 figures, 5 tables. Submitted to Astronomy&Astrophysics. Comments welcomed

References in corpus (11)

• Cosmological parameters from combining the Lyman-alpha forest with CMB, galaxy clustering and SN constraints
• Cosmological Simulations of Intergalactic Medium Enrichment from Galactic Outflows
• Constraint on neutrino masses from SDSS-III/BOSS Ly$\alpha$ forest and other cosmological probes
• The thermal history of the intergalactic medium down to redshift z=1.5: a new curvature measurement
• The Second Data Release of the KODIAQ Survey
• A combined analysis of 3D Weak Lensing, Lyman-alpha forest and WMAP year three data
• Neural networks as optimal estimators to marginalize over baryonic effects
• How informative are summaries of the cosmic 21-cm signal?
• Possible evidence for a large-scale enhancement in the Lyman-$\alpha$ forest power spectrum at redshift $\text{z}\ge 4$
• A ~6 Mpc overdensity at z = 2.7 detected along a pair of quasar sight lines: filament or protocluster?
• Ly$\alpha$NNA: A Deep Learning Field-level Inference Machine for the Lyman-$\alpha$ Forest

Cited by in corpus (1)

• Deep learning-driven likelihood-free parameter inference for 21-cm forest observations