Date: 29.10.2024

Priyanka Jalan

FLAME: Fitting Lyα Absorption lines using Machine Learning

Abstract:
We introduce FLAME, a machine learning algorithm designed to fit Voigt profiles to HI Lyman-alpha absorption lines using deep convolutional neural networks. FLAME integrates two algorithms: the first determines the number of components required to fit absorption lines, and the second calculates the Doppler parameter b, the HI column density NHI, and the velocity separation of individual components. For the current version of FLAME, we trained it on low-redshift forests observed with the Far Ultraviolet gratings of the COS aboard the HST. Drawing on this data, we trained FLAME on ~1M simulated Voigt profiles, forward-modeled to mimic absorption lines observed with HST-COS, to classify lines as either single or double components, and then determine Voigt profile fitting parameters. FLAME shows impressive accuracy on the simulated data by identifying more than 98% (90%) of single (double) component lines. It determines b values within 15 (18) km/s and log NHI cm2 values within 0.3 (0.8) for 90% of the single (double) component lines. However, when applied to real data, FLAME's component classification accuracy drops by ~10%. Despite this, there is a reasonable agreement between the b and NHI distributions obtained from traditional Voigt profile fitting methods and FLAME's predictions. Our mock HST-COS data analysis, designed to emulate real data parameters, demonstrated that FLAME could achieve consistent accuracy comparable to its performance with simulated data. FLAME's performance validates the use of machine learning for Voigt profile fitting, underscoring the significant potential of machine learning for detailed analysis of absorption lines.