Studying the effects that the local environments of galaxies have on their interstellar medium (ISM) properties is crucial for understanding galaxy evolution and large scale structure of the universe. In order to do that we need precise measurements of ISM properties like Star Formation Rate (SFR), metallicity (Z), ionization parameter (U), gas pressure, and extinction. Accurate estimation of redshift and emission line fluxes from a galaxy's spectrum is the first step in measuring these ISM properties. Current techniques for these measurements still rely on time-consuming manual efforts or error-prone cross-correlation codes that are already struggling to process the vast quantities of spectroscopic data that currently exist. With future NASA missions like JWST, Euclid, Roman, and SPHEREx expected to produce even larger amounts of spectroscopic data, a fast and reliable alternative to the current techniques of spectroscopic measurements is the need of the hour. To that end, we train a Convolutional Neural Network (CNN) to estimate redshift directly from an input spectrum. We generate a library of synthetic spectra spanning a wide range of parameter values and use it to train the CNN and later evaluate its performance. We obtain a normalized mean absolute deviation (NMAD) value of 0.0086 and an outlier fraction of 5.36% for our test set. This accuracy and precision is comparable to the current best photometric redshifts estimated using SED fitting codes and is lower than the subset of high quality spectroscopic data estimated using time and labour-intensive techniques. In comparison, our CNN is able to process ~30,000 spectra in around five seconds giving it an important advantage over the current methods of redshift estimation. We plan to extend this technique to estimating other ISM properties from galaxy spectra in the future.
Astrophysical Sciences and Technology (MS)
Department, Program, or Center
School of Physics and Astronomy (COS)
Pattnaik, Rohan, "Automating the measurements of galaxy redshifts and ISM properties using CNN" (2021). Thesis. Rochester Institute of Technology. Accessed from
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