Statistical Prediction of [CII] Observations by Constructing Probability Density Functions using SOFIA, Herschel, and Spitzer Observations

TL;DR

This paper introduces a statistical method using probability density functions to accurately predict [CII] emission from infrared continuum images, aiding efficient planning of far-infrared observations and maximizing scientific returns.

Contribution

It presents a novel statistical approach that relates [CII] emission to multiple dust tracers using PDFs, improving prediction accuracy over previous methods.

Findings

Achieved less than 30% uncertainty in 80% of the observation area

Demonstrated high-quality predictions for the star-forming region RCW 120

Method supports future far-infrared missions like GUSTO and FIR Probe

Abstract

We present a statistical algorithm for predicting the [CII] emission from Herschel and Spitzer continuum images using probability density functions between the [CII] emission and continuum emission. The [CII] emission at 158 $\mu$m is a critical tracer in studying the life cycle of interstellar medium and galaxy evolution. Unfortunately, its frequency is in the far infrared (FIR), which is opaque through the troposphere and cannot be observed from the ground except for highly red-shifted sources (z $\gtrsim$ 2). Typically [CII] observations of closer regions have been carried out using suborbital or space observatories. Given the high cost of these facilities and limited time availability, it is important to have highly efficient observations/operations in terms of maximizing science returns. This requires accurate prediction of the strength of emission lines and, therefore, the time required for their observation. However, [CII] emission has been hard to predict due to a lack of strong correlations with other observables. Here we adopt a new approach to making accurate predictions of [CII] emission by relating this emission simultaneously to several tracers of dust emission in the same region. This is done using a statistical methodology utilizing probability density functions (PDFs) among [CII] emission and Spitzer IRAC and Herschel PACS/SPIRE images. Our test result toward a star-forming region, RCW 120, demonstrates that our methodology delivers high-quality predictions with less than 30\% uncertainties over 80\% of the entire observation area, which is more than sufficient to test observation feasibility and maximize science return. The {\it pickle} dump files storing the PDFs and trained neural network module are accessible upon request and will support future far-infrared missions, for example, GUSTO and FIR Probe.