This is an exact copy of the official NRC Astro2020 white paper LaTeX template. Re-published for community use on Overleaf by Jason Tumlinson (STScI / JHU).
This is the LaTeX template for the Royal Society Open Science – a fast, open journal publishing high quality research across all of science, engineering and mathematics.
A Latex template for the preparation of IAU Symposia Proceedings downloaded from
http://www.iau.org/static/scientific_meetings/authors/.
The package contains: Class File (iau.cls), Instructions, a Sample PDF and a Sample TeX file
Template for white paper submissions to the 2020 Long Range plane for Canadian astronomy, LRP2020. For further information see the call for white papers
With the detectors currently off, LIGO has detected and gathered an abundance of data from the second observing run (O2). Some of which, captures the most recent triggers that are potential candidates for future gravitational waves, are analyzed more thoroughly. My responsibility as a student researcher is to perform independent checks on four of the most recent Compact Binary Coalescence (CBC) triggers. In order to do so, I compare the \(h(t)\) Omega scans of these events to the Gravity Spy classes. Omega scans are a detector characterization tool to help measure the Signal-to-Noise-Ratio (SNR) of transient noises during detections. This helps scientists distinguish the difference between a gravitational wave signal, which looks like a `chirp' versus a glitch in the data. Gravity Spy is a citizen science program that helps LIGO in classifying glitches to improve machine learning for gravitational wave signals. For each event I determine if it looks like one of the known categories of solved or unsolved glitches seen in the Advanced LIGO detectors? My results are then recorded in the O2 event detection checklist. Omega scans are a `burst-type' search pipeline that detect glitches efficiently. The Omega scan is labeled using time measured in seconds on the x-axis, frequency measured in Hz on the y-axis and the signal measured is normalized to demonstrate how `loud' the noise is.