LIGO @ Kenyon

We are members of the LIGO Scientific Collaboration and run a LIGO group at Kenyon.  LIGO stands for Laser Interferometer Gravitational-wave Observatory.  There are two LIGO observatories in the United States.  These instruments were the first to directly detect the existence of gravitational waves on September 14, 2015!  This momentous discovery of the gravitational waves from two colliding black holes has received a wide array of press and attention, including the co-founders of the LIGO experiment being awarded the 2017 Nobel Prize in Physics.  Since 2015, LIGO has discovered dozens more binary black hole mergers through their gravitational wave signatures and several binary neutron star mergers.  Most notably, on August 17, 2017, LIGO detected gravitational waves from the collision of two neutron stars.  This event also emitted radiation across the electromagnetic spectrum and was observed by hundreds of telescopes around the world.  This was the first multi-messenger astronomy event that involved gravitational waves, and for reference of the importance of this discovery and the science it has enabled, the detection paper for this event has been cited over 2,500 times in only three years.  You can read more about LIGO and the exciting discoveries we have been involved in as members of the collaboration at www.ligo.org.

Current activities of the LIGO research group at Kenyon College involve the maintenance and further development of the low-latency calibration pipeline for Advanced LIGO, which is the critical first step in low-latency gravitational-wave science, the ongoing development and running of a modeled search for gravitational waves from intermediate mass black hole binary systems, the development and investigation of methods to use gravitational- wave detections to probe the elusive equation of state of neutron star matter, and the development of machine learning algorithms used to predictively veto glitches from LIGO data.

Projects

Calibration

Calibration is the necessary bridge between the instrument and data analysis. Data analysts search the interferometer strain, or fractional change in length of the interferometer arms, for gravitational-wave signals. However, the output of the interferometer does not directly represent the interferometer strain. The calibration effort is the process of reconstructing the strain from the interferometer output.  This process involves modeling the physics of the interferometer, digitizing these models, computing the strain, and thoroughly understanding all of the systematic errors that impact this process.

Prof. Maddie Wade is co-chair of the calibration working group within LIGO.  She did a lot of the original development work on the official LIGO calibration pipeline that computes the strain data and continues to help maintain this software.  Prof. Maddie Wade, along with several students, has also studied ways to increase the speed of the calibration process in order to enable more low-latency science, improve the accuracy of the digitized filters used in the calibration process, and improve the overall accuracy of the calibrated strain data by account for real-time changes in the calibration model.  Additionally, Prof. Maddie Wade and students have studied the impact of calibration errors on astrophysical results, such as the estimation of the parameters of a discovered gravitational wave source and the detection of compact binary events like merging black holes and neutron stars.  

Searching for intermediate mass black hole binaries

An intermediate-mass black hole (IMBH) is a black hole (BH) with a mass above the upper edge of the stellar-mass BH mass range, which is a few tens of solar masses, and below the lower edge of the supermassive BH mass range, which is roughly a hundred thousand solar masses.   Little is known about IMBHs, but their relatively large masses enable aLIGO to probe cosmological redshifts up to z ∼ 2. Recently, the LIGO Scientific Collaboration published on an observation of two stellar mass black holes merging to form an IMBH, which is the first detection of its kind and not only solidifies their existence but demonstrates how they sometimes form.

Prof. Les Wade, along with several students,  is a lead developer of a matched filter search for coalescing IMBH binaries from a total binary mass of fifty solar masses to six hundred solar masses and component mass ratios between 10:1 and 1:1. This is a search in which gravitational-wave data is filtered through a bank of theoretically modeled waveforms in order to find matching signals hidden in the data. The difficultly of a matched filter search for IMBH binaries is that such signals can be very short and therefore be hard to distinguish from noise transients in the data relative to compact binary sources with smaller masses.

Neutron star equation of state parameter estimation

Neutron stars are some of the most interesting astrophysical bodies known.  They contain the mass of roughly one and a half of our Sun squished into the size of a city spinning faster than a kitchen blender.  Just a single teaspoon of neutron star matter weighs 10 million pounds.  The physics of matter at this density is not well understood, and the mathematical relationship that described the inner workings of these stars is called the neutron star equation of state.

On August 17, 2017, LIGO picked up the gravitational ripples from two merging neutron stars.  Prof. Leslie Wade and two of his research students had been developing software to measure the neutron star equation of state from binary neutron star mergers like this.  Using this software, the LIGO Scientific Collaboration reported on the first gravitational-wave constraint on the neutron star equation of state.  However, several more observations will need to be made in order to get a precise measurement.  Prof. Leslie Wade and students continue developing robust models for equation of state inference.

Machine learning and LIGO data quality

Gravitational wave signals from merging compact objects are signals that quickly enter and exit the LIGO detectors.  These types of signals are known as transient signals.  Unfortunately, terrestrial noise sources also show up as transient signals in ground-based interferometers like LIGO.  One of the major challenges in the analysis of LIGO data is to tease signals out of the noisy background of transient terrestrial signals.  

Our group works with the LIGO group at Penn State University to develop algorithms for predicting the existence of transient noise events in the detector based on data from auxiliary sensors, such as seismometers and thermometers, placed all over the detectors.  We focus on adapting machine learning algorithms - specifically artificial neural networks and recurrent neural networks - to tackle this problem.

Current Students

Isa Braun '26 - Isa is developing a machine learning algorithm for mapping from tidal parameters to neutron star equation of state parameters with several different equation of state models.

James Hart '24 - James is developing software for neutron star equation of state parameter estimation in bilby.

Kuba Kopczuk '26 - 

Luke Wilson '25 - 

Michael Van Keuren '24 - 

Emmanuel Makelele '25 - 

Josephine Smith '26 - 

David Chintala '26 - 

Teddy Masters '25 - 

Past Students

Kalista Wayt '23 - Kali is developing a sophisticated machine learning algorithm that will be tuned to different types of glitches for use in predicting the presences of glitches in LIGO data.

Olivia Wilks '23 - Olivia is developing equation of state models with phase transitions for equation of state inference.

Sophie Schmitz, '25 - Sophie developed a hyperparameter tuning method for machine learning algorithms used to predict the presence of glitches in LIGO data.

Phillip Diamond, '25 - Phillip investigated the effectiveness of different features in a machine learning algorithm used to predict the presence of glitches in LIGO data.

Jeremy Baier, '22 - Jeremy upgraded equation of state software to be robust to equation of states with sharp phase transitions and implemented this new software in the bilby inference package.

Quinn Curren, '22 - Quinn developed an effective machine learning to identify pulsar candidates in data from radio telescope surveys.

Ezra Moguel, '21 - Ezra worked on improving the latency of LIGO calibration through new and innovative filter design techniques.  He has also worked to develop analysis and visualization techniques for the machine learning algorithms being developed to identify noise in LIGO data.

Paul Neubauer, '21 - Paul optimized the artificial neural network used for determining the quality of LIGO data and continuing development of a recurrent neural network for use in determining LIGO data quality.

Joe Lucaccioni, '21 - Joe developed and implemented a generalized polytropic model for measuring the neutron star equation of state from gravitational wave detections of binary neutron star mergers into the official LIGO software infrastructure.

Devon Nothard, '20 - Devon worked on implementing a neutron star equation of state model that is everywhere causal.

Maddie Stover, '20 - Maddie studied the impact of LIGO calibration errors on searches and parameter estimation for coalescing binary systems.

Chase Frederick, '20 - Chase worked on improving techniques for determining the accuracy of filters used in LIGO calibration.

Georgia Stolle-McAllister, '20 - Georgia performed a search for intermediate mass black hole binary systems on LIGO's second observing run data.

Burke Irwin, '19 - Burke developed methods for visualizing the results of an equation of state parameter estimation.

Kyle Rose, '19 - Kyle explored  improvements to the inputs of machine learning algorithms used to classify loud, transient noise events in LIGO data.

Campbell Fee, '18 - Campbell studied the effects of calibration error on gravitational-wave searches.

Matthew Carney, '18 - Matt developed LIGO parameter estimation software's ability to measure the neutron star equation of state.

Donald Moffa, '18 - Doni explored machine learning algorithms for the use of classifying loud, transient noise events in LIGO data.

Theresa Chmiel, '17 - Tracy worked on modeling Advanced LIGO calibration error and its effects on parameter estimation.

John Zellweger, '17 - Jack worked on optimizing algorithms to search for intermediate mass black hole binaries.

Radio and Optical Astronomy Research (ROAR) group

We run the ROAR group at Kenyon with the help of our colleague (and real astronomer) Paula Turner.  Our group is an integrated research and education group focussing on radio and optical astronomy.   Radio astronomy activities include taking observations with radio telescopes, participating in the NANOStars group (the undergraduate research group that is part of the NANOGrav collaboration), and building our very own radio telescope.   In particular, we remotely control one of the world's largest radio telescopes, the Green Bank Telescope in West Virginia, to search for rapidly rotating neutron stars called pulsars.  In addition, the group also studies variable stars using the 20-inch optical telescope in the Miller Observatory at Kenyon.  The group is open to anyone with an interest in astronomy.   We generally have about 15 active student members, and usually 5-10 of them are trained to run solo observations  on the GBT for the GBNCC survey.   

We use a team structure in the ROAR group.  Students are broken up into teams of 3-6 people with a team leader for each group.   The ROAR group meets as a large group once a week and each team must meet outside of the large group on a weekly or bi-weekly basis.  Teams report back to the large group on their activities on a bi-weekly basis.  

Our motto in the ROAR group is that we both do astronomy and learn astronomy together.

Our operations have been modified slightly during the COVID-19 pandemic, but we still operate on the same principle of doing and learning about astronomy together.

Doing astronomy

We are active in two different astronomy wavelengths in the ROAR group - optical and radio astronomy.  Both optical and radio observing sessions are performed using the team structure in ROAR.  Data analysis activities can be performed either in teams or as a large group depending on the activity. 

Optical astronomy

We use our telescopes housed at Kenyon's own Miller Observatory to perform optical astronomy observations.  Prof. Paula Turner runs observations focused on variable stars in our galaxy.  Students are then able to analyze the data taken during their observations and draw conclusions from these analyses.

Radio astronomy

We use the GBT and Arecibo Telescope for radio observations of pulsars.  At Kenyon , we perform observations using only the GBT but hope to branch out to the Arecibo Telescope in the near future.  Students both run remote observing sessions that survey the sky looking for new pulsars and perform data analysis activities on data collected through this survey.  Students rank pulsar candidates in terms of their likelihood of being an astrophysical pulsar signal versus noise or RFI through the CyberSKA candidate rankers.  By participating in survey observations and pulsar candidate ranking, students are contributing to the effort to add pulsars to the pulsar timing arrays used by collaborations such as NANOGrav.

Learning astronomy

Students in ROAR engage in discussions about modern astronomy topics and encounter astronomy material in interactive lessons both in the large group meetings and in team meetings.  Our large group meetings often involve active discussions about recently published astronomy articles, fun activities to teach students more about a specific topic in astronomy, or discussions about videos or articles that were targeted at teaching students some introductory astronomy material.

Mount Vernon High School Astronomy Club

We have partnered with the local Mount Vernon High School (MVHS) to help start an astronomy club for students.  The Kenyon College ROAR group members help design activities for the MVHS astronomy club and help coordinate and run the MVHS astronomy club meetings.  Members of the MVHS astronomy club engage in hands-on activities learning about astronomy and participate in current data analysis activities through the Pulsar Search Collaboratory.  We also organize activities and events that involve both the MVHS astronomy club members and Kenyon ROAR group members working together collaboratively or just having fun up at the Miller Observatory.

GBT training trips

Each year we support a group of students to attend a week-long GBT observer training workshop at the GBT in Green Bank, West Virginia.  Students have found this trip both an invigorating and excellent education experience.  After receiving GBT observer training, students can become team leaders and run solo remote observing sessions through ROAR at Kenyon.  Here are some pictures from the most recent trip to the GBT for observation training!