2025 Winner
The U.S. National Science Foundation Laser Interferometer Gravitational-wave Observatory (NSF LIGO) Laboratory congratulates Jane Glanzer of Caltech for winning this year's LIGO Laboratory Award for Excellence in Detector Characterization and Calibration.
Glanzer is recognized for critical and timely contributions to both calibration and detector characterization for the fourth observing run (O4), including improved calibration of LIGO data at high frequencies, mitigation of LIGO detector glitches, and production of key LIGO data products.
Dr. Jane Glanzer is a postdoctoral scholar at the California Institute of Technology. She received her PhD in Physics from Louisiana State University in 2024 working with Dr. Gabriela Gonzalez. Dr. Glanzer works on mitigating detector noise that can negatively impact the detectors' astrophysical reach, and on producing high-quality calibrated gravitational wave data.
Glanzer’s work has impacted all areas of gravitational-wave science by improving the sensitivity of the search algorithms used to identify candidate signals and the performance of the gravitational-wave detectors themselves. Glanzer stepped up to lead the production of data quality vetoes in O4, used by all astrophysical searches for gravitational waves to exclude periods of data likely to cause a high rate of false positive detections. As the LIGO Detector Characterization group’s detector improvement subgroup lead, Glanzer has also led efforts to use techniques from gravitational-wave data analysis to increase the sensitivity of our interferometers.
Additionally, Glanzer's calibration work in the past year has included deployment of a high frequency measurement system that allows well-constrained calibration uncertainty budgets up to frequencies of 5 kHz. This improved calibration at higher frequencies improves our ability to recover post-merger signals from compact binary coalescences as well as gravitational waves from other sources, such as core-collapse supernovae or isolated neutron stars. Glanzer is currently developing time-dependent calibration infrastructure for a forthcoming upgraded calibration pipeline. Tracking and accounting for changes in the interferometers’ actuation and sensing functions on timescales as short as minutes will lead to more accurate LIGO data calibration and improved understanding of astrophysical signals in future observation runs.
Glanzer’s contributions to LIGO detector characterization go back over six years, including her time as a graduate student at Louisiana State University. Her broad past contributions include work on mitigating fast scattering [1], one of the most prevalent noise sources in the LIGO detectors at the time, and assessments based on the machine learning algorithm Gravity Spy [2] in LIGO-Virgo’s third observing run. Glanzer was part of the team validating LIGO gravitational wave data before its release to the public as part of the Gravitational Wave Open Science Center. Glanzer has been proactive in the mitigation of new glitches appearing in the LIGO detectors by responding to commissioner requests and monitoring detector output. This has recently included analysis of vibration coupling, and using regression and time-lagged cross-correlation methods to investigate semi-hourly dips in the binary neutron star (BNS) range of the LIGO Livingston detector.
For her work, Glanzer will receive a $1,000 prize and present an invited seminar at one of the LIGO Laboratory sites (LIGO-Hanford, LIGO-Livingston, Caltech, or MIT) to share her achievements with LIGO Laboratory members. Glanzer will also be presented with an award certificate at the next meeting of the LIGO-Virgo-KAGRA collaboration.
[1] J. Glanzer et al 2023 Class. Quantum Grav. 40 195015 https://iopscience.iop.org/article/10.1088/1361-6382/acf01f
[2] J. Glanzer et al 2023 Class. Quantum Grav. 40 065004 https://iopscience.iop.org/article/10.1088/1361-6382/acb633
2025 Honorable Mentions
Sofía Álvarez-López (Massachusetts Institute of Technology) – for development of GSpyNetTree, a tool that distinguishes between true gravitational wave signals and detector noise, an important contribution to the automation of event validation.
Debasmita Nandi (Louisiana State University) – for studies of glitches caused by scattered light in LIGO Livingston, suggesting a model for the source of the scattered light for glitches seen in Observing Run 4 data.
Avani Patel (National Central University) – for enabling the delivery of offline calibrated LIGO data for a period in Observing run 4 when online calibration was unsuitable.
Kiet Pham (University of Minnesota) – for key contributions to LIGO data quality for stochastic and continuous wave gravitational wave searches, including development of STAMP-PEM and Stochmon tools.
Preeti Sharma (Louisiana State University) – for studies of the impact of earthquakes on LIGO interferometer locking during Observing Run 4.
