| NSF Org: |
PHY Division Of Physics |
| Recipient: |
|
| Initial Amendment Date: | July 22, 2019 |
| Latest Amendment Date: | August 1, 2021 |
| Award Number: | 1912630 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Pedro Marronetti
pmarrone@nsf.gov (703)292-7372 PHY Division Of Physics MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | August 1, 2019 |
| End Date: | December 31, 2022 (Estimated) |
| Total Intended Award Amount: | $374,998.00 |
| Total Awarded Amount to Date: | $374,998.00 |
| Funds Obligated to Date: |
FY 2020 = $114,999.00 FY 2021 = $114,999.00 |
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
1201 W UNIVERSITY DR EDINBURG TX US 78539-2909 (956)665-2889 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
One W University Boulevard Brownsville TX US 78520-0008 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | LIGO RESEARCH SUPPORT |
| Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT 01002122DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.049 |
ABSTRACT
This award supports research in LIGO instrumentation and data analysis and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. Gravitational waves (GW) have been directly detected in 2015. So far, two different types of sources have been detected, viz. binary black holes and binary neutron stars. However, there is a wide repertoire of potential sources yet to be detected. The third generation of GW detectors are also in the offing. To address these new challenges in the next decade, this award supports experimental innovations and novel detector characterization and data analysis techniques to further enhance probability of detection of new sources and further extend the GW visibility field. Core collapse supernovae (CCSN) are one of such highly anticipated yet equally challenging sources. The science payoffs from such a detection will be huge, but it dares to elude us because of the low occurrence rates and weak signal strengths. The award will implement a new technique that, based on recent studies, is expected to enhance the detection sensitivity of CCSN. At the same time, further data quality studies will be conducted to study and mitigate noise generated by turbulent airflow. On the instrumental side, research will be conducted to calculate the length response from the advanced LIGO detectors to better understand the high frequency response. While this research will reflect on fundamental understanding of a wide variety of issues, it will also be a great opportunity to train the undergraduate and graduate students in GW research and strengthen STEM workforce. The algorithms and numerical models that will be developed during this study will have a broader application beyond the GW data analysis.
With the upcoming O3 run of the LIGO detectors, it is anticipated the detection of other types of sources and even unknown ones. With the goal of significantly increasing the science reach of the advanced detectors, the UTRGV team will work on projects in the following major areas. 1. Noise characterization: the development of a numerical model to generate realistic finely-sampled temperature fields and run a full hydrodynamic simulation, to determine the frequency distribution of turbulent vortices, and to see how turbulent airflow acts back on the temperature field. 2. Instrumentation research: studies of the aLIGO interferometer configuration in the interferometer model, and evaluating the residual uncertainties at high frequencies. 3. Efficient methods for GW emission from core collapse supernovae: development and application of innovative data analysis algorithms geared towards enhancement of efficiency in detecting weak unmodeled GW signals from core collapse supernovae burst sources. A data pre-processing method (called "TSD"), derived from the Harmonic Regeneration Noise Reduction (HRNR) technique, will be integrated with existing network analysis pipelines to boost their sensitivity to post-core-bounce-phase supernova signals, followed by characterization of performance enhancement and waveform reconstruction for such signals injected in observation-run data.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The goal of this project was to extend the scientific reach of the Laser Interferometer Gravitational-wave Observatory (LIGO) and other next generation gravitational wave detectors. LIGO is a ground-based gravitational wave observatory with two coincidence detectors in the US. These detectors are 4km long Michelson interferometers, one located in Livingston, LA, one in Hanford, WA. The current generation of LIGO is called Advanced LIGO (aLIGO) and is operational since 2015. The efforts in this project were split in three directions Photon calibrator and LIGO calibration improvements, characterization of gravity gradient noise and development of tools to search for unmodeled GW emitting sources.
The research was performed by faculty researchers and graduate students and the following outcomes were achieved:
GW generated by core collapse supernova (CCSN) led to the development of a data analysis pipeline using CCSN waveforms by integrating a convolutional neural network (CNN) in the analysis. This method was able to detect signals that were missed by other analysis pipelines and has consistently shown a broader-band reconstruction of the detected signals as compared to similar analysis pipelines. The research in this sub-project has contributed significantly to the O3 All-sky search for short gravitational-wave bursts analysis by using CCSN waveforms in the coherent wave burst (cWB) data analysis pipeline.
Newtonian noise modeling was investigated and an analytic model of atmospheric Newtonian noise due to thermal variations was created. The model was verified through numerical 3d simulations that have confirmed the general scaling laws that were predicted by the model.
The Photon calibrators are one of the main calibration tools for the aLIGO detectors. A Photon Calibrator test setup copying the aLIGO version was set up in the lab at UTRGV to study the noise limitations in the absolute power measurements of the Photon Calibrators for LIGO. Thermal changes on the photodetector or surrounding electronics have been identified as one of the largest effects on calibration uncertainties and investigations are continuing.
All sub-project activities as mentioned are tightly coordinated with the respective LIGO Scientific Collaboration working groups. The results have been presented through presentations and were disseminated through publications.
This project involved undergraduate, masters and PhD students from UTRGV, a Hispanic-serving institution, in gravitational-wave research and education. Through participation in the project the students have received direct training in GW data analysis and instrumentation research and thereby added to the community of researchers in this emerging field. These experiences are transferable and have a direct impact on the STEM workforce in the area through skills such as data analysis, noise analysis, optics, electronics, and computing.
Last Modified: 06/15/2023
Modified by: Volker M Quetschke
Please report errors in award information by writing to: awardsearch@nsf.gov.
