Caltech LIGO SURF

Seismic noise causes the LIGO mirrors to move and thereby, the position of the laser beams on the mirrors. The coupling of laser noise sources from the laser to the gravitational-wave readout is modulated by this beam motion. In this project, we will use the video cameras from a prototype interferometer on the Caltech campus that monitor the interferometer mirrors to determine the beam position as a function of time with high accuracy using a supervised learning approach with convolutional neural networks to reject image noise. This beam spot motion will then be used as an input to nonlinear regression algorithms to improve the LIGO sensitivity.

Knowledge of Python, image processing, and CNNs will be useful. recommended.

With the advent of gravitational-wave astronomy, techniques to extend the reach of the detectors are desired. In addition to the stellar mass black hole and neutron star mergers already detected, many more are below the surface of the noise, available for detection if the noise is reduced enough. This project applies machine learning algorithms to gravitational-wave detector data and auxiliary channels to reduce the noise due to instrumental artifacts (analogous to noise-canceling headphones). This framework is generic enough to subtract both linear and non-linear coupling mechanisms, and teach us about the mechanisms which limiting detector sensitivities. Using realistic assumptions about coupling mechanisms, we seek to reduce the noise floor to lead to sensitivity improvements for binary systems. The expectation is that this work can be generalized to other time series regression analyses in all areas of science.

Interest and knowledge of Python, Machine Learning methods, and FFT / signal processing are recommended.

Fast lock acquisition and high duty cycle operation of the LIGO interferometers relies on hundreds of feedback loops keeping the various subsystems under control. Improving the performance of these feedback loops, particularly extending linear control to non-linear control, could dramatically improve the ease of keeping the interferometers in observing mode. This project will explore the applications of novel machine learning techniques to design and implement non-linear feedback control on a prototype system (inverted pendulum on a cart), and extend it to some of the simpler subsystems of the 40m prototype interferometer.

Experience with mechanics, electronics, and python/machine learning/Neural-Network tools are desirable.

One limiting source of noise for gravitational wave detectors is thermal (Brownian) noise, and it is also a source of decoherence in a broad class of optomechanical measurement devices operating at or near their quantum limits. This project uses a recently developed testbed for measuring the internal friction of thin disk resonators to explore open problems in controlling and optimizing such systems, such as: (i) Testing candidate materials for low-loss coatings for LIGO optics, (ii) Optimal experimental design for measuring resonators with nonconventional topologies, (iii) New or improved methods for fast, non-contact temperature readout and control.

Experience with vacuum systems, cryogenics, FEM, and precision measurement would be useful for this project.

Seismic activity is one of the major noise sources for gravitational wave detectors. For understanding and eliminating seismic noises, it is effective to scrutinize the source and statistics of persistent and transient seismic activities. We form a seismic radiometer, employing the multiple seismometers located at the LIGO 40m prototype interferometer. Using the time-series data of the seismometer outputs, we evaluate the seismic heat map around the campus, which allows us to investigate anthropogenic activities and its statistics. We utilize known artificial vibration to precisely calibrate the response of the seismometers. We hope to build an interactive graphical display of this seismic heatmap on a web site as a final product of the project.

Interest and knowledge of Python and signal processing are recommended.

Low-noise and cryo-compatible sensors and actuators will be required to control the suspended mirrors of the next-generation gravitational wave interferometers. This project will involve design work, prototyping, and testing of a compact optical sensor and linear motor assembly in a cryogenic setting. Factors to be considered include the sensing noise, the actuator strength and its magnetic noise coupling, the range and tolerances required to accommodate ground motion and thermal expansion effects, and the thermal loading and temperature characteristics of the opto-electronics.

Thermal noise is a limiting noise source in many of the LIGO sensors. Cooling of photodiodes and optical components to cryogenic temperatures can reduce this noise greatly. A way to achieve refrigeration with minimal disturbance is by pumping a phononic transition of a solid with a red-detuned laser, thus achieving cooling by fluorescence.

One of the primary goals in GW astronomy is to identify weak signals in very noisy detector data. The inspiral and merger (coalescence) of compact stars (neutron stars and black holes) produce a GW signal that is well modeled using numerical simulations of General Relativity to predict the signal waveform. We search for these signals using matched filtering. Matched filter searches on LIGO data use hundreds of thousands of waveforms models that span different object masses and spins. To date, dozens of such signals have been detected in this way.

Students interested in searching for compact binaries can study the origin, evolution, and morphology of these signals to learn as much as possible about their implications for astrophysics and cosmology.

Gravitational waves carry information about the astrophysical sources that create them, which can be measured with precision by comparing observed data to models and simulations. In particular, source masses, angular momenta, location on the sky, and distance from Earth (among other things) all affect the amplitude or phase evolution of an observed GW signal.

Students who work on parameter estimation will be at the interface between theory and experiment, analyzing LIGO data and developing methods to improve our knowledge of the explosive sources of gravitational waves.

Interest in / knowledge of Python, signal processing, and statistics is recommended.

Projects that may be offerefd in Summer 2022:

• Improving searches for GWs from compact binaries
  In the current era of gravitational astronomy, there are now significant gravitational wave events discovered on a weekly basis. Beyond the loud signals that are discovered, there should be many more weaker signals present in the data. This project aims to help improve the analyses that identify these signals in order to increase the total number of confident gravitational wave events that we can uncover, as well as better differentiate weak signals from other sources of noise in the detectors.
• Exceptional Binary Black Hole merger events
  Amongst the dozens of binary black hole merger events detected by LIGO and Virgo from 2015 - 2020 are ones that push the limits of our GR-based models. This project will pursue deep studies into exceptional BBH events detected by LIGO and Virgo, exhibiting very high or very low masses, high spins, orbital precession, higher order modes, possibly eccentricity.
• Improving sensitivities of parameterized tests of general relativity
  The GW events detected so far by LIGO and Virgo are consistent with being from compact binary mergers, and the data seem well described by gravitational waveforms predicted by General Relativity (GR). Contributions can be added to those waveforms with extra parameters to describe departures from the GR prediction; so far, these parameters are found to be consistent with GR. However, these analyses are difficult to interpret, and they can be made significantly more sensitive to beyond-GR physics. In this project we will explore some ways to improve the sensitivity and interpretation of such analyses, and apply them to the LIGO data.
• Detecting evidence of the quantum nature of black holes using GWs from binary black hole mergers, detected by LIGO and Virgo
  Although we do not have a quantum theory of strong-field gravity that can make firp predictions about what happens when two black holes merge, general physical principles argue that the effect can be measurable as a small distortion of the gravitational waveform emitted by such systems and observed by LIGO. In this project we will explore the detectability of such distortions.
• Detecting GW memory with binary black hole mergers
  General Relativity predicts that after a gravitational wave burst passes through a region of space (eg, the space occupied by the LIGO detectors), it leaves it permanently distorted (by a little bit) - GW memory. This is a very low-frequency effect, for which our detectors are not well optimized. In this project we will explore the detectability of GW memory.
• Studies of the BBH population: masses, spins, mass gaps
  LIGO and Virgo have detected dozens of GWs from binary black hole mergers in recent years. By studying the properties of this ensemble of events (masses, spins, redshifts), we can hope to infer the mechanisms by which these systems form, evolve, and finally merge. The primary tool is the development of parameterized models, confronting the data with Bayesian inference, using python coding.
• Interactive data visualization with gravitational wave parameter estimation
  The LIGO/Virgo collaboration now makes available high dimensional data sets of parameter estimation results, known as posterior samples, representing the full set of information known about the physical parameters of sources, such as mass, spin, and position. In principle, these data could be easily accessed through web based tools, in particular javascript libraries. This project will ask the student to utilize javascript plotting libraries to create meaningful visualizations for the astrophysics community.

Rapidly spinning neutron stars in our galaxy will produce gravitational waves that are essentially at one frequency, with very long duration - essentially continuous waves. In order to produce GWs, these sources cannot be perfectly spherical, instead they must have some asphericity. This roughly amounts to something like a 1 mm high mountain sitting on the surface of a neutron star, the shape and height of which can teach us about how matter behaves at super-nuclear densities. Students will have the opportunity to look for potential LIGO signals that correlate with known pulsars in frequency and sky location, as well as sources corresponding to neutron stars not observed with light-based telescopes.

Interest in / experience with programming, signal processing, and Bayesian statistics is recommended.

In the new era of gravitational wave astronomy, our primary goal is to measure source parameters and draw astrophysical inferences from observed signals, as accurately as possible. To do this effectively, we require an accurate estimate of the calibrated strain, due to gravitational waves passing through our detectors. This is especially important for testing General Relativity using well modeled waveforms. Students who work in LIGO calibration will combine precision controls engineering with computationally efficient signal processing to provide such data in real time.

Interest in / experience with signal processing and Python is recommended; stunents will also learn some techniques from controls engineering.

Some of the GW signals that LIGO detects, such as the binary neutron star merger GW170817, will have gamma-ray, X-ray, optical/infrared, radio, or neutrino counterparts. In many ways this can be thought of as witnessing the same event with several different senses. But for many compact binary source types, LIGO is the one to catch them first, locate the source in the sky, and inform astronomers where to point their telescopes. Students who get involved in multi-messenger astronomy will analyze data and develop software to learn as much as possible from joint observations with other telescopes scattered across the world and in space.

Interest in / knowledge of Python, signal processing, image processing, and Bayesian statistics is recommended.

Projects that may be offered in Summer 2022:

• Detectability of Neutron star-black hole mergers with optical telescopes
  The light emission from mergers between neutron stars and black holes has never before been detected. However, LIGO's third observing run (O3; from April 2019-April 2020) has yielded several distant neutron star-black hole (NSBH) gravitational-wave candidates. This project involves attempting to address the question of how detectable this type of event is with ground-based, optical telescopes like the Zwicky Transient Facility (ZTF). We will be using LIGO estimates of the chirp mass and inclination angle of the binary to narrow down the suite of plausible models describing the light emission for the past NSBH events in O3, and use a survey simulator software known as SimSurvey to determine the efficiency of ZTF in targeted searches for these events, while taking into account the probability that certain events could be terrestrial. Using the results, we aim to build an optimal observing strategy for detecting NSBH systems with optical telescopes like ZTF.

Breakthroughs in physics are often made at extremes, and the weakest of all interactions -- gravity -- is no exception. General relativity is the prevailing theory of gravity, which describes gravitation as curvature in space and time rather than as a force. Since the early 20th century, tests of general relativity were all done using measurements of Solar System objects where gravitation is relatively weak. However, with every LIGO observation of extremely compact sources such as neutron stars and black holes comes a unique opportunity to test general relativity using extreme spacetime curvature, pushing the theory to its limits. Students who work on such projects will test this foundational principle of modern physics using the cutting-edge LIGO experiment.

Numerical relativity is a powerful tool for simulating the gravitational wave signatures of binary black hole mergers, as well as the recoil response of the system after merger. One key direction in current research is using simulations, population models, and observations of black holes to understand more about the evolution of black hole progenitor stars and binary black hole systems. Another direction is contributing to the body of waveforms that searches for modeled gravitational waves signals in LIGO data can draw on by improving the accuracy of phenomenological models and/or the efficiency of numerical simulations.

Interest in / experience with signal processing and Bayesian statistics are recommended; students will also learn a great deal about general relativity.

Some anticipated sources of gravitational waves are not well modeled, and therefore do not have predictable waveforms. Examples are: galactic core-collapse supernovae, magnetar bursts, hypothetical cosmic string cusps, and compact binary mergers that are not well described by General Relativity (or by the waveforms predicted by General Relativity that may leave out crucial effects such as eccentric orbits). For such sources we use model-agnostic transient (or 'burst') gravitational wave searches. Burst searches are particularly useful to explore perhaps the most exciting potential gravitational wave signal of all: the unknown. As these searches are more susceptible to noise sources, current work explores methods to better differentiate astrophysical signals from detector noise.

Interest in / experience with programming in a terminal and/or via a remote connection is recommended.

It is very likely that echoes of the Big Bang are currently reverberating around the Universe in the form of gravitational waves, which form a stochastic cosmological background (by analogy with the cosmic microwave background). In addition, there will be a stochastic astrophysical "foreground" formed by the superposition of many weak signals from compact binary mergers, core collapse supernovae, and all other astrophysical sources in the universe. A key science goal for Advanced LIGO is to detect this background by searching for long-lasting coherent power between multiple gravitational wave detectors.

Interest in / experience with programming and statistics is recommended.