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Raw space-based gravitational-wave data like LISA's phase measurements are dominated by laser frequency noise. The standard technique to make this data usable for science is time-delay interferometry (TDI), which cancels laser noise terms by forming suitable combinations of delayed measurements. We recently introduced the basic concepts of an alternative approach which, unlike TDI, does not rely on independent knowledge of temporal correlations in the dominant noise. Instead, our automated Principal Component Interferometry (aPCI) processing only assumes that one can produce some linear combinations of the temporally nearby regularly spaced phase measurements, which cancel the laser noise. Then we let the data reveal those combinations. Our previous work relies on the simplifying additional assumption that the filters which lead to the laser-noise-free data streams are time-independent. In LISA, however, these filters will vary as the constellation armlengths evolve. Here, we discuss a generalization of the basic aPCI concept compatible with data dominated by a still unmodeled but slowly varying noise covariance. Despite its independence on any model, aPCI successfully mitigates laser frequency noise below the other noises' level, and its sensitivity to gravitational waves is the same as the state-of-the-art second-generation TDI, up to a 2\\% error.

The future space-based gravitational wave observatory LISA will consist of a constellation of three spacecraft in a triangular constellation, connected by laser interferometers with 2.5 million-kilometer arms. Among other challenges, the success of the mission strongly depends on the quality of the cancellation of laser frequency noise, whose power lies eight orders of magnitude above the gravitational signal. The standard technique to perform noise removal is time-delay interferometry (TDI). TDI constructs linear combinations of delayed phasemeter measurements tailored to cancel laser noise terms. Previous work has demonstrated the relationship between TDI and principal component analysis (PCA). We build on this idea to develop an extension of TDI based on a model likelihood that directly depends on the phasemeter measurements. Assuming stationary Gaussian noise, we decompose the measurement covariance using PCA in the frequency domain. We obtain a comprehensive and compact framework that we call PCI for \"principal component interferometry,\" and show that it provides an optimal description of the LISA data analysis problem.

With a laser interferometric gravitational-wave detector in separate free flying spacecraft, the only way to achieve detection is to mitigate the dominant noise arising from the frequency fluctuations of the lasers via postprocessing. The noise can be effectively filtered out on the ground through a specific technique called time-delay interferometry (TDI), which relies on the measurements of time-delays between spacecraft and careful modeling of how laser noise enters the interferometric data. Recently, this technique has been recast into a matrix-based formalism by several authors, offering a different perspective on TDI, particularly by relating it to principal component analysis (PCA). In this work, we demonstrate that we can cancel laser frequency noise by directly applying PCA to a set of shifted data samples, without any prior knowledge of the relationship between single-link measurements and noise, nor time-delays. We show that this fully data-driven algorithm achieves a gravitational-wave sensitivity similar to classic TDI.

The LISA Pathfinder (LPF) mission succeeded outstandingly in demonstrating key technological aspects of future space-borne gravitational-wave detectors, such as the Laser Interferometer Space Antenna (LISA). Specifically, LPF demonstrated with unprecedented sensitivity the measurement of the relative acceleration of two free-falling cubic test masses. Although most disruptive non-gravitational forces have been identified and their effects mitigated through a series of calibration processes, some faint transient signals of yet unexplained origin remain in the measurements. If they appear in the LISA data, these perturbations (also called glitches) could skew the characterization of gravitational-wave sources or even be confused with gravitational-wave bursts. For the first time, we provide a comprehensive census of LPF transient events. Our analysis is based on a phenomenological shapelet model allowing us to derive simple statistics about the physical features of the glitch population. We then implement a generator of synthetic glitches designed to be used for subsequent LISA studies, and perform a preliminary evaluation of the effect of the glitches on future LISA data analyses.

Space-based interferometric gravitational wave instruments such as the ESA/NASA Laser Interferometer Space Antenna (LISA) observe gravitational waves by measuring changes in the light travel time between widely-separated spacecraft. One potential noise source for these instruments is interaction with the solar wind, in particular the free electrons in the interplanetary plasma. Variations in the integrated column density of free electrons along the laser links will lead to time-of-flight delays which directly compete with signals produced by gravitational waves. In this paper we present a simplified model of the solar plasma relevant for this problem, anchor key parameters of our model using data from the NASA Wind/SWE instrument, and derive estimates for the effect in the LISA measurement. We find that under normal solar conditions, the gravitational-wave sensitivity limit from the free-electron effect is smaller than other noise sources that are expected to limit LISA's sensitivity.

I would like to thank Gabriela Gonzalez for hiring me on from the start of my time at LSU, for advising me on my path through academics and research, and for showing perhaps superhuman patience with me. I would like to thank Keith Riles for giving me my first non-retail job, introducing me to research and LIGO, and continuing to occasionally offer good advice and assistance long after I was no longer his responsibility.I would like to gratefully acknowledge professors Jeffrey Blackmon, Joel Tohline, Joseph Giaime, Hwang Lee, and James Madden, for agreeing to take the time and effort of serving on my thesis committee.I would like to thank the entire Inspiral/CBC group for their helping me to understand the searches, their code, my code, and for giving me all those triggers to work with in the first place. In particular, I’d like to thank Patrick Brady, Duncan Brown, Stephen Fairhurst, Nick Fotopoulis, Drew Keppel, and Eirini Messaritaki.I would also like to thank the entire Detchar/Glitch group, for their collaboration in stemming the seemingly infinite tide of blips and glitches in the data. I’d like to specifically thank Laura Cadonati, Marco Cavaglia, Nelson Christenson, John Zweizig, Josh Smith, Andrew Lundgren, Larne Pekowsky.Thanks to my fellow Astrowatchers, especially first shifters Berit Bernake, Pinkesh Patel, Evan Goetz, Phillip Roberts, and Junyi Zang. I’d also like to thank Matt West and Nicolas Smith in particular for help along the way. I would like to thank Anamaria Effler for teaching me much of what I know about H2 and interferometer operations in general, Fred Raab for supporting Astrowatch with gusto, and all the LHO staff for their indispensable help.I’d like to thank Rupal Amin, Sarah Caudill, Chad Hanna, Jeff Kissel, Andres Rodriguez, Kate Dooley, Tobin Fricke, and all my fellow students. In particular, thanks to Chad for wise advice, patient explanations, and this LaTeX template, and thanks to Tobin for helping fix my laptop that broke 10 days before my defense.Thank you Rachel, for maintaining my sanity through it all.

The Laser Interferometer Space Antenna (LISA) mission is being developed by ESA with NASA participation. As it has recently passed the Mission Adoption milestone, models of the instruments and noise performance are becoming more detailed, and likewise prototype data analyses must as well. Assumptions such as Gaussianity, stationarity, and data continuity are unrealistic, and must be replaced with physically motivated data simulations, and data analysis methods adapted to accommodate such likely imperfections. To this end, the LISA Data Challenges have produced datasets featuring time-varying and unequal constellation armlength, and measurement artifacts including data interruptions and instrumental transients. In this work, we assess the impact of these data artifacts on the inference of Galactic Binary and Massive Black Hole properties. Our analysis shows that the treatment of noise transients and gaps is necessary for effective parameter estimation, as they substantially corrupt the analysis if unmitigated. We find that straightforward mitigation techniques can significantly if imperfectly suppress artifacts. For the Galactic Binaries, mitigation of glitches was essentially total, while mitigations of the data gaps increased parameter uncertainty by approximately 10%. For the Massive Black Hole binaries the particularly pernicious glitches resulted in a 30% uncertainty increase after mitigations, while the data gaps can increase parameter uncertainty by up to several times. Critically, this underlines the importance of early detection of transient gravitational waves to ensure they are protected from planned data interruptions.

The Laser Interferometer Space Antenna (LISA) mission is being developed by ESA with NASA participation. As it has recently passed the Mission Adoption milestone, models of the instruments and noise performance are becoming more detailed, and likewise prototype data analyses must as well. Assumptions such as Gaussianity, Stationarity, and continuous data continuity are unrealistic, and must be replaced with physically motivated data simulations, and data analysis methods adapted to accommodate such likely imperfections. To this end, the LISA Data Challenges have produced datasets featuring time-varying and unequal constellation armlength, and measurement artifacts including data interruptions and instrumental transients. In this work, we assess the impact of these data artifacts on the inference of Galactic Binary and Massive Black Hole properties. Our analysis shows that the treatment of noise transients and gaps is necessary for effective parameter estimation. We find that straightforward mitigation techniques can significantly suppress artifacts, albeit leaving a non-negligible impact on aspects of the science.

This white paper presents an analysis of Astro2020 science priorities and NASA's future astrophysics mission architecture, advocating for a coordinated fleet of \\$1--2B missions, smaller than typical Flagship observatories, but strategically designed to complement them, i.e. a ``Next Generation Great Observatories\" program. The study addresses opportunities in current mission planning, design, and implementation and proposes a strategic approach to maximize scientific return on investment while strengthening partnerships across NASA divisions, other government organizations, universities, and industry.

By listening to gravity in the low frequency band, between 0.1 mHz and 1 Hz, the future space-based gravitational-wave observatory LISA will be able to detect tens of thousands of astrophysical sources from cosmic dawn to the present. The detection and characterization of all resolvable sources is a challenge in itself, but LISA data analysis will be further complicated by interruptions occurring in the interferometric measurements. These interruptions will be due to various causes occurring at various rates, such as laser frequency switches, high-gain antenna re-pointing, orbit corrections, or even unplanned random events. Extracting long-lasting gravitational-wave signals from gapped data raises problems such as noise leakage and increased computational complexity. We address these issues by using Bayesian data augmentation, a method that reintroduces the missing data as auxiliary variables in the sampling of the posterior distribution of astrophysical parameters. This provides a statistically consistent way to handle gaps while improving the sampling efficiency and mitigating leakage effects. We apply the method to the estimation of galactic binaries parameters with different gap patterns, and we compare the results to the case of complete data.