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We introduce a new capability of the Neil Gehrels Swift Observatory, dubbed “continuous commanding,” that achieves 10 s latency response time on orbit to unscheduled target-of-opportunity requests received on the ground. We show that this will allow Swift to respond to premerger (early-warning) gravitational-wave (GW) detections, rapidly slewing the Burst Alert Telescope (BAT) across the sky to place the GW origin in the BAT field of view at or before merger time. This will dramatically increase the GW/gamma-ray burst (GRB) codetection rate and enable prompt arcminute localization of a neutron star merger. We simulate the full Swift response to a GW early-warning alert, including input sky maps produced at different early-warning times, a complete model of the Swift attitude control system, and a full accounting of the latency between the GW detectors and the spacecraft. 60 s of early warning can double the rate of a prompt GRB detection with arcminute localization, and 140 s guarantees observation anywhere on the unocculted sky, even with localization areas ≫1000 deg2. While 140 s is beyond current GW detector sensitivities, 30–70 s is achievable today. We show that the detection yield is now limited by the latency of LIGO/Virgo cyberinfrastructure and motivate a focus on its reduction. Continuous commanding has been integrated as a general capability of Swift, significantly increasing its versatility in response to the growing demands of time-domain astrophysics. We demonstrate this potential on an externally triggered fast radio burst (FRB), slewing 81° across the sky, and collecting X-ray and UV photons from the source position <150 s after the trigger was received from the Canadian Hydrogen Intensity Mapping Experiment, thereby setting the earliest and deepest such constraints on high-energy activity from nonrepeating FRBs. The Swift Team invites the community to consider and propose novel scientific applications of ultra-low-latency UV, X-ray, and gamma-ray observations.

We describe representative observing scenarios for early warning detection of binary neutron star mergers with the current generation of ground-based gravitational wave detectors as they approach design sensitivity. We incorporate recent estimates of the infrastructure latency and detector sensitivities to provide up-to-date predictions. We use Fisher analysis to approximate the associated localizations, and we directly compare to Bayestar to quantify biases inherited from this approach. In particular, we show that Advanced LIGO and Advanced Virgo will detect and distribute ≲1 signal with signal-to-noise ratio greater than 15 before a merger in their fourth observing run provided they maintain a 70% duty cycle. This is consistent with previous early warning detection estimates. We estimate that 60% of all observations and 8% of those detectable 20 s before a merger will be localized to ≲100 deg2. If KAGRA is able to achieve a 25 Mpc horizon, 70% of these binary neutron stars will be localized to ≲100 deg2 by a merger. As the Aundha–Hanford–KAGRA–Livingston–Virgo network approaches design sensitivity over the next ∼10 yr, we expect one (six) early warning alerts to be distributed 60 (0) s before a merger. Although adding detectors to the Hanford–Livingston–Virgo network at design sensitivity impacts the detection rate at ≲50% level, it significantly improves localization prospects. Given uncertainties in sensitivities, participating detectors, and duty cycles, we consider 103 future detector configurations so electromagnetic observers can tailor preparations toward their preferred models.

The detection of gravitational waves by Advanced LIGO in 2015 marked the start of a new era in astrophysics. These small ripples in space-time - first predicted in the early 20th century by Albert Einstein - encode properties of the progenitor system and provide a powerful new way to probe distant and extreme astrophysical environments. My dissertation focuses on contributions I have made in facilitating the multi-messenger detection of electromagnetically bright sources and using LIGO’s observations (or lack thereof) to constrain models of the dark matter. I describe the motivation for Advanced LIGO searches for sub-solar mass ultracompact binaries, as well as two recent searches I carried out with the LIGO-Virgo Scientific Collaboration. No confident detections were made in these searches, but the null result allowed us to place the tightest constraint to date on a particular model of the dark matter. I also discuss my contributions to efforts to detect binary neutron stars. Although the first BNS detection, GW170817, was a model multi-messenger discovery, there remains much to be learned about the extreme environment of the coalescence that can only be resolved by additional, prompt observations. I describe a subthreshold search for BNS that aims to increase our catalog of joint discoveries by facilitating searches for temporal or spatial coincidence, as well as recent attempts to detect BNS prior to merger to enable prompt electromagnetic followup.

We describe realistic observing scenarios for early warning detection of binary neutron star mergers with the current generation of ground-based gravitational-wave detectors as these approach design sensitivity. Using Fisher analysis, we estimate that Advanced LIGO and Advanced Virgo will detect one signal before merger in their fourth observing run provided they maintain a 70\\% duty cycle. 60\\% of all observations and 8\\% of those detectable 20 seconds before merger will be localized to \\( 100 deg^2\\). If KAGRA is able to achieve a 25 Mpc horizon, these prospects increase to \\( 2\\) early detections with 70\\% of all BNS localized to \\( 100 deg^2\\) by merger. As the AHKLV network approaches design sensitivity over the next \\(10\\) years, we expect up to 1 (14) detections made 100 (10) seconds before merger. Although adding detectors to the HLV network impacts the detection rate at \\( 50\\%\\) level, it improves localization prospects and increases the completeness of compact binary surveys. Given uncertainties in sensitivities, participating detectors, and duty cycles, we consider 103 future detector configurations so electromagnetic observers can tailor preparations towards their preferred models.

The nature of dark matter remains one of the biggest open questions in physics. Intriguingly, it has been suggested that dark matter may be explained by another recently observed phenomenon: the detection of gravitational waves by LIGO. LIGO's detection of gravitational waves from merging stellar mass black holes renewed attention toward the possibility that dark matter consists solely of black holes created in the very early universe and that these primordial black holes are what LIGO is presently observing. Subsequent work on this topic has ruled out the possibility that dark matter could consist solely of black holes similar to those that LIGO has detected with masses above 10 solar masses. However, LIGO's connection to dark matter remains an open question and in this work we consider a distribution of primordial black holes that accounts for all of the dark matter, is consistent with LIGO's observations arising from primordial black hole binaries, and resolves tension in previous surveys of microlensing events in the Milky Way halo. The primordial black hole mass distribution that we consider offers an important prediction--LIGO may detect black holes smaller than have ever been observed with ~1% of the black holes it detects having a mass less than the mass of our Sun. Approximately one year of operating advanced LIGO at design sensitivity should be adequate to begin to see a hint of a primordial black hole mass distribution. Detecting primordial black hole binaries below a solar mass will be readily distinguishable from other known compact binary systems, thereby providing an unambiguous observational window for advanced LIGO to pin down the nature of dark matter.

Tests of general relativity with gravitational wave observations from merging compact binaries continue to confirm Einstein's theory of gravity with increasing precision. However, these tests have so far only been applied to signals that were first confidently detected by matched-filter searches assuming general relativity templates. This raises the question of selection biases: what is the largest deviation from general relativity that current searches can detect, and are current constraints on such deviations necessarily narrow because they are based on signals that were detected by templated searches in the first place? In this paper, we estimate the impact of selection effects for tests of the inspiral phase evolution of compact binary signals with a simplified version of the GstLAL search pipeline. We find that selection biases affect the search for very large values of the deviation parameters, much larger than the constraints implied by the detected signals. Therefore, combined population constraints from confidently detected events are mostly unaffected by selection biases, with the largest effect being a broadening at the \\(10\\) % level for the \\(-1\\)PN term. These findings suggest that current population constraints on the inspiral phase are robust without factoring in selection biases. Our study does not rule out a disjoint, undetectable binary population with large deviations from general relativity, or stronger selection effects in other tests or search procedures.

Gravitational-wave detection pipelines have helped to identify over one hundred compact binary mergers in the data collected by the Advanced LIGO and Advanced Virgo interferometers, whose sensitivity has provided unprecedented access to the workings of the gravitational universe. The detectors are, however, subject to a wide variety of noise transients (or glitches) that can contaminate the data. Although detection pipelines utilize a variety of noise mitigation techniques, glitches can occasionally bypass these checks and produce false positives. One class of mitigation techniques is the signal consistency check, which aims to quantify how similar the observed data is to the expected signal. In this work, we describe a new signal consistency check that utilizes a set of bases that spans the gravitational-wave signal space and convolutional neural networks (CNN) to probabilistically identify glitches. We recast the basis response as a grayscale image, and train a CNN to distinguish between gravitational-waves and glitches with similar morphologies. We find that the CNN accurately classifies \\( 99\\%\\) of the responses it is shown. We compare these results to a toy detection pipeline, finding that the two methods produce similar false positive rates, but that the CNN has a significantly higher true positive rate. We modify our toy model detection pipeline and demonstrate that including information from the network increases the toy pipeline's true positive rate by \\(4-7\\%\\) while decreasing the false positive rate to a data-limited bound of \\( 0.1\\%\\).

Matched-filter based gravitational-wave search pipelines identify candidate events within seconds of their arrival on Earth, offering a chance to guide electromagnetic follow-up and observe multi-messenger events. Understanding the detectors' response to an astrophysical transient across the searched signal manifold is paramount to inferring the parameters of the progenitor and deciding which candidates warrant telescope time. We describe a framework that uses artificial neural networks to interpolate gravitational waves and, equivalently, the signal-to noise ratio (SNR) across sufficiently local patches of the signal manifold. Our machine-learning based model generates a single waveform in 6 milliseconds on a CPU and 0.4 milliseconds on a GPU. When using a GPU to generate batches of waveforms simultaneously, we find that we can produce \\(10^4\\) waveforms in \\( 1\\) ms. This is achieved while remaining faithful, on average, to 1 part in \\(10^4\\) (1 part in \\(10^5\\)) for binary black hole (binary neutron star) waveforms. The model we present is designed to directly utilize intermediate detection pipeline outputs in the hopes of facilitating a better real-time understanding of gravitational-wave candidates.

As gravitational-wave observations expand in scope and detection rate, the data analysis infrastructure must be modernized to accommodate rising computational demands and ensure sustainability. We present a scalable gravitational-wave search pipeline which modernizes the GstLAL pipeline by adapting the core filtering engine to the PyTorch framework, enabling flexible execution on both Central Processing Units (CPUs) and Graphics Processing Units (GPUs). Offline search results on the same 8.8 day stretch of public gravitational-wave data indicate that the GstLAL and the PyTorch adaptation demonstrate comparable search performance, even with float16 precision. Lastly, computational benchmarking results show that the GPU float16 configuration of the PyTorch adaptation executed on an A100 GPU can achieve a speedup factor of up to 169 times compared to GstLAL's performance on a single CPU core.

We explore a new paradigm to study dissipative dark matter models using gravitational-wave observations. We consider a dark atomic model which predicts the formation of binary black holes such as GW190425 while obeying constraints from large-scale structure, and improving on the missing satellite problem. Using LIGO and Virgo gravitational-wave data from 12th September 2015 to 1st October 2019, we show that interpreting GW190425 as a dark matter black-hole binary limits the Chandrasekhar mass for dark matter to be below 1.4 \\(M_\\) at \\(> 99.9\\%\\) confidence implying that the dark proton is heavier than 0.95 GeV, while also suggesting that the molecular energy-level spacing of dark molecules lies near \\(10^-3\\) eV and constraining the cooling rate of dark matter at low temperatures.