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Yijun Wang

Publications and source records attributed to Yijun Wang.

At least 73 records · Page 4Linked to original sources

Tolerance to Astrophysical Model Uncertainty in Dark Siren Hubble Measurement with Third-generation Gravitational-wave Detectors

Gravitational-wave (GW) events can serve as standard sirens for cosmology, as the luminosity distance to source can be directly measured from the waveform amplitude. Specifically, the ``dark'' siren method involves inferring cosmological parameters, e.g. the Hubble constant, by comparing the luminosity distance distribution and that of the redshift, typically obtained through a combination of galaxy survey catalog and theoretical models. Especially with the prospect of third-generation GW detectors, the statistical uncertainty of the Hubble measurement can be suppressed to a percent level. However, incorrect assumption in galaxy population models can lead to systematic bias, which becomes increasingly relevant as third-generation GW detectors can detect large-redshift sources beyond the reach of currently available galaxy catalogs. In this work, we adopt a Fisher information formalism and study the maximum model error tolerance given specific total error budget. We find that, to achieve a total error budget of 1% in the Hubble constant, the galaxy mass function redshift evolution should be known to O(1%). We find that galaxy redshift uncertainty, survey magnitude limit and GW angular localization error are important factors. We find galaxy clustering also improves model error tolerance, thus our results provide a conservative benchmark to future analysis using real galaxy catalogs. We also investigate the effective bright siren scenario and highlight that the dark siren selection strategy should be catered to measurement uncertainty and the target total error budget. This work thus highlights the challenge in the dark siren method and quantifies requirements on both the galaxy catalog and GW measurement to contribute to constraining cosmology.

gr-qc↗

Black holes regulate cool gas accretion in massive galaxies

The nucleus of almost all massive galaxies contains a supermassive black hole (BH). The feedback from the accretion of these BHs is often considered to have crucial roles in establishing the quiescence of massive galaxies, although some recent studies show that even galaxies hosting the most active BHs do not exhibit a reduction in their molecular gas reservoirs or star formation rates. Therefore, the influence of BHs on galaxy star formation remains highly debated and lacks direct evidence. Here, based on a large sample of nearby galaxies with measurements of masses of both BHs and atomic hydrogen (HI), the main component of the interstellar medium, we show that the HI gas mass to stellar masses ratio ($μ_{\rm HI} = M_{\rm HI}/M_{\star}$) is more strongly correlated with BH masses ($M_{\rm BH}$) than with any other galaxy parameters, including stellar mass, stellar mass surface density and bulge masses. Moreover, once the $μ_{\rm HI}-M_{\rm BH}$ correlation is considered, $μ_{\rm HI}$ loses dependence on other galactic parameters, demonstrating that $M_{\rm BH}$ serves as the primary driver of $μ_{\rm HI}$. These findings provide important evidence for how the accumulated energy from BH accretion regulates the cool gas content in galaxies, by ejecting interstellar medium gas and/or suppressing gas cooling from the circumgalactic medium.

astro-ph.GA↗

NOEMA formIng Cluster survEy (NICE): Characterizing eight massive galaxy groups at $1.5 < z < 4$ in the COSMOS field

The NOEMA formIng Cluster survEy (NICE) is a large program targeting 69 massive galaxy group candidates at $z>2$ in six deep fields. We report spectroscopic confirmation of eight groups at $1.65\leq z\leq3.61$ in COSMOS. Homogeneously selected as significant overdensities of red IRAC sources with red Herschel colors, four groups are confirmed by CO and [CI] with NOEMA 3mm observations, three are confirmed with ALMA, and one is confirmed by H$α$ from Subaru/FMOS. We constructed the integrated FIR SEDs for the eight groups, obtaining total IR SFR $=260-1300~{\rm M_\odot}$~yr$^{-1}$. We adopted six methods to estimate the dark matter masses, including stellar mass to halo mass relations, overdensity with galaxy bias, and NFW profile fitting to radial stellar mass density. We found the radial stellar mass density are consistent with a NFW profile, supporting that they are collapsed structures hosted by a single dark matter halo. The best halo mass estimates are $\log(M_{\rm h}/{\rm M_\odot})=12.8-13.7$ with uncertainty of 0.3 dex. From halo mass estimates, we derive baryonic accretion rate ${\rm BAR}=(1-8)\times10^{3}\,{\rm M_{\odot}/yr}$ for this sample. We find a quasi-linear correlation between the integrated SFR/BAR and the theoretical halo mass limit for cold streams, $M_{\rm stream}/M_{\rm h}$, with ${\rm SFR/BAR}=10^{-0.46\pm0.22}\left({M_{\rm stream}/M_{\rm h}}\right)^{0.71\pm0.16}$ with a scatter of $0.40\,{\rm dex}$. Further, we compare halo masses and stellar masses with simulations, and find all structures are consistent with being progenitors of $M_{\rm h}(z=0)>10^{14}\,{\rm M_{\odot}}$ galaxy clusters, and the most massive central galaxies have stellar masses consistent with brightest cluster galaxies (BCGs) progenitors in the TNG300 simulation. The results strongly suggest these structures are forming massive galaxy clusters via baryonic and dark matter accretion.

astro-ph.GA↗

The fundamental plane of black hole activity for low-luminosity radio active galactic nuclei across 1 < z < 4

The fundamental plane of black hole activity (BHFP) describes the correlation between radio luminosity ($L_R$), X-ray luminosity ($L_X$), and black hole mass. It reflects a disc-jet connection. However, dependence of BHFP on various physical properties of active galactic nuclei (AGNs) and host galaxies is unclear, especially for low-luminosity AGNs, which is important for understanding accretion physics in AGNs. Here we explore the dependence of BHFP on radio loudness, Eddington ratio ($λ_E$), redshift, and galaxy star formation properties at 0.1 < z < 4 for radio AGNs. Based on current deep and large surveys, our studies extend to low luminosities and high redshifts. From GOODS and COSMOS fields, we constructed a homogeneous radio AGN sample including 208 objects. We divided the radio AGN sample into 141 radio-quiet (RQ) AGNs and 67 radio-loud (RL) AGNs according to the radio loudness defined by the $L_R$-to-$L_X$ ratio. This ratio shows a bimodal distribution that is well described by two Gaussian models. Their cross point corresponds to a radio loudness threshold of $\log (L_R/L_X) = -2.73$. RQ and RL AGNs show different BHFPs, indicating a dependence of BHFP on the radio loudness. For both RQ and RL AGNs, BHFP shows a dependence on $λ_E$, but no dependence on redshift. BHFP shows a dependence on the galaxy star formation properties for RQ AGNs, while for RL AGNs this dependence disappears. BHFP sheds important light on the accretion physics and X-ray emission origins. X-ray emission of RQ AGNs originates from a combination of advection-dominated accretion flow (ADAF) and synchrotron radiation from jet at $0.01 < λ_E < 0.1$, and from jet at $0.1 < λ_E < 1$. X-ray emission in RL AGNs originates from a combination of ADAF and jet at $λ_E < 0.01$, from jet at $0.01 < λ_E < 0.1$, and from a combination of standard thin disc and jet at $λ_E > 0.1$.

astro-ph.GA↗

Noema formIng Cluster survEy (NICE): Discovery of a starbursting galaxy group with a radio-luminous core at z=3.95

The study of distant galaxy groups and clusters at the peak epoch of star formation is limited by the lack of a statistically and homogeneously selected and spectroscopically confirmed sample. Recent discoveries of concentrated starburst activities in cluster cores have opened a new window to hunt for these structures based on their integrated IR luminosities. Hereby we carry out the large NOEMA (NOrthern Extended Millimeter Array) program targeting a statistical sample of infrared-luminous sources associated with overdensities of massive galaxies at z>2, the Noema formIng Cluster survEy (NICE). We present the first result from the ongoing NICE survey, a compact group at z=3.95 in the Lockman Hole field (LH-SBC3), confirmed via four massive (M_star>10^10.5M_sun) galaxies detected in CO(4-3) and [CI](1-0) lines. The four CO-detected members of LH-SBC3 are distributed over a 180 kpc physical scale, and the entire structure has an estimated halo mass of ~10^13Msun and total star formation rate (SFR) of ~4000Msun/yr. In addition, the most massive galaxy hosts a radio-loud AGN with L_1.4GHz, rest = 3.0*10^25W/Hz. The discovery of LH-SBC3 demonstrates the feasibility of our method to efficiently identify high-z compact groups or forming cluster cores. The existence of these starbursting cluster cores up to z~4 provides critical insights into the mass assembly history of the central massive galaxies in clusters.

astro-ph.GA↗

Decoding Natural Images from EEG for Object Recognition

Electroencephalography (EEG) signals, known for convenient non-invasive acquisition but low signal-to-noise ratio, have recently gained substantial attention due to the potential to decode natural images. This paper presents a self-supervised framework to demonstrate the feasibility of learning image representations from EEG signals, particularly for object recognition. The framework utilizes image and EEG encoders to extract features from paired image stimuli and EEG responses. Contrastive learning aligns these two modalities by constraining their similarity. With the framework, we attain significantly above-chance results on a comprehensive EEG-image dataset, achieving a top-1 accuracy of 15.6% and a top-5 accuracy of 42.8% in challenging 200-way zero-shot tasks. Moreover, we perform extensive experiments to explore the biological plausibility by resolving the temporal, spatial, spectral, and semantic aspects of EEG signals. Besides, we introduce attention modules to capture spatial correlations, providing implicit evidence of the brain activity perceived from EEG data. These findings yield valuable insights for neural decoding and brain-computer interfaces in real-world scenarios. The code will be released on https://github.com/eeyhsong/NICE-EEG.

cs.HC↗

On the Connection between the Repeated X-ray Quasi-periodic Oscillation and Warm Absorber in the Active Galaxy RE~J1034+396

We conduct an in-depth spectral analysis of $\sim1{\rm ~Ms}$ XMM-Newton data of the narrow line Seyfert 1 galaxy RE J1034+396. The long exposure ensures high spectral quality and provides us with a detailed look at the intrinsic absorption and emission features toward this target. Two warm-absorber (WA) components with different ionization states ($\log (ξ/{\rm erg~cm~s}^{-1}) \sim 4$ and $\log (ξ/{\rm erg~cm~s}^{-1}) \sim 2.5-3$) are required to explain the intrinsic absorption features in the RGS spectra. The estimated outflow velocities are around $-1400{\rm ~km~s}^{-1}$ and $-(100-300){\rm ~km~s}^{-1}$ for the high- and low-ionization WA components, respectively. Both absorbers are located beyond the broad-line region and cannot significantly affect the host environment. We analyze the warm absorbers in different flux states. We also examine the May-2007 observation in the low and high phases of quasi-periodic oscillation (QPO). In contrast to previous analyses showing a negative correlation between the high-ionization WA and the QPO phase, we have found no such variation in this WA component. We discover a broad emission bump in the spectral range of $\sim12-18$ Angstrom, covering the primary features of the high-ionization WA. This emission bump shows a dramatic change in different source states, and its intensity may positively correlate with the QPO phase. The absence of this emission bump in previous work may contribute to the suggested WA-QPO connection.

astro-ph.HE↗

Cosmic evolution of radio-excess AGNs in quiescent and star-forming galaxies across $0 < z < 4$

Recent deep and wide radio surveys extend the studies for radio-excess active galactic nuclei (radio-AGNs) to lower luminosities and higher redshifts, providing new insights into the abundance and physical origin of radio-AGNs. Here we focus on the cosmic evolution, physical properties and AGN-host galaxy connections of radio-AGNs selected from a sample of ~ 500,000 galaxies at 0 < z < 4 in GOODS-N, GOODS-S, and COSMOS fields. Combining deep radio data with multi-band, de-blended far-infrared (FIR) and sub-millimeter data, we identify 1162 radio-AGNs through radio excess relative to the FIR-radio relation. We study the cosmic evolution of 1.4 GHz radio luminosity functions (RLFs) for star-forming galaxies (SFGs) and radio-AGNs, which are well described by a pure luminosity evolution of $L_*\propto (1+z)^{-0.31z+3.41}$ and a pure density evolution of $Φ_*\propto (1+z)^{-0.80z+2.88}$, respectively. We derive the turnover luminosity above which the number density of radio-AGNs surpasses that of SFGs. This crossover luminosity increases as increasing redshift, from $10^{22.9}$ W Hz$^{-1}$ at z ~ 0 to $10^{25.2}$ W Hz$^{-1}$ at z ~ 4. At full redshift range (0 < z < 4), we further derive the probability ($p_{radio}$) of SFGs and quiescent galaxies (QGs) hosting a radio-AGN as a function of stellar mass ($M_*$), radio luminosity ($L_R$), and redshift (z), which yields $p_{radio}\propto (1+z)^{3.54}M_*^{1.02}L_R^{-0.90}$ for SFGs, and $p_{radio}\propto (1+z)^{2.38}M_*^{1.39}L_R^{-0.60}$ for QGs, respectively. It indicates that radio-AGNs in QGs prefer to reside in more massive galaxies with larger $L_R$ than those in SFGs, and radio-AGN fraction increases towards higher redshift in both SFGs and QGs with a more rapid increase in SFGs. Further, we find that the radio-AGN fraction depends on accretion states of BHs and redshift in SFGs, while in QGs it also depends on BH (or galaxy) mass.

astro-ph.GA↗

Detecting gravitational lensing in hierarchical triples in galactic nuclei with space-borne gravitational-wave observatories

Stellar-mass binary black holes (BBHs) may merge in the vicinity of a supermassive black hole (SMBH). It is suggested that the gravitational-wave (GW) emitted by a BBH has a high probability to be lensed by the SMBH if the BBH's orbit around the SMBH (i.e., the outer orbit) has a period of less than a year and is less than the duration of observation of the BBH by a space-borne GW observatory. For such a BBH + SMBH triple system, the de Sitter precession of the BBH's orbital plane is also significant. In this work, we thus study GW waveforms emitted by the BBH and then modulated by the SMBH due to effects including Doppler shift, de Sitter precession, and gravitational lensing. We show specifically that for an outer orbital period of 0.1 yr and an SMBH mass of $10^7 M_\odot$, there is a 3\%-10\% chance for the standard, strong lensing signatures to be detectable by space-borne GW detectors such as LISA and/or TianGO. For more massive lenses ($\gtrsim 10^8 M_\odot$) and more compact outer orbits with periods <0.1 yr, retro-lensing of the SMBH might also have a 1%-level chance of detection. Furthermore, by combining the lensing effects and the dynamics of the outer orbit, we find the mass of the central SMBH can be accurately determined with a fraction error of $\sim 10^{-4}$. This is much better than the case of static lensing because the degeneracy between the lens' mass and the source's angular position is lifted by the outer orbital motion. Including lensing effects also allows the de Sitter precession to be detectable at a precession period 3 times longer than the case without lensing. Lastly, we demonstrate that one can check the consistency between the SMBH's mass determined from the orbital dynamics and the one inferred from gravitational lensing, which serves as a test on theories behind both phenomena. The statistical error on the deviation can be constrained to a 1% level.

gr-qc↗

Visual tracking brain computer interface

Brain-computer interfaces (BCIs) offer a way to interact with computers without relying on physical movements. Non-invasive electroencephalography (EEG)-based visual BCIs, known for efficient speed and calibration ease, face limitations in continuous tasks due to discrete stimulus design and decoding methods. To achieve continuous control, we implemented a novel spatial encoding stimulus paradigm and devised a corresponding projection method to enable continuous modulation of decoded velocity. Subsequently, we conducted experiments involving 17 participants and achieved Fitt's ITR of 0.55 bps for the fixed tracking task and 0.37 bps for the random tracking task. The proposed BCI with a high Fitt's ITR was then integrated into two applications, including painting and gaming. In conclusion, this study proposed a visual BCI-based control method to go beyond discrete commands, allowing natural continuous control based on neural activity.

cs.HC↗

High-performance cVEP-BCI under minimal calibration

The ultimate goal of brain-computer interfaces (BCIs) based on visual modulation paradigms is to achieve high-speed performance without the burden of extensive calibration. Code-modulated visual evoked potential-based BCIs (cVEP-BCIs) modulated by broadband white noise (WN) offer various advantages, including increased communication speed, expanded encoding target capabilities, and enhanced coding flexibility. However, the complexity of the spatial-temporal patterns under broadband stimuli necessitates extensive calibration for effective target identification in cVEP-BCIs. Consequently, the information transfer rate (ITR) of cVEP-BCI under limited calibration usually stays around 100 bits per minute (bpm), significantly lagging behind state-of-the-art steady-state visual evoked potential-based BCIs (SSVEP-BCIs), which achieve rates above 200 bpm. To enhance the performance of cVEP-BCIs with minimal calibration, we devised an efficient calibration stage involving a brief single-target flickering, lasting less than a minute, to extract generalizable spatial-temporal patterns. Leveraging the calibration data, we developed two complementary methods to construct cVEP temporal patterns: the linear modeling method based on the stimulus sequence and the transfer learning techniques using cross-subject data. As a result, we achieved the highest ITR of 250 bpm under a minute of calibration, which has been shown to be comparable to the state-of-the-art SSVEP paradigms. In summary, our work significantly improved the cVEP performance under few-shot learning, which is expected to expand the practicality and usability of cVEP-BCIs.

cs.HC↗

Cross-subject dual-domain fusion network with task-related and task-discriminant component analysis enhancing one-shot SSVEP classification

This study addresses the significant challenge of developing efficient decoding algorithms for classifying steady-state visual evoked potentials (SSVEPs) in scenarios characterized by extreme scarcity of calibration data, where only one calibration is available for each stimulus target. To tackle this problem, we introduce a novel cross-subject dual-domain fusion network (CSDuDoFN) incorporating task-related and task-discriminant component analysis (TRCA and TDCA) for one-shot SSVEP classification. The CSDuDoFN framework is designed to comprehensively transfer information from source subjects, while TRCA and TDCA are employed to exploit the single available calibration of the target subject. Specifically, we develop multi-reference least-squares transformation (MLST) to map data from both source subjects and the target subject into the domain of sine-cosine templates, thereby mitigating inter-individual variability and benefiting transfer learning. Subsequently, the transformed data in the sine-cosine templates domain and the original domain data are separately utilized to train a convolutional neural network (CNN) model, with the adequate fusion of their feature maps occurring at distinct network layers. To further capitalize on the calibration of the target subject, source aliasing matrix estimation (SAME) data augmentation is incorporated into the training process of the ensemble TRCA (eTRCA) and TDCA models. Ultimately, the outputs of the CSDuDoFN, eTRCA, and TDCA are combined for SSVEP classification. The effectiveness of our proposed approach is comprehensively evaluated on three publicly available SSVEP datasets, achieving the best performance on two datasets and competitive performance on one. This underscores the potential for integrating brain-computer interface (BCI) into daily life.

cs.HC↗

Breathing Life into Faces: Speech-driven 3D Facial Animation with Natural Head Pose and Detailed Shape

The creation of lifelike speech-driven 3D facial animation requires a natural and precise synchronization between audio input and facial expressions. However, existing works still fail to render shapes with flexible head poses and natural facial details (e.g., wrinkles). This limitation is mainly due to two aspects: 1) Collecting training set with detailed 3D facial shapes is highly expensive. This scarcity of detailed shape annotations hinders the training of models with expressive facial animation. 2) Compared to mouth movement, the head pose is much less correlated to speech content. Consequently, concurrent modeling of both mouth movement and head pose yields the lack of facial movement controllability. To address these challenges, we introduce VividTalker, a new framework designed to facilitate speech-driven 3D facial animation characterized by flexible head pose and natural facial details. Specifically, we explicitly disentangle facial animation into head pose and mouth movement and encode them separately into discrete latent spaces. Then, these attributes are generated through an autoregressive process leveraging a window-based Transformer architecture. To augment the richness of 3D facial animation, we construct a new 3D dataset with detailed shapes and learn to synthesize facial details in line with speech content. Extensive quantitative and qualitative experiments demonstrate that VividTalker outperforms state-of-the-art methods, resulting in vivid and realistic speech-driven 3D facial animation.

cs.CV↗

Giant nonlinear optical wave mixing in van der Waals compound MnPSe3

Optical nonlinearities, one of the most fascinating properties of two-dimensional (2D) materials, are essential for exploring novel physics in 2D systems and developing next-generation nonlinear optical applications. While tremendous efforts have been made to discover and optimize second-order nonlinear optical responses in various 2D materials, higher odd-order nonlinear processes, which are in general much less efficient than second order ones, have been paid less attention despite their scientific and applicational significance. Here we report giant odd-order nonlinear optical wave mixing in a correlated van der Waals insulator MnPSe3 at room temperature. Illuminated by two near-infrared femtosecond lasers simultaneously, it generates a series of degenerate and non-degenerate four- and six-wave mixing outputs, with conversion efficiencies up to the order of $10^{-4}$ and $10^{-6}$ for the four- and six-wave mixing processes, respectively, far exceeding the efficiencies of several prototypical nonlinear optical materials (GaSe, LiNbO3). This work highlights the intriguing prospect of transition metal phosphorous trichalcogenides for future research of the nonlinear light matter interactions in 2D systems and for potential nonlinear photonic applications.

physics.optics↗

A Surgery-Detection Two-Dimensional Panorama of Signal Acquisition Technologies in Brain-Computer Interface

Brain-computer interface (BCI) technology is an interdisciplinary field that allows individuals to connect with the external world. The performance of BCI systems relies predominantly on the advancements of signal acquisition technology. This paper aims to present a comprehensive overview of signal acquisition technologies in BCI by examining research articles published in the past decade. Our review incorporates both clinician and engineer perspectives and presents a surgery-detection two-dimensional panorama of signal acquisition technologies in BCI. We classify the technologies into nine distinct categories, providing representative examples and emphasizing the significant challenges associated with each modality. Our review provides researchers and practitioners with a macroscopic understanding of BCI signal acquisition technologies and discuss the field's major issues today. Future development in BCI signal acquisition technology should prioritize the integration of diverse disciplines and perspectives. Striking a balance among signal quality, trauma, biocompatibility, and other relevant factors is crucial. This will promote the advancement of BCI technology, enhancing its efficiency, safety, and reliability, and ultimately contributing to a promising future for humanity.

cs.HC↗

Estimating and approaching maximum information rate of noninvasive visual brain-computer interface

The mission of visual brain-computer interfaces (BCIs) is to enhance information transfer rate (ITR) to reach high speed towards real-life communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are achievable. In this study, we investigate the information rate limits of the primary visual channel to explore whether we can and how we should build visual BCI with higher information rate. Using information theory, we estimate a maximum achievable ITR of approximately 63 bits per second (bps) with a uniformly-distributed White Noise (WN) stimulus. Based on this discovery, we propose a broadband WN BCI approach that expands the utilization of stimulus bandwidth, in contrast to the current state-of-the-art visual BCI methods based on steady-state visual evoked potentials (SSVEPs). Through experimental validation, our broadband BCI outperforms the SSVEP BCI by an impressive margin of 7 bps, setting a new record of 50 bps. This achievement demonstrates the possibility of decoding 40 classes of noninvasive neural responses within a short duration of only 0.1 seconds. The information-theoretical framework introduced in this study provides valuable insights applicable to all sensory-evoked BCIs, making a significant step towards the development of next-generation human-machine interaction systems.

cs.HC↗

DiffColor: Toward High Fidelity Text-Guided Image Colorization with Diffusion Models

Recent data-driven image colorization methods have enabled automatic or reference-based colorization, while still suffering from unsatisfactory and inaccurate object-level color control. To address these issues, we propose a new method called DiffColor that leverages the power of pre-trained diffusion models to recover vivid colors conditioned on a prompt text, without any additional inputs. DiffColor mainly contains two stages: colorization with generative color prior and in-context controllable colorization. Specifically, we first fine-tune a pre-trained text-to-image model to generate colorized images using a CLIP-based contrastive loss. Then we try to obtain an optimized text embedding aligning the colorized image and the text prompt, and a fine-tuned diffusion model enabling high-quality image reconstruction. Our method can produce vivid and diverse colors with a few iterations, and keep the structure and background intact while having colors well-aligned with the target language guidance. Moreover, our method allows for in-context colorization, i.e., producing different colorization results by modifying prompt texts without any fine-tuning, and can achieve object-level controllable colorization results. Extensive experiments and user studies demonstrate that DiffColor outperforms previous works in terms of visual quality, color fidelity, and diversity of colorization options.

cs.CV↗

Gravitational Wave Detection with Relative Astrometry using Roman's Galactic Bulge Time Domain Survey

Gravitational waves (GWs) are a new avenue of observing our Universe. So far, we have seen them in the ~10-100 Hz range, and there are hints that we might soon detect them in the nanohertz regime. Multiple efforts are underway to access GWs across the frequency spectrum; however, parts of the frequency space are currently not covered by any planned or future observatories. Photometric surveys can bridge the microhertz gap in the spectrum between LISA and Pulsar Timing Arrays (PTAs) through relative astrometric measurements. Similar to PTA measurements, these astrometric measurements rely on the correlated spacetime distortions produced by gravitational waves at Earth, which induce coherent, apparent stellar position changes on the sky. To detect microhertz GWs with an imaging survey, a combination of high relative astrometric precision, a large number of observed stars, and a high cadence of exposures are needed. Roman's proposed core community survey, the Galactic Bulge Time Domain Survey (RGBTDS), would have all of these components. RGBTDS would be sensitive to GWs with frequencies ranging from $7.7\times 10^{-8}$ Hz to $5.6\times 10^{-4}$ Hz, which opens up a unique GW observing window for supermassive black hole binaries and their waveform evolution. We note that small changes to the survey could enhance Roman's sensitivity to GWs, making it possible to observe the GW background signal that PTAs have recently hinted at with an SNR $\sim$ 70.

astro-ph.GA↗