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Measurements of Black Hole and Accretion Properties from Observations of Strong Field Gravitational Lensing
Get PDFAdvancements in Very Long Baseline Interferometry (VLBI) have ushered in a new era of high-resolution imaging that now allow us to probe electromagnetic signatures around black holes on unprecedentedly short gravitational length scales. These include the first images of black hole shadows produced from observations of the near-horizon environments of the supermassive black holes Sgr A∗ and M87∗ by the Event Horizon Telescope (EHT). Accessing this information, however, requires the development of novel techniques that can infer the properties of sources from the visibility domain products of VLBI. In this thesis, we present developments towards techniques for extracting information about black holes and their accretion environments from VLBI observations of low luminosity active galactic nuclei (LLAGN). We first measure the geometric features of the images of M87∗ from data acquired during the 2018 EHT campaign, where we show that the appearance of the source is consistent with the Kerr hypothesis. We then outline a novel, automatic differentiable, general relativistic ray tracing code which we use to define a dual-cone emission model for LLAGN. We show that the dual-cone model can accurately reproduce synthetic observations of the averaged accretion flows present in general relativistic magnetohydrodynamic (GRMHD) simulations of M87∗ and Sgr A∗. We then fit the dual-cone model to the EHT’s 2017 observations of M87∗ to measure properties of the system’s accretion flow and central supermassive black hole. We then study the effects of intrinsic source variability on the inference capabilities of the dual-cone model by performing a multi-epoch fit to the EHT’s 2017 and 2018 observations of M87∗, and present paths towards mitigating these effects. Finally, we discuss the development of a Gaussian Markov random field model as a potential solution for variability mitigation when performing snapshot and multi-epoch measurements from observations of M87∗ and Sgr A∗Physic
Fusobacterium-Stromal Cell Interactions in Colorectal Cancer
No full textColorectal cancer (CRC) is the second most common cause of cancer related deaths among adults within the United States. The complexity of the CRC tumor microenvironment (TME), comprising immune cells and stromal cells, such as cancer-associated fibroblasts (CAFs), and microbiota, further complicate the challenge in developing effective therapeutic targets. Fusobacterium, a Gram-negative, anaerobic oral microbe that has been shown to invade human gingival fibroblasts and promote a pro-inflammatory environment in the mouth, has also been associated with worsened CRC patient prognosis, specifically in CRC patients displaying a stromal cell-rich microenvironment. Additionally, substantial evidence has established a positive correlation between Fusobacterium, stromal cells, and immune dysregulation during CRC development. Thus, understanding the multifactorial components of the CRC TME may reveal potential therapeutic targets. We hypothesized, thus, that Fusobacterium within the CRC TME similarly play a pivotal role in inducing a pro-inflammatory environment through its interactions with CAFs which exacerbates CRC development.
We sought to investigate the interactions between Fusobacterium species (spp.) and fibroblasts by first developing optimized techniques for indirect immunofluorescence microscopy-based detection of Fusobacterium on Formalin-Fixed, Paraffin-Embedded (FFPE) sections. This was an important initial step, as Fusobacterium spp. have not been visually presented in FFPE of mouse colons within published works to date, however successful detection of Fusobacterium spp. within the colon allows us to further localize Fusobacterium in the gut and further evaluate its impact on normal tissue and in conditions of colonic pathology. In these experiments, bacterial cultures of Fusobacterium spp. (Fn7-1, Fn7-3, and Fn25586) were each incubated with resected colons of Germ-Free (GF) mice, which are completely devoid of microorganisms, to assess and ensure the validation of our antibody detection. Fusobacterium spp. presence was assessed using primary antibodies against each species, and a fluorescent secondary antibody specific to the host of the primary antibody.
After successfully detecting and optimizing the detection of Fusobacterium on resected colons of GF mice, we used the optimized techniques to assess Fusobacterium localization in the colons of Altered-Schaedler Flora (ASF) mice following oral inoculation. These mice contain a well-defined, limited consortia of eight, non-pathogenic microorganisms, typically employed for the controlled study of physiologically relevant microorganisms and their impacts on a host. In assessment of the potential influence of Fusobacterium and colonic fibroblasts, we assessed the expression of CAF markers within the colons of Fusobacterium-inoculated ASF mice, which revealed a correlation between the presence of Fusobacterium and upregulated expression of two CAF markers, namely platelet-derived growth factor receptor alpha (PDGFR)-a and alpha smooth muscle actin (aSMA). This suggested that the presence of Fusobacterium may have influence on the transition between normal fibroblasts to a more CAF-like phenotype, validated by the upregulated expression of CAF-specific markers.
Next, we sought to investigate these findings in Specific Pathogen-Free (SPF) mice, which contain a conventional microbiota composition devoid of a specific pathogen. A time-point experiment tracking the abundance of orally inoculated Fusobacterium showed that Fusobacterium abundance falls below the threshold of detection after 24-hours, and therefore does not stably colonize the colons of these mice for further investigation. Due to this result, we sought to implement in vitro experiments consisting of isolated mouse colonic fibroblasts in coculture with Fusobacterium or microbially associated elements. The in vitro co-culture studies revealed increases in interleukin (Il)-6, and Pdgfr-a. An increase in fibroblast secretion of IL-6 was also observed, further supporting our hypothesis that Fusobacterium induces a pro-inflammatory phenotype.
We next investigated whether Fusobacterium presence may further promote a pro-inflammatory microenvironment within a mouse tumor model. SPF mice were orthotopically-injected with organoids harboring mutations in KRAS, p53, and NOTCH, which mimic a stromal-cell rich TME and result in primary tumor formation and metastasis to the liver. Mice were then tail-vein intravenously inoculated with Fusobacterium, and Fusobacterium presence within tumors was validated by qPCR. Immunofluorescence imaging on mouse colon tumors of Fusobacterium-inoculated mice revealed upregulation of CAF marker expression when compared to non-inoculated mice. Through immunofluorescence imaging, RNA expression analysis, in vitro coculture, and in vivo inoculation studies, this thesis aims to provide a deeper analysis of Fusobacterium and its potential impact on inducing a pro-inflammatory environment through its interaction with colon and colonic fibroblasts.Graduate Educatio
Breathing Underwater: From Oxygen Sensing to Behavior in Larval Zebrafish
Get PDFOxygen (O₂) is essential for nearly all animal life, serving as the final electron acceptor in the mitochondrial electron transport chain and enabling the efficient generation of ATP to meet the energetic demands of multicellular organisms. However, despite its abundance in the atmosphere, oxygen availability is often unpredictable in aquatic environments. Many organisms, particularly aquatic vertebrates, must contend with acute drops in environmental oxygen or chronic hypoxic conditions during development. Understanding how vertebrates sense, respond to, and survive fluctuations in oxygen levels remains an important area of biological inquiry. Larval zebrafish (Danio rerio), with their optical transparency, genetic tractability, and quantifiable behaviors, provide a powerful model to investigate the neural circuits and behavioral strategies underlying hypoxia adaptation.
This dissertation explores how larval zebrafish behaviorally and physiologically respond to hypoxia, employing a multidisciplinary approach that integrates behavioral assays, in vivo calcium imaging, laser ablation techniques, and serial electron microscopy-based circuit reconstruction. The studies presented here reveal that acute hypoxia triggers a robust and stereotyped motor behavior characterized by rhythmic pectoral fin movements. These movements likely serve an adaptive role by enhancing water flow across respiratory surfaces, thereby facilitating oxygen uptake. Calcium imaging experiments suggested specific motor neurons whose activity correlates with hypoxia-induced fin movements. Laser ablation of suggested nerve bundles confirmed the neural pathways controlling this behavior.
Beyond acute responses, this dissertation examines the developmental consequences of chronic hypoxia exposure. Larval zebrafish raised under sustained low oxygen conditions exhibited altered growth patterns, including slow growth along the anteroposterior axis and a delayed onset of swim bladder inflation. Behavioral assays assessing the optomotor response revealed that chronic hypoxia impairs sensorimotor coordination during critical stages of development, potentially affecting the larvae’s ability to navigate and forage effectively. These findings suggest that oxygen availability during early development has lasting impacts on morphology and neural circuit function.
Together, these studies illuminate the strategies by which vertebrates transform oxygen-sensing into coordinated motor responses and offer a foundation for further investigations into the strategies for counteracting hypoxia, the interplay between environmental stress and nervous system development, and potential translational relevance for understanding hypoxia-related challenges in human health.Biology, Molecular and Cellula
Finding Beauty in Pain: working through pigment and blood in the works of Hervé Guibert, Léon-Gontran Damas, and Caio Fernando Abreu
Get PDFThis study focuses on issues of race and AIDS. I bring these issues together to think about how
they have both been understood through similar processes of stigmatization and shaming, and to
analyze the narrative responses created by writers in order to handle the grief associated with the
weight of such stereotyping, and to create beauty out of ugliness. The literature of AIDS and the
long history of racial stigma emerge in response to such grief,and the act of writing becomes an
act of mourning for the writers I study (Léon-Gontran Damas [France], Caio Fernando Abreu
[Brazil)] and Hervé Guibert [France]). All three write from an acute state of bereavement which
propels them to push against marginalization. They embrace their minoritized selves, and
reconstruct their diseased and racialized or outcasted bodies along terms that help them honor
their differences rather than reject them. My two objectives with this dissertation are: first, to
show how Damas, Abreu, and Guibert reject the external gaze and push against monolithic ideas
of what it means to exist in a corporeal and spiritual sense; and second, to focus on questions of
reception and appreciation, and on the impact the writing has on their readers.Romance Languages and Literature
Stereotypes that Dumbfound: Comprehensive Investigations Across Content, Methods, and Demography
Get PDFStereotypes help humans navigate a complex social world by offering heuristics about the characteristics of social groups. Nevertheless, stereotypes can hinder decision-making along two paths. First, even when stereotypes are accurate at the group-level, meaning they reflect statistically significant differences between groups (e.g., height differences between men and women), stereotypes can prevent accurate inferences at the individual-level. Second, when stereotypes are inaccurate at the group-level, all inferences that follow – at the group-level and individual-level – are necessarily inaccurate.
Across five chapters, I examine stereotypes with group-level accuracy and group-level inaccuracy to obtain general insights about their magnitude (how robust is the stereotype?), pervasiveness (which groups or places exhibit the stereotype most strongly or weakly?), mechanisms (what features drive the effect?), and malleability (can even entrenched stereotypes change?). In doing so, I show how both types of stereotypes can dumbfound because they (a) impede accurate inferences, (b) conflict with ground-truth data and/or (c) contradict participants’ own stated beliefs and values.
Chapter I (Morehouse et al., 2022; CRESP) presents 7 experiments (N > 7,000) probing the nature of a stereotype with group-level accuracy: surgeon=male. In particular, I examine the magnitude, prevalence, mechanisms, and malleability of this gender-occupation stereotype, and whether it is sufficiently strong to prevent logical inferences. A Supplemental Chapter (Morehouse, Pan, Contreras, & Banaji, 2024; ICML) extends this work by exploring whether a Large Language Model – GPT-4 – similarly exhibits gender-occupation stereotypes across a set of 1,016 diverse occupations. Additionally, I tested whether systematic changes to the input prompt influence the degree of observed bias.
Chapter II (Morehouse et al., 2025; Scientific Reports) leverages an archival dataset with over 600,000 respondents to interrogate the “American=White” stereotype. Although the US has been historically majority-White, this stereotype lacks group-level accuracy because all Americans, regardless of ethnic ancestry, are American. Beyond benchmarking stereotype strength at the societal-level, I uncover individual-level and regional-level predictors of this American=White effect and use time-series models to examine whether it has changed over the past 17 years (2007-2023).
Chapter III (Morehouse, Maddox & Banaji, PNAS) reports 13 experiments (N > 60,000) to test a stereotype that dumbfounds by defying biological fact and participants’ explicitly held beliefs: “Human=White.” In addition to probing its existence, I examine whether this stereotype is pervasive across U.S. demographic groups (e.g., gender and political ideology) and conduct exploratory analyses to assess its emergence in non-US countries. Finally, Chapter IV (Morehouse, Ueda, Saiki, & Banaji, in prep) investigates whether these findings are unique to dominant groups in Western contexts or represent a more universal “Human=Own (Dominant) Group” effect. Specifically, four samples of Japanese participants (tested in two Japanese writing systems, Katakana and Kanji) were recruited to test the magnitude and prevalence of a “Human=Japanese” effect in Japan.
Together, these five chapters harness data from ~700,000 respondents across 37 experiments to demonstrate that (1) even stereotypes with group-level accuracy prevent simple inferences; (2) implicit stereotypes with group-level inaccuracy are surprisingly robust and pervasive across groups and geography; (3) certain demographic characteristics consistently predict stereotype strength; and (4) even widely held stereotypes are malleable; they are sensitive to targeted interventions and the passage of time. In doing so, this body of work illuminates the features that create, maintain, and change stereotypes that dumbfound.Psycholog
Civic Discourse Pedagogies in American Higher Education: Tracing Historical Patterns, Enduring Paradigms, and Future Possibilities
Get PDFCivic discourse on college campuses has become a flashpoint in American politics. Amid deep political division, rising threats to freedom of inquiry and speech, and declining public trust, colleges and universities are seeking ways to teach and practice the communication across difference so essential to their civic, educational, and scholarly missions. Importantly, this is not the first time institutions of higher education have sought new approaches to civic discourse education in response to profound societal changes and challenges. They have done so repeatedly, stretching back to higher education’s earliest days in colonial America. In this dissertation, I draw on histories of American politics, higher education, ideas, and philosophy to make two core arguments. First, I demonstrate that the emergence of civic discourse pedagogies in American higher education has followed a historical pattern: Societal change has generated new visions of politics; together with the transformative forces that shaped them, these visions have led to new conceptions of higher education’s mission and new ideas about the kinds of citizens higher education should aim to produce. These conceptions have demanded new approaches to civic education, spurring the creation of new ways to teach civic discourse. Second, I argue that these new pedagogies were not just instructional innovations. Rather, they were products of new paradigms of civic discourse practice and pedagogy, rooted in specific notions of personhood, power, and social epistemology. I build this argument by analyzing three examples of pedagogical emergence: the development of forensic debate in the mid-1700s; deliberative discussion in the early 20th century; and intergroup dialogue in the late 1980s. These models of pedagogical emergence and paradigm development constitute new scholarly contributions—ones that provide analytical tools that can help us understand and reframe our contemporary challenges of civic discourse. In my final chapters, I demonstrate this point by applying these models to our recent history. Ultimately, my hope is that viewing campus civic discourse in historical perspective can support the vital work of sustaining conversations across difference amid the critical challenges facing higher education today.Educatio
Attenuation of the Post-Movement Beta Rebound in Patients with Epilepsy
No full textEpilepsy is a condition defined by persistent risk of seizures: episodes of aberrant electrical activity that disrupt normal brain function and place individuals at higher risk of psychiatric disorders, cognitive impairment, and death. Clinicians today do not have high performing, highly accessible measures for diagnosing epilepsy. The current mainstay of data gathering in epilepsy is electroencephalography (EEG) of spontaneous brain activity interpreted by human experts. EEG often does not yield abnormal electrical signatures of epilepsy, but these known signatures have high sensitivity and specificity for epilepsy. Because long-term neural changes in response to seizures can make future seizures more likely, a good prognosis in epilepsy requires early diagnosis and treatment. Because antiseizure medications (ASMs) can have serious adverse cognitive and behavioral effects, avoiding harm also requires accurate diagnosis. Improving the performance and accessibility of epilepsy diagnostic tools is therefore crucial for improving patient outcomes.
Seizures occur in the brain when the level of aberrant, synchronous excitatory activity in a focal epileptogenic circuit or generalized epileptogenic network exceeds the brain’s “seizure threshold” and involves wider areas of the brain. The seizure threshold depends on a balance of excitatory and inhibitory activity. A measure of cortical inhibitory tone could therefore inform epilepsy diagnosis, yet no clinical tool currently reports this property of the brain. Post-movement beta rebound (PMBR) is a response that occurs after movement detectable on quantitative EEG (qEEG) and is a putative marker of cortical inhibition. This dissertation introduces a method for quantifying the PMBR in human patients and tests the hypothesis that the PMBR is reduced in participants with epilepsy, congruent with the reduced seizure threshold in epilepsy, to evaluate whether the PMBR might be used to identify individuals with epilepsy.
This dissertation advances a method of systematic data processing for artifact rejection from EEG recordings as well as a method of PMBR quantification. Both methods are based on principled searches for parameters in a pilot dataset composed of EEG from 14 adults without epilepsy who completed a cue-response task. These protocols were then applied to a second dataset of 28 children and adults without epilepsy and 53 children and adults with epilepsy. These 81 participants completed an auditory discrimination go/no-go task during recording, which enabled assessment of behavioral performance and the analysis of trials containing stereotypical movements performed in a controlled context. A permutation testing protocol was devised to create an individualized measure of PMBR and enable evaluation of the test characteristics of the PMBR.
Participants without epilepsy displayed a positive correlation between age and PMBR power (r2 = 0.23, p = 1.2*10-2). PMBR power sharply increased shortly before adulthood (age 17.6 years). Participants with epilepsy did not have an association between age and PMBR power, and mature (> 17.6 years) participants without epilepsy had significantly greater PMBR power than participants with epilepsy (t-test p = 4.6*10-3). Participants without epilepsy still had significantly greater PMBR power than participants with epilepsy when participants with epilepsy were restricted to 1) patients without gross structural anomalies, 2) patients without moderate to severe cognitive disorders, 3) patients with focal epilepsy, 4) patients with generalized epilepsy, 5) patients with only absence seizures, 6) patients with drug resistant epilepsy, 7) patients without psychiatric disorders, 8) patients without any GABAergic ASMs, and 9) patients for whom recordings were captured at both their full dose of ASMs and when having withheld all ASMs. Permutation testing confirmed that more participants without epilepsy had greater PMBR power than participants with epilepsy (Z-test p = 1.2*10-4). Classification of epilepsy or not based on PMBR permutation testing had an ROC AUC of 0.92-0.95, with 67% specificity at 100% sensitivity and 69% sensitivity at 100% specificity, and high intra-individual reliability (r = 0.631).
Overall, this dissertation characterizes the maturational trajectory of the PMBR as detected on EEG, finds that the PMBR is attenuated in epilepsy, develops an individualized PMBR test, and assesses the PMBR test’s characteristics as a potential diagnostic biomarker for epilepsy. The findings of this work demonstrate the promise of the PMBR as an exemplar of a qEEG biomarker that could improve epilepsy diagnosis, care, and outcomes.Neuroscienc
Taking Care: Home Cooking and Community in the Late 19th and Early 20th Century Chinese Diaspora
No full textHow did members of the late nineteenth and early twentieth century Chinese diaspora care for spatially extended communities? Taking Care uses multiple methodologies from archaeology, ethnography, and history to investigate the complex, circulatory web of relations that shifted the social organization of food production and consumption across the Chinese diaspora. This dissertation explores different spatial and social configurations of home cooking, their impact on social experience at the time, and their influence on contemporary Chinese American conceptions of food’s connection with community care. I argue that home cooking was a primary means of care in northern California and the Guangdong Province. Home cooking is a flexible concept encompassing the places and care practices of feeding oneself and one’s intimate community. Recovering cooking practices at residential institutions, ephemeral homes of itinerant laborers, and family configurations, I re-site the locus of inquiry from the use of material food evidence for reifying race or labor-based identities (e.g., Chinese Railroad Worker, Chinese Miner, Chinese Farmer, etc.) to exploring the lived experiences—the structures, patterns, and material choices of daily life—of people who could, at any given moment, labor in any number of different ways and not once consider that type of work constitutive of their identity. Instead, investigating the care provided by home cooking offers an approach for understanding how communities form and maintain collective memories and identity.Anthropolog
On Statistical Learning for Structural Data: Data Fusion and Semi-supervised Learning
Get PDFNowadays, with the rise of the large data era, data tends to become more complex and structural. The data may incorporate various different sources, with distinct data quality, sample size, or set of covariates. For instance, in ecological inference and validation studies of epidemiology, it is common that the predictor and response of interest are gathered in different datasets. However, many statistical estimands of interest (e.g., in regression or causality) are functions of the joint distribution of multiple random variables. In this scenario, the only possible approach is one of data fusion, where multiple independent data sets, each measuring a subset of the random variables of interest, are combined for inference. In general, since all random variables are never observed jointly, their joint distribution, and hence also the estimand which is a function of it, is only partially identifiable. Unfortunately, the endpoints of the partially identifiable region depend in general on entire conditional distributions, rendering them hard both operationally and statistically to estimate. Inspired by this challenge, in the first chapter, we present a novel outer-bound on the region of partial identifiability (and establish conditions under which it is tight) that depends only on certain conditional first and second moments. This allows us to derive semiparametrically efficient estimators of our endpoint outer-bounds that only require the standard machine learning toolbox which learns conditional means. We prove asymptotic normality and semiparametric efficiency of our estimators and provide consistent estimators of their variances, enabling asymptotically valid confidence interval construction for our original partially identifiable estimand. We demonstrate the utility of our method in simulations and a data fusion problem from economics.
Beyond multi-source datasets, specialized formats-—such as network data-—are also a crucial component of structured data. With the large amount of networks and graphs in the modern data age, such as social networks (Facebook, LinkedIn, etc.), citation networks, and medical networks (e.g., propagation networks of treatment, infection networks of viruses), a proper theory for unveiling the underlying network sub-structures like communities becomes increasingly essential. Motivated by social network analysis and network-based recommendation systems, in the second chapter, we study a semi-supervised community detection problem in which the objective is to estimate the community label of a new node using the network topology and partially observed community labels of existing nodes. The network is modeled using a degree-corrected stochastic block model, which allows for severe degree heterogeneity and potentially non-assortative communities. We propose an algorithm that computes a `structural similarity metric' between the new node and each of the K communities by aggregating labeled and unlabeled data. The estimated label of the new node corresponds to the value of k that maximizes this similarity metric. Our method is fast and numerically outperforms existing semi-supervised algorithms. Theoretically, we derive explicit bounds for the misclassification error and show the efficiency of our method by comparing it with an ideal classifier. Our findings highlight, to the best of our knowledge, the first semi-supervised community detection algorithm that offers theoretical guarantees.
An extension of community detection is mixed membership estimation (MME), which is a classical problem in network data analysis. It extends community detection by allowing a node to have fractional memberships in multiple communities. One major challenge in mixed membership estimation is to discover the underlying simplex structure of the high-dimensional data, which corresponds to the membership of each individual. Previous literature mainly focuses on leveraging the data itself to identify the simplex in an unsupervised learning fashion. However, in many scenarios, prior information may be available. For instance, the past user history of certain individuals in a social network may provide a clue to their membership. To incorporate this class of knowledge, in the third chapter, we consider a semi-supervised setting where the membership vectors of a subset of nodes are given. This problem is significantly more challenging than semi-supervised community detection, and to the best of our knowledge, it has not been studied in most previous literature. We discover an insightful structural equation for utilizing the known labels, which inspires a delicate way of extending unsupervised mixed-membership estimation algorithms to the semi-supervised setting. Compared to unsupervised algorithms for identifying the vertex structure, our method does not require the existence of pure nodes and needs fewer regularity conditions on the vertices. Additionally, assuming a degree-corrected mixed membership model, we provide theoretical guarantees of our algorithm, and show that it is efficient in the sense that its error rate is the same as a least squares problem with more prior information on the vertices. To the best of our knowledge, this is the first semi-supervised vertex hunting algorithm with a theoretical guarantee. We also demonstrate the excellent performance of our algorithm in several empirical studies, illustrating that with only a tiny fraction of the label information, our method can dramatically outperform unsupervised algorithms.Statistic
Revealing the formation of massive quiescent galaxies using various techniques
No full textUnderstanding why and how galaxies stop forming stars (``quenched'') and become quiescent has been a key question in galaxy evolution. This thesis aims to understand the formation of massive quiescent galaxies at various epochs by combining three different approaches: 1) Galaxy formation simulations, 2) Ground- & space-based spectroscopic observations, and 3) Stellar population synthesis models.
In the local Universe (z~0), star-forming activity/quiescence is closely linked to galaxy morphology. To understand how galaxy quenching and morphological transformation are related, I use a high-resolution cosmological galaxy simulation (Illustris-TNG50). I track the evolution of massive quiescent galaxies down to z=0 and show how quenching histories/timescales and mechanisms differ for quiescent galaxies with different morphology.
Cosmic noon (z~2) is the epoch when galaxy growth and feedback are the most active. I focus on young quiescent galaxies at z~2, as they likely still hold the evidence of powerful rapid quenching they underwent recently. I perform Magellan/FIRE spectroscopic observations of young quiescent galaxies at z~2, infer their star formation histories from spectral energy distribution fitting, and analyze their ionized emission lines and their sizes. JWST has enabled us to obtain a larger spectroscopic sample of massive quiescent galaxies at cosmic noon. I study the quenching histories and mechanisms of 14 massive quiescent galaxies at z~2 from deep JWST/NIRSpec observations (JWST Cycle-1 program: the Blue Jay survey). I reconstruct their star formation histories and investigate their neutral and ionized gas properties to understand the physical mechanism behind rapid quenching.
Finally, I develop new self-consistent alpha-enhanced stellar population models, which will be essential in deriving accurate physical properties of high-z galaxies where alpha-enhancement is expected to be common. For future work, I will apply these new alpha-enhanced models to early massive quiescent galaxies (z>3) to reveal their true formation histories. Since these galaxies are likely descendants of the first galaxies and progenitors of the massive quiescent galaxies in the local universe, revealing their accurate formation histories will shed light on how the first galaxies grow into the most massive galaxies we see today.Astronom