Quasar clustering measurements are a powerful probe of the physical processes governing the growth of supermassive black holes and their coevolution with host galaxies. Wide-field quasar surveys reveal a dramatic increase in the clustering strength of quasars at the high redshift, with measurements at z~4 that have been challenging to reproduce theoretically, even assuming that quasars are hosted by the most massive dark matter halos inhabiting the early Universe. In the first project of my Ph.D., we revisited this decade-long problem using new, large-volume N-body cosmological simulations. We developed a model that successfully reproduces the observed quasar clustering and demographics by leveraging a novel method for computing the halo mass function and halo cross-correlation functions across multiple simulations. Using a conditional luminosity function framework, this model captures the stochastic relationship between quasar luminosity and halo mass and predicts key observables, including the quasar auto-correlation and luminosity functions, host mass function, and duty cycle. (Figure from Pizzati et al. 2024a)

Since its launch in late 2021, the James Webb Space Telescope (JWST) has been rapidly advancing our understanding of the high-redshift Universe. Among JWST's instruments, the NIRCam Wide Field Slitless Spectroscopy (WFSS) mode is particularly powerful for spectroscopically identifying high-redshift quasars and galaxies across vast comoving volumes. Using NIRCam/WFSS observations of bright high-z quasar fields, surveys such as EIGER and ASPIRE detected numerous [OIII]-emitting galaxies associated with these quasars, uncovering a diverse range of quasar large-scale environments and measuring for the first time the quassar-galaxy cross-correlation function at z>6. By building upon the empirical quasar population model described above, we have proposed a simple framework to jointly infer the properties of quasars and [OIII] emitting galaxies using the measurements of the clustering and luminosity functions of these populations coming from JWST. We applied the model to observations from the EIGER survey and inferred the luminosity-halo mass relation, the halo host mass function, and the duty cycle/occupation fraction of quasars and galaxies at z~6. The model represents a way to effectively exploit the constraining power offered by JWST observations and will be applied to upcoming data from several JWST surveys. (Figure from Pizzati et al. 2024b)

The dramatic leap in sensitivity brought by JWST has also led to wildly unexpected discoveries, such as the presence of an abundant population of broad-line high-z AGN candidates appearing as "little red dots" (LRDs) in JWST imaging. When correcting for obscuration effects, these LRDs have surprisingly large bolometric luminosities, comparable to the ones of the UV-selected quasars whose properties have been studied for decades. This is incredibly surprising, since UV-luminous quasars are selected from wide-field 1400 deg\(^2\) deep imaging surveys, whereas JWST AGN are identified in surveys of not more than ~300-600 arcmin\(^2\). Such a massive difference indicates that these AGN are far more abundant than comparably luminous UV-unobscured quasars, implying that our understanding of SMBH growth and quasar/AGN activity in the early Universe needs to be thoroughly revised. By comparing the properties of JWST AGN/LRDs to the ones of UV-selected quasars, we have concluded that LRDs outnumber quasars by a large factor that rapidly evolves with redshift. Interestingly, this suggests that the large population of LRDs cannot be accommodated in the same halos where unobscured quasars live. Hence, LRDs may represent a different evolutionary phase of early SMBHs. This hypothesis will be ultimately tested by constraining the clustering of LRDs, for which we have developed a successful mock analysis based on a quasar population model. (Figure from Pizzati et al. 2024c)

Cosmological hydrodynamical simulations follow the non-linear evolution of structures in the Universe by modeling a large variety of processes that are important to the physics of galaxies, stars, and black holes. In the last decade, these simulations have become capable of reproducing a large number of observed galaxy properties. As a consequence, they have started to play a key role in shaping our understanding of the formation and evolution of galaxies in the Universe, as well as the relation between galaxies and the supermassive black holes (SMBHs) that are harbored at their center. While much effort has been devoted to the study of the connection between SMBHs and their hosting galaxies in terms of gas fueling and AGN feedback, only few studies have focused on the global properties of SMBHs and their evolution over cosmic time. Specifically, SMBHs are visible at cosmological distances when they turn into bright quasars. Quasars are, however, extremely rare objects, hence their statistical properties can only be studied theoretically when large simulated volumes are available. In a project that I am co-supervising, we use the cosmological simulations from the FLAMINGO suite (which are the largest ever run) to reproduce two basic observational probes of quasar activity: the quasar luminosity function and the quasar autocorrelation function. We study how well FLAMINGO can match observational constraints on these quantities at different redshifts. We also investigate which black holes contribute to the bright quasar population, and split their relative contribution in terms of different properties. Thanks to the state-of-the-art capabilities of FLAMINGO, for the first time we can test the ability of large-volume hydrodynamical simulations to capture the evolution of massive black holes and quasar activity across cosmic time. (Figure from Schaye et al. 2023).

Investigating the complex environments of galaxies during the Epoch of Reionization is one of the most pressing research goals of modern astrophysics. In this context, the advent of telescopes such as ALMA and NOEMA have opened a new window on the primordial Universe, allowing us to shed light on the obscured star formation and ISM line emission at rest-frame FIR wavelengths up to z~7. One of the most compelling findings made by ALMA is that a significant fraction of z>4 galaxies is surrounded by extended (10-15 kpc) [C II]-emitting haloes that are not predicted by even the most advanced zoom-in simulations. As part of my Master Thesis, I worked with Andrea Ferrara, Andrea Pallottini, and the cosmology group at SNS on finding a plausible mechanism to explain the formation of these halos. We focused on the hypothesis that these halos result from the remnants of past – or ongoing – outflow activity. We explored this idea by using a semi-analytical model for an outflow that undergoes catastrophic cooling in the inner region of the halo. By computing the abundance of singly ionized Carbon and simulating the resulting [C II] emission, we compared our model with data from, e.g., the ALMA ALPINE program, and we conclude that outflows represent a promising answer to explain the origin of the observed [C II] halos. Our model points to the presence of extended [CII] emission at high redshifts as a tangible sign of star-formation-driven feedback mechanisms being already in place well into the Epoch of Reionization. (Figure from NASA; see also my Master Thesis)

Constraining the strength of gas turbulence in protoplanetary discs is crucial for understanding the physics of gas accretion and planet formation. A promising method to gauge the level of gas turbulence in discs is to measure the vertical scale height of the dust component - which is expected to be coupled to the gas component through gas-dust coupling. This has become possible in the last few years thanks to the very high-resolution observations provided by the ALMA telescope. These observations uncovered a large amount of features in the 2d images of protoplanetary discs, including dark gaps and emission rings. As shown by Pinte et al. 2016, it is possible to exploit these features to uncover the 3-d morphology of discs. The idea is simple: due to projection effects, a gap in a disc emission profiles will be partly filled by the emission coming from the neighbouring regions. This effect is stronger along the minor axis of the disc, whereas the major axis is only marginally affected. Hence, one can compare the gap contrast along the major and minor axes to infer the amount of "filling" that has taken place. The extent of this gap filling is directly dependent on the vertical structure of the disc. In collaboration with Giovanni Rosotti and Benoit Tabone, we applied this method to high-resolution DSHARP observations. We built a radiative transfer model to reproduce the observed gap constrast for different values of the dust scale heights. and found that the scale heights that yield a better agreement with data are generally small, implying low levels of gas turbulence in discs. This represents an important step towards understanding the physical processes that govern the formation of planets in discs. (Figure from Pizzati et al. 2023).

With the first direct detection of gravitational waves in 2015, the era of gravitational-wave astronomy has begun. This first decade of observations has already provided a wealth of information about compact binary mergers of neutron stars and black holes. The future of this research field looks even brighter, with the next generation of detectors such as the Cosmic Explorer and the Einstein Telescope expected to observe hundreds of thousands of binary coalescence events each year. With huge leaps in sensitivity, however, new challenges arise. One of the most pressing issues for the next generation of detectors is that signals from coalescing binaries will be so frequent that they will start to overlap in the time domain. When two signals overlap, the standard data-analysis pipelines used to detect and infer the properties of these signals are not guaranteed to work. In particular, multiple signals overlapping in the time/frequency domain may result in biases in the final parameter estimates. In a work done with Bangalore Sathyaprakash, Surabhi Sachdev, and Anuradha Gupta, we quantified the biases arising by using current parameter inference pipelines to constrain the parameters of a signal in the presence of multiple overlapping ones. We showed that, by setting a prior on the coalescence time, it is possible to correctly infer the properties of multiple overlapping signals even with the current data-analysis infrastructure, provided that the coalescence times of the signals in the detector frame are more than ~1-2 seconds apart. Signals whose coalescence epochs lie within ~0.5 seconds of each other, however, suffer from significant biases, requiring future developments of new strategies and algorithms. (Figure from Pizzati et al. 2022)