Publications

First-principles calculations and analysis of the thermodynamic, structural, and electronic properties of liquid SiO

Can non-invasive imaging with fluorescence lifetime imaging microscopy (FLIM) detect metabolic differences in euploid versus aneuploid human blastocysts? FLIM has identified significant metabolic differences between euploid and aneuploid blastocysts. Prior studies have demonstrated that FLIM can detect metabolic differences in mouse oocytes and embryos and in discarded human blastocysts.

Angle-resolved photoelectron spectroscopy is an extremely powerful probe of materials to access the occupied electronic structure with energy and momentum resolution. However, it remains blind to those dynamic states above the Fermi level that determine technologically relevant transport properties. In this work, we extend band structure mapping into the unoccupied states and across the entire Brillouin zone by using a state-of-the-art high repetition rate, extreme ultraviolet fem- tosecond light source to probe optically excited samples. The wide-ranging applicability and power of this approach are demonstrated by measurements on the 2D semiconductor WSe2, where the energy-momentum dispersion of valence and conduction bands are observed in a single experiment. This provides a direct momentum-resolved view not only on the complete out-of-equilibrium band gap but also on its renormalization induced by electron-hole interaction and screening. Our work establishes a new benchmark for measuring the band structure of materials, with direct access to the energy-momentum dispersion of the excited-state spectral function.

We study the electronic structure and correlated phases of twisted bilayers of platinum diselenide using large-scale ab initio simulations combined with the functional renormalization group. PtSe

Experimental Particle Physics has been at the forefront of analyzing the world's largest datasets for decades. The HEP community was among the first to develop suitable software and computing tools for this task. In recent times, new toolkits and systems for distributed data processing, collectively called "Big Data" technologies have emerged from industry and open source projects to support the analysis of Petabyte and Exabyte datasets in industry. While the principles of data analysis in HEP have not changed (filtering and transforming experiment-specific data formats), these new technologies use different approaches and tools, promising a fresh look at analysis of very large datasets that could potentially reduce the time-to-physics with increased interactivity. Moreover these new tools are typically actively developed by large communities, often profiting of industry resources, and under open source licensing. These factors result in a boost for adoption and maturity of the tools and for the communities supporting them, at the same time helping in reducing the cost of ownership for the end-users. In this talk, we are presenting studies of using Apache Spark for end user data analysis. We are studying the HEP analysis workflow separated into two thrusts: the reduction of centrally produced experiment datasets and the end-analysis up to the publication plot. Studying the first thrust, CMS is working together with CERN openlab and Intel on the CMS Big Data Reduction Facility. The goal is to reduce 1 PB of official CMS data to 1 TB of ntuple output for analysis. We are presenting the progress of this 2-year project with first results of scaling up Spark-based HEP analysis. Studying the second thrust, we are presenting studies on using Apache Spark for a CMS Dark Matter physics search, comparing Spark's feasibility, usability and performance to the ROOT-based analysis.

The 2019/2020 Australian New Year’s wildfires injected record amounts of smoke and biomass burning products into the lower stratosphere. The Aura Microwave Limb Sounder (MLS) tracked the evolution of distinct plumes of fire-influenced air as they rapidly spiraled up to the mid-stratosphere. In the months following the fires, smoke spread throughout the Southern Hemisphere (SH) stratosphere. We contrast the evolution of the SH midlatitude lower stratosphere in 2020 with the 17-year MLS record. Long after the coherent plumes dispersed, data from MLS and other satellite instruments show unprecedented persistent and pervasive depletion in HCl (50–60% below climatology) and enhancements in ClO and ClONO2 that were not transport related; peak anomalies occurred in mid-2020. We conclude that the observed perturbations likely arose from heterogeneous chlorine activation on widespread smoke particles. The sustained chlorine activation was far weaker than in typical winter polar vortices, inducing at most minor changes in ozone.

Excitons play a dominant role in the optoelectronic properties of atomically thin van der Waals (vdW) semiconductors. These excitons are amenable to on-demand engineering with diverse control knobs, including dielectric screening, interlayer hybridization, and moir© potentials. However, external stimuli frequently yield heterogeneous excitonic responses at the nano- and meso-scales, making their spatial characterization with conventional diffraction-limited optics a formidable task. Here, we use a scattering-type scanning near-field optical microscope (s-SNOM) to acquire exciton spectra in atomically thin transition metal dichalcogenide microcrystals with previously unattainable 20‚Äânm resolution. Our nano-optical data revealed material- and stacking-dependent exciton spectra of MoSe2, WSe2, and their heterostructures. Furthermore, we extracted the complex dielectric function of these prototypical vdW semiconductors. s-SNOM hyperspectral images uncovered how the dielectric screening modifies excitons at length scales as short as few nanometers. This work paves the way towards understanding and manipulation of excitons in atomically thin layers at the nanoscale.

Discoveries of gaps in data have been important in astrophysics. For example, there are kinematic gaps opened by resonances in dynamical systems, or exoplanets of a certain radius that are empirically rare. A gap in a data set is a kind of anomaly, but in an unusual sense: Instead of being a single outlier data point, situated far from other data points, it is a region of the space, or a set of points, that is anomalous compared to its surroundings. Gaps are both interesting and hard to find and characterize, especially when they have non-trivial shapes. We present methods to address this problem. First, we present a methodological approach to identify critical points, a criterion to select the most relevant ones and use those to trace the `valleys' in the density field. We then build on the observed properties of critical points to propose a novel gappiness criterion that can be computed at any point in the data space. This allows us to identify a broader variety of gaps, either by highlighting regions of the data-space that are `gappy' or by selecting data points that lie in local under densities. We also explore methodological ways to make the detected gaps robust to changes in the density estimation and noise in the data. We illustrate our methods on the velocity distribution of nearby stars in the Milky Way disk plane, which exhibits gaps that could originate from different processes. Identifying and characterizing those gaps could help determine their origins.

The 2mm Mapping Obscuration to Reionization with ALMA (MORA) Survey was designed to detect high redshift (z≳4), massive, dusty star-forming galaxies (DSFGs). Here we present two, likely high redshift sources, identified in the survey whose physical characteristics are consistent with a class of optical/near-infrared (OIR) invisible DSFGs found elsewhere in the literature. We first perform a rigorous analysis of all available photometric data to fit spectral energy distributions and estimate redshifts before deriving physical properties based on our findings. Our results suggest the two galaxies, called MORA-5 and MORA-9, represent two extremes of the "OIR-dark" class of DSFGs. MORA-5 (zphot=4.3+1.5−1.3) is a significantly more active starburst with a star-formation rate of 830+340−190M⊙yr−1 compared to MORA-9 (zphot=4.3+1.3−1.0) whose star-formation rate is a modest 200+250−60M⊙yr−1. Based on the stellar masses (M⋆≈1010−11M⊙), space density (n∼(5±2)×10−6Mpc−3, which incorporates two other spectroscopically confirmed OIR-dark DSFGs in the MORA sample at z=4.6 and z=5.9), and gas depletion timescales (<1Gyr) of these sources, we find evidence supporting the theory that OIR-dark DSFGs are the progenitors of recently discovered 3<z<4 massive quiescent galaxies.

Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we quantify the SARS-CoV-2 concentration and track its dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4–10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral load as a convolution of back-dated new clinical cases with the average population-level viral shedding function. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. This finding suggests that SARS-CoV-2 concentrations in wastewater may be primarily driven by viral shedding early in infection. This work shows that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and infer early viral shedding dynamics for newly infected individuals, which are difficult to capture in clinical investigations.