Reconstructing the Invisible: Dr. Diana Scognamiglio on Dark Matter and Precision Cosmology

In contemporary cosmology, the challenge is no longer simply observing the Universe — it is reconstructing what cannot be seen. As next-generation surveys generate unprecedented volumes of data, astrophysicists are developing increasingly sophisticated methods to map the invisible distribution of dark matter, control systematic uncertainties, and extract cosmological parameters with extraordinary precision. The frontier of research now lies at the intersection of weak gravitational lensing, large-scale structure, high-performance computing, and advanced statistical inference.

It is within this demanding scientific landscape that Dr. Diana Scognamiglio’s work acquires particular relevance. Trained in physics and astrophysics in Italy and later completing her PhD at the University of Bonn’s Argelander Institute for Astronomy — one of Europe’s most research-intensive environments — she developed a research profile centered on dark matter distribution, galaxy evolution, and the reconstruction of the cosmic web. Her doctoral work strengthened her methodological rigor and critical independence, shaping an approach grounded in precision, validation of assumptions, and careful treatment of limitations.

Her career has since taken her across multiple scientific cultures, including research environments in Germany, Russia, and the United States. Through her work within large international collaborations linked to major missions such as JWST, Euclid, and the Roman Space Telescope, she has contributed to high-resolution dark matter mass mapping and the study of ultra-compact massive galaxies as relics of early cosmic structure formation. Her research integrates large-scale data analysis, simulations, and machine learning techniques — not as black-box tools, but as physically constrained methods designed to preserve interpretability and control systematic uncertainties in precision cosmology.

For SKYCR Dialogues, inviting Dr. Scognamiglio is especially meaningful because her work reflects the direction in which modern astrophysics is evolving. She represents a generation of cosmologists who combine deep physical insight with computational sophistication, navigating petabyte-scale datasets while addressing some of the most fundamental questions in physics: the nature of dark matter, the growth of structure, and the underlying architecture of the Universe. Her perspective offers our readers not only technical insight into frontier research, but also a clear view of how cosmology is being reshaped in the era of data-driven science.

– Homer Dávila: Do you recall the moment or experience that first sparked your interest in astrophysics? Was it an early calling or something that developed gradually?
– Diana Scognamiglio: It wasn’t a single moment — it grew naturally over time. As a child, I was endlessly curious. I would spend hours in my parents’ garden looking up at the night sky, trying to recognize constellations, and reading astronomy books long before I fully understood them. What fascinated me was the idea that the Universe — something so vast and mysterious — could actually be studied and understood.
I also remember watching television programs about space exploration and thinking, one day I want to be one of those scientists. I wanted to study mysterious things and perhaps even contribute to a space mission. At the time, it felt like a distant dream, but it planted something inside me.

At school, my teachers encouraged my interest in mathematics and physics, telling me I had a natural inclination for numbers and logical thinking. So, I combined the two — the sky and equations — and realized this could be my path.

“Using weak gravitational lensing, I reconstruct high-resolution maps of dark matter to study the growth of the cosmic web.”

You completed your PhD at the University of Bonn within a highly research-intensive environment. How did that period shape your scientific identity and research direction?

The University of Bonn, and in particular the Argelander Institute for Astronomy where I worked, was a very formative environment for me. It was intellectually intense but also strongly collaborative. I have very good memories of my time there and of my mentors and collaborators. During my PhD, I learned how to think independently and critically. I understood that science is not just about obtaining results — it is about questioning assumptions, validating methods, and being honest about limitations. That period pushed me toward precision and rigor. At the same time, being part of a large research environment taught me the value of collaboration. I learned how much stronger scientific work becomes through discussion and shared expertise. It was also during that time that my interest in dark matter and galaxy evolution became more clearly defined, setting the direction for my later work.

Your career has taken you across Italy, Russia, Germany, and now the United States. How have these international experiences influenced your approach to collaboration and research?

Moving across countries has been one of the most enriching aspects of my career. Each scientific culture has a slightly different style — different expectations, communication patterns, and problem-solving approaches. Italy gave me strong foundational training and a sense of creativity in thinking.

“How is dark matter distributed in the Universe, and how does it shape the formation and evolution of cosmic structures?”

Germany strengthened my rigor and discipline in research methodology.
Working internationally, including Russia and now the U.S., has taught me efficiency, flexibility, and adaptability. Large collaborations in astrophysics involve hundreds of scientists across continents. My international background has made me comfortable navigating those environments. It has taught me that good science depends not only on expertise, but also on communication, trust, and openness to different perspectives.

You are currently a Postdoctoral Researcher at NASA’s Jet Propulsion Laboratory. What are the central scientific questions driving your current work?
I recently transitioned to Duke University, where I am continuing the research projects I began at NASA’s Jet Propulsion Laboratory, now expanding my involvement within the Roman Space Telescope collaboration.

“Progress is incremental, and maintaining patience is essential.”

My work is driven by one central question: how is dark matter distributed in the Universe, and how does it shape the formation and evolution of cosmic structures?
Using weak gravitational lensing, I reconstruct high-resolution maps of dark matter to study the growth of the cosmic web. In parallel, I investigate ultra-compact massive galaxies as relic systems that provide insight into early galaxy formation and their connection to dark matter halos.

“Science is not just about obtaining results — it is about questioning assumptions, validating methods, and being honest about limitations.”

With current and upcoming large surveys from Euclid and Roman, we are in period in which advanced data analysis techniques — including machine learning — and detailed mass mapping will allow us to probe fundamental questions about dark matter and dark energy with unprecedented accuracy.

Your research integrates large-scale data analysis, simulations, and machine learning techniques. How is artificial intelligence transforming modern astrophysics in practice?

Artificial intelligence is becoming essential in astrophysics — and not only there — not as a replacement for physics or human creativity, but as a powerful tool. Modern telescopes generate datasets that are simply too large and complex to handle with traditional methods alone. Machine learning helps us classify objects, denoise images, simulate galaxies, recognize patterns, and much more. AI is transforming astrophysics by increasing efficiency and enabling new kinds of analysis. However, it works best when combined with strong physical understanding. It cannot be treated as a black box. We need methods that respect physical constraints and allow for interpretability.

“Artificial intelligence works best when combined with strong physical understanding. It cannot be treated as a black box.”

What are the biggest challenges researchers face today when working with increasingly massive astronomical datasets?

The scale of the data is both a gift and a challenge. We are facing a true data tsunami, with surveys producing petabytes of data, requiring a transformation in how scientific research is conducted. The main challenges include managing and storing data efficiently, developing scalable and fast — and in some cases also real-time — algorithms and pipelines, and, from the perspective of my research, rigorously controlling systematic uncertainties.

“I try to remember why I entered astrophysics in the first place: curiosity and the desire to do what I love most.”

In modern precision cosmology, even small systematic biases can lead to significant shifts in inferred cosmological parameters. As statistical uncertainties shrink with increasingly large datasets, controlling systematics becomes the dominant limiting factor.

With missions such as JWST, Euclid, and the Rubin Observatory producing unprecedented volumes of data, how must the next generation of astrophysicists prepare differently?

To be honest, I don’t think we can wait for the next generation of astrophysicists — we need to start now! The field requires hybrid expertise. We must understand physics deeply, but also be fluent in data science, high-performance computing, and statistical inference. That’s not easy…

“The field requires hybrid expertise: deep physical understanding combined with data science and high-performance computing.”

The era of analyzing small datasets alone is over. Being an astrophysicist today means collaborating across disciplines, writing code, working with machine learning tools, and contributing to large international teams. Equally important is adaptability. Technology evolves rapidly, and scientists must be prepared to learn continuously.

Looking back at your journey so far, what has been a particularly defining or transformative moment in your scientific career?

This is a difficult question. The launches of JWST and Euclid were certainly defining moments for me. Seeing these missions move from concept to reality — and knowing I would contribute to the scientific goals they were designed to enable — was profoundly meaningful.

“The Universe — something so vast and mysterious — could actually be studied and understood.”

Another particularly significant and most recent milestone was watching our ultra-high-resolution dark matter map emerge from JWST data. Reconstructing the invisible structure of the Universe — turning faint distortions of galaxies into a tangible map of dark matter with unprecedented detail — brought together years of training, coding, debugging, collaboration, physical insight, and persistence.

At the same time, I still believe the truly transformative moment may lie ahead. Every discovery expands the horizon of what we do not yet understand — and that sense of unfinished exploration is what makes this field so exciting.

In a highly competitive and demanding research environment, how do you maintain motivation and long-term perspective?

I try to remember why I entered astrophysics in the first place: curiosity and the desire to do what I love most. Competition and deadlines are part of the job, but at its core, we are exploring fundamental questions about existence. Keeping that perspective helps, but I am not saying it’s easy. I also deeply value collaboration and mentorship. Science is not an individual race — it is a collective effort. Seeing myself grow and contributing to large collaborations gives me a sense of purpose beyond personal achievements. Finally, I remind myself that research is a long-term journey. Progress is incremental, and maintaining patience is essential.

“From the largest cosmic scales to the smallest particles — that kind of unification would be extraordinary to witness.”

If you could witness one major breakthrough in astrophysics during your lifetime, what would you most hope it to be — and why?

What I most hope to witness is the definitive identification of the nature of dark matter. Whether through a particle detection experiment, a cosmological signature, or a breakthrough in theory, understanding what dark matter is made of would reshape both astrophysics and fundamental physics.

It would answer one of the deepest open questions about the Universe and demonstrate how observations of galaxies and large-scale structure connect to the most fundamental building blocks of nature. That kind of unification — from the largest cosmic scales to the smallest particles — would be extraordinary to witness… and even more so if I could contribute to it.

Editorial Reflection

As cosmology transitions into an era defined by petabyte-scale surveys and unprecedented observational precision, the challenge is no longer limited to detecting structure in the Universe—it is about reconstructing the invisible with methodological rigor. The next frontier is not simply deeper observation, but disciplined interpretation.

Dr. Diana Scognamiglio’s work reflects a fundamental shift in how we approach cosmic structure. Dark matter is not observed directly; it is inferred through subtle distortions, statistical patterns, and gravitational signatures embedded in vast datasets. Extracting meaningful structure from that complexity demands more than computational speed—it requires conceptual clarity, systematic control, and physical interpretability.

Precision cosmology has revealed a paradox: as statistical uncertainties shrink, systematic effects become the dominant frontier. In this environment, the ability to question assumptions, validate methodologies, and integrate machine learning without surrendering physical understanding becomes essential. The future of cosmology will not be shaped solely by larger telescopes, but by researchers capable of navigating data, theory, and uncertainty simultaneously.

What emerges from this dialogue is not merely a discussion about dark matter maps or survey missions. It is a portrait of a field undergoing transformation—from observation to reconstruction, from detection to modeling, from isolated analysis to global collaboration. The invisible scaffolding of the Universe is slowly coming into focus, not through spectacle, but through rigor.

If the coming decades are to resolve the nature of dark matter and refine our cosmological models, it will be through the disciplined synthesis of physics, computation, and critical reasoning. Conversations like this are not ceremonial—they reveal the intellectual architecture supporting modern cosmology.

We are no longer just observing the Universe. We are learning to map what cannot be seen.

— Homer Dávila
Astrophysicist
Founder, SKYCR