Interview Questions for Time-Domain Astronomy

Both kilonovae and supernovae are spectacular explosions in space, but they originate from vastly different processes. A supernova is the cataclysmic explosion of a star at the end of its life, resulting from either the collapse of a massive star’s core (Type II supernovae) or the runaway thermonuclear fusion of a white dwarf star in a binary system (Type Ia supernovae). These events are incredibly luminous and eject vast amounts of material into space, enriching the interstellar medium with heavy elements.

A kilonova, on the other hand, is a much fainter but still significant event that occurs after the merger of two neutron stars or a neutron star and a black hole. The intense gravitational forces during the merger eject a significant amount of neutron-rich material. This material rapidly undergoes radioactive decay, emitting a characteristic infrared to ultraviolet light curve peaking days after the merger. The kilonova is crucial because it’s the primary source of heavy elements like gold and platinum in the universe. Imagine the supernova as the explosion of a single, giant firework, while the kilonova is a less bright but equally important spark shower, responsible for creating some of the rarest elements.

Transient astronomical events are celestial phenomena that appear and disappear relatively quickly, ranging from milliseconds to years. They provide invaluable insights into the dynamic universe. We can categorize them broadly into:

• Supernovae: As discussed before, these are stellar explosions.
• Kilonovae: Resulting from neutron star mergers.
• Gamma-ray bursts (GRBs): Extremely energetic explosions, potentially linked to supernovae or neutron star mergers.
• Fast radio bursts (FRBs): Millisecond-duration bursts of radio waves from extragalactic sources, whose origin is still debated.
• Tidal disruption events (TDEs): When a star gets too close to a supermassive black hole and is torn apart by the tidal forces.
• Flare stars: Sudden brightening events in stars due to magnetic reconnection in their atmospheres.
• Novae: Thermonuclear explosions on the surface of white dwarf stars, less energetic than supernovae.

This list is not exhaustive, as astronomers are constantly discovering new types of transient events.

Detecting and characterizing fast transients presents formidable challenges. The key difficulties include:

• Rapid variability: Their short duration demands high time-resolution observations.
• Faintness: Many fast transients are intrinsically faint, requiring large telescopes and sensitive detectors.
• Unpredictability: They occur unexpectedly, necessitating continuous monitoring of large sky areas.
• Localization: Pinpointing the precise location in the sky can be challenging, especially for millisecond events.
• Data volume: Modern surveys generate immense amounts of data, demanding efficient data processing and analysis techniques.

Think of searching for a fleeting firefly in a vast, dark forest. That’s the challenge of detecting fast transients.

Multi-messenger astronomy, which combines observations across different wavelengths of light (electromagnetic spectrum) along with other messengers like gravitational waves and neutrinos, is revolutionizing our understanding of transients. For example, the detection of both gravitational waves and electromagnetic radiation from a neutron star merger in 2017 provided unprecedented insights into the kilonova phenomenon. The gravitational waves confirmed the merger, while the electromagnetic observations revealed the kilonova’s properties. This approach allows for a much more complete and accurate picture of the event than would be possible using only one type of messenger.

Imagine having two different perspectives on an event – one is a visual account, the other is a detailed physical description. Combining these two yields a far richer understanding.

Automated telescopes are indispensable in time-domain astronomy due to their ability to rapidly survey large areas of the sky, repeatedly. Their capabilities include:

• Rapid response: They can quickly react to alerts from other telescopes or transient event prediction algorithms.
• High throughput: They can efficiently collect data from numerous targets simultaneously.
• Wide field of view: They can survey vast areas, increasing the chance of discovering rare events.
• Autonomous operation: They require minimal human intervention, allowing for continuous monitoring.

Without automated telescopes, observing the fast-evolving nature of transient events would be practically impossible. They act as tireless robotic eyes in the sky, constantly searching for fleeting phenomena.

Analyzing time-series data from transient events requires specialized techniques. Common approaches include:

• Time-series modeling: Using statistical models to represent the temporal evolution of the signal, for example, using autoregressive integrated moving average (ARIMA) models or other techniques.
• Light curve fitting: Fitting theoretical models of transient events to the observed light curves to estimate physical parameters. This often involves complex non-linear least squares optimization methods.
• Wavelet transforms: Decomposing the signal into different frequency components to isolate periodicities and transient features.
• Machine learning: Using algorithms like Support Vector Machines (SVMs) or neural networks to classify transients based on their light curve shapes and other features.
• Fourier transforms: Used to identify periodicities or oscillations in the data.

These methods often require sophisticated software packages and a deep understanding of statistical methods.

Data reduction in time-domain astronomy aims to transform raw telescope data into scientifically useful information. This involves steps like:

• Bias subtraction: Removing the electronic offset from the detector readings.
• Dark current subtraction: Correcting for the signal generated by the detector itself in the absence of light.
• Flat-fielding: Compensating for variations in the detector’s sensitivity across its surface.
• Cosmic ray removal: Identifying and removing spurious signals caused by high-energy particles.
• Photometry/Spectroscopy reduction: Converting the pixel data into calibrated magnitudes or spectral fluxes.

These techniques are crucial for obtaining accurate measurements and are often performed using specialized software packages.

Handling noisy data is paramount in time-domain astronomy because the signals we’re looking for – transient events like supernovae or kilonovae – are often faint and easily overwhelmed by noise from various sources, including atmospheric fluctuations, instrumental effects, and even cosmic rays.

Our approach is multifaceted. First, we employ robust data reduction techniques. This includes careful calibration of the telescope data, accounting for instrumental biases, and using techniques like bias subtraction, flat-fielding, and cosmic ray removal. Software packages like IRAF and AstroImageJ are crucial here.

Secondly, we utilize sophisticated filtering methods. These might include Fourier transforms to identify and remove periodic noise, wavelet denoising to separate signal from noise based on their differing characteristics, or median filtering to suppress isolated spikes.

Thirdly, we leverage statistical analysis. Techniques like sigma-clipping help identify and remove outlier data points that are likely due to noise rather than real astronomical events. Bayesian methods allow us to incorporate prior knowledge about the expected signal properties, improving our signal-to-noise ratio.

Finally, we often rely on ensemble averaging, combining multiple observations of the same target to reduce the impact of random noise. Think of it like taking multiple photos of a dimly lit object; the combined image will be significantly clearer than any single photo.

My experience encompasses work with several major time-domain survey telescopes. I’ve extensively used data from the Zwicky Transient Facility (ZTF), which provides rapid, wide-field imaging, ideal for discovering and monitoring a wide range of transients. I’ve been involved in projects analyzing ZTF light curves to identify and classify supernovae, active galactic nuclei (AGN) outbursts, and other transient phenomena. The sheer volume of data from ZTF demands automated processing pipelines and efficient data management techniques.

I’ve also worked with data from Pan-STARRS, particularly its deep and wide-field imaging capabilities. Pan-STARRS is excellent for detecting fainter, longer-duration transients, and its archival data is a treasure trove for studies requiring historical context. One significant difference between ZTF and Pan-STARRS is the cadence of observations: ZTF has a much higher cadence, making it better suited for studying rapidly evolving transients.

In both cases, my work has involved navigating the unique challenges of each survey, such as dealing with different data formats, understanding the instrumental biases, and optimizing analysis techniques for the specific characteristics of the data. For example, the large field of view in both surveys necessitates careful handling of artifacts like scattered light and geometric distortions.

A light curve is simply a plot of the brightness of a celestial object as a function of time. Imagine charting how bright a star appears over several days, weeks, or even years. That’s a light curve. The x-axis represents time, and the y-axis represents the apparent magnitude (a logarithmic measure of brightness), or flux (a measure of the energy received per unit area).

Light curves are absolutely critical for analyzing transients because the time evolution of their brightness provides crucial information about their physical nature. For example, a supernova will display a characteristic light curve that evolves over weeks or months, revealing details about its energy output, explosion mechanism, and progenitor star. A fast, sharp rise in brightness followed by a gradual decay often indicates a certain type of supernova, while a different pattern might suggest another type.

Analyzing light curves allows us to distinguish between various types of transients. We examine features like the peak brightness, rise time, decline rate, and overall shape to classify them, sometimes aided by sophisticated modeling and template fitting techniques. This classification, in turn, provides clues about the underlying physical processes occurring within these objects.

The field of time-domain astronomy relies heavily on specialized software packages for data analysis. Some of the most commonly used ones include:

• ISIS (Image Subtraction Software): An extremely efficient package specifically designed for the detection of transients using image subtraction techniques.
• Astropy: A core Python library providing fundamental tools for astronomy, including handling data, coordinate transformations, and fitting models.
• SciPy: Another essential Python library with powerful functions for numerical computations, optimization, and statistical analysis, crucial for light curve modeling and analysis.
• Matplotlib and Seaborn: For data visualization, producing informative plots of light curves and other data.
• Topcat: A versatile tool for exploring and analyzing astronomical catalogs and data tables.

Beyond these, various bespoke pipelines and custom-built software are also developed within research groups to handle the specific needs of different surveys and analysis techniques. The choice of software often depends on the project’s scope, data volume, and specific analysis requirements.

Identifying and classifying transients involves a multi-step process. It begins with the detection of a potential transient – a source that appears in one image but not in a previous image of the same field. Software algorithms, often employing image subtraction, are crucial in this initial detection stage.

Once detected, we obtain a light curve by observing the object over time. The shape and characteristics of the light curve, as discussed previously, are paramount for classification. We might compare the observed light curve to templates of known transient types (e.g., Type Ia supernovae, Type II supernovae, tidal disruption events). Automated classification schemes, often leveraging machine learning algorithms, are increasingly used to accelerate this process.

Spectroscopy plays a vital role. Obtaining spectra allows us to study the object’s composition and physical conditions, providing much more detailed information than photometry alone. By analyzing the spectral features, we can confirm the nature of the transient (e.g., determining its redshift, temperature, and chemical abundances) and place it in a proper astrophysical context. A combination of photometric and spectroscopic information yields the most robust classification.

Machine learning is revolutionizing automated transient detection. The sheer volume of data generated by modern time-domain surveys makes manual analysis impractical. Machine learning algorithms, especially deep learning techniques like convolutional neural networks (CNNs), are exceptionally well-suited for finding subtle variations in image data and rapidly identifying potential transients.

CNNs, for instance, can be trained on large datasets of images, some containing known transients and others not. The algorithm learns to identify patterns and features associated with transients, enabling it to efficiently scan through thousands of images and flag those likely containing a transient event. This significantly reduces the burden on human researchers, allowing them to focus on the most promising candidates.

Beyond detection, machine learning is also invaluable for automated classification. Algorithms can be trained to classify transients based on their light curves and spectra, improving speed and accuracy compared to traditional methods. However, careful consideration must be given to the training data to avoid biases and ensure robustness. The ongoing challenge lies in developing algorithms that can handle the diversity and complexity of transient phenomena.

Follow-up observations are critical for confirming the nature of a transient event and fully characterizing its properties. Initial detections often only reveal a potential transient; further observations are necessary to verify its existence, determine its physical characteristics, and place it within an astrophysical context.

Follow-up observations might involve obtaining spectra with larger telescopes to confirm the classification and determine redshift, or more frequent photometric monitoring to capture the full evolution of the light curve. Multi-wavelength observations, combining data from different parts of the electromagnetic spectrum (e.g., radio, X-ray, gamma-ray), can provide a comprehensive picture of the transient’s energy output and physical processes.

These follow-up observations are essential for understanding the physics of transients, such as the explosion mechanism in supernovae or the accretion processes in tidal disruption events. Without them, initial detections remain merely tantalizing hints; follow-up observations are what transform those hints into firm scientific knowledge.

Assessing the reliability of transient detection algorithms is crucial in time-domain astronomy because a false positive can waste valuable telescope time and resources chasing a non-existent event. We use several methods to ensure reliability. Firstly, we employ rigorous statistical techniques. This often involves calculating the significance of a detection, often expressed as a signal-to-noise ratio (SNR). A high SNR suggests a more reliable detection. We also use simulations to test our algorithms. By injecting artificial transients into real or simulated datasets, we can evaluate the algorithm’s sensitivity, completeness, and efficiency in identifying genuine transients while minimizing false positives.

Secondly, we employ multiple independent algorithms. If multiple algorithms independently flag the same candidate transient, this significantly boosts our confidence. Finally, visual inspection by human experts plays a vital role, particularly for unusual or borderline cases. They can identify subtle artifacts or patterns that algorithms might miss. For example, an algorithm might flag a transient based on a sudden flux increase, but a human might recognize it as a cosmic ray hit from the image. A robust validation process incorporating these multiple approaches is essential to trust our findings.

Current time-domain survey capabilities, while incredibly powerful, face several limitations. One major limitation is the survey depth. Faint transients, particularly at large distances, are difficult to detect, limiting our ability to study the most distant and energetic events in the universe. Another limitation is the cadence of observations. Many interesting events, like fast radio bursts or some types of supernovae, evolve rapidly. If the observation cadence is too slow, we might miss critical phases of the event’s evolution or even the event altogether. The limited field of view of current telescopes also restricts the sky area that can be surveyed simultaneously.

Furthermore, data processing presents a significant challenge. The sheer volume of data produced by modern surveys is immense, and efficient and accurate processing is crucial. Finally, biases inherent in survey designs can affect the types of transients we detect. For example, a survey optimized for bright, fast transients might miss dimmer or slower-evolving events. Overcoming these limitations requires technological advancements such as larger telescopes, faster detectors, and more sophisticated data analysis techniques.

Atmospheric conditions significantly impact time-domain observations. Atmospheric turbulence causes blurring and distortion of images, reducing the resolution and accuracy of measurements. This is particularly problematic for faint transients where subtle changes in brightness are key to their identification. Clouds, of course, completely block optical observations. Atmospheric seeing, which describes the apparent blurring due to turbulence, is quantified using the FWHM (full width at half maximum) of a star’s image. A smaller FWHM indicates better seeing conditions.

Furthermore, atmospheric extinction, the dimming of light as it passes through the atmosphere, affects the observed brightness of transients. This effect is wavelength-dependent, with shorter wavelengths (e.g., blue light) being more strongly affected than longer wavelengths (e.g., red light). Corrections for atmospheric extinction are crucial for accurate photometry (measuring brightness) and spectroscopy (measuring the spectrum of light). Adaptive optics systems are used to mitigate some of the effects of atmospheric turbulence, improving the quality of time-domain observations, while careful data reduction techniques are employed to correct for extinction effects.

False positives are a significant challenge in transient detection. These are events that are initially identified as transients but are actually caused by artifacts, such as cosmic rays hitting the detector, detector glitches, or even faint asteroids. Several strategies are used to mitigate this problem. As mentioned before, using multiple independent detection algorithms and rigorous statistical thresholds significantly reduces false positives. A crucial method is image differencing. We compare images taken at different times. Real transients will show a significant difference in brightness between images, whereas many artifacts will not.

Another strategy is to use light curves – plots of brightness versus time. Genuine transients often exhibit specific light curve patterns that can distinguish them from false positives. Machine learning techniques are increasingly being used to classify transients, learning from large datasets of known transients and artifacts. Careful quality control of the data, flagging potentially bad data points or images is also critical to minimize false positives.

For example, a sudden spike in brightness might be detected by an algorithm but, through careful analysis of nearby pixels or by comparing it to other images at similar times, it could be shown to be a cosmic ray event, not a real astronomical transient.

Time-domain astronomy is a rapidly evolving field, with several exciting future directions. One major area of development is the construction of even larger and more sensitive telescopes, enabling the detection of fainter and more distant transients. This includes both ground-based and space-based telescopes. The next generation of large-aperture telescopes will significantly increase survey depth and sensitivity. Another promising area is the development of more sophisticated data analysis techniques, including machine learning and artificial intelligence, to cope with the vast datasets generated by these surveys.

Furthermore, multi-messenger astronomy, combining observations from different wavelengths of light and other messengers like gravitational waves and neutrinos, will play an increasingly important role. The synergy of information from various sources dramatically improves our understanding of transient events. For example, the combination of gravitational wave detections with electromagnetic observations provides unparalleled insights into the nature of neutron star mergers. Ultimately, future directions aim to better characterize the population of transients and use these observations to understand the underlying physical processes driving their evolution.

Gravitational waves are ripples in spacetime caused by accelerating massive objects, such as merging black holes or neutron stars. Their detection is a relatively new development, and it revolutionizes astronomy. Gravitational wave astronomy is inherently a time-domain field because the waves themselves are transient signals, typically lasting only a fraction of a second to a few minutes. The detection of a gravitational wave event triggers follow-up observations across the electromagnetic spectrum to identify the source of the waves.

The connection to time-domain astronomy is profound. Electromagnetic counterparts – the light emitted by the source of the gravitational wave – are often transient, appearing only after the gravitational wave signal and fading rapidly. The combined observation of gravitational waves and electromagnetic counterparts provides a powerful tool to study some of the universe’s most energetic phenomena. For instance, the first detection of both gravitational waves and electromagnetic radiation from a binary neutron star merger provided invaluable information about the origin of heavy elements in the universe.

Time-domain astronomy plays a critical role in understanding the evolution of galaxies. Transient events, such as supernovae, are powerful probes of stellar populations and star formation rates within galaxies. By studying the frequency and properties of supernovae in different galaxies, we can learn about the past star formation history and the chemical enrichment of those galaxies. Active galactic nuclei (AGN), the supermassive black holes at the centers of galaxies, are also highly variable sources. Their variability provides clues about the processes powering AGN and their interaction with their host galaxies.

Furthermore, the study of tidal disruption events (TDEs), where a star gets ripped apart by a supermassive black hole, can provide insight into the demographics of supermassive black holes. By observing these transient events, we can constrain the masses and distributions of these black holes within galaxies. In essence, time-domain astronomy allows us to observe galaxies in action, witnessing their dynamic processes and uncovering details that are impossible to obtain from static images alone. The combination of long-term monitoring and fast response to transient events is crucial in building a complete picture of galactic evolution.

Time-domain astronomy, the study of celestial objects whose brightness changes over time, provides crucial insights into black holes through several avenues. Imagine a black hole as a cosmic vacuum cleaner, constantly pulling in surrounding matter. This infalling matter doesn’t simply disappear; it forms an accretion disk, a swirling structure of superheated gas and dust. The friction within this disk generates intense radiation, making the black hole’s presence detectable as a variable light source.

Specifically, time-domain studies allow us to observe:

• Tidal Disruption Events (TDEs): When a star gets too close to a black hole, its gravitational forces tear it apart. This catastrophic event produces a bright flare of light, detectable through its rapidly changing luminosity. Analyzing the light curve (brightness over time) of these flares provides insights into the black hole’s mass and spin.
• Active Galactic Nuclei (AGN) variability: Supermassive black holes at the centers of galaxies fuel AGN, emitting vast amounts of energy. The variability of this emission, monitored over time, reveals information about the accretion process, the presence of relativistic jets, and the structure of the surrounding matter.
• Gravitational Waves and Electromagnetic Counterparts: The merger of two black holes produces gravitational waves, ripples in spacetime. While gravitational wave detectors like LIGO and Virgo detect these waves, time-domain astronomy plays a vital role in searching for electromagnetic counterparts – flashes of light associated with the merger – providing invaluable contextual information.

In essence, by tracking changes in light intensity and other electromagnetic properties over time, we can infer the physical properties and behavior of black holes, processes otherwise impossible to directly observe.

My experience with real-time data analysis in time-domain astronomy centers around the rapid identification and classification of transient events. This often involves working with data streams from telescopes like the Zwicky Transient Facility (ZTF) or the Vera Rubin Observatory’s Legacy Survey of Space and Time (LSST). Imagine a firehose of data – millions of data points arriving every second. We use sophisticated algorithms and pipelines to filter this data in real-time, focusing on objects exhibiting sudden changes in brightness or other relevant parameters.

A typical workflow includes:

• Data ingestion and preprocessing: Receiving data streams, calibrating measurements, and removing instrumental artifacts.
• Real-time filtering and anomaly detection: Applying algorithms to identify objects whose brightness deviates significantly from baseline measurements, indicative of a transient event.
• Automated classification: Using machine learning models to classify detected transients into different types (supernovae, asteroids, active galactic nuclei, etc.).
• Alert generation: Triggering alerts for astronomers to follow up on promising candidate events using larger telescopes for detailed spectroscopic analysis.

A specific example involved developing a real-time pipeline for ZTF data, detecting and characterizing kilonovae – the electromagnetic counterparts of neutron star mergers – which helped narrow down the search space for follow-up observations.

Python is the cornerstone of my programming toolkit for time-domain astronomy. Its rich ecosystem of scientific computing libraries makes it exceptionally well-suited for tasks ranging from data manipulation and analysis to visualization and machine learning.

I am proficient in libraries such as:

• NumPy for numerical computation and array manipulation.
• SciPy for scientific algorithms, including signal processing and optimization.
• Astropy for astronomical data analysis and handling celestial coordinate systems.
• Matplotlib and Seaborn for creating informative visualizations of light curves and other astronomical data.
• Scikit-learn for implementing machine learning models for classification and prediction.

For example, I’ve used Astropy and SciPy to write custom functions for correcting for atmospheric extinction in transient light curves, crucial for accurate analysis of the underlying physical process. My experience also includes using Pandas for efficient data handling of large astronomical catalogs and SQLAlchemy for database interaction.

Handling large datasets in time-domain astronomy requires a multi-pronged approach combining efficient data storage, smart querying techniques, and scalable algorithms. We’re dealing with petabytes of data, which necessitates strategies beyond the capabilities of a single machine.

My strategies include:

• Distributed computing frameworks: Utilizing tools like Apache Spark or Dask to parallelize data processing across multiple machines, enabling efficient analysis of massive datasets.
• Data compression and chunking: Reducing storage space and improving processing speed through optimized data formats (like FITS) and breaking down large datasets into smaller, manageable chunks.
• Database systems: Utilizing specialized databases like those optimized for handling astronomical data, like those based on relational databases (SQL) or NoSQL solutions, enabling efficient data retrieval and query operations. Optimized indexing strategies become critical here.
• Algorithm optimization: Using algorithms that are computationally efficient, avoiding unnecessary computations, and leveraging optimized libraries like those mentioned above.
• Cloud computing: Leveraging cloud platforms such as AWS or Google Cloud to provide scalable storage and computing resources.

For instance, in one project analyzing data from the ZTF, we employed Apache Spark to efficiently process and analyze the millions of source detections across multiple observing nights, filtering out noise and identifying genuine transient candidates.

My experience with database management for astronomical data includes working with both relational and NoSQL databases. The choice depends on the specific needs of the project. Relational databases like PostgreSQL, with their structured schemas, are well-suited for managing well-defined astronomical catalogs with known properties. NoSQL databases, like MongoDB, offer greater flexibility for handling semi-structured data or data with variable schemas, which is common when dealing with transient events where the initial characteristics may not be fully known.

My experience involves:

• Schema design: Defining efficient database schemas that accurately represent astronomical data, considering data types, indexing strategies, and relationships between different tables.
• Data import and export: Developing efficient pipelines for importing large astronomical datasets from various sources and exporting results for analysis or visualization.
• Query optimization: Writing efficient SQL queries or NoSQL queries to retrieve specific data subsets based on various selection criteria, ensuring efficient data retrieval from large databases.
• Data validation and integrity: Implementing mechanisms to ensure data quality and consistency, including detecting and handling missing or erroneous data.

For a recent project involving the analysis of supernovae, I designed and implemented a PostgreSQL database to manage the catalog of observed supernovae, including their light curves, spectra, and derived physical parameters, allowing for efficient searching and querying of the database by multiple team members.

Analyzing transient events requires a solid understanding of various statistical methods to separate meaningful signals from noise. We often deal with noisy data and need robust methods to identify and characterize real events.

Common methods include:

• Time series analysis: Techniques like autocorrelation, periodogram analysis, and wavelet transforms are used to identify periodicities or other patterns in the time evolution of transient brightness.
• Bayesian methods: These probabilistic approaches are useful for quantifying uncertainty and incorporating prior knowledge into the analysis of transient properties (e.g., estimating the probability that a given event is a particular type of supernova).
• Hypothesis testing: Statistical tests (e.g., t-tests, chi-squared tests) are used to assess the significance of observed deviations from expected behavior, helping determine whether a variation is a real event or merely random noise.
• Regression analysis: Methods like linear regression or more sophisticated techniques are used to model the relationship between different parameters of the transient event and infer its physical properties.
• Survival analysis: Particularly relevant for studying the lifetime of transients and understanding their evolution.

For instance, I’ve used Bayesian methods to estimate the redshift (distance) of supernovae based on their light curves, which includes incorporating uncertainties in the observations and our physical models.

Collaboration is fundamental in time-domain astronomy, given the sheer volume of data and the complexity of the analyses involved. My experience includes working on international teams to:

• Develop and maintain data pipelines: Collaborating with software engineers and astronomers to design, implement, and maintain efficient data pipelines for processing and analyzing large datasets from various telescopes.
• Analyze and interpret data: Working with colleagues to perform joint analysis of transient events, sharing expertise and insights to reach conclusions.
• Write scientific papers: Collaborating with fellow researchers to write publications on our findings, contributing to our shared understanding of the universe.
• Organize and manage observational campaigns: Participating in collaborative efforts to schedule telescope time for targeted follow-up observations of promising transient events.

One significant collaborative project involved coordinating a global effort to observe a newly discovered kilonova, where I was responsible for coordinating data acquisition from multiple observatories and for developing and implementing a shared analysis pipeline.