Serendipitous Spectral Characterizations of Asteroids with JWST

Abstract

Asteroids are time capsules. In fact, they are the only remnants of the early solar systems that are mostly untouched, unchanged. Studying them can thus inform our understanding of the solar system; from the formation and evolution of planets, to the delivery of key planetary resources such as water, and the origins of life on Earth. Each process affects these asteroids in distinct ways, which are increasingly within reach owing to leaps in remote-sensing capabilities. Recently, the power of serendipitous asteroid detections with JWST was demonstrated to reveal unprecedented insights into the main-belt. Here, I propose to build upon this achievement and mine the JWST archival data for asteroids for which JWST gathered spectroscopic information serendipitously. I will then analyze their spectra, which contain both reflected sunlight and thermal emission, to reveal the chemical composition and physical properties of their surface. This research will also advance our knowledge of asteroids by identifying their taxonomic types.

Research Period
Sept 2025 – Present

Research Guidance
Guidance under Professor Julien de Wit and Dr. Artem Burdanov, Massachusetts Institute of Technology (MIT)

Hypothesis
Serendipitous JWST spectra contain enough information to distinguish broad asteroid surface types, such as carbon rich and silicate rich compositions, without requiring dedicated observations or full thermal modeling.

Motivation
Asteroids preserve material from different stages of Solar System history and provide a record of how rock, metal, and volatiles were distributed during planet formation. Studying their surface composition helps constrain where water bearing minerals formed and how early Solar System materials evolved. This work connects to my interest in planetary geology and geomorphology by using spectra to identify mineralogical fingerprints and constrain the composition of asteroid surfaces. This project builds on my prior work modeling physical processes in small bodies and extends it toward understanding early Solar System materials through surface composition.

Research Breakdown
The research problem was deconstructed into several manageable tasks:

  1. Archive Search and Target Identification: I searched the full JWST archive using the Mikulski Archive for Space Telescopes (MAST) to identify serendipitous asteroid detections, where asteroids crossed the field of view during unrelated observations. I ran an existing archive search script locally to identify where known asteroids crossed JWST observation fields and retrieve metadata for candidate detections.
  2. Motion Verification and Data Selection: Candidate asteroids were verified by identifying point sources that moved between exposures while background stars remained fixed. This motion based confirmation ensured that the detected source was an asteroid rather than a static background object and allowed reliable selection of exposures for spectral extraction.
  3. Spectral Extraction with JWST Pipeline: I adapted the public JWST pipeline to run on my own asteroid observations and validated the workflow by running it on provided demonstration data. The pipeline corrects detector effects, aligns multiple integrations, and extracts a one dimensional spectrum that includes both reflected sunlight and thermal emission.
  4. Initial Spectral Assessment: Extracted spectra were inspected for signal quality, wavelength coverage, and stability. This step established whether the data contain sufficient information to support later reflectance conversion, feature identification, and surface composition analysis.

Quantifiable Outcomes
1. Archive Search Results: Identified dozens of serendipitous asteroid detections in JWST observations, including targets in both MIRI and NIRISS WFSS data, demonstrating that archival searches can systematically recover asteroid spectra without dedicated observations.
2. Confirmed Asteroid Detection: Successfully identified and verified the motion of asteroid 2008 TN215 and 2012 QE2 in NIRISS WFSS data using metadata inspection and image based motion confirmation.
3. Pipeline Based Spectral Extraction: Produced a one dimensional spectrum of asteroid 2008 TN215 using the JWST pipeline, establishing a complete workflow from archival identification to extracted spectral data.
4. Spectral Readiness for Composition Analysis: Verified that the extracted spectrum has sufficient signal quality and wavelength coverage to support future reflectance conversion, feature measurement, and surface composition classification.

Skills Acquired

  1. MAST Archive Searching and Metadata Interpretation: Gained experience querying the JWST archive through MAST, interpreting observation metadata, and identifying serendipitous targets within large archival datasets.
  2. JWST Pipeline Execution and Adaptation: Developed comfort running the public JWST pipeline on both demonstration datasets and independently identified asteroid observations, including troubleshooting configuration, file structure, and pipeline stages.
  3. Spectroscopic Data Handling: Learned to manage and organize large volumes of JWST imaging and spectroscopic data, including handling multi integration exposures and tracking data products across pipeline stages.
  4. Asteroid Identification in Imaging Data: Built experience identifying moving targets in JWST images by comparing exposures and distinguishing asteroid motion from fixed background sources.
  5. Workflow Reproducibility and Validation: Gained practice validating analysis workflows by cross checking results between demonstration data and independently processed archival observations.

Key Learnings

  1. Serendipitous data requires careful validation: Archival detections are powerful but require explicit motion confirmation and metadata checks to avoid misidentifying background sources or artifacts.
  2. Pipeline output is not science ready by default: Extracted spectra require additional inspection and preparation before they can be interpreted in terms of surface composition.
  3. Composition constraints depend on wavelength coverage and signal quality: The ability to identify mineralogical features is limited by spectral range, resolution, and signal to noise, requiring careful assessment of what can and cannot be claimed from the data.
  4. Workflow validation builds scientific confidence: Running the same pipeline on demonstration data and independent archival targets is essential for identifying failure modes and building trust in results.