1. 1 RESOLVING VOLCANISM ON IO WITH APERTURE MASKING INTERFEROMETRY (AMI) Chima mcGruder University of Tennessee, Knoxville

2. the Non-Redundant Mask (NRM) on the Near-InfraRed Imager and Slitless Spectrograph (NIRISS) Point Spread Function(psf) = diffraction pattern of a point source viewed through the aperture NIRISS Filter wavelength range is about 3.5μm to 5 μm

3. Io: Jupiter’s closest moon The most geologically active body in our solar system Has highest radiation flux in IR This is perfect because JWST is made for IR Io’s total emissions Io’s total output Io’s volcanic emission Io’s reflected light emission NIRISS is optimized in range 3.5μm to 5μm Brightness Limits of Galilean Satellites + Titan Wavelength (μm) 10 σ Surface Brightness (Jy/square-arcsec)

4. IMAGE PROCESSING First: Made the JWST aperture and its point spread function (psf) Second: Created my fake Io with volcanoes Third: Convolved the object (Io) with the psf of the aperture which formed the image. Fourth: Compared my results with the results that Deepashri created PSF of JWST aperture Image of Io produced by convolution Image of Io (the object) 1 pixel volcano NRM PSF of JWST aperture

5. ACQUIRING KEY DATA ABOUT IO We want to know the parameters of an observed image of Io. These parameters includes Io’s positioning, Io’s brightness, volcanic fluxes, and volcanic positions. Used forward modeling to minimize the differences between my produced data and the real data (Deepashri’s data), in order to find the best likely parameters for Io. Results Found that it is extremely difficult to optimize the positional and intensity parameters at the same time. Must use separate methods to find both Incorporating Poisson noise and many volcanoes yields > 3% error, while the positional parameters are given

6. NEXT STEPS Set program to run with all types of noise that would be expected to affect the image Use a method that first finds the positional parameters of the Image then the flux/brightness parameters Try different methods to reduce the error all together. MCMC (Markov chain Monte Carlo) Why Will prepare us for JWST’s launch by allowing us to optimize our usage of the mask and telescope by already having established algorithms that measure Io’s volcanic parameters. Will be able to have more precise photometric monitoring of Io‘s volcanic activities.

7. THANK YOU! With continuous assistance and support from : Alexandra Greenbaum Johns Hopkins University NSF graduate fellow, non-redundant aperture masking algorithms lead (My Second advisor) Anand Sivaramakrishnan JWST Aperture Masking Team lead (My advisor) Deepashri Thatte STScI NIRISS AMI co-lead John Stansberry NIRCam+STScI, SSOAP chair NAC ( National Astronomy Consortium )

8. NAC EXPERIENCE

9. NAC EXPERIENCE ( POSITIVES ) Enjoyed the weekly meeting: Made me feel really comfortable with my presentation and gave me a lot of perspective on what to expect after graduation (graduate school or not) Loved the social component with the NAC members: The unrequired NAC socializations like lunch, the baseball game, and hanging in Lou’s office made me feel really comfortable approaching all of the NAC mentors about any of the many questions I would have. Personal appreciation is really important to form good experiences.

10. NAC EXPERIENCE ( NEGATIVES ) Did not like the size of the group: It was such a small group in STScI, three, this might have been unavoidable, but still I wish our group could have been bigger so I would have had more NAC members to connect with. Recommendation : Coordinate with the other groups more. I do not know exactly how much the other groups communicated with each other, but I felt like each NAC group did there own thing with little connection or consistency with the other groups. I saw some of the other members in the beginning, middle (DC baseball game), and end. I believe this disconnection is more noticeable with a small NAC group like STScI’s.

11. THANK YOU! Lou Strolger (STScI NAC head) Kartik Sheth (NAC president)