About Me

My name is Imad Pasha, and I'm currently a second year graduate student and NSF Fellow at Yale University. In 2017, I graduated from University of California, Berkeley with B.A. degrees in Astrophysics and Physics (and a minor in Creative Writing). In spring 2018, I worked as a Junior Specialist in the Berkeley Astronomy Department under my undergraduate thesis advisor, Prof. Mariska Kriek.

I am primarily interested in the formation and subsequent evolution of galaxies — how they build up their stellar populations, what mechanisms drive a galaxy to quench and become quiescent (or not), and what observational tracers we can use to learn more about the internal and external processes driving their evolution. In particular, I have spent time looking into how Bayesian SED-fitting inference frameworks can leverage photometry to extract properties of stellar populations, as well as what the next era of telescopes (JWST, LSST, etc) will provide as new indicators for star formation rates, star formation histories, masses, etc.

I have done research in several areas of astronomy, including computational hydrodynamics, inference-driven modeling, and X-ray imaging. I plan to pursue astronomical research and teaching as a career, as well as journalism (I worked for four years at The Daily Californian).

Astronomy on Tap

I'm thrilled to be one of the organizers (and occasionally MC) of New Haven's edition of Astronomy on Tap, a ~monthly event in which various members of the astronomy department, from graduate students to faculty, give public talks over beers at a local bar. The events are remarkably well attended, forming a fantastic bridge between the science we do and a local group of interested locals!

Teaching

The Yale Summer Program in Astrophysics

Yale | Summer 2019

The YSPA is an intensive, 4 week long program in which selected high schoolers around the country come to Yale to participate in classes and research projects. During this session, I served as one of the instructors, teaching various lessons in Python.


Astronomy 250: Research Methods in Astrophysics

Yale | Spring 2019

I served as the Teaching Fellow for the undergraduate course in research methods for intended astrophysics majors, (Prof. Marla Geha). I developed materials and taught several workshops and classes, especially surrounding pythonic methods for data reduction and other analysis techniques for astronomical data.


Astronomy 150: Earth in its Cosmic Context

Yale | Fall 2018

I served as the Teaching Fellow for the undergraduate course Earth in its Cosmic Context (Prof. Greg Laughlin), designing and teaching sections every week and holding office hours.[Evaluations]


Astronomy 98/198: Python For Astronomers (Instructor)

UC Berkeley | Spring 2015, 2016, 2017

For several years, I was the the instructor of the 2 unit, P/NP Python course in the astronomy department.

My co-instructor and I developed the content for this course from scratch, including writing a textbook, which is freely available here. We are always working on updating this book, which is designed to be entirely introductory (no prior coding experience), and oriented towards those going into astronomy research and data analysis.[Evaluations]


Astronomy 120: Optical and Infrared Laboratory (GSI/TA)

UC Berkeley | Fall 2016

In Fall 2016, I served as one of the two GSIs for the Astronomy Department’s upper division lab class in observational techniques in astronomy.

The class covers the fundamentals of photon statistics, measurements and uncertainty, and data analysis in python. A course description can be found here. Highlights of the lab include an extrasolar planet detection lab as well as measurements of the Earth-Sun distance via measurements of doppler shifts on the Sun’s surface. [Evaluation]

Resources

LEARNASTROPYTHON.COM

Programming is an integral part of an astronomer or astrophysicist's toolbox. But the prospect of learning how to program can be super intimidating. There is also an overwhelming number of different resources from which one can learn to code, which vary in difficulty and in applicability to astronomy.

When I took over teaching UC Berkeley Astronomy's Python course, I didn't set out to write a textbook. But as I designed the syllabus for my class — which is designed for those with no prior programming experience — I realized that not many resources start with the absolute basics (what is a terminal? syntax? a variable?), but end up in a place where a student would be prepared to set out programming in a research setting. My attempt to do so is enshrined in the book I co-wrote with Chris Agostino (my co-instructor) and the tutorials/exercises I developed over the years. I've made the textbook, and increasingly more guided and unguided tutorials, available on a hosted website.

Did I do a good job? I'm always looking for feedback on my tutorials and text, and am constantly updating both to hopefully be a resource students starting out can utilize as they begin to learn to code.


TripleSpec User Guide

An introductory guide to using the TripleSpec instrument at Palomar Observatory --- one of the instruments that can be applied for by Yale graduate students. I wrote this up during a long observing run; hope it might come in handy for someone! Access the guide.

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Measuring Heavy Metal
Absorption Features in
Distant Quiescent Galaxies

Until now, there has been no direct measurement of absorption features in the spectra of quiescent galaxies at high redshift. To do so requires a LOT of integration time on the world's most powerful telescope, Keck Observatory -- each galaxy in this study has 12+ hours of integration time.

Publications

Brackett-gamma Star
Formation Rates in
the era of JWST

Astronomers have been trying to measure the Star Formation Rates (SFRs) of other galaxies for a long time. It's not an easy problem: Because we can't actually see star formation happening (as stars form in thick birthclouds of gas and dust that are too small to be resolved anywhere other than our galaxy and its satellites), we have to rely on what we call "tracers" of star formation --- observable properties that correlate closely with recent star formation. There are better and worse tracers to use, usually those with better (tighter) correlations (and thus more accurate SFRs) are also more observationally challenging and time consuming to obtain.

For example, a galaxy's UV Luminosity (the amount of light it produces in the rest-frame ultraviolet) is a good tracer of star formation, because only the youngest, hottest, most massive stars are hot enough to produce a significant amount of UV radiation. With the adoption of an Initial Mass Function (IMF; another source of uncertainty), one can translate the amount of light coming from O/B stars (those massive, hot stars) into a number of O/B stars, which can be extrapolated to determine the recent rate of formation of all stars. O/B stars only live a few hundred million years (compared to many billions for low mass stars), so we call this the "instantaneous" SFR. UV light is also very easy to see from the ground on earth when galaxies are redshifted, moving the light emitted in the UV into the optical, where our atmosphere is transparent. Thus, large surveys of many thousands of galaxies' UV luminosities could be obtained.

The problem is, UV light emitted by stars is the most affected by dust attenuation (absorption). Dust grains love to absorb light and re-emit it at longer wavelengths, and they do so preferentially for light approximately the wavelength of their grain size, and there are many more very small grains than larger grains. In short, a large fraction (as much as over 50 percent) of UV starlight never makes it to us, but rather is absorbed by dust in the galaxy and re-emitted in the mid to far infrared.

Hydrogen recombination emission lines are also a good tracer of star formation. The gas around and near hot O/B stars gets excited and ionized (electron knocked off), and as they recombine and the electron cascades down to the ground state, the hydrogen emits lights of various set wavelengths. These are so well studied they have names, such as the Lyman series (transitions to the ground state), Balmer series (transition to the n=2 state), Paschen, Brackett, Pfund, etc. series. The most famous of all these lines is dubbed H-alpha, and it's the transition from n=3 to n=2 (a Balmer line). It's one of the brightest emission lines, and is in the restframe optical (6563 angstroms). The issue with H-alpha is that it is also affected by dust, just like the UV, and thus has an associated scatter in its correlation with SFR.

There are many ways to try to correct for these dust effects, including the use of UV and FIR data, a "Balmer decrement" correction using H-alpha and H-beta, or combining multiple methods. One promising avenue in the future is the use of restframe near-IR hydrogen emission lines, in the Paschen and Brackett series. Most galaxies are optically thin at these wavelengths; that is, the are not enough dust grains at sizes corresponding to these longer wavelengths for a large fraction of light to be absorbed. In short, they are a "dust free" (or more dust free) measurement of the same SFR traced by H-alpha.

The main issue with lines like Pa-alpha, Pa-beta, and Br-gamma, are that restframe near-IR lines get redshifted to wavelengths in a region unobservable from the ground due to our atmosphere blocking those wavelengths. To get at the region just redward of near-IR, one needs a space based telescope. Additionally, obtaining spectra (to measure the emission line) used to be very inefficient, a one object at a time endeavor. But with the advent of Multi-object spectrographs, it is now possible to observe ~20-30 objects at once, drastically improving observing efficiency.

The James Webb Space Telescope (JWST) combines both things needed to observe Paschen and Brackett lines out to farther distances beyond the local universe -- it has a multi-object spectrograph with coverage out to 5 micron (brackett gamma, the least dust affected line, is at restframe 2.166 micron).

In this work, I analyze how much "cleaner" these lines will be compared with what we have now, using suites of models from the Bayesian inference framework Prospector. First, I use measurements taken using the Palomar TripleSpec instrument to ensure Prospector has good predictions of Br-gamma, Pa-beta, and H-alpha, and then I compare (in model space) the corelations with the marginalized SFRs generated by fitting the full photometry available for these galaxies (yet another way of measuring SFRs, albeit a computationally intensive one).

The paper for this work is in prep, and I hope it's available to everyone soon!

Panchromatic Modeling of
Composite Spectral Energy
Distributions

Coming Soon...