McGill Cosmic Dawn Intensity Mapping Group

Scientific Vision We develop and apply novel analysis algorithms to data from intensity mapping telescopes to understand the currently unexplored epoch of Cosmic Dawn and to construct the largest ever 3D map of our cosmos. These maps enable us to probe the fundamental nature of our Universe and how it came to be.

Group Vision We are all apprentices, whether of each other, of other scientists, or of our Universe. Everyone has something to learn, and the ability to be self-critical is crucial. Science is our goal, but we are people first and scientists second; mutual respect for each other as individuals is therefore a non-negotiable requirement. We are honest about our weaknesses and are receptive to constructive feedback. While academia can be competitive, within our group and our colleagues we do not compete. Instead, we collaborate and help each other as we “fail our way to success”.

From observations to theory and back

Data analysis with the Hydrogen Epoch of Reionization Array (HERA) HERA (top right picture on the previous page) is a next-generation radio telescope seeking to directly measure the distribution, ionization state, and temperature of neutral hydrogen in the intergalactic medium (IGM) at high redshifts (6 < z < 20). These measurements will the first direct observations of these redshifts. The goal is to understand Cosmic Dawn (when first-generation stars and galaxies formed) and reionization (when these first luminous objects systematically ionized the hydrogen in the IGM), which are two crucial—but missing—chapters in our cosmic timeline. The success of HERA depends critically on our ability to ~0.2 terabytes of raw data per day into maps of the 21cm line emission from hydrogen, which are then converted into statistical descriptors of the signal. Significant software development work is required to make this cutting-edge telescope a success! Pros: Opportunities to work with lots of incredible scientists within a big collaboration, and to learn how to write “professional” software; Timing of HERA data availability (next 1 to 5 years) is optimal for MSc/PhD students; Your contributions will be crucial to the success of a cutting-edge telescope. Cons: Understanding the nature of the first stars and galaxies sounds glamorous, but the day-to-day work will involve frustrating efforts to get icky data to “behave”. Example: https://arxiv.org/abs/1502.06016 Applying machine learning to cosmology How do we extract every bit of information that our Universe is providing to us in our observations? With observations that are complex as those of reionization, machine learning provides an alternative approach to traditional statistics. Neural networks can learn to automatically recognize interesting features in our data that teach us about the properties of the first galaxies. It may also messy astrophysical signatures to be bypassed in our quest to use cosmological observations to learn “fundamental” parameters such as neutrino masses. Pros: Learn new techniques from computer science relevant both to physics and industry. Cons: There will likely be lots of speculative failures before we find techniques that work for our problems. Example: https://arxiv.org/abs/1711.02033

Interpretation of HERA data/Cosmic Dawn and Reionization Theory HERA is poised to answer a wide variety of questions regarding Cosmic Dawn. For example, what was the minimum mass required for a first-generation galaxy to form? How luminous were these first galaxies? How strongly did they emit in X-rays? Do the observations allow room for exotic emission mechanisms such as dark matter annihilations? Answering these questions from HERA data requires theoretical models and simulations. There is a crucial need for speeding up semi-numerical simulations, as well as for new ways to understand the physics of Cosmic Dawn. The former is amenable to computational techniques such as emulation, while the latter may be amenable to new effective field theory and perturbation theory techniques. Pros: Close contact with the actual physics of what we are trying to study. Cons: Be prepared for lots of nasty algebra! Examples: https://arxiv.org/abs/1705.04688, https://arxiv.org/abs/astro-ph/0607628 Theory of data analysis Given the specifications of an experiment (e.g., its noise properties, which part of the sky it’s looking at), what is the optimal way to analyze the data, such that the error bars on our final results will be minimized? For example, do we use spherical harmonic Bessel functions or Fourier modes in our analyses? Pros: Elegant mathematics; an opportunity to flex your linear algebra/differential equations/ complex analysis muscles. Cons: One step removed from actual analysis; harder to gain the satisfaction of seeing your methods work on real data. Examples: https://arxiv.org/abs/1609.04401

Cross-correlations with other telescopes HERA uses the neutral hydrogen 21cm line to observe the effects of the first galaxies on the intergalactic medium. In the next few years, telescopes such as CCAT-prime will be coming online. CCAT-prime will survey the sky for emission from ionized carbon, which should trace early galaxies (i.e., the causes of reionization). How do we take advantage of this complementarity? What can we learn from it, and how should we design future telescopes to maximize the science return? Pros: Forward-looking projects (get into a field early!) Cons: Real data probably won’t be available during MSc/PhD Example: https://arxiv.org/abs/1107.3553

The extended Global Sky Model (eGSM) It turns out that the very basic question of “what does the sky look like in all directions at all wavelengths?” is just not very well known, particular in low-frequency radio. Given patchy coverage of the sky (both in direction and wavelength) from previous surveys, what is our “best guess” model of what the sky looks like? Can we use statistics to fill in the missing information? Pros: Data already taken and processed to a great extent, so emphasis is on clever analysis algorithms rather than mundane processing steps. Cons: A lot of the existing data isn’t of the highest quality. Example: https://arxiv.org/abs/1605.04920

Bring your own ideas The best part of working with students is that they can lead your research in new and exciting ideas that you never thought of! I encourage students to come up with their own projects in addition to the ones that I propose. I am delighted to listen to pitches, and look forward to collaborating (and learning) with you!

Connections to other cosmological probes How do 21cm measurements from instruments like HERA complement traditional cosmological probes such as the CMB? It turns out that reionization is considered a “nuisance” effect in CMB measurements. Thus, by understanding reionization better, we can potentially improve other precision cosmological constraints. This may include constraints on neutrino masses, spatial curvature, or the nature of primordial density fluctuations from inflation. What is the best way to get at these constraints? Pros: Opportunities to build bridges with people working in other branches of cosmology. Cons: A bit more of a mature field, so greater creativity is required to come up with a high-impact project. Example: https://arxiv.org/abs/1509.08463

Instrumentation and field work My background is not in instrumentation. However, I collaborate very closely with instrumentalists, as I believe that our data analyses are only as good as our understanding of our instruments. Thus, I encourage my students to get a taste for instrumentation and field work. Opportunities exist, for example, to help with deployments of new antennas for HERA. In addition, I am open to co-supervisions with my more experimental colleagues such as Cynthia Chiang, Matt Dobbs, and Jon Sievers.

Contact me Please feel free to contact me with questions. Email is best:

Adrian Liu acliu@physics.mcgill.ca