Example Research Projects for KIPAC Post-bac Scholars

The Triggering of Active Galactic Nuclei in Cluster Environments

 KIPAC Mentor: Steve Allen Adi Foord, Sanskriti Das

  • Project Description:

Active galactic nuclei (AGN) are thought to play an important role in shaping the observed properties of galaxies. However, many aspects of the relationship between AGN and their host galaxies remain uncertain, including the mechanisms that trigger AGN activity. Galaxy clusters contain large numbers of galaxies in close proximity. Physical processes such as tidal interactions and mergers, and ram pressure forces from the diffuse intergalactic medium, are also maximized in such environments. By comparing the incidence of AGN in cluster galaxies to galaxies in the field, and how this varies as a function of cluster mass, radius and redshift, we can hope to disentangle the impacts of the physical processes at play.

  • Skills needed: Some programming experience (preferably python). A basic knowledge of astronomy and astronomical data would be beneficial.
  • Skills scholars will develop: Skills scholars will develop: coding in python, experience with optical and X-ray astronomical data, statistical model building and fitting, uncertainty estimation.

Deciphering the Physics of the Interstellar Medium

 KIPAC Mentor: Susan Clark, Mehrnoosh Tahani

  • Project Description:

The interstellar medium (ISM) is the material between the stars in galaxies. The ISM includes gas, dust, high-energy particles, and magnetic fields, and is the material out of which new stars are formed. Many key physical processes in the ISM, including star formation and the flow of gas between physical states, remain poorly understood. Our group works with a broad range of observational tracers, including polarized light that encodes properties of interstellar magnetic fields, and measurements of the emission or absorption of interstellar gas and dust. Potential projects include:

  1. A statistical analysis comparing ultraviolet absorption line measurements to the structure of diffuse neutral hydrogen emission, to investigate the formation of molecular gas in the diffuse ISM.
  2. An analysis of already-obtained polarized thermal dust emission data toward a non-star-forming molecular cloud, to investigate the possible role of magnetic fields in hindering star formation.
  • Skills needed: Some background in physics, some programming (preferably python). Willingness to learn new things and work collaboratively.
  • Skills scholars will develop: Programming and data analysis skills, statistical analysis, image analysis, basics of widely-used astronomical software and file formats, knowledge of interstellar medium physics.

Quantifying the Effect of Magnetic Fields in the Star Formation History of a Galaxy

 KIPAC Mentor: Enrique Lopez Rodriguez, Sergio Martin-Alvarez

  • Project Description:

Stars form in molecular clouds through a delicate balance between gravity, turbulence, and magnetic forces. These molecular clouds are influenced by the galaxy dynamics and threaded by the galactic magnetic fields (B-fields). However, B-fields have been complicated to measure and characterize due to difficulties with instrumentation and modeling. Recent observational and computational developments have opened a new window of exploration to characterize the B-fields in nearby galaxies.

Observationally, the B-fields in the multi-phase interstellar medium (ISM) can now be investigated using polarized emission in radio and far-infrared. Computationally, cosmological magnetohydrodynamical (MHD) simulations are now able to resolve individual molecular clouds in galaxies. Thus, the observational trends across the multi-phase ISM of galaxies can be explored using cosmological MHD simulations to obtain the underlying physics linking galactic B-fields and star formation activity.

The post-bac scholar will work with already-acquired radio and far-infrared data of spiral galaxies and with a sample of synthetic observations from MHD simulations. The project aims to study the relationship between star formation rate and thermal and non-thermal polarized emission. This work will allow us to quantify the importance of the B-field in the formation of stars across the disk of a Milky Way-like galaxy.

  • Skills needed: Basics in astronomy and statistical analysis.
  • Skills scholars will develop: Coding in python, imaging analysis, bayesian fitting and uncertainties, galaxy evolution, and magnetohydrodynamics.

Developing an Auxiliary Interferometer for LIGO

 KIPAC Mentor: Brian Lantz, Edgard Bonilla

  • Project Description:

The LIGO group at Stanford is developing a Seismic Platform Interferometer (SPI) to measure, and enable real time control of, the separation between the tables which support the LIGO optics. The LIGO optical system comprises many optics which are mounted to actively controlled tables which isolate the optics from ground motion. With proper measurement, the relative positions of the table can (hopefully!) be stabilized to 10 nanometers rms.

We are now running two of these SPI systems in the lab. The project is to work with us to fully characterize the performance and the noise of the interferometer, help make it robust to environmental disturbance and easy to use in a system to be running at the LIGO detectors.

  • Skills needed: Some programming (preferably Matlab or Python), willingness to learn to LIGO data acquisition system. Experience building simple circuits, bolting together mechanical systems, or taking Fourier transforms would be useful. Mechanics and E&M is also useful.
  • Skills scholars will develop: Building precision measurement tools, measuring many of the ways they can fail, and figuring out how to fix them. Describing systems in both the time and the frequency domain. Integrating new sensors into active servo control systems.

Getting Ready for the Rubin Observatory “Legacy Survey of Space & Time”

 KIPAC Mentor: Pat Burchat, Sid Mau

  • Project Description:

The LSST Dark Energy Science Collaboration is preparing to explore dark energy and fundamental physics with the Legacy Survey of Space & Time (LSST) – a massive astronomical data set that Rubin Observatory in Chile will soon begin to produce. We are addressing challenges that need to be overcome to do both precise and accurate measurements of "weak gravitational lensing". We use analytical calculations, simulations, modeling, and analysis of existing astronomical images to thoroughly understand and then correct potential systematic biases. 

The post-bac scholar would join one or more of these projects:

  1. Developing and testing the accuracy of high fidelity simulations needed to understand potential biases – e.g., due to instrumental or atmospheric effects, or limitations in our image-analysis algorithms. 
  2. Quantifying the level of residual correlations in the point spread function (which can bias cosmological signatures) across the very large field of view of the Rubin telescope, 
  3. Understanding and reducing the impacts of blended (overlapping) objects on weak lensing signals. 
  • Skills needed: Programming experience (preferably python). Basic knowledge of statistics. Experience with machine learning and some prior knowledge of cosmology are helpful.
  • Skills scholars will develop: Astronomical image analysis, data analysis, statistical analysis, developing software packages in python. 

Guiding the Rubin Observatory Legacy Survey of Space & Time Discovery of New Physics

 KIPAC Mentors: Agnès Ferté, Risa Wechsler

  • Project Description:

The Rubin Observatory is currently under construction in Chile: by continuously imaging the sky for 10 years with a fast 8-meter telescope, this observatory will revolutionize our view of the night sky. One of its goals is to understand the nature of dark energy, a mysterious fluid at the origin of the acceleration of the universe's expansion. An alternative to dark energy to explain cosmic acceleration is to posit that our understanding of gravity is not correct on cosmic scales. We will be able to test these alternatives with unprecedented precision thanks to Rubin Observatory's data. Whether dark energy or a change in gravity laws are needed in our cosmological model, answering these questions will lead to discovery of new physics.

However, there are many models that can be tested, requiring a lot of computational and human efforts to develop the pipeline, models and perform the analysis in each model.

In this project, the post-bac scholar will use machine learning algorithms to categorize models according to their impact on data. We will then be able to say which model is the most interesting to be tested with Rubin Observatory. The post-bac will join the international Dark Energy Science Collaboration (DESC) to produce a training set using DESC tools, develop an unsupervised learning algorithm to sort out models and perform the analysis. This way, the post-bac will be able to get involved in one the largest cosmological collaboration and get expertise on tools that will be key to lead future cosmological analysis.

  • Skills needed: Python coding, basics in statistics.
  • Skills scholars will develop: Machine learning, data analysis, teamwork.

Measuring the Accelerating Expansion of the Universe With Big Data: Simulating All the Gravitational Lenses in Rubin’s LSST

 KIPAC Mentors: Phil Marshall, Sydney Erickson, Martin Millon, Ralf Kaehler, Aaron Roodman

  • Project Description:

Rubin Observatory’s Legacy Survey of Space and Time (LSST) will contain a treasure trove of gravitational lenses: massive galaxies that are magnifying and multiply-imaging more distant galaxies. These fascinating and beautiful systems can be used to measure distance in the Universe, and, combined with their apparent recession speed, constrain the kinematics of the expansion of the Universe and allow us to explore the properties of the mysterious Dark Energy thought to be driving that expansion. To date, we have 9 well-measured time delay lenses that together constrain the present-day expansion rate to within a couple of %. With Rubin we should be able to find and use 100s of similar, well-measured, systems. In our group, we are wondering: how much better can we do by including the thousands more lenses that the LSST data will contain? To do this, we are developing fast, AI-powered image modeling techniques, and assembling the big, joint inference that is needed, so that when the survey starts we are ready to go.

In this project, the post-bac scholar will work closely with other members of the SLAC Strong Lensing Group to build a life-size mock sample of realistic simulated LSST gravitational lenses that will enable full-scale end-to-end tests of our modeling pipeline. They will then design and carry out some controlled tests of the pipeline’s performance using their sample, and thus help quantify the amount of information contributed by various sub-samples. They will join the international LSST Dark Energy Science Collaboration (DESC) and use DESC and Rubin tools for their simulations. This way, the post-bac will be able to get involved in one the largest cosmological collaborations and gain valuable Rubin expertise.

  • Skills needed: Python coding, basics in statistics.
  • Skills scholars will develop: Machine learning, data analysis, teamwork.

Large Scale Structure Formation in the Universe

 KIPAC Mentor: Tom Abel, Bryen Irving

  • Project Description:

The project would involve using computer simulations to study the distribution and behavior of dark matter in the Universe, with a focus on understanding the formation of large scale structure such as galaxy clusters and superclusters in the cosmic web. 

The first step of the project would involve learning how to analyze simulation results using the yt software (http://yt-project.org

Once the post-bac scholar is comfortable with the tools and techniques, they would begin to run their own simulations of dark matter, starting with simple models and gradually increasing the complexity and realism of the simulations. The post-bac would also analyze the results of the simulations, looking for patterns and trends in the distribution of dark matter and comparing their results to observational data and theoretical models.

The ultimate goal of the project would be to make new discoveries and contribute to the understanding of how dark matter shapes the large scale structure in the Universe. The post-bac would gain valuable experience in the field of astrophysics and cosmology, and would have the opportunity to work with leading experts in the field of dark matter simulations.

  • Skills needed: Some programming experience with python and/or julia background.
  • Skills scholars will develop: Large scale data analysis and visualization. Cosmological models and theory of structure formation.

Quantum Sensing for Particle Astrophysics

 KIPAC Mentor: Noah Kurinsky

  • Project Description:

Conventional detectors used for astrophysics, including in the search for dark matter, observing galaxies and clusters, and exploring the early universe, probe wavelengths and energies that are visible with the naked eye. To probe invisible wavelengths, and improve sensitivity to individual light particles (single photons), we are developing novel sensor technologies based on superconducting thin films that will open up new energy and sensitivity regimes across many fields of astrophysics. We aim to be able to detect single photons at the meV level with backgrounds at or less than a single photon per day.

The post-bac scholar involved in this project will learn about engineering for cryogenic systems, using superconducting technology to detect light and particle interactions, and participate in our development of quantum sensors for astrophysical applications. Many of our projects involve trying to detect dark matter terrestrially, and our sensors can also be used to measure optical and infrared spectra from stars and galaxies without dispersive optics.

  • Skills needed: Background in general physics, some programming experience, willingness to learn about work in a cryogenics lab.
  • Skills scholars will develop: Experience with superconducting sensors, operating cryostats, programming for real-time systems in python, data analysis, understanding of spectroscopy and signal processing.

Needle in a Haystack: Dark Matter Searches With Noble Liquids

 KIPAC Mentor: Maria Elena Monzani, Maris Arthurs, Tyler Anderson

  • Project Description:

The nature and origin of dark matter are among the most compelling mysteries of contemporary science. The LUX-ZEPLIN (LZ) collaboration has recently started operating a new dark matter detector, filled with 10 tons of liquified xenon gas, maintained at almost atomic purity and stored in a refrigerated titanium cylinder a mile underground in a former gold mine in Lead, South Dakota. The experiment is slated to acquire 5 PB of data over its lifetime (or 5 billion particle interactions).

However, due to their elusive nature, only a handful of dark matter particles would be discovered in the process. Finding those particles is an extreme "needle in a haystack" challenge, requiring an unprecedented level of analytical prowess and statistical accuracy. This project will leverage advanced Machine Learning techniques to increase the sensitivity of our measurements. Opportunities for experimental work in the laboratory will also be available.

  • Skills needed: Some programming experience (for example: python, jupyter, C, C++). Basic physics knowledge would be helpful, and potential lab skills.
  • Skills scholars will develop: Advance their coding skills, becoming proficient in one or more languages. Machine Learning algorithms and data analysis skills. Laboratory/detector development skills if desired by the applicant.

Tools and Techniques for Wave-like Dark Matter

 KIPAC Mentor: Chelsea Bartram

  • Project Description:

The SLAC Expansive Axion Search group will develop R&D for wideband axion dark matter experiments over a range of frequencies from as low as 5 MHz up to 10s of GHz. The axion is a dark matter candidate that also solves the Strong CP problem. It is typically detected using an ultra low noise receiver chain in a strong (multi-Tesla) magnetic field. Post-bac scholars will have the opportunity to participate in DM Radio, ADMX and CM-wave cavity haloscope collaborations, in addition to performing broadband R&D that is specific to the Expansive Axion Search group. The ADMX collaboration has real axion search data in the pipeline that the post-bacs will be able to analyze.

  • Skills needed: Some programming (preferably Python), willingness to learn or past experience with microwave simulation software. Background in physics or EE would be useful.
  • Skills scholars will develop: Software development / coding skills, analysis, ability to use simulation tools such as COMSOL and/or HFSS. Understanding of microwave technology, quantum sensing, cryogenics and strong magnets. Axion dark matter searches have strong overlap with the technology used in quantum computing.

Spectral Calibration for Cosmic Microwave Background Telescopes

 KIPAC Mentor: Kirit Karkare, David Goldfinger

  • Project Description:

The Cosmic Microwave Background (CMB) is an image of the universe at only 380,000 years old, and provides a unique view of the fundamental physics underlying our cosmological model. CMB experiments like BICEP Array and eventually CMB-S4 aim to test the physics of cosmic inflation, the leading paradigm to explain the origin of the Universe and the primordial fluctuations. However, a major challenge in these measurements is separating the CMB from other components in the microwave sky like Galactic dust and synchrotron. Component separation requires us to know the spectral response of our detectors extremely precisely. We can measure the detector bandpasses using a Fourier Transform spectrometer (FTS).

The post-bac scholar would work with the SLAC CMB group to build a new FTS that is suitable for use on BICEP Array and CMB-S4 receivers. They would begin by understanding how the FTS measurement is made and processing archival data taken with previous BICEP experiments. They will then design a new FTS that is optimized for the CMB-S4 optics, including building an optical model that allows various systematic effects to be understood. Finally, they will build the interferometer and validate it on existing CMB receivers. The FTS would eventually be deployed to the South Pole and see use in our world-leading constraints on inflation.

  • Skills needed: Basic background in physics and optics, programming in Matlab/Python useful but not necessary. Primarily a willingness to learn and spend time in the lab.
  • Skills scholars will develop: Microwave optics, programming in Python/Matlab, data analysis and Fourier transforms, cryogenics, lab electronics, fabrication of scientific instruments. General lab skills.

Ice Penetrating Radar: Science and Engineering To Explore Ice Sheets and Icy Moons

 KIPAC Mentor: Dusty Schroeder

  • Project Description:

The Stanford Radio Glaciology research group focuses on the subglacial and englacial conditions of rapidly changing ice sheets and the use of ice penetrating radar to study them and their potential contribution to the rate of sea level rise. In general, we work on the fundamental problem of observing, understanding, and predicting the interaction of ice and water in Earth and planetary systems. Radio echo sounding is a uniquely powerful geophysical technique for studying the interior of ice sheets, glaciers, and icy planetary bodies. It can provide broad coverage and deep penetration as well as interpretable ice thickness, basal topography, and englacial radio stratigraphy. Our group develops techniques that model and exploit information in the along-track radar echo character to detect and characterize subglacial water, englacial layers, bedforms, and grounding zones. In addition to their utility as tools for observing the natural world, our group is interested in radio geophysical instruments as objects of study themselves. We actively collaborate on the development of flexible airborne and ground-based ice penetrating radar for geophysical glaciology, which allow radar parameters, surveys, and platforms to be finely tuned for specific targets, areas, or processes. We also collaborate on the development of satellite-borne radars, for which power, mass, and data are so limited that they require truly optimized designs. Post-bac projects are available in support of both ice penetrating radar instrument development and data analysis.

  • Skills needed: Some background in physics and/or scientific programming.
  • Skills scholars will develop: Depending on the project, some mix of Computational Electromagnetics, Exploration or Near Surface Geophysics, Geophysical Glaciology, Ice Sheet Hydrology, Ice Sheet Modeling, Planetary Geophysics, Radar Science and Engineering, Remote Sensing, Signal Processing