KIPAC Mentor: Zeesh Ahmed

• Project Description:

The Cosmic Microwave Background (CMB) is the oldest observable light, providing critical insights into the origins and evolution of our Universe. The CMB group at KIPAC builds, deploys, and operates state-of-the-art telescopes to observe the cosmic microwave background, using collected data to investigate cosmic inflation and the large-scale structure of the Universe.

Potential Projects:

1. Detector Readout Design and Prototyping: Contribute to the development of cryogenic and warm detector readout components for the proposed SPT-3G+ delensing camera, enhancing future CMB measurements.
2. Data Analysis for BICEP: Investigate the sources and impacts of data cuts and mode loss in the BICEP experiment, focusing on optimizing large-angular-scale B-mode polarization measurements from the South Pole.
3. Instrumental Performance Evaluation: Analyze the instrumental performance and stability of the Simons Observatory Large Aperture Telescope Receiver, preparing for its initial science operations starting in mid-2025.

• Skills needed: Interest in cosmology, basic programming (Python), physics/engineering background, analytical thinking
• Skills scholars will develop: Advanced instrumentation design, cosmological data analysis, cryogenic systems experience, interdisciplinary collaboration, scientific communication

 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.

 KIPAC Mentor: Christian Aganze, Risa Wechsler

• Project Description:

Low-mass stars and brown dwarfs (ultracool dwarfs, UCDs, <0.1 solar mass) constitute a significant fraction of the stellar population in the Galaxy (at least ~25%). While UCDs are abundant, they are intrinsically faint (<10,000 solar luminosity); hence, UCD samples have been limited to the immediate solar neighborhood (<100 pc). Little is known about distant, metal-poor UCDs. Deep samples with JWST grism spectroscopy allow the detection of distant (1-50 kpc ) objects, allowing us to link UCDs to the broader star formation history of the Galaxy.  This project aims to search for distant UCDs in the Cycle 1 PASSAGE survey and characterize metal-poor UCDs, which are likely thick disk and halo objects, using machine learning techniques. We will use recently developed UCD atmosphere and evolutionary models to estimate stellar parameters for the final sample and put constraints on their surface densities in JWST deep fields.  Moreover, in photometric surveys, UCDs are often confused with other faint extragalactic high-redshift sources, and part of this project involves developing an efficient selection method for future JWST surveys. 

• Skills needed: some background in physics/astronomy, programming in Python, ideally some experience with machine learning models (CNNs and random forests), and data analysis skills.
• Skills scholars will develop: scientific writing, knowledge of stellar spectroscopy and model fitting techniques; the candidate will practice scientific presentation. These skills will prepare candidates for a successful career as a graduate student researcher.

Cosmology and Dark Matter with Strong Gravitational Lensing

 KIPAC Mentors: Adam Bolton, Phil Marshall

• Project Description:

When two distant galaxies or quasars fall by chance along the same line of sight, the image of the more distant object is distorted into multiple images or a complete ring through a phenomenon known as “strong gravitational lensing”. Strong lensing provides a unique tool for measuring the relationship between normal matter and dark matter, and for measuring the fundamental cosmological parameters that drive the geometry and expansion rate of the Universe as a whole. The combination of massive cosmological surveys, such as Rubin-LSST and DESI, with AI/ML-based classification and inference methods is enabling a revolution in the discovery of large samples of lenses and their application to cosmology and dark matter research. Possible Projects:

1. Developing and training AI-based tools to discover new kinds of strong gravitational lenses within massive spectroscopic and imaging surveys
2. Astrophysical modeling of Hubble Space Telescope images of strong lenses discovered within large surveys
3. Development and application of inference frameworks for constraining cosmological parameters from observational samples of strong lenses
4. Optimization of strategies and samples to monitor for multiply imaged supernovae in known lenses

• Skills needed: basic familiarity with scientific programming, statistics, and data analysis; interest in astrophysics, cosmology, and/or AI-enabled scientific research.
• Skills scholars will develop: Astronomical imaging and spectroscopic data analysis; scientific programming and scripting in Python; AI and machine learning for data mining and inference; knowledge of astrophysical relationships between quasars, galaxies, and dark matter; knowledge of cosmological applications of strong gravitational lensing; Bayesian and hierarchical-Bayesian inference methods.

Quantum Sensing for Particle Astrophysics

 KIPAC Mentor: Noah Kurinsky

• Project Description:

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.