Caleb Michael Levy

Caleb Michael Levy

Senior at Colgate University hoping to pursue a PhD in theoretical physics.

Publications

Constraining Dark Matter properties with the first generation of stars

Published in Physical Review D, 2021

Dark matter (DM) can be trapped by the gravitational field of any star since collisions with nuclei in dense environments can slow down the DM particle below the escape velocity at the surface of the star. If captured, the DM particles can self-annihilate, and, therefore, provide a new source of energy for the star. We investigate this phenomenon for the capture of DM particles by the first generation of stars, Population III (Pop III) stars, by using the multiscatter capture formalism. Pop III stars are particularly good DM captors, since they form in DM-rich environments, at the center of DM minihalos, at redshifts z∼15. Assuming a DM-proton scattering cross section at the current deepest exclusion limits provided by the XENON1T experiment, we find that captured DM annihilations at the core of Pop III stars can lead, via the Eddington limit, to upper bounds in stellar masses that can be as low as a few solar masses if the ambient DM density at the location of the Pop III star is sufficiently high. Conversely, when Pop III stars are identified, one can use their observed mass to place bounds on dark matter properties. Using adiabatic contraction to estimate the ambient DM density in the environment surrounding Pop III stars, we place projected upper limits on the scattering cross section across a range of stellar masses and find bounds that are competitive with, or deeper than, those provided by the most sensitive current direct detection experiments for both spin-independent and spin-dependent (SD) interactions, for a wide range of DM masses. Most intriguingly, we find that Pop III stars could be used to probe the SD proton-DM cross section below the neutrino floor, i.e. the region of parameter space where DM direct detection experiments will soon become overwhelmed by neutrino backgrounds.

Recommended citation: C. Ilie, C. Levy, J. Pilawa, and S. Zhang. "Constraining Dark Matter properties with the first generation of stars." 2021. Phys. Rev. D 104, 123031. https://doi.org/10.1103/PhysRevD.104.123031

Multicomponent multiscatter capture of dark matter

Published in Physical Review D, 2021

In recent years, the usefulness of astrophysical objects as dark matter (DM) probes has become more and more evident, especially in view of null results from direct-detection and particle-production experiments. The potentially observable signatures of DM gravitationally trapped inside a star, or another compact astrophysical object, have been used to forecast stringent constraints on the nucleon–dark matter interaction cross section. Currently, the probes of interest are at high redshifts, Population III (Pop III) stars that form in isolation or in small numbers, in very dense DM minihalos at z∼15–40, and, in our own Milky Way, neutron stars, white dwarfs, brown dwarfs, exoplanets, etc. None of these objects are truly single component and, as such, capture rates calculated with the common assumption made in the literature of single-component capture, i.e., capture of DM by multiple scatterings with one single type of nucleus inside the object, are not accurate. In this paper, we present an extension of this formalism to multicomponent objects and apply it to Pop III stars, thereby investigating the role of He in the capture rates of Pop III stars. As expected, we find that the inclusion of the heavier He nuclei leads to an enhancement of the overall capture rates, further improving the potential of Pop III stars as dark matter probes.

Recommended citation: C. Ilie and C. Levy. "Multicomponent multiscatter capture of dark matter." 2021. Phys. Rev. D 104, 083033. https://doi.org/10.1103/PhysRevD.104.083033

Detecting Low Surface Brightness Galaxies with Mask R-CNN

Published in Machine Learning and the Physical Sciences Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS), 2021

Low surface brightness galaxies (LSBGs), galaxies that are fainter than the dark night sky, are famously difficult to detect. Nonetheless, studies of these galaxies are essential to improve our understanding of the formation and evolution of low-mass galaxies. In this work, we train a deep learning model using the Mask R-CNN framework on a set of simulated LSBGs inserted into images from the Dark Energy Survey (DES) Data Release 2 (DR2). This deep learning model is combined with several conventional image pre-processing steps to develop a pipeline for the detection of LSBGs. We apply this pipeline to the full DES DR2 coadd image dataset, and preliminary results show the detection of 22 large, high-quality LSBG candidates that went undetected by conventional algorithms. Furthermore, we find that the performance of our algorithm is greatly improved by including examples of false positives as an additional class during training.

Recommended citation: Machine Learning and the Physical Sciences Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS), Remote, Dec 2021. Levy, C., Drlica-Wagner, A., et al. "Detecting Low Surface Brightness Galaxies with Mask R-CNN". https://ml4physicalsciences.github.io/2021/files/NeurIPS_ML4PS_2021_111.pdf