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The observed magnifications and light curves of the quadruply imaged iPTF16geu supernova (SN) offers a unique opportunity to study a lens system with a variety of independent constraints. The four observed positions can be used to constrain the macrolens model. The magnifications and light curves at the four SN positions are more useful to constrain microlensing models. We define the macrolens model as a combination of a baryonic component that traces the observed light distribution, and a dark matter halo component. We constrained the macrolens model using the positional constraints given by the four observed images, and compared it with the best model obtained when magnification constraints were included. We found that the magnification cannot be explained by a macrolens model alone, and that contributions from substructures such as microlenses are needed to explain the observed magnifications. We considered microlens models based on the inferred stellar mass from the baryonic component of the macrolens model, and used the observed magnification and light curves to constrain the contribution from microlenses. We computed the likelihood of a variety of macro and micro lens models where we varied the dark matter halo, baryonic component, and microlens configurations. We used information about the position, magnification, and, for the first time, the light curves of the four observed SN images. We combined macrolens and microlens models in order to reproduce the observations; the four SN positions, magnifications, and lack of fluctuations in the light curves. After marginalizing over the model parameters, we found that larger stellar surface mass densities are preferred. This result suggests that the mass of the baryonic component is dominated by its stellar component. We conclude that microlensing from the baryonic component suffices to explain the observed flux ratios and light curves.
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Learning Optical Map in Liquid Xenon Detector with Poisson Likelihood Loss
Dual-phase liquid xenon time projection chambers (LXeTPC) have been successfully applied in rare event searches in astroparticle physics because of their ability to reach low backgrounds and detect small scintillation signals with photosensors. Accurate modeling of optical properties is essential for reconstructing particle interactions within these detectors as well as for developing data selection criteria. This is commonly achieved with discretized maps derived from Monte Carlo simulation or approximated with empirical analytical models. In this work, we employ a novel approach to this using a neural network trained with a Poisson log-likelihood ratio loss to model the mapping from light source location to the expected light intensity for each photosensor. We demonstrate its effectiveness by integrating it into a likelihood fitter for position reconstruction, simultaneously providing insights into the uncertainty associated with the reconstructed position.
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- PAR ID:
- 10487158
- Publisher / Repository:
- NeurIPS Workshops
- Date Published:
- Journal Name:
- NeurIPS Machine Learning in the Physical Sciences
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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