Domain Adaptation for Simulation-Based Dark Matter Searches Using Strong Gravitational Lensing

Abstract

Clues to the identity of dark matter have remained surprisingly elusive, given the scope of experimental programs aimed at its identification. While terrestrial experiments may be able to nail down a model, an alternative, and equally promising, method is to identify dark matter based on astrophysical or cosmological signatures. A particularly sensitive approach is based on the unique signature of dark matter substructure on galaxy-galaxy strong lensing images. Machine learning applications have been explored in detail for extracting just this signal. With limited availability of high quality strong lensing data, these approaches have exclusively relied on simulations. Naturally, due to the differences with the real instrumental data, machine learning models trained on simulations are expected to lose accuracy when applied to real data. This is where domain adaptation can serve as a crucial bridge between simulations and real data applications. In this work, we demonstrate the power of domain adaptation techniques applied to strong gravitational lensing data with dark matter substructure. We show with simulated data sets of varying complexity, that domain adaptation can significantly mitigate the losses in the model performance. This technique can help domain experts build and apply better machine learning models for extracting useful information from strong gravitational lensing data expected from the upcoming surveys.

Galaxy Spin Classification. I. Z-wise versus S-wise Spirals with the Chirality Equivariant Residual Network

“…We note that in principle, one can implement a more advanced equivariant network that manifestly accounts for all the relevant symmetries (e.g., Sosnovik et al 2019;Weiler & Cesa 2019;Zhang 2019;Cesa et al 2022). Also, domain adaptation techniques may help improve the performance when the model needs to be applied to data from different surveys (Ben-David et al 2010;Alexander et al 2021). We leave these directions for future research, since our current architecture already works well in the various benchmarks demonstrated in this paper.…”

Locating Hidden Exoplanets in ALMA Data Using Machine Learning

“…Any differences in performance of the models between the codes, e.g., if the models consistently performed better on SPH data, would be difficult to interpret, and the cause could be hard to identify. It is possible that the method of domain adaptation (Ben-David et al 2010), which has already found success in astronomy (Vilalta et al 2019;Alexander et al 2021;Ćiprijanović et al 2022), could be used to encourage the models to overcome any differences between data sets. This is an avenue that is ripe for exploration in future work.…”

Machine Learning and Cosmology

“…Further work [10][11][12] proposed applying parameter inference and uncertainty quantification methods in order to characterize the properties of lensed sources and lensing galaxies. More recently, with an eye towards the large sample of gravitational lenses that will be imaged by forthcoming cosmological surveys like Euclid and LSST, there has been significant effort towards understanding how to utilize machine learning to optimally exploit this data towards source/lens characterization [13][14][15][16], Hubble constant inference [17], and characterization of dark matter substructure within the lensing galaxies [18][19][20][21][22][23][24][25][26][27][28][29] in a scalable manner.…”