The aim of this work is to obtain precise atmospheric parameters and chemical abundances automatically for solar twins and analogs to find signatures of exoplanets, as well as to assess how peculiar the Sun is compared to these stars and to analyze any possible fine structures in the Galactic thin disk. We developed a neural network (NN) algorithm using Python to obtain these parameters for a sample of 99 solar twins and solar analogs previously studied in the literature from normalized high-quality spectra from HARPS, with a resolving power of R $\sim$ 115000 and a signal-to-noise ratio S/N > 400. We obtained precise atmospheric parameters and abundance ratios [X/Fe] of 20 chemical elements (Li, C, O, Na, Mg, Al, Si, S, Ca, Sc, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, Y, and Ba). The results are in line with the literature, with average differences and standard deviations of $(2 \pm 27)$ K for T$_{\rm eff}$, $(0.00 \pm 0.06)$ dex for log g, $(0.00 \pm 0.02)$ dex for [Fe/H], $(-0.01 \pm 0.05)$ km s$^{-1}$ for microturbulence velocity, $(0.02 \pm 0.08)$ km s$^{-1}$ for the macro turbulence velocity, and $(-0.12 \pm 0.26)$ km s$^{-1}$ for the projected rotational velocity (vsin$i$). Regarding the chemical abundances, most of the elements agree with the literature within 0.01 - 0.02 dex. The abundances were corrected from the effects of the Galactic chemical evolution and analyzed with the condensation temperature (T$_{\rm cond}$) to verify whether the stars presented depletion of refractories compared to volatiles. We found that the Sun is more depleted in refractory elements compared to volatiles than 89% of the studied solar analogs, with a significance of 9.5$\sigma$ when compared to the stars without detected exoplanets. We also found the possible presence of three subpopulations in the solar analogs: one Cu-rich, one Cu-poor, and the last one slightly older and poor in Na.
Increasing resolution and coverage of astrophysical and climate data necessitates increasingly sophisticated models, often pushing the limits of computational feasibility. While emulation methods can reduce calculation costs, the neural architectures typically used--optimised via gradient descent--are themselves computationally expensive to train, particularly in terms of data generation requirements. This paper investigates the utility of the Extreme Learning Machine (ELM) as a lightweight, non-gradient-based machine learning algorithm for accelerating complex physical models. We evaluate ELM surrogate models in two test cases with different data structures: (i) sequentially-structured data, and (ii) image-structured data. For test case (i), where the number of samples $N$ >> the dimensionality of input data $d$, ELMs achieve remarkable efficiency, offering a 100,000$\times$ faster training time and a 40$\times$ faster prediction speed compared to a Bi-Directional Recurrent Neural Network (BIRNN), whilst improving upon BIRNN test performance. For test case (ii), characterised by $d >> N$ and image-based inputs, a single ELM was insufficient, but an ensemble of 50 individual ELM predictors achieves comparable accuracy to a benchmark Convolutional Neural Network (CNN), with a 16.4$\times$ reduction in training time, though costing a 6.9$\times$ increase in prediction time. We find different sample efficiency characteristics between the test cases: in test case (i) individual ELMs demonstrate superior sample efficiency, requiring only 0.28% of the training dataset compared to the benchmark BIRNN, while in test case (ii) the ensemble approach requires 78% of the data used by the CNN to achieve comparable results--representing a trade-off between sample efficiency and model complexity.
The 21cm line from the spin-flip transition of neutral hydrogen (HI) provides a unique window into the Epoch of Reionization (EoR), the final phase transition of our Universe. The Square Kilometre Array (SKA) enables precise measurements of 21cm fluctuations that trace ionization, temperature, and density fluctuations of the intergalactic medium (IGM). Nevertheless, a direct reconstruction of the timeline of the EoR in terms of the progress of ionization remains an ongoing challenge due to the highly non-Gaussian nature and thus intractable likelihood of the 21cm signal. Here, we present EoRFlow, a simulation-based inference (SBI) framework for reconstructing the global neutral hydrogen fraction $x_{\mathrm{HI}}(z)$ directly from 2D cylindrically averaged power spectra (2DPS) of the 21cm signal. We validate our method on realistic mock datasets for SKA-Low. Bypassing the need for explicit likelihood formulations, our approach enables fast, unbiased posterior estimation of the $x_{\mathrm{HI}}$ evolution in narrow redshift slices, allowing for piecewise reconstruction of the global reionization history. By directly inferring the reionization history from 21cm power spectra, our framework provides a scalable and robust path forward for 21cm cosmology in the SKA era.
Large language models (LLMs) have shown strong capabilities in complex reasoning, and test-time scaling techniques can enhance their performance with comparably low cost. Many of these methods have been developed and evaluated on mathematical reasoning benchmarks such as AIME. This paper investigates whether the lessons learned from these benchmarks generalize to the domain of advanced theoretical physics. We evaluate a range of common test-time scaling methods on the TPBench physics dataset and compare their effectiveness with results on AIME. To better leverage the structure of physics problems, we develop a novel, symbolic weak-verifier framework to improve parallel scaling results. Our empirical results demonstrate that this method significantly outperforms existing test-time scaling approaches on TPBench. We also evaluate our method on AIME, confirming its effectiveness in solving advanced mathematical problems. Our findings highlight the power of step-wise symbolic verification for tackling complex scientific problems.