Abstracts of Interest
Selected by:
Fedor Tairli
Abstract: 2408.08474
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Title:Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution
View PDF HTML (experimental)Abstract:Recent discoveries by neutrino telescopes, such as the IceCube Neutrino Observatory, relied extensively on machine learning (ML) tools to infer physical quantities from the raw photon hits detected. Neutrino telescope reconstruction algorithms are limited by the sparse sampling of photons by the optical modules due to the relatively large spacing ($10-100\,{\rm m})$ between them. In this letter, we propose a novel technique that learns photon transport through the detector medium through the use of deep learning-driven super-resolution of data events. These ``improved'' events can then be reconstructed using traditional or ML techniques, resulting in improved resolution. Our strategy arranges additional ``virtual'' optical modules within an existing detector geometry and trains a convolutional neural network to predict the hits on these virtual optical modules. We show that this technique improves the angular reconstruction of muons in a generic ice-based neutrino telescope. Our results readily extend to water-based neutrino telescopes and other event morphologies.
Abstract: 2408.08676
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Title:Fine-tuning LLMs for Autonomous Spacecraft Control: A Case Study Using Kerbal Space Program
View PDF HTML (experimental)Abstract:Recent trends are emerging in the use of Large Language Models (LLMs) as autonomous agents that take actions based on the content of the user text prompt. This study explores the use of fine-tuned Large Language Models (LLMs) for autonomous spacecraft control, using the Kerbal Space Program Differential Games suite (KSPDG) as a testing environment. Traditional Reinforcement Learning (RL) approaches face limitations in this domain due to insufficient simulation capabilities and data. By leveraging LLMs, specifically fine-tuning models like GPT-3.5 and LLaMA, we demonstrate how these models can effectively control spacecraft using language-based inputs and outputs. Our approach integrates real-time mission telemetry into textual prompts processed by the LLM, which then generate control actions via an agent. The results open a discussion about the potential of LLMs for space operations beyond their nominal use for text-related tasks. Future work aims to expand this methodology to other space control tasks and evaluate the performance of different LLM families. The code is available at this URL: \texttt{this https URL}.
Abstract: 2408.08873
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Title:Accelerating Giant Impact Simulations with Machine Learning
View PDF HTML (experimental)Abstract:Constraining planet formation models based on the observed exoplanet population requires generating large samples of synthetic planetary systems, which can be computationally prohibitive. A significant bottleneck is simulating the giant impact phase, during which planetary embryos evolve gravitationally and combine to form planets, which may themselves experience later collisions. To accelerate giant impact simulations, we present a machine learning (ML) approach to predicting collisional outcomes in multiplanet systems. Trained on more than 500,000 $N$-body simulations of three-planet systems, we develop an ML model that can accurately predict which two planets will experience a collision, along with the state of the post-collision planets, from a short integration of the system's initial conditions. Our model greatly improves on non-ML baselines that rely on metrics from dynamics theory, which struggle to accurately predict which pair of planets will experience a collision. By combining with a model for predicting long-term stability, we create an efficient ML-based giant impact emulator, which can predict the outcomes of giant impact simulations with a speedup of up to four orders of magnitude. We expect our model to enable analyses that would not otherwise be computationally feasible. As such, we release our full training code, along with an easy-to-use API for our collision outcome model and giant impact emulator.
Abstract: 2408.09303
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Title:Primordial black hole numbers: standard formulas and charts
View PDF HTML (experimental)Abstract:This brief note presents standard computations of primordial black hole mass $M$ given perturbations of scale $k$, and their late-time abundance $\Omega_\text{PBH}$ given their initial density fraction $\beta$. I recap the assumptions made in these computations and present formulas and reference charts useful for a working cosmologist.
Abstract: 2408.10897
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Title:Search for the Hawking radiation of primordial black holes: prospective sensitivity of LHAASO
View PDF HTML (experimental)Abstract:Primordial black holes (PBHs) with initial mass $\sim5\times10^{14}$ g are evaporating due to Hawking radiation, leading to bursts of very-high-energy gamma rays. In this work, we investigate the prospective sensitivity of the Large High Altitude Air Shower Observatory (LHAASO) to measure the local burst rate density of PBHs. Our findings reveal that LHAASO is capable of searching for the PBH bursts within a distance $\sim0.1$ pc from the sun and thereby measure the local burst rate density $\sim$ 1164 (or 699)$\,\mathrm{pc}^{-3}\,\mathrm{yr}^{-1}$ at $99\%$ confidence level during a 3 (or 5) year observing run. This stands for a sensitivity that is one order of magnitude stronger than the strongest observational constraint from the High Altitude Water Cherenkov Observatory (HAWC). In addition, we further suggest an observing strategy to search for the PBH bursts during upcoming observing runs of LHAASO.
Abstract: 2408.11166
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Title:The Discovery of Three Galactic Wolf-Rayet Stars
View PDF HTML (experimental)Abstract:Wolf-Rayet stars (WRs) are evolved massive stars in the brief stage before they undergo core collapse. Not only are they rare, but they also can be particularly difficult to find due to the high extinction in the Galactic plane. This paper discusses the discovery of three new Galactic WRs previously classified as H$\alpha$ emission stars, but thanks to Gaia spectra, we were able to identify the broad, strong emission lines that characterize WRs. Using the Lowell Discovery Telescope and the DeVeny spectrograph, we obtained spectra for each star. Two are WC9s, and the third is a WN6 + O6.5 V binary. The latter is a known eclipsing system with a 4.4 day period from ASAS-SN data. We calculate absolute visual magnitudes for all three stars to be between -7 and -6, which is consistent with our expectations of these subtypes. These discoveries highlight the incompleteness of the WR census in our local volume of the Milky Way and suggest the potential for future Galactic WR discoveries from Gaia low-dispersion spectra. Furthermore, radial velocity studies of the newly found binary will provide direct mass estimates and orbital parameters, adding to our knowledge of the role that binarity plays in massive star evolution.
Abstract: 2408.11353
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Title:Multi-messenger connection in high-energy neutrino astronomy
View PDF HTML (experimental)Abstract:Low fluxes of astrophysical neutrinos at TeV energies and the overwhelming background of atmospheric neutrinos below that, render the current paradigm of neutrino astronomy as a severely statistics limited one. While many hints have emerged, all the evidence gathered by IceCube and ANTARES, over the course of almost a decade and a half of operation, have fallen short of providing any conclusive answer to the puzzle of the origin of high-energy cosmic rays and neutrinos. The advancement of the field is thus closely associated with not only the neutrino observatories coming online in the next few years, but also on the coordinated efforts of the EM, GW and cosmic ray communities to develop dedicated channels and infrastructure that allows for swift and comprehensive multi-messenger follow-up of relevant events detected in any of the sectors. This paper highlights the strides that have been already taken in that direction and the fruits that they have born, as well as the challenges that lie ahead.
Abstract: 2408.11960
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Title:Application of Convolutional Neural Networks to time domain astrophysics. 2D image analysis of OGLE light curves
View PDF HTML (experimental)Abstract:In recent years the amount of publicly available astronomical data has increased exponentially, with a remarkable example being large scale multiepoch photometric surveys. This wealth of data poses challenges to the classical methodologies commonly employed in the study of variable objects. As a response, deep learning techniques are increasingly being explored to effectively classify, analyze, and interpret these large datasets. In this paper we use two-dimensional histograms to represent Optical Gravitational Lensing Experiment (OGLE) phasefolded light curves as images. We use a Convolutional Neural Network (CNN) to classify variable objects within eight different categories (from now on labels): Classical Cepheid (CEP), RR Lyrae (RR), Long Period Variable (LPV), Miras (M), Ellipsoidal Binary (ELL), Delta Scuti (DST), Eclipsing Binary (E), and spurious class with Incorrect Periods (Rndm). We set up different training sets to train the same CNN architecture in order to characterize the impact of the training. The training sets were built from the same source of labels but different filters and balancing techniques were applied. Namely: Undersampling (U), Data Augmentation (DA), and Batch Balancing (BB). The best performance was achieved with the BB approach and a training sample size of $\sim$370000 stars. Regarding computational performance, the image representation production rate is of $\sim$76 images per core per second, and the time to predict is $\sim$ 60$\, \mu\text{s}$ per star. The accuracy of the classification improves from $\sim$ 92%, when based only on the CNN, to $\sim$ 98% when the results of the CNN are combined with the period and amplitude features in a two step approach. This methodology achieves comparable results with previous studies but with two main advantages: the identification of miscalculated periods and the improvement in computational time cost.
Abstract: 2408.12563
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Full Text: [ PostScript, PDF]
Title:Climate Bistability at the Inner Edge of the Habitable Zone due to Runaway Greenhouse and Cloud Feedbacks
View PDF HTML (experimental)Abstract:Understanding the climate dynamics at the inner edge of the habitable zone (HZ) is crucial for predicting the habitability of rocky exoplanets. Previous studies using Global Climate Models (GCMs) have indicated that planets receiving high stellar flux can exhibit climate bifurcations, leading to bistability between a cold (temperate) and a hot (runaway) climate. However, the mechanism causing this bistability has not been fully explained, in part due to the difficulty associated with inferring mechanisms from small numbers of expensive numerical simulations in GCMs. In this study, we employ a two-column (dayside and nightside), two-layer climate model to investigate the physical mechanisms driving this bistability. Through mechanism-denial experiments, we demonstrate that the runaway greenhouse effect, coupled with a cloud feedback on either the dayside or nightside, leads to climate bistability. We also map out the parameters that control the location of the bifurcations and size of the bistability. This work identifies which mechanisms and GCM parameters control the stellar flux at which rocky planets are likely to retain a hot, thick atmosphere if they experience a hot start. This is critical for the prioritization of targets and interpretation of observations by the James Webb Space Telescope (JWST). Furthermore, our modeling framework can be extended to planets with different condensable species and cloud types.
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