Science Space Deep Space Space Telescope Two neutron stars may have formed the first known'superkilonova' The historic explosion was 1.3 billion light-years away from Earth. Breakthroughs, discoveries, and DIY tips sent every weekday. A double blast of dying stars may be the first observed case of a long-hypothesized, never proven "superkilonova." Although astronomers are still searching for concrete answers, a study published in may detail the historic explosion about 1.3 billion light-years from Earth. Most of the universe's massive stars end their lives in a blaze of glory as supernovae, but that's not always the case.

We study the performance of the Topological Uncertainty (TU) constructed with a trained feedforward neural network (FNN) for Anomaly Detection. Generally, meaningful information can be stored in the hidden layers of the trained FNN, and the TU implementation is one tractable recipe to extract buried information by means of the Topological Data Analysis. We explicate the concept of the TU and the numerical procedures. Then, for a concrete demonstration of the performance test, we employ the Neutron Star data used for inference of the equation of state (EoS). For the training dataset consisting of the input (Neutron Star data) and the output (EoS parameters), we can compare the inferred EoSs and the exact answers to classify the data with the label $k$. The subdataset with $k=0$ leads to the normal inference for which the inferred EoS approximates the answer well, while the subdataset with $k=1$ ends up with the unsuccessful inference. Once the TU is prepared based on the $k$-labled subdatasets, we introduce the cross-TU to quantify the uncertainty of characterizing the $k$-labeled data with the label $j$. The anomaly or unsuccessful inference is correctly detected if the cross-TU for $j=k=1$ is smaller than that for $j=0$ and $k=1$. In our numerical experiment, for various input data, we calculate the cross-TU and estimate the performance of Anomaly Detection. We find that performance depends on FNN hyperparameters, and the success rate of Anomaly Detection exceeds $90\%$ in the best case. We finally discuss further potential of the TU application to retrieve the information hidden in the trained FNN.

Breakthroughs, discoveries, and DIY tips sent every weekday. While the Hubble and James Webb Space Telescopes continue to offer astronomers revolutionary glimpses of our universe, their upcoming sibling may very well upstage them. Scheduled to launch in 2027, NASA's Nancy Grace Roman Space Telescope is designed with a field of view at least 100 times larger than Hubble's, with the potential to document light from over a billion galaxies over its career. Combined with timelapse recording capabilities, Roman will help researchers to better understand exoplanets, infrared astrophysics, and the nature of dark matter. According to a study published on July 15 in The Astrophysics Journal, Roman is poised to eventually capture an estimated 100,000 celestial explosions over its lifetime.

Breakthroughs, discoveries, and DIY tips sent every weekday. Nothing is permanent--not even the universe itself. At least, that's what current models of physics tell us about the nature of existence. Luckily for humanity, most astrophysicists' estimates don't have the universe's grand finale scheduled for around 10¹¹⁰⁰ years (that's a 1 followed by 1,100 zeros). However, based on new calculations that include the peculiar nature of certain black hole particles, the universe's curtains may fall much sooner than expected--cosmically speaking.

The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from complex data sets. This is particularly relevant for quantum chromodynamics (QCD), the theory of strong interactions, with its inherent limitations in observational data and demanding computational approaches. This perspective highlights advances and potential of physics-driven learning methods, focusing on predictions of physical quantities towards QCD physics, and drawing connections to machine learning(ML). It is shown that the fusion of ML and physics can lead to more efficient and reliable problem-solving strategies. Key ideas of ML, methodology of embedding physics priors, and generative models as inverse modelling of physical probability distributions are introduced. Specific applications cover first-principle lattice calculations, and QCD physics of hadrons, neutron stars, and heavy-ion collisions. These examples provide a structured and concise overview of how incorporating prior knowledge such as symmetry, continuity and equations into deep learning designs can address diverse inverse problems across different physical sciences.

In 1977, the Ohio State University's Big Ear radio telescope captured a signal from space so strange that scientists are still baffled by it almost 50 years later. For decades, scientists have struggled to find any natural process capable of producing the 72-second burst which prompted astronomer Jerry Ehman to write'WOW!' on the telescope's readout. Now, new analysis of the so-called WOW! signal has revealed that it might have been caused by a hugely powerful laser slamming into Earth. Experts say this was not the first salvo of an alien invasion, but rather the entirely natural product of a rare alignment between a collapsed star and a cloud of cool hydrogen. Unfortunately for alien-hunters, scientists from the University of Puerto Rico at Arecibo say this new evidence shows that the WOW! signal is not evidence of life beyond Earth.

Mergers of binary neutron stars (BNSs) emit signals in both the gravitational-wave (GW) and electromagnetic (EM) spectra. Famously, the 2017 multi-messenger observation of GW170817 led to scientific discoveries across cosmology, nuclear physics, and gravity. Central to these results were the sky localization and distance obtained from GW data, which, in the case of GW170817, helped to identify the associated EM transient, AT 2017gfo, 11 hours after the GW signal. Fast analysis of GW data is critical for directing time-sensitive EM observations; however, due to challenges arising from the length and complexity of signals, it is often necessary to make approximations that sacrifice accuracy. Here, we develop a machine learning approach that performs complete BNS inference in just one second without making any such approximations. This is enabled by a new method for explicit integration of physical domain knowledge into neural networks. Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger; (ii) improved localization precision by $\sim30\%$ compared to approximate low-latency methods; and (iii) detailed information on luminosity distance, inclination, and masses, which can be used to prioritize expensive telescope time. Additionally, the flexibility and reduced cost of our method open new opportunities for equation-of-state and waveform systematics studies. Finally, we demonstrate that our method scales to extremely long signals, up to an hour in length, thus serving as a blueprint for data analysis for next-generation ground- and space-based detectors.

We combine pulsar population synthesis with simulation-based inference to constrain the magneto-rotational properties of isolated Galactic radio pulsars. We first develop a flexible framework to model neutron-star birth properties and evolution, focusing on their dynamical, rotational and magnetic characteristics. In particular, we sample initial magnetic-field strengths, $B$, and spin periods, $P$, from log-normal distributions and capture the late-time magnetic-field decay with a power law. Each log-normal is described by a mean, $\mu_{\log B}, \mu_{\log P}$, and standard deviation, $\sigma_{\log B}, \sigma_{\log P}$, while the power law is characterized by the index, $a_{\rm late}$, resulting in five free parameters. We subsequently model the stars' radio emission and observational biases to mimic detections with three radio surveys, and produce a large database of synthetic $P$-$\dot{P}$ diagrams by varying our input parameters. We then follow a simulation-based inference approach that focuses on neural posterior estimation and employ this database to train deep neural networks to directly infer the posterior distributions of the five model parameters. After successfully validating these individual neural density estimators on simulated data, we use an ensemble of networks to infer the posterior distributions for the observed pulsar population. We obtain $\mu_{\log B} = 13.10^{+0.08}_{-0.10}$, $\sigma_{\log B} = 0.45^{+0.05}_{-0.05}$ and $\mu_{\log P} = -1.00^{+0.26}_{-0.21}$, $\sigma_{\log P} = 0.38^{+0.33}_{-0.18}$ for the log-normal distributions, and $a_{\rm late} = -1.80^{+0.65}_{-0.61}$ for the power law at $95\%$ credible interval. Our approach represents a crucial step towards robust statistical inference for complex population-synthesis frameworks and forms the basis for future multi-wavelength analyses of Galactic pulsars.

If aliens were to contact Earth, what would it sound like? Such a scenario has been imagined countless times in science fiction but in reality we have no proof extraterrestrials even exist. That hasn't dampened the excitement that an advanced civilisation might be out there, however, and the discovery of a mysterious stellar object which emits a five-minute pulse every 22 minutes will only serve to intensify that. What's more, the scientists who detected it aren't 100 per cent sure what it is. An international team of astronomers led by Australia's Curtin University think it could be an ultra-long period magnetar -- a rare type of star with the most powerful known magnetic fields in the universe.

Born and raised in Shanghai, Tan came to MIT to study high-energy astrophysics and wrote his dissertation on computational modeling of neutron stars. "Coming from China at that time, I had very little experience with computers," he says. "I was fortunate to find many helpful students during my time there." Tan also met his wife, Hong (Zhang) Tan, SM '88, PhD '96, at MIT. The pair were married in the MIT Chapel and today have two sons.