Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representations

The generalization of machine learning (ML) models to out-of-distribution (OOD) examples remains a key challenge in extracting information from upcoming astronomical surveys. Interpretability approaches are a natural way to gain insights into the OOD generalization problem. We use Centered Kernel Alignment (CKA), a similarity measure metric of neural network representations, to examine the relationship between representation similarity and performance of pre-trained Convolutional Neural Networks (CNNs) on the CAMELS Multifield Dataset. We find that when models are robust to a distribution shift, they produce substantially different representations across their layers on OOD data. However, when they fail to generalize, these representations change less from layer to layer on OOD data. We discuss the potential application of similarity representation in guiding model design, training strategy, and mitigating the OOD problem by incorporating CKA as an inductive bias during training.Comment: Accepted to Machine Learning and the Physical Sciences Workshop, NeurIPS 202

Image Improvement and Restoration in Optical Time Series. I. The Method

Globular clusters (GCs) are considered strong candidates for hosting rogue (free-floating) planets. Since they are not bound to a star, they are undetectable by any traditional detection methods: transit, radial velocity, or direct imaging. Gravitational microlensing (ML), which causes transient brightening of background stars by passing foreground masses, is, on the other hand, an established method of detecting planets and proves promising for application in GCs. By employing the image subtraction technique, differential photometry on the time-series images of GCs could extract variability events, build light curves and inspect them for the presence of microlensing. However, instrumental anomalies and varying observing conditions over a long observational campaign period result in the distortion of stellar Point Spread Function (PSF), which affects the subtraction quality and leads to false-positive transient detection and large-scale noise structure in the subtracted images. We propose an iterative image reconstruction method as a modification to the Scaled Gradient Projection (SGP) algorithm, called the Flux-Conserving Scaled Gradient Projection (FC-SGP), to restore the shapes of stars while preserving their flux well within the photometrically accepted tolerance. We perform an extensive empirical comparative study of FC-SGP with different image restoration algorithms like the Richardson-Lucy (RL) and the original SGP algorithms, using several physically motivated metrics and experimental convergence analysis. We find that FC-SGP could be a promising approach for astronomical image restoration. In the future, we aim to extend its application to different image formats while maintaining the performance of the proposed algorithm.Comment: Submitted to MNRA

TCF periodogram's high sensitivity: A method for optimizing detection of small transiting planets

We conduct a methodological study for statistically comparing the sensitivities of two periodograms for weak signal planet detection in transit surveys: the widely used Box-Least Squares (BLS) algorithm following light curve detrending and the Transit Comb Filter (TCF) algorithm following autoregressive ARIMA modeling. Small depth transits are injected into light curves with different simulated noise characteristics. Two measures of spectral peak significance are examined: the periodogram signal-to-noise ratio (SNR) and a False Alarm Probability (FAP) based on the generalized extreme value distribution. The relative performance of the BLS and TCF algorithms for small planet detection is examined for a range of light curve characteristics, including orbital period, transit duration, depth, number of transits, and type of noise. The TCF periodogram applied to ARIMA fit residuals with the SNR detection metric is preferred when short-memory autocorrelation is present in the detrended light curve and even when the light curve noise had white Gaussian noise. BLS is more sensitive to small planets only under limited circumstances with the FAP metric. BLS periodogram characteristics are inferior when autocorrelated noise is present. Application of these methods to TESS light curves with small exoplanets confirms our simulation results. The study ends with a decision tree that advises transit survey scientists on procedures to detect small planets most efficiently. The use of ARIMA detrending and TCF periodograms can significantly improve the sensitivity of any transit survey with regularly spaced cadence.Comment: 30 pages, 13 figures, submitted to AAS Journal

Systematic analysis of jellyfish galaxy candidates in Fornax, Antlia, and Hydra from the S-PLUS survey: A self-supervised visual identification aid

© 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/We study 51 jellyfish galaxy candidates in the Fornax, Antlia, and Hydra clusters. These candidates are identified using the JClass scheme based on the visual classification of wide-field, twelve-band optical images obtained from the Southern Photometric Local Universe Survey. A comprehensive astrophysical analysis of the jellyfish (JClass > 0), non-jellyfish (JClass = 0), and independently organized control samples is undertaken. We develop a semi-automated pipeline using self-supervised learning and similarity search to detect jellyfish galaxies. The proposed framework is designed to assist visual classifiers by providing more reliable JClasses for galaxies. We find that jellyfish candidates exhibit a lower Gini coefficient, higher entropy, and a lower 2D Sérsic index as the jellyfish features in these galaxies become more pronounced. Jellyfish candidates show elevated star formation rates (including contributions from the main body and tails) by 1.75 dex, suggesting a significant increase in the SFR caused by the ram-pressure stripping phenomenon. Galaxies in the Antlia and Fornax clusters preferentially fall towards the cluster's centre, whereas only a mild preference is observed for Hydra galaxies. Our self-supervised pipeline, applied in visually challenging cases, offers two main advantages: it reduces human visual biases and scales effectively for large data sets. This versatile framework promises substantial enhancements in morphology studies for future galaxy image surveys.Peer reviewe

Yash-10/Periodogram-Comparison-Optimize-Planet-Detection: PCOSTPD: Periodogram Comparison for Optimizing Small Transiting Planet Detection

R code for comparing the sensitivity of the BLS and TCF transit periodogram algorithms to small exoplanets. It calculates the False Alarm Probability (FAP) based on extreme value theory and signal-to-noise ratio (SNR) metrics to quantify periodogram peak significance. The code can be extended for comparing any set of periodograms

galmask: A Python package for unsupervised galaxy masking

Galaxy morphological classification is a fundamental aspect of galaxy formation and evolution studies. Various machine learning tools have been developed for automated pipeline analysis of large-scale surveys, enabling a fast search for objects of interest. However, crowded regions in the image may pose a challenge as they can lead to bias in the learning algorithm. In this Research Note, we present galmask, an open-source package for unsupervised galaxy masking to isolate the central object of interest in the image. galmask is written in Python and can be installed from PyPI via the pip command.Comment: Submitted to RNAA

astropy/regions: v0.8

<p>See the <a href="https://astropy-regions.readthedocs.io/en/stable/changelog.html">changelog</a> for release notes.</p&gt

pasqal-io/Pulser: Release v0.16.2

<h2>Bugfix</h2> <ul> <li>Temporarily restrict scipy<1.12 to avoid <code>qutip</code> incompatiblity (#632)</li> </ul> <h2>Changelog</h2> <p>6925aaa Temporarily restrict scipy<1.12 to avoid <code>qutip</code> incompatiblity (#632)</p&gt

pasqal-io/Pulser: Release v0.15.3

<h2>Bugfix</h2> <ul> <li>Fixes the use of a Parametrized phase in EOM mode.</li> </ul> <h2>Changelog</h2> <p>e1dcf47 Release v15.3 (#611)</p&gt

The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package

International audienceThe Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates on the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project