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Jorge Martinez-Palomera

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astro-ph.EP astro-ph.IM astro-ph.SR astro-ph.HE Machine Learning

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preprint2026arXiv

Trajectory-Agnostic Asteroid Detection in TESS with Deep Learning

We present a novel method for extracting moving objects from TESS data using machine learning. Our approach uses two stacked 3D U-Nets with skip connections, which we call a W-Net, to filter background and identify pixels containing moving objects in TESS image time-series data. By augmenting the training data through rotation of the image cubes, our method is robust to differences in speed and direction of asteroids, requiring no assumptions for either parameter range which are typically required in "shift-and-stack" type algorithms. We also developed a novel method for learned data scaling that we call Adaptive Normalization, which allows the neural network to learn the ideal range and scaling distribution required for optimal data processing. We built a code for creating TESS training data with asteroid masks that served as the foundation of our effort (tess-asteroid-ml), which we publicly released for the benefit of the community. Our method is not limited to TESS, but applicable for implementation in other similar time-domain surveys, making it of particular interest for use with data from upcoming missions such as the Nancy Grace Roman Space Telescope and NEOSurveyor.

preprint2021arXiv

Kepler Bonus: Aperture Photometry Light Curves of EXBA Sources

NASA's Kepler mission observed background regions across its field of view for more than three consecutive years using custom designed super apertures (EXBA masks). Since these apertures were designed to capture a region of the sky rather than single targets, the Kepler Science Data Processing pipeline produced Target Pixel Files, but did not produce light curves for the sources within these background regions. In this work we produce light curves for $9,327$ sources observed in the EXBA masks. These light curves are generated using aperture photometry estimated from the instrument's Pixel Response Function (PRF) profile computed from Kepler's full-frame images. The PRF models enable the creation of apertures that follow the characteristic shapes of the PSF in the image and the computation of flux completeness and contamination metrics. The light curves are available at MAST as a High Level Science Product (kbonus-apexba). Alongside this dataset, we present kepler-apertures, a Python library to compute PRF models and use them to perform aperture photometry on Kepler-like data. Using light curves from the EXBA masks we found an exoplanet candidate around Gaia EDR3 2077240046296834304 consistent with a large planet companion with a $0.81 R_J$ radius. Additionally, we report a catalog of 69 eclipsing binaries. We encourage the community to exploit this new dataset to perform in depth time domain analysis, such as eclipsing binaries demographic and others.

preprint2020arXiv

deepSIP: Linking Type Ia Supernova Spectra to Photometric Quantities with Deep Learning

We present {\tt deepSIP} (deep learning of Supernova Ia Parameters), a software package for measuring the phase and -- for the first time using deep learning -- the light-curve shape of a Type Ia supernova (SN~Ia) from an optical spectrum. At its core, {\tt deepSIP} consists of three convolutional neural networks trained on a substantial fraction of all publicly-available low-redshift SN~Ia optical spectra, onto which we have carefully coupled photometrically-derived quantities. We describe the accumulation of our spectroscopic and photometric datasets, the cuts taken to ensure quality, and our standardised technique for fitting light curves. These considerations yield a compilation of 2754 spectra with photometrically characterised phases and light-curve shapes. Though such a sample is significant in the SN community, it is small by deep-learning standards where networks routinely have millions or even billions of free parameters. We therefore introduce a data-augmentation strategy that meaningfully increases the size of the subset we allocate for training while prioritising model robustness and telescope agnosticism. We demonstrate the effectiveness of our models by deploying them on a sample unseen during training and hyperparameter selection, finding that Model~I identifies spectra that have a phase between $-10$ and 18\,d and light-curve shape, parameterised by $Δm_{15}$ , between 0.85 and 1.55\,mag with an accuracy of 94.6\%. For those spectra that do fall within the aforementioned region in phase-- $Δm_{15}$ space, Model~II predicts phases with a root-mean-square error (RMSE) of 1.00\,d and Model~III predicts $Δm_{15}$ values with an RMSE of 0.068\,mag.

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