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公式

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e^iπ + 1 = 0

∫₀^∞e^−x² dx =√π2

main.tex

\documentclass{article}
\usepackage{amsmath}
\begin{document}
\begin{align*}
  e^{i\pi} + 1 &= 0 \\[1ex]
  \int_{0}^{\infty} e^{-x^2} dx &= \frac{\sqrt{\pi}}{2}
\end{align*}
\end{document}

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letx.app/editor/exoplanet-thesis

Exoplanet Detection Thesis刚刚编辑

main.tex

1% Exoplanet & Star Detection, main.tex
2\documentclass[11pt]{article}
3\usepackage{graphicx, amsmath, wrapfig}
4
5\title{Synergistic ML \& CV for Exoplanet Detection}
6\author{Shihab Shahriar Antor \and Ashraful Kabir Alif}
7
8\begin{document}
9\maketitle
10
11\begin{abstract}
12  We detect faint exoplanets in JWST/MIRI imagery.
13\end{abstract}
14
15\section{Introduction}Alif
16Direct imaging is limited by \emph{stellar glare}.Shihab
17\begin{wrapfigure}{r}{0.45\linewidth}
18  \includegraphics[width=\linewidth]{jwst-miri}
19  \caption{JWST/MIRI direct imaging.}
20\end{wrapfigure}
21
22\section{Method}
23We train a contrastive objective:
24\begin{equation}
25  \mathcal{L} = -\log \frac{e^{s^+}}{\sum e^{s_i}}
26\end{equation}
27\section{Results}
28\end{document}

无问题行 16，列 5465 个字符LaTeX

pdfLaTeX1 / 4100%

Synergistic Application of Machine Learning and Computer Vision for the Detection of Exoplanets and Stars

Shihab Shahriar Antor Ashraful Kabir Alif

Department of Computer Science, BRAC University

Abstract— We present a framework that detects faint exoplanets in JWST/MIRI coronagraph imagery using contrastive and few-shot learning, separating planetary signals from bright host stars under low signal-to-noise. On held-out fields it outperforms CNN and GNN baselines while using far fewer labels.

Keywords: exoplanets, direct imaging, contrastive learning, few-shot learning, JWST

1. Introduction

Figure 1: JWST/MIRI direct imaging of a faint exoplanet (b) beside its host star.

Direct imaging of exoplanets is fundamentally limited by the overwhelming glare of the host star. Modern coronagraphs on JWST/MIRI suppress this stellar light, yet faint planetary signals remain buried in structured noise. As shown in Figure 1, the planet sits within the diffraction pattern of its star, making detection a needle-in-a-haystack problem. We cast this as a representation-learning task and show that contrastive objectives recover signals that pixel-wise classifiers miss.

2. Method

We learn a discriminative embedding with a contrastive objective, pulling together augmented views of one source and pushing apart star and planet candidates:

𝓛= −loge^s⁺Σ e^s_i(1)

A few-shot head then adapts to new instruments from a handful of labelled detections, which is critical given the scarcity of confirmed direct-imaging targets.

3. Results

On held-out fields the model recovers 92% of injected planets at a false-positive rate below 0.1 per frame, outperforming CNN and GNN baselines by 14 and 9 points of recall respectively.

Preprint, drafted collaboratively on LetXJune 2026

Exoplanet Thesis

main.tex

Synergistic Application of Machine Learning and Computer Vision for the Detection of Exoplanets and Stars

Shihab Shahriar Antor Ashraful Kabir Alif

Department of Computer Science, BRAC University

Keywords: exoplanets, direct imaging, contrastive learning, few-shot learning, JWST

1. Introduction

Figure 1: JWST/MIRI direct imaging of a faint exoplanet (b) beside its host star.

2. Method

We learn a discriminative embedding with a contrastive objective, pulling together augmented views of one source and pushing apart star and planet candidates:

𝓛= −loge^s⁺Σ e^s_i(1)

A few-shot head then adapts to new instruments from a handful of labelled detections, which is critical given the scarcity of confirmed direct-imaging targets.

3. Results

On held-out fields the model recovers 92% of injected planets at a false-positive rate below 0.1 per frame, outperforming CNN and GNN baselines by 14 and 9 points of recall respectively.

Preprint, drafted collaboratively on LetXJune 2026

无问题行 16，列 5465 个字符LaTeX

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Direct Imaging of Exoplanets

Coronagraphs on JWST/MIRI suppress the host star’s glare, yet faint planetary signals stay buried in structured stellar noise.

We recover them with contrastive and few-shot learning, separating planet candidates from the star even at very low signal-to-noise.

已保存 · 版本历史

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\documentclass[11pt]{article}
\usepackage{graphicx}
\begin{document}
\section{Results}
Our model reaches 94.2\% accuracy on the
held-out set; the baseline reaches 89.7\%.
F1 was 0.94 against 0.88.
Training converged after 40 epochs.
\end{document}