Mr. Timothy L. Makinen Profile

Timothy L. Makinen

at Harvard-Smithsonian Ctr for Astrophysics

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 25 August 2022 Presentation + Paper

Searching for time-domain anomalies in high energy catalogs

Rafael Martínez-Galarza, Timothy Makinen

Proceedings Volume 12186, 121860J (2022) https://doi.org/10.1117/12.2629504

KEYWORDS: X-rays, Photons, Data modeling, Neural networks, X-ray astronomy, Astronomy, X-ray telescopes, X-ray sources, X-ray detectors, Sensors

Read Abstract +

The era of time-domain astronomy has brought new challenges and opportunities to X-ray observational astronomy. Recent discoveries indicate that fast extragalactic X-ray transients, planetary transits, and other exotic events can be extracted from rich X-ray telescope archives. Such anomalies have distinct signatures at the time series level, changes in patterns in the arrival times and energies of photons. Event files are inherently of variable length, posing an issue for data representation for deep learning applications. We compare two data representation methods for variable-length time series data: i) 2D histograms in the time and energy domain, and ii) connected graph representation. We train regression networks to a) predict posterior moments for global summaries of each time series and b) demonstrate an anomaly detection framework for unlabeled data. Trained networks are asked to predict archival summaries for a test data set, bypassing the need for an expensive processing pipeline. Outliers beyond a threshold in the predictive residual error distribution are flagged as potential anomalies for follow-up review. We test this method on a toy on-the-fly toy simulator and proceed to an application to real Chandra archival data. We present constructed Chandra graph and histogram data sets, as well as a toy model and regression network library written differentially in JAX, open-source tools for the X-ray community to enhance archival searches.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years