skip to main content
10.1145/3555776.3577606acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
research-article

Elastic Data Binning for Transient Pattern Analysis in Time-Domain Astrophysics

Published: 07 June 2023 Publication History

Abstract

Time-domain astrophysics analysis (TDAA) involves observational surveys of celestial phenomena that may contain irrelevant information because of several factors, one of which is the sensitivity of the optical telescopes. Data binning is a typical technique for removing inconsistencies and clarifying the main characteristic of the original data in astrophysics analysis. It splits the data sequence into smaller bins with a fixed size and subsequently sketches them into a new representation form. In this study, we introduce a novel approach, called elastic data binning (EBinning), to automatically adjust each bin size using two statistical metrics based on the Student's t-test for linear regression and Hoeffding inequality. We demonstrate the successful representation of various characteristics in the light curve data gathered from the Kiso Schmidt telescope using our approach and the applicability of our approach for transient pattern analysis using real world data.

References

[1]
C. C. Aggarwal. 2017. An Introduction to Outlier Analysis. Springer International Publishing, Cham.
[2]
M. Aizawa et al. 2022. Fast optical flares from M dwarfs detected by a one-second-cadence survey with Tomo-e Gozen. PASJ 74, 5 (Aug. 2022), 1069--1094.
[3]
A. Blázquez-García, A. Conde, U. Mori, and J. A. Lozano. 2021. A Review on Outlier/Anomaly Detection in Time Series Data. ACM Comput. Surv. 54, 3, Article 56 (Apr 2021), 33 pages.
[4]
G. Chiarot and C. Silvestri. 2021. Time series compression: a survey. CoRR abs/2101.08784 (2021). arXiv:2101.08784 https://arxiv.org/abs/2101.08784
[5]
G. Cormode. 2022. Current Trends in Data Summaries. SIGMOD Rec. 50, 4 (Jan 2022), 6--15.
[6]
P. Esling and C. Agon. 2012. Time-Series Data Mining. ACM Comput. Surv. 45, 1, Article 12 (Dec 2012), 34 pages.
[7]
Astropy Collaboration et al. 2018. The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package. The Astronomical Journal 156, 3 (Aug 2018), 123.
[8]
J. Gama, I. Žliobaitundefined, A. Bifet, M. Pechenizkiy, and A. Bouchachia. 2014. A Survey on Concept Drift Adaptation. ACM Comput. Surv. 46, 4, Article 44 (March 2014), 37 pages.
[9]
S. Gharghabi et al. 2017. Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels. In 2017 IEEE International Conference on Data Mining (ICDM). 117--126.
[10]
G. Helou and C. A. Beichman. 1990. The confusion limits to the sensitivity of submillimeter telescopes. In Liege International Astrophysical Colloquia (Liege International Astrophysical Colloquia), B. Kaldeich (Ed.), Vol. 29. 117--123.
[11]
E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra. 2001. Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowledge and Information Systems 3, 3 (01 Aug 2001), 263--286.
[12]
T. Kim and C.H. Park. 2020. Anomaly pattern detection for streaming data. Expert Systems with Applications 149 (2020), 113252.
[13]
J. Lin, E. Keogh, S. Lonardi, and B. Chiu. 2003. A Symbolic Representation of Time Series, with Implications for Streaming Algorithms (DMKD '03). Association for Computing Machinery, New York, NY, USA, 2--11.
[14]
Lott, B., Escande, L., Larsson, S., and Ballet, J. 2012. An adaptive-binning method for generating constant-uncertainty/constant-significance light curves with Fermi-LAT data. A&A 544 (2012), A6.
[15]
S. Malinowski, T. Guyet, R. Quiniou, and R. Tavenard. 2013. 1d-SAX: A Novel Symbolic Representation for Time Series. In Advances in Intelligent Data Analysis XII, A. Tucker, F. Höppner, A. Siebes, and S. Swift (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 273--284.
[16]
J. R. Martínez-Galarza et al. 2021. A method for finding anomalous astronomical light curves and their analogues. Monthly Notices of the Royal Astronomical Society 508, 4 (Sep 2021), 5734--5756.
[17]
T. Phungtua-Eng, Y. Yamamoto, and S. Sako. 2021. Detection for Transient Patterns with Unpredictable Duration using Chebyshev Inequality and Dynamic Binning. In 2021 Ninth International Symposium on Computing and Networking Workshops (CANDARW). 454--458.
[18]
T. Phungtua-Eng, Y. Yamamoto, and S. Sako. 2021. Dynamic Binning for the Unknown Transient Patterns Analysis in Astronomical Time Series. In 2021 IEEE International Conference on Big Data (Big Data). 5988--5990.
[19]
T. Phungtua-Eng, Y. Yamamoto, and S. Sako. 2022. Supplementary material. https://sites.google.com/view/elasticdatabinning
[20]
U. Rebbapragada, P. Protopapas, C. E. Brodley, and C. Alcock. 2009. Finding anomalous periodic time series. Machine Learning 74, 3 (01 Mar 2009), 281--313.
[21]
Khalid S. 2006. 1 - Introduction. In Introduction to Data Compression (Third Edition) (third edition ed.), Khalid S. (Ed.). Morgan Kaufmann, Burlington, 1--11.
[22]
G. Shevlyakov and M. Kan. 2020. Stream Data Preprocessing: Outlier Detection Based on the Chebyshev Inequality with Applications. In 2020 26th Conference of Open Innovations Association (FRUCT). 402--407.
[23]
R. Sulo, T. Berger-Wolf, and R. Grossman. 2010. Meaningful Selection of Temporal Resolution for Dynamic Networks. In Proceedings of the Eighth Workshop on Mining and Learning with Graphs (MLG '10). Association for Computing Machinery, New York, NY, USA, 127--136.
[24]
B. D. Warner. 2016. A Practical Guide to Lightcurve Photometry and Analysis (2nd ed. ed.). Springer Cham, Cham, Switzerland.
[25]
B. L. Welch. 1938. The Significance of the Difference Between Two Means when the Population Variances are Unequal. Biometrika 29, 3/4 (1938), 350--362.
[26]
C. M. Yeh et al. 2016. Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets. In 2016 IEEE 16th International Conference on Data Mining (ICDM). 1317--1322.
[27]
Y. Zhu et al. 2016. Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins. In 2016 IEEE 16th International Conference on Data Mining (ICDM). 739--748.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
March 2023
1932 pages
ISBN:9781450395175
DOI:10.1145/3555776
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 June 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. data binning
  2. data sketching
  3. hoeffding inequality
  4. student's t-test
  5. lightcurve

Qualifiers

  • Research-article

Conference

SAC '23
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 62
    Total Downloads
  • Downloads (Last 12 months)10
  • Downloads (Last 6 weeks)1
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media