AC07
Choose when to observe a changing sky
A star keeps changing between camera exposures. Learn which periods can imitate its sampled brightness, how a short flash slips through the gaps, and how an observed OGLE light curve tests the search.
L1
Why a telescope can give a star the wrong period
Regular observing gaps let different rhythms fit the same star. Changing the visit times can expose the hidden alternatives.
L2
Count the cycles between exposures
Derive regular-sampling aliases, a sampling window and exposure averaging, then calculate how observing gaps hide a short flash.
L3
Fit a period and measure the risk of a false peak
Derive a weighted floating-mean search, calibrate its full-grid maximum and separate signal detection from choosing the correct alias.
L3
Search an observed OGLE variable star and count the aliases
Replay a frozen RR Lyrae light curve, inspect its observing window, calibrate full-search false alarms and measure injected-period recovery.
L4
Follow an alert from first exposure to the next telescope
Read pinned ZTF and Fink papers, separate detection latency from classification, and inspect how follow-up selection changes a training sample.
Sources and calculation scope
- VanderPlas, Understanding the Lomb–Scargle Periodogram, arXiv v1
28 March 2017; Sections 3.2/4.2 and Figures 7–14 for windows and aliases; Sections 6.1–6.2 for weighted floating mean; Section 7.1 and 7.4.2/Figure 26 for grid and FAP. Published journal counterpart: ApJS 236,16 (2018). This guide pins the arXiv version.
- Zechmeister & Kürster, generalized Lomb–Scargle, arXiv v1
16 January 2009, Section 2 equations 4–18 and Appendix A.1: weighted sine/cosine/offset normal equations and normalized chi-square reduction. The replay uses orthogonal projections as an independent numerical formulation.
- Astropy 8.0.2 Lomb–Scargle documentation
Consulted 28 October 2026; page updated 15 October 2026. Additional Arguments, standard normalization, peak significance and RR Lyrae example. Default floating mean, same-times/same-grid FAP, and no general average-Nyquist cutoff for irregular data.
- OGLE-IV LMC RR Lyrae official data README
Column definitions for HJD minus 2450000, I-band magnitude and uncertainty; public-data citation requirement. Frozen per-object file OGLE-LMC-RRLYR-00001.dat and catalog row are retained with SHA-256 and HTTP metadata.
- Soszyński et al., OGLE Magellanic RR Lyrae catalog
Version 2,14 July 2016; Acta Astronomica 66,131. Catalog attribution and data context; exact tutorial values come from the archived measured file and the selected RRab catalog row, not a generated curve.
- Patterson et al., ZTF Alert Distribution System, arXiv v1
6 February 2019; III.3,IV,V, Table 1 and Figure 2. Packet/filter/distribution stages and explicitly dated performance, with different latency reference points. These numbers are not presented as current infrastructure measurements.
- Fraga et al., Transient classifiers for Fink, arXiv v2
29 November 2024; A&A 692,A208. Sections2,4,6,7: simulated ELAsTiCC benchmark, precision/recall and classifier combination. Benchmark performance is distinct from real-survey calibrated class probabilities.
- Möller et al., real-time active-learning follow-up, arXiv v1
Pinned v1,26 February2025; Sections4.1–4.5,5.1–5.2,6 and Figure1. Real 2023–2024 ZTF/Fink/ANU campaign, selection and label-update loop. Related accepted journal article DOI10.1017/pasa.2025.20; v2 is12March2025. Proposed automated earlier follow-up is identified as a proposal.