Data Reduction Pipeline Research Articles

Robust Support for Semi-automated Reductions of Keck/NIRSPEC Data Using PypeIt

We present a data reduction pipeline (DRP) for Keck/Near InfraRed SPECtrograph (NIRSPEC) built as an addition to the PypeIt Python package. The DRP is capable of reducing multi-order echelle data taken both before and after the detector upgrade in 2018. As part of developing the pipeline, we implemented major improvements to the capabilities of the PypeIt package, including manual wavelength calibration for multi-order data and new output product that returns a coadded spectrum order-by-order. We also provide a procedure for correcting telluric absorption in NIRSPEC data by using the spectra of telluric standard stars taken near the time of the science spectra. At high resolutions, this is often more accurate than modeling-based approaches.

Robust and automatic beamstop shadow outlier rejection: combining crystallographic statistics with modern clustering under a semi-supervised learning strategy.

During the automatic processing of crystallographic diffraction experiments, beamstop shadows are often unaccounted for or only partially masked. As a result of this, outlier reflection intensities are integrated, which is a known issue. Traditional statistical diagnostics have only limited effectiveness in identifying these outliers, here termed Not-Excluded-unMasked-Outliers (NEMOs). The diagnostic tool AUSPEX allows visual inspection of NEMOs, where they form atypical pattern: clusters at the low-resolution end of the AUSPEX plots of intensities or amplitudes versus resolution. To automate NEMO detection, a new algorithm was developed by combining data statistics with a density-based clustering method. This approach demonstrates a promising performance in detecting NEMOs in merged data sets without disrupting existing data-reduction pipelines. Re-refinement results indicate that excluding the identified NEMOs can effectively enhance the quality of subsequent structure-determination steps. This method offers a prospective automated means to assess the efficacy of abeamstop mask, as well as highlighting the potential of modern pattern-recognition techniques for automating outlier exclusion during data processing, facilitating future adaptation to evolving experimental strategies.

FAST Ultra-Deep Survey: Data Release for FUDS0

We have used the Five-hundred-meter Aperture Spherical radio Telescope (FAST) to conduct a blind ultradeep survey for neutral hydrogen (H i). We present the complete results from the first of six fields (FUDS0). These observations of 95 hr allowed us to achieve a high-sensitivity (∼50 μJy beam−1) and a high-frequency resolution (22.9 kHz) over an area of 0.72 deg2. We detected 128 galaxies in H i distributed over the redshift range of 0 < z < 0.4, with H i masses in the range of 6.67≤log(MHI/h70−2M⊙)≤10.92 , and three faint high-velocity clouds, with a peak column density of N H i ≤ 3.1 × 1017 cm−2. Of the galaxies, 95 are new detections and six have z > 0.38, where no unlensed H i emission has previously been directly detected. Estimates of completeness and reliability are presented for the catalog. The consistency of continuum and H i flux estimates with the NRAO VLA Sky Survey and the Arecibo Ultra-Deep Survey, respectively, confirms the accuracy of the calibration method and data reduction pipeline developed for the full FAST Ultra-Deep Survey.

HST SHEL: Enabling Comparative Exoplanetology with HST/STIS

The Hubble Space Telescope (HST) has been our most prolific tool to study exoplanet atmospheres. As the age of JWST begins, there are a wealth of HST archival data that are useful to strengthen our inferences from JWST. Notably, HST/Space Telescope Imaging Spectrograph (STIS), with its 0.3–1 μm wavelength coverage, extends past JWST’s 0.6 μm wavelength cutoff and holds an abundance of potential information: alkali (Na, K) and molecular (TiO, VO) species opacities, aerosol information, and the presence of stellar contamination. However, time-series observations with HST suffer from significant instrumental systematics and can be highly dependent on choices made during the transit fitting process. This makes comparing transmission spectra of planets with different data reduction methodologies challenging, as it is difficult to discern whether an observed trend is caused by differences in data reduction or underlying physical processes. Here we present the Sculpting Hubble’s Exoplanet Legacy (SHEL) program, which aims to build a consistent data reduction and light-curve analysis methodology and associated database of transmission spectra from archival HST observations. In this paper, we present the SHEL analysis framework for HST/STIS and its low-resolution spectroscopy modes, G430L and G750L. We apply our methodology to four notable hot Jupiters, WASP-39 b, WASP-121 b, WASP-69 b, and WASP-17 b, and use these examples to discuss nuances behind analysis with HST/STIS. Our results for WASP-39 b, WASP-121 b, and WASP-17 b are consistent with past publications, but our analysis of WASP-69 b differs and shows evidence of either a strong scattering slope or stellar contamination. The data reduction pipeline and tutorials are available on Github and Zenodo.

Photometry of Saturated Stars with Neural Networks

We use a multilevel perceptron (MLP) neural network to obtain photometry of saturated stars in the All-Sky Automated Survey for Supernovae (ASAS-SN). The MLP can obtain fairly unbiased photometry for stars from g ≃ 4 to 14 mag, particularly compared to the dispersion (15%–85% 1σ range around the median) of 0.12 mag for saturated (g < 11.5 mag) stars. More importantly, the light curve of a nonvariable saturated star has a median dispersion of only 0.037 mag. The MLP light curves are, in many cases, spectacularly better than those provided by the standard ASAS-SN pipelines. While the network was trained on g-band data from only one of ASAS-SN’s 20 cameras, initial experiments suggest that it can be used for any camera and the older ASAS-SN V-band data as well. The dominant problems seem to be associated with correctable issues in the ASAS-SN data reduction pipeline for saturated stars more than the MLP itself. The method is publicly available as a light-curve option on ASAS-SN Sky Patrol v1.0.

Systematically Revisiting All NuSTAR Spins of Black Holes in X-Ray Binaries

We extend our recent work on black hole spin in X-ray binary systems to include an analysis of 189 archival NuSTAR observations from 24 sources. Using self-consistent data reduction pipelines, spectral models, and statistical techniques, we report an unprecedented and uniform sample of 36 stellar-mass black hole spin measurements based on relativistic reflection. This treatment suggests that prior reports of low spins in a small number of sources were generally erroneous: our comprehensive treatment finds that those sources tend to harbor black holes with high spin values. Overall, within 1σ uncertainty, ∼86% of the sample are consistent with a ≥ 0.95, ∼94% of the sample are consistent with a ≥ 0.9, and 100% are consistent with a ≥ 0.7 (the theoretical maximum for neutron stars; a = cJ/GM 2). We also find that the high-mass X-ray binaries (those with A-, B-, or O-type companions) are consistent with a ≥ 0.9 within the 1σ errors; this is in agreement with the low-mass X-ray binary population and may be especially important for comparisons to black holes discovered in gravitational wave events. In some cases, different spectra from the same source yield similar spin measurements but conflicting values for the inclination of the inner disk; we suggest that this is due to variable disk winds obscuring the blue wing of the relativistic Fe K emission line. We discuss the implications of our measurements, the unique view of systematic uncertainties enabled by our treatment, and future efforts to characterize black hole spins with new missions.

ROME/REA: Three-year, Tri-color Timeseries Photometry of the Galactic Bulge

The Robotic Observations of Microlensing Events/Reactive Event Assessment Survey was a Key Project at Las Cumbres Observatory (hereafter LCO) which continuously monitored 20 selected fields (3.76 sq.deg) in the Galactic Bulge throughout their seasonal visibility window over a three-year period, between 2017 March and 2020 March. Observations were made in three optical passbands (SDSS −g′ , −r′ , −i′ ), and LCO’s multi-site telescope network enabled the survey to achieve a typical cadence of ∼10 hr in i′ and ∼15 hr in g′ and r′ . In addition, intervals of higher cadence (<1 hr) data were obtained during monitoring of key microlensing events within the fields. This paper describes the Difference Image Analysis data reduction pipeline developed to process these data, and the process for combining the photometry from LCO’s three observing sites in the Southern Hemisphere. The full timeseries photometry for all ∼8 million stars, down to a limiting magnitude of i ∼ 18 mag is provided in the data release accompanying this paper, and samples of the data are presented for exemplar microlensing events, illustrating how the tri-band data are used to derive constraints on the microlensing source star parameters, a necessary step in determining the physical properties of the lensing object. The timeseries data also enables a wealth of additional science, for example in characterizing long-timescale stellar variability, and a few examples of the data for known variables are presented.

Spectroflat: A generic spectrum and flat-field calibration library for spectro-polarimetric data

Flat-fielding spectro-polarimetric data with one spatial and one spectral dimension is inherently difficult as the imprint of the spectral lines needs to be separated from other wavelength-dependent instrumental effect (e.g., fringes or prefilter profiles) and wavelength-independent effects (e.g., dust and sensor response). Current approaches for spectrometers are often based on moving the grating or they depend on optical models and/or on lab calibration data. They are also limited to small spectral regions and are instrument-specific. Approaches that would be suitable for polarimeters have not been reported yet. We present an approach that allows for flat-field calibration data to be to obtained for diffraction-grating-based, long-slit spectrographs combined with temporally modulated polarimetry from high-resolution solar telescopes. This approach is based on nominal flat-fielding procedures performed during the instrument's science operations. We performed a precise and field-dependent correction of the spectrographic distortion effect (resulting in curved spectral lines, typically denoted as a "smile" effect) to ensure the orthogonality of the spectral and spatial dimensions. We identified distortions by tracking the position of multiple spectral lines within the full spectral field of view. From the raw modulated flats, we then removed the solar line imprints and derived separate flat-fields for sensor and slit dust features. Optionally, wavelength calibration and continuum correction can be included in this process. We have created generic Python libraries that can be plugged into existing Python-based data reduction pipelines or used as a standalone calibration tool. We show that for spectrographs covering many spectral lines, a correction of the smile distortion based on optical models alone is not sufficient. Our results demonstrate a suppression of fringes, sensor artifacts, and fixed-pattern imprints in demodulated data by one order of magnitude. For intensity images, the photon noise level can be closely attained after calibration. Our correction works across the full spectral range. The algorithm was tested for different wavelength regimes with emission (EUV range) or absorption (near-UV, VIS, IR range) spectra, on data acquired with ground-based (SST/TRIPPLE-SP, GREGOR/GRIS), balloon-borne (SUNRISE-III/SUSI), and space-based (SolO/SPICE) instruments. The data calibrated with our method offer robust and precise inversion results. We have extended existing spectroscopic flat-field techniques to modern instruments with large imaging sensors covering many spectral lines simultaneously, and with polarimetric capabilities, where methods described so far are not adequate. We believe that our method is applicable as a standard calibration approach for most modern high resolution large-FOV, long-slit spectrographs -- both with and without polarimetric capabilities.

Stingray Sensor System for Persistent Survey of the GEO Belt.

The Stingray sensor system is a 15-camera optical array dedicated to the nightly astrometric and photometric survey of the geosynchronous Earth orbit (GEO) belt visible above Tucson, Arizona. The primary scientific goal is to characterize GEO and near-GEO satellites based on their observable properties. This system is completely autonomous in both data acquisition and processing, with human oversight reserved for data quality assurance and system maintenance. The 15 ZWO ASI1600MM Pro cameras are mated to Sigma 135 mm f/1.8 lenses and are controlled simultaneously by four separate computers. Each camera is fixed in position and observes a 7.6-by-5.8-degree portion of the GEO belt, for a total of a 114-by-5.8-degree field of regard. The GAIA DR2 star catalog is used for image astrometric plate solution and photometric calibration to GAIA G magnitudes. There are approximately 200 near-GEO satellites on any given night that fall within the Stingray field of regard, and all those with a GAIA G magnitude brighter than approximately 15.5 are measured by the automated data reduction pipeline. Results from an initial one-month survey show an aggregate photometric uncertainty of 0.062 ± 0.008 magnitudes and astrometric accuracy consistent with theoretical sub-pixel centroid limits. Provided in this work is a discussion of the design and function of the system, along with verification of the initial survey results.

ASIVA – Platform for observational and computational analysis of stellar variables

The astronomical data analysis consists of two crucial process; data reduction of the captured images and data analysis of the derived magnitudes. We present the platform ASIVA, a data analysis platform which comes along with a data reduction pipeline. The data reduction pipeline gives flexibility to analyse the FITS images and also perform image alignment for detecting the correct image coordinates for required objects. It can be custom scheduled with cron jobs so that it picks the latest data and appends the results accordingly. The data analysis platform allows user to effectively analyse the ensemble data and perform accurate data processing and grouping with ease. It is integrated with a custom algorithm to detect the variable stars from an ensemble with its relative standard deviations. The statistical, spectral and non-linear dynamics features can be extracted from time series data which can be eventually used for in-depth analysis. To validate the capability, we have analysed 15 nights of Orion Nebula Cluster field in I filter which had 1585 images. ASIVA reduces manual effort to a great extent thus saves analysis time and excludes human errors.

Using multiobjective optimization to reconstruct interferometric data

Context. In very long baseline interferometry (VLBI), signals recorded at multiple antennas are combined to form a sparsely sampled virtual aperture with an effective diameter set by the largest separation between the antennas. Due to the sparsity of the sampled aperture, VLBI imaging constitutes an ill-posed inverse problem. Various algorithms have been employed to deal with the VLBI imaging, including the recently proposed multiobjective evolutionary algorithm by decomposition (MOEA/D) described in the first paper of this series. Aims. Among the approaches to the reconstruction of the image features in total intensity from sparsely sampled visibilities, extensions to the polarimetric and the temporal domain are of great interest for the VLBI community in general and the Event Horizon Telescope Collabroration (EHTC) in particular. Based on the success of MOEA/D in presenting an alternative claim of the image structure in a unique, fast, and largely unsupervised way, we study the extension of MOEA/D to polarimetric and time dynamic reconstructions in this paper. Methods. To this end, we utilized the multiobjective, evolutionary framework introduced for MOEA/D, but added the various penalty terms specific to total intensity imaging time-variable and polarimetric variants, respectively. We computed the Pareto front (the sample of all non-dominated solutions) and identified clusters of close proximities. Results. We tested MOEA/D with synthetic data sets that are representative for the main science targets and instrumental configuration of the EHTC and its possible successors. We successfully recovered the polarimetric and time-dynamic signature of the ground truth movie (even with relative sparsity) and a set of realistic data corruptions. Conclusions. MOEA/D has been successfully extended to polarimetric and time-dynamic reconstructions and, specifically, in a setting that would be expected for the EHTC. It offers a unique alternative and independent claim to the already existing methods, along with a number of additional benefits, namely: it is the first method that effectively explores the problem globally and compared to regularized maximum likelihood (RML) methods. Thus, it waives the need for parameter surveys. Hence, MOEA/D is a novel, useful tool to characterize the polarimetric and dynamic signatures in a VLBI data set robustly with a minimal set of user-based choices. In a consecutive work, we will address the last remaining limitation for MOEA/D (the number of pixels and numerical performance), so that MOEA/D can firmly solidify its place within the VLBI data reduction pipeline.

Multiple Patchy Cloud Layers in the Planetary-mass Object SIMP 0136+0933

Multiwavelength photometry of brown dwarfs and planetary-mass objects provides insight into their atmospheres and cloud layers. We present near-simultaneous J- and K s -band multiwavelength observations of the highly variable T2.5 planetary-mass object, SIMP J013656.5+093347. We reanalyze observations acquired over a single night in 2015 using a recently developed data reduction pipeline. For the first time, we detect a phase shift between J- and K s -band light curves, which we measure to be 39.°9−1.1+3.6 . Previously, phase shifts between near-infrared and mid-infrared observations of this object were detected and attributed to probing different depths of the atmosphere, and thus different cloud layers. Using the Sonora Bobcat models, we expand on this idea to show that at least two different patchy cloud layers must be present to explain the measured phase shift. Our results are generally consistent with recent atmospheric retrievals of this object and other similar L/T transition objects.

The ArgusSpec Prototype: Autonomous Spectroscopic Follow-up of Flares Detected by Large Array Telescopes

ArgusSpec is a prototype autonomous spectroscopic follow-up instrument designed to characterize flares detected by the Argus Pathfinder telescope array by taking short exposure (30 s) broadband spectra (370–750 nm) at low resolutions (R ∼ 150 at 500 nm). The instrument is built from consumer off-the-shelf astronomical equipment, assembled inside a shipping container, and deployed alongside the Argus Pathfinder at a dark sky observing site in Western North Carolina. In this paper, we describe the hardware design, system electronics, custom control software suite, automated target acquisition procedure, and data reduction pipeline. We present initial on-sky test data used to evaluate system performance and show a series of spectra taken of a small flare from AD Leonis. The $35k prototype ArgusSpec was designed, built, and deployed in under a year, largely from existing parts, and has been operating on-sky since 2023 March. With current hardware and software, the system is capable of receiving an observation, slewing, performing autonomous slit acquisition, and beginning data acquisition within an average of 32 s. With Argus Pathfinder’s 1 s cadence survey reporting alerts of rising sources within 2 s of onset, ArgusSpec can reach new targets well within a minute of the start of the event. As built, ArgusSpec can observe targets down to a 20σ limiting magnitude of m V ≈ 13 at 30 s cadence with an optical resolution of R ∼ 150 (at 500 nm). With automated rapid acquisition demonstrated, later hardware upgrades will be based on a clean-sheet optical design, solving many issues in the current system, significantly improving the limiting magnitude, and potentially enabling deep spectroscopy by the coaddition of data from an array of ArgusSpec systems. The primary science driver behind ArgusSpec is the characterization of the blackbody evolution of flares from nearby M-dwarfs. Large flares emitted by these stars could have significant impacts on the potential habitability of any orbiting exoplanets, but our current understanding of these events is in large part built on observations from a handful of active stars. ArgusSpec will characterize large numbers of flares from across the night sky, building a spectroscopic library of the most extreme events from a wide variety of stellar masses and ages.

WARP: The Data Reduction Pipeline for the WINERED Spectrograph

We present a data reduction pipeline written in Python for data obtained with the near-infrared cross-dispersed echelle spectrograph, WINERED, which yields a 0.91–1.35 μm spectrum with the resolving power of Rmax≡λ/Δλ=28,000 or 70,000 depending on the observing mode. The pipeline was developed to efficiently extract the spectrum from the raw data with high quality. It comprises two modes: the calibration and the science mode. The calibration mode automatically produces the flat-fielding image, bad pixel map, echellogram distortion map and the dispersion solution from the set of the calibration data. Using calibration images and parameters, the science data of astronomical objects can be reduced automatically using the science mode. The science mode is also used for the real-time quick look at the data during observations. An example of the spectra reduced with WARP is presented. The effect of the highly inclined slit image on the spectral resolution is discussed.

The Breakthrough Listen Search for Intelligent Life: Technosignature Search of 97 Nearby Galaxies

The Breakthrough Listen search for intelligent life is, to date, the most extensive technosignature search of nearby celestial objects. We present a radio technosignature search of the centers of 97 nearby galaxies, observed by Breakthrough Listen at the Robert C. Byrd Green Bank Telescope. We performed a narrowband Doppler drift search using the turboSETI pipeline with a minimum signal-to-noise parameter threshold of 10, across a drift rate range of ±4 Hz s−1, with a spectral resolution of 3 Hz and a time resolution of ∼18.25 s. We removed radio frequency interference (RFI) by using an on-source/off-source cadence pattern of six observations and discarding signals with Doppler drift rates of 0. We assess factors affecting the sensitivity of the Breakthrough Listen data reduction and search pipeline using signal injection and recovery techniques and apply new methods for the investigation of the RFI environment. We present results in four frequency bands covering 1–11 GHz, and place constraints on the presence of transmitters with equivalent isotropic radiated power on the order of 1026 W, corresponding to the theoretical power consumption of Kardashev Type II civilizations.

The Large Array Survey Telescope—Pipeline. I. Basic Image Reduction and Visit Coaddition

The Large Array Survey Telescope (LAST) is a wide-field telescope designed to explore the variable and transient sky with a high cadence and to be a test-bed for cost-effective telescope design. A LAST node is composed of 48 (32 already deployed), 28 cm f/2.2 telescopes. A single telescope has a 7.4 deg2 field of view and reaches a 5σ limiting magnitude of 19.6 (21.0) in 20 (20 × 20) s (filter-less), while the entire system provides a 355 deg2 field of view. The basic strategy of LAST is to obtain multiple 20 s consecutive exposures of each field (a visit). Each telescope carries a 61 Mpix camera, and the system produces, on average, about 2.2 Gbit s−1. This high data rate is analyzed in near real-time at the observatory site, using limited computing resources (about 700 cores). Given this high data rate, we have developed a new, efficient data reduction and analysis pipeline. The LAST data pipeline includes two major parts: (i) Processing and calibration of single images, followed by a coaddition of the visit’s exposures. (ii) Building the reference images and performing image subtraction and transient detection. Here we describe in detail the first part of the pipeline. Among the products of this pipeline are photometrically and astrometrically calibrated single and coadded images, 32 bit mask images marking a wide variety of problems and states of each pixel, source catalogs built from individual and coadded images, Point-Spread Function photometry, merged source catalogs, proper motion and variability indicators, minor planets detection, calibrated light curves, and matching with external catalogs. The entire pipeline code is made public. Finally, we demonstrate the pipeline performance on real data taken by LAST.

Gaia Focused Product Release: Sources from Service Interface Function image analysis

Context. Gaia’s readout window strategy is challenged by very dense fields in the sky. Therefore, in addition to standard Gaia observations, full Sky Mapper (SM) images were recorded for nine selected regions in the sky. A new software pipeline exploits these Service Interface Function (SIF) images of crowded fields (CFs), making use of the availability of the full two-dimensional (2D) information. This new pipeline produced half a million additional Gaia sources in the region of the omega Centauri (ω Cen) cluster, which are published with this Focused Product Release. We discuss the dedicated SIF CF data reduction pipeline, validate its data products, and introduce their Gaia archive table. Aims. Our aim is to improve the completeness of the Gaia source inventory in a very dense region in the sky, ω Cen. Methods. An adapted version of Gaia’s Source Detection and Image Parameter Determination software located sources in the 2D SIF CF images. These source detections were clustered and assigned to new SIF CF or existing Gaia sources by Gaia s cross-match software. For the new sources, astrometry was calculated using the Astrometric Global Iterative Solution software, and photometry was obtained in the Gaia DR3 reference system. We validated the results by comparing them to the public Gaia DR3 catalogue and external Hubble Space Telescope data. Results. With this Focused Product Release, 526 587 new sources have been added to the Gaia catalogue in ω Cen. Apart from positions and brightnesses, the additional catalogue contains parallaxes and proper motions, but no meaningful colour information. While SIF CF source parameters generally have a lower precision than nominal Gaia sources, in the cluster centre they increase the depth of the combined catalogue by three magnitudes and improve the source density by a factor of ten. Conclusions. This first SIF CF data publication already adds great value to the Gaia catalogue. It demonstrates what to expect for the fourth Gaia catalogue, which will contain additional sources for all nine SIF CF regions.

Custom JWST NIRSpec/IFU and MIRI/MRS Data Reduction Pipelines for Solar System Targets

We present custom JWST data reduction pipelines for JWST NIRSpec/IFU and MIRI/MRS observations of solar system objects. The pipelines simplify the process of reducing the JWST observations, and include custom steps to significantly improve the data quality. Our custom processing routines include a “desaturation” routine to reduce the effect of saturation while still maintaining high signal-to-noise ratio, and custom flat field correction code to remove the significant artifacts found in MIRI/MRS observations. The pipelines also automatically generate a series of quick look plots and animations to simplify exploration of a dataset. These custom JWST pipelines can be downloaded from https://github.com/JWSTGiantPlanets/pipelines.

CLASS Data Pipeline and Maps for 40 GHz Observations through 2022

The Cosmology Large Angular Scale Surveyor (CLASS) is a telescope array that observes the cosmic microwave background over 75% of the sky from the Atacama Desert, Chile, at frequency bands centered near 40, 90, 150, and 220 GHz. This paper describes the CLASS data pipeline and maps for 40 GHz observations conducted from 2016 August to 2022 May. We demonstrate how well the CLASS survey strategy, with rapid (∼10 Hz) front-end modulation, recovers the large-scale Galactic polarization signal from the ground: the mapping transfer function recovers ∼67% (85%) of EE and BB (VV) power at ℓ = 20 and ∼35% (47%) at ℓ = 10. We present linear and circular polarization maps over 75% of the sky. Simulations based on the data imply the maps have a white noise level of and correlated noise component rising at low-ℓ as ℓ −2.4. The transfer-function-corrected low-ℓ component is comparable to the white noise at the angular knee frequencies of ℓ ≈ 18 (linear polarization) and ℓ ≈ 12 (circular polarization). Finally, we present simulations of the level at which expected sources of systematic error bias the measurements, finding subpercent bias for the Λ cold dark matter EE power spectra. Bias from E-to-B leakage due to the data reduction pipeline and polarization angle uncertainty approaches the expected level for an r = 0.01 BB power spectrum. Improvements to the instrument calibration and the data pipeline will decrease this bias.

Optimized temporal binning of comparison star measurements for differential photometry

ABSTRACT Ground-based, high precision observations of the light curves of objects such as transiting exoplanets rely on the application of differential photometry. The flux of the target object is measured relative to a comparison star in the same field, allowing correction for systematic trends in the light curve, mainly due to atmospheric effects including the variation of extinction with airmass. However, the precision of the light curve is then limited by the random noise for the measurements of both the target object and the comparison star. For time-resolved photometry using short exposure times of up to a few tens of seconds, the time-scale of the systematic variations due to atmospheric (or other) effects can be much longer than the cadence of the observations. In this case, the overall signal-to-noise ratio of the observation may be improved significantly by applying some temporal binning to the measurements of the comparison star, before comparison with the target object, without reducing the cadence of the overall light curve. In this paper, we will describe a data reduction pipeline for implementing this method which optimizes the number of frames to be binned for the comparison star, and we present example results for time-resolved photometric data. An example of applying the technique on an exoplanet transit light curve of WASP-166b is presented using four comparison stars of different magnitudes.