Pith. sign in

REVIEW 4 major objections 3 minor 1 cited by

PyLMT: A transient detection pipeline for the 4-m International Liquid Mirror Telescope

T0 review · 4 major / 3 minor · reviewed 2026-08-09 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper presents PyLMT, an automated pipeline that detects transient and variable sources in the nightly images of the 4-m International Liquid Mirror Telescope, using image subtraction and convolutional-neural-network classifiers to…

desk verdict A solid, externally validated transient pipeline for ILMT, but the headline precision figure is in-sample and needs out-of-sample confirmation. read the letter →

arxiv 2502.00556 v1 pith:I7HA5LLU submitted 2025-02-01 astro-ph.IM astro-ph.HE

Kumar Pranshu , Kuntal Misra , Bhavya Ailawadhi , Monalisa Dubey , Naveen Dukiya , Sara Filali , Paul Hickson , Brajesh Kumar
show 2 more authors
Vibhore Negi Jean Surdej
This is my paper · ORCID
classification astro-ph.IMastro-ph.HE
keywords transientdetectionimagesubtractionconvolutionalneuralnetworksreal/bogusclassificationtime-domainastronomyliquidmirrortelescopesupernovadiscoveryasteroid
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper presents PyLMT, an automated pipeline that detects transient and variable sources in the nightly images of the 4-m International Liquid Mirror Telescope. The pipeline subtracts a reference image from each science frame, uses convolutional-neural-network classifiers to separate real astrophysical sources from subtraction artifacts, and then sorts the survivors by whether the underlying host looks like a galaxy, a point source, or nothing. On real ILMT frames the authors report a detection precision of 0.91, test accuracies of 94-98% for the real/bogus classifiers, and 98.6% accuracy for the host-morphology classifier. The pipeline has been running operationally and produced 12 extragalactic transients in one observing cycle, including two previously unknown objects. The broader aim is to show that a small, dedicated survey telescope with limited training data can run real-time transient discovery with off-the-shelf machine-learning tools.

What carries the argument

The load-bearing component is the adaptive two-classifier detection strategy. Every source in the subtracted image is first pre-classified as point-host or non-point-host; point-host sources go to a high-precision CNN trained with a deliberately artifact-heavy dataset, while all other sources go to a high-recall CNN. This is what reduces false positives from about 35% to about 10% while keeping faint, extended-host transients visible. Around it sit the image-subtraction module, which fits an optimal convolution kernel to match the point-spread function of the reference frame to the science frame, and the host-morphology ensemble, which combines one 31x31-pixel CNN and two 102x102-pixel CNNs to label candidates as extended-host, point-host, or hostless.

What would settle it

Take a set of ILMT frames from a different observing season or a different filter, run the pipeline exactly as described, and manually vet every alert; if precision on the fresh cycle falls materially below 0.91 or the false-positive fraction rises above the reported 10%, the same-season training assumption is falsified. A narrower check is to repeat the asteroid-recovery test with a coadded reference image and count whether the recovery fraction of 231 out of 293 improves.

Watch

Extended reading notes

Core claim

The central claim is that a three-module pipeline can find and classify transients in ILMT images in near real-time with a precision high enough for follow-up. Image subtraction is handled by a module built on delta-basis kernel optimisation, which matches the point-spread functions of the science and reference frames; transient detection then routes sources through two CNN classifiers, a high-recall one for non-point-host candidates and a high-precision one for point-host candidates, because improperly subtracted stars are the dominant false-positive class. This adaptive split raised precision on 30 i-band frames from 0.65 to 0.91. A second CNN ensemble classifies candidates as extended-host, point-host, or hostless, and catalogue cross-matching rejects known solar-system bodies and bright variable stars. Running on 761 frames from the November 2023-May 2024 cycle, the pipeline detected 12 extragalactic transients, including two new discoveries, AT 2023yjc and 2024fxn, reported to the public transient registry.

Load-bearing premise

The load-bearing premise is that the same-season ILMT frames used to train the real/bogus CNNs are representative of the frames the pipeline will see in later observing cycles; if the telescope's seeing, artifacts, or sky background change enough, the 0.91 precision will not transfer.

Editorial extensions

If this is right

  • A full night of up to 35 ILMT frames can be processed in roughly 17 hours, making the pipeline fast enough to run continuously between nights.
  • The adaptive point-host/non-point-host split cuts the false-positive share in final alerts from about 35% to about 10% without sacrificing faint non-point-host transients.
  • Extended-host candidates are prioritized for follow-up, which is where supernovae are most likely to be found; four of the twelve operational detections were confirmed supernovae.
  • The same pipeline recovers 231 of 293 visually confirmed catalogued asteroids, including magnitudes fainter than 21, so it serves as a solar-system survey as well.
  • Operational use on 761 frames yielded 12 extragalactic transients, including two previously unreported objects, demonstrating that the pipeline is ready for routine use.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The reported performance numbers are computed on ILMT data from the same observing cycle used to train the CNNs, so the paper's key assumption is that those same-season samples represent the operational survey; a fresh cycle in a different filter would test this directly.
  • Because the current reference images are single non-coadded frames, building the planned library of coadded references should improve subtraction residuals and push recall for faint transients higher than the values reported here.
  • The point-host versus non-point-host routing strategy is tied to ILMT's PSF and its dominant false-positive source; it would be a testable transfer question whether the same two-model scheme improves precision on other survey telescopes without retraining.
  • If the 12-transient yield from 761 frames persists, ILMT will become a regular supplier of supernova candidates that need only external spectroscopic confirmation, which would expand the sample of early-time transients available for follow-up.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 3 minor

Summary. The paper presents PyLMT, a transient-detection pipeline for the 4-m International Liquid Mirror Telescope. The pipeline performs image subtraction (ILMTDiff), source detection and real/bogus classification with CNNs (TransiSearch), and host-morphology classification of candidates (NovaNet), followed by catalogue cross-matching against SIMBAD, Gaia, and IMCCE/SkyBot. The authors report a pipeline precision of 0.91 measured on 30 i-band images from October 2022, real/bogus classifier test accuracies of 93.97% and 98.07%, a NovaNet candidate-classifier accuracy of 98.6%, and 12 extragalactic transients detected in the November 2023–May 2024 cycle, including two previously unknown transients reported to the TNS. The paper also describes asteroid recovery with MPC cross-matching and gives a median execution time of about 29 minutes per frame.

Significance. If the reported performance holds, PyLMT provides a complete, operational transient-detection capability for a new survey telescope and has already produced externally validated discoveries (AT 2023yjc, AT 2024fxn, plus confirmed SNe). The pipeline's use of TDI imaging, segmented subtraction, adaptive real/bogus classification, and candidate classification is a useful contribution to the time-domain literature. The public availability of the underlying ILMT images is a strength. However, the two central performance figures—the 0.91 precision and the 98.6% NovaNet accuracy—are less well supported than the text implies: the precision is an in-sample estimate from the same season used for training, and the NovaNet accuracy is inconsistent with the published confusion matrix.

major comments (4)
  1. [§4.2] The reported pipeline precision of 0.91 is computed on 30 i-band images from 28–30 October 2022, and the real/bogus classifiers were trained on cutouts from the same October–November 2022 cycle (Appendix B). This is an in-sample estimate: the validation frames share the PSF, background, artifact morphology, and specific field locations with the training data. The operational 2023–24 cycle is not evaluated for false positives: §4.4 reports 12 extragalactic transients in 761 frames but gives no total candidate count or false-positive rate. Please add an out-of-sample precision measurement—even a small sample of vetted candidates from the 2023–24 cycle—or explicitly state that 0.91 is an in-sample figure and not a demonstrated survey precision.
  2. [§4.3, Table 8] The claimed classification accuracy of 98.6% does not match the confusion matrix in Table 8. Summing the diagonal gives 14+185+1284 = 1483 correct classifications out of 1518 total, which is 97.7%, not 98.6%. Please correct the stated accuracy or the confusion matrix, and update the abstract, Section 4.3, and Section 5 accordingly.
  3. [§3.2 vs Table C1] The text states that the TransiSearch module 'extracts all the sources above 4σ significance' (Section 3.2), but Table C1 lists the default detection_threshold as 5 standard deviations above noise. This discrepancy directly affects the reported sensitivity, the precision measurement in Section 4.2, and the interpretation of detections. Please specify which threshold was used for the 30-image validation and for the 2023–24 search.
  4. [§3.5] The adaptive detection scheme relies on a CNN-based preemptive classification of all sources into 'point-host' and 'non-point-host' before applying the high-precision or high-recall real/bogus classifier. The paper does not specify which CNN performs this split, how it was trained, or what its accuracy is. This is load-bearing because the claimed reduction in false positives (from about 35% to about 10%) depends on this split, yet no evaluation of the split itself is presented. Please identify the classifier and report its training data and performance.
minor comments (3)
  1. [Appendix A] Appendix A states that only non-coadded single reference images were used because limited imaging data was available. This is an acknowledged limitation, but its impact on subtraction artifacts and hence on precision should be quantified or at least discussed explicitly in the validation section.
  2. [Table 7] The caption of Table 7 refers to 'true positive class distribution', but the table only includes classifications of visually confirmed real sources; it is unclear whether false negatives from TransiSearch are included. Please clarify whether the entries are conditional on the source having been correctly detected.
  3. [References] The reference entry 'Makhlouf, K. Turpin, D. Corre, D. Karpov, S. Kann, D. A. Klotz, A. 2022' has an inconsistent author-name formatting that should be brought into line with the journal style.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the pipeline evaluation is partly in-sample, but the derivation does not reduce to its inputs.

full rationale

The paper's central claims are (1) an image-subtraction module (ILMTDiff) whose quality is comparable to external codes, (2) CNN real/bogus and host-morphology classifiers with reported validation accuracies, (3) a pipeline precision of 0.91 measured on ILMT frames, and (4) operational detections of 12 extragalactic transients, including two TNS-reported discoveries. None of these reduce by construction to the paper's own inputs. ILMTDiff is a concrete implementation of Bramich (2008) delta-basis kernel optimisation (Eqs. A1-A2) and is benchmarked against HOTPANTS, OIS, and Properimage, which are independent external tools. The CNN classifiers were trained on cutouts from the October-November 2022 ILMT cycle and validated on an 80:20 split of that same dataset; the 0.91 precision was measured by running the full pipeline on 30 i-band images from 28-30 October 2022, i.e., from the same commissioning cycle as the training data. This is a genuine in-sample/generalization limitation and should temper any claim that 0.91 will hold operationally, but it is not a fitted parameter being renamed as a prediction: the precision is a measured pipeline-level statistic, not a quantity forced by the training procedure. The operational 2023-24 detections are supported by TNS reports and confirmed SNe, which are external evidence independent of the in-sample precision. Self-citations (Pranshu et al. 2024a overview; TNS discovery reports) are not load-bearing assumptions: they point to earlier descriptions or announce independent discoveries. Appendix A candidly states that non-coadded single reference images were used because limited data were available; this is an acknowledged sensitivity limitation, not a circular step. No uniqueness theorem is imported from the authors, and no ansatz is smuggled in via citation. Overall, no claim in the derivation chain is equivalent to its own input by definition.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The pipeline's central claims rest on hand-chosen detection and classification thresholds, on the standard least-squares PSF-matching formalism, and on the domain assumption that a single reference image from the same ILMT survey provides adequate templates. No new physical entities are introduced.

free parameters (5)
  • detection_threshold = 5 sigma (text says 4 sigma in Section 3.2)
    Threshold for source extraction in difference images; directly controls recall and false-positive rate.
  • classification_threshold = 0.5
    Cut on CNN real/bogus score to classify sources as real or bogus.
  • selection_thresh = (30000, 30000, 2, 6)
    Bounds on Gaussian amplitude and sigma for candidate sources, hand-tuned to ILMT PSF.
  • flux_c_min = 1000 ADUs
    Minimum aperture flux for a candidate to be accepted.
  • variable_star_mag_cut = Gaia G < 19 mag
    Threshold to reject probable variable stars during catalogue cross-matching.
assumptions (4)
  • standard math Least-squares kernel optimisation converges to a PSF-matching kernel that yields accurate subtraction residuals (Bramich 2008, Moré 1978).
    The ILMTDiff module relies on numerical optimisation of Eq. A1/A2; poor convergence would create bogus residuals.
  • domain assumption The ILMT TDI images, after preprocessing, are astrometrically aligned well enough that residual misalignment is below the source extraction scale.
    Astrometric errors are cited as a source of bogus detections; the pipeline uses WCS or astroalign (Section 3, Appendix A).
  • domain assumption A single, non-coadded reference image with better seeing is an adequate template for subtraction.
    Appendix A states the current work used non-coadded single reference images; co-added references may be needed for fainter transients.
  • ad hoc to paper Point-host candidates are predominantly false positives, motivating the adaptive detection split.
    Section 3.5 asserts this based on inspection of candidate outputs; it is an empirical assumption, not derived.

how reviews work

0 comments
Cite this review

Pith. "Pith review of PyLMT: A transient detection pipeline for the 4-m International Liquid Mirror Telescope." pith.science (2026). https://pith.science/paper/I7HA5LLU

@misc{pith2026250200556,
  author       = {Pith},
  title        = {Pith review of: PyLMT: A transient detection pipeline for the 4-m International Liquid Mirror Telescope},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/I7HA5LLU}},
  note         = {Machine review of arXiv:2502.00556}
}
read the original abstract

The International Liquid Mirror Telescope (ILMT) is a 4-m aperture, zenith-pointing telescope with a field-of-view of 22', situated in the foothills of the Himalayas. The telescope operates in continuous survey mode, making it a useful instrument for time-domain astronomy, particularly for detecting transients, variable stars, active galactic nuclei variability, and asteroids. This paper presents the PyLMT transient detection pipeline to detect such transient/varying sources in the ILMT images. The pipeline utilises the image subtraction technique to compare a pair of images from the same field, identifying such sources in subtracted images with the help of convolutional neural networks (CNN) based real/bogus classifiers. The test accuracies determined for the real/bogus classifiers ranged from 94% to 98%. The resulting precision of the pipeline calculated over candidate alerts in the ILMT frames is 0.91. It also houses a CNN-aided transient candidate classifier that classifies the transient/variable candidates based on host morphology. The test accuracy of the candidate classifier is 98.6%. It has the provision to identify catalogued asteroids and other solar system objects using public databases. The median execution time of the pipeline is approximately 29 minutes per image of 17 minutes exposure. Relevant CNNs have been trained on data acquired with the ILMT during the cycle of October-November 2022. Subsequent tests on those images have confirmed the detection of numerous catalogued asteroids, variable stars, and other uncatalogued sources. The pipeline has been operational and has detected 12 extragalactic transients, including 2 new discoveries in the November 2023-May 2024 observation cycle.

Figures

Figures reproduced from arXiv: 2502.00556 by the authors.

Figure 1
Figure 1. An image of the 4-m ILMT showing the mirror bowl (bottom) mounted on the air bearing, the metallic structure (painted black), four safety pillars (painted yellow), and the CCD-corrector assembly (top). 3 PIPELINE DESCRIPTION The pipeline consists of three modules namely ILMTDiff, TransiSearch, and NovaNet for performing image subtraction, transient detection, and transient candidate classification, respec￾tively. A … view at source ↗
Figure 2
Figure 2. Schematic diagram of the PyLMT transient detection pipeline. The diagram illustrates the three basic steps for carrying out the image subtraction (ILMTDiff), transient detection (TransiSearch), and candidate classification (NovaNet). An additional catalogue crossmatching step identifies catalogued host galaxies and filters out the catalogued minor planets and bright variable stars [PITH_FULL_IMAGE:figures/full_fig_… view at source ↗
Figure 3
Figure 3. A reduced i ′ -band ILMT frame of size 22′ along declination and 198′ along RA. The first 22′ along RA of the acquired full raw frame (not shown here) corresponds to TDI ramping and is therefore sliced out during pre-processing. (a) Science image (b) Reference image (c) Difference image [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Image subtraction performed on a 1051×1051 pixel (∼ 5 ′ .66 × 5 ′ .66) cutout image of an r ′ -band ILMT frame using the difference imaging algorithm (ILMTDiff). for pairs of common individual sources in science and reference cutouts. A scipy (Virtanen et al. 2020) hos…
Figure 5
Figure 5. Figure 5: Samples from the bogus dataset for the real/bogus dataset. Such artefacts occurring in subtracted images generally result from image misalign￾ment due to poor astrometric calibration, PSF mismatch during subtraction, saturated stars, CCD artefacts and cosmic hits. Each…
Figure 7
Figure 7. Figure 7 [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Samples of point-source training data for the NovaNet transient candidate classifier. Each cutout is of size 102×102 pixels (∼ 33′′ × 33′′). jected for being probable variable stars. The magnitude threshold is set to ensure that any distant SNe candidate, whose host ga…
Figure 9
Figure 9. Figure 9: Samples for hostless class training data for the NovaNet transient candidate classifier. Each cutout is of size 102×102 pixels (∼ 33′′ × 33′′). fication status. Extended-host candidates that do not correspond to known solar-system objects are prioritised for follow-up …
Figure 10
Figure 10. Figure 10: Flowchart illustrating candidate filtering performed using the catalogue cross-matching step in the pipeline [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]
Figure 11
Figure 11. Figure 11: Image subtraction was performed on 2K×2K pixel (∼ 11′ × 11′ ) cutouts of (a) science and (b) reference images of the ILMT using (c) ILMTDiff and (d) HOTPANTS. It can be inferred from the illustration that the quality of image subtraction with the custom ILMTDiff algor…
Figure 12
Figure 12. Figure 12: The ROC curves and PR curves for high-precision and high-recall CNN classifiers. a few artificially generated ‘SN-like’ sources to account for the corre￾sponding sample scarcity in the original dataset of detected sources. Such sources were generated by embedding poin…
Figure 14
Figure 14. Figure 14: S/N ratio vs recall plot for high precision and high recall CNN￾based real/bogus classifier [PITH_FULL_IMAGE:figures/full_fig_p011_14.png]
Figure 13
Figure 13. Figure 13: 51×51 pixel (∼ 16′′ .5 × 16′′ .5) cutouts of sources in subtracted images along with their science and reference image cutouts and the corre￾sponding real/bogus classifier scores. for the realisation of the project. PH acknowledges financial sup￾port from the Natural …
Figure 16
Figure 16. Figure 16: Distribution of MPC obtained V-band magnitudes of asteroids detected with the pipeline [PITH_FULL_IMAGE:figures/full_fig_p012_16.png]
Figure 17
Figure 17. Figure 17: Left column – top to bottom: PyLMT discovery of the new transients AT 2023yjc and 2024fxn on 13th November 2023 and 5th April 2024, respectively. The bottom panel illustrates the detection of the catalogued type-Ia SN 2023wuk. The images from left to right illustrate …
Figure 18
Figure 18. Figure 18: Left column – From top to bottom: Detection of variability in the known T-Tauri star IRAS 04108+2910, the known QSO SDSS_J221646.70+292050.8 and the catalogued asteroid 2000 AY36. All these sources were rejected from the filtered list of candidates but were included i…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SN 2022xus: bridging the gap between Type IIP and IIL supernovae

    astro-ph.HE 2026-07 conditional novelty 4.0 of 10

    SN 2022xus is a transitional Type II supernova with a 94.8-day plateau, a decline rate of 1.23 mag per 100 days, mixed IIP/IIL spectral features, and a likely 12-15 solar-mass progenitor.

Reference graph

Works this paper leans on

63 extracted references · 35 canonical work pages · cited by 1 Pith paper

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  2. [2]

    Abadi M., et al., 2016, TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems ( @eprint arXiv 1603.04467 )

    arXiv 2016

  3. [3]

    Acero-Cuellar T., Bianco F., Dobler G., Sako M., Qu H., LSST Dark Energy Science Collaboration 2023, @doi [ ] 10.3847/1538-3881/ace9d8 , https://ui.adsabs.harvard.edu/abs/2023AJ....166..115A 166, 115

    work page doi:10.3847/1538-3881/ace9d8 2023

  4. [4]

    S., Yildirim C., Klimenko S., Garner A., 2019, @doi [ ] 10.3847/1538-3881/ab3c4b , https://ui.adsabs.harvard.edu/abs/2019AJ....158..172A 158, 172

    Ackley K., Eikenberry S. S., Yildirim C., Klimenko S., Garner A., 2019, @doi [ ] 10.3847/1538-3881/ab3c4b , https://ui.adsabs.harvard.edu/abs/2019AJ....158..172A 158, 172

    work page doi:10.3847/1538-3881/ab3c4b 2019

  5. [5]

    Ailawadhi B., et al., 2024, @doi [Bulletin de la Societe Royale des Sciences de Liege] 10.25518/0037-9565.11892 , https://ui.adsabs.harvard.edu/abs/2024BSRSL..93..820A 93, 820

    work page arXiv 2024

  6. [6]

    Andreoni I., Jacobs C., Hegarty S., Pritchard T., Cooke J., Ryder S., 2017, @doi [ ] 10.1017/pasa.2017.33 , https://ui.adsabs.harvard.edu/abs/2017PASA...34...37A 34, e037

    work page doi:10.1017/pasa.2017.33 2017

  7. [7]

    C., Weaver B

    Bailey S., Aragon C., Romano R., Thomas R. C., Weaver B. A., Wong D., 2007, @doi [ ] 10.1086/519832 , https://ui.adsabs.harvard.edu/abs/2007ApJ...665.1246B 665, 1246

    work page doi:10.1086/519832 2007

  8. [8]

    Becker A., 2015, HOTPANTS: High Order Transform of PSF ANd Template Subtraction , Astrophysics Source Code Library, record ascl:1504.004

    work page 2015

Show all 63 references
  1. [9]

    C., Kulkarni S

    Bellm E. C., Kulkarni S. R., Intermediate Palomar Transient Factory Collaboration 2017, in American Astronomical Society Meeting Abstracts \#229. p. 313.01

    2017

  2. [10]

    C., et al., 2019, @doi [ ] 10.1088/1538-3873/aaecbe , https://ui.adsabs.harvard.edu/abs/2019PASP..131a8002B 131, 018002

    Bellm E. C., et al., 2019, @doi [ ] 10.1088/1538-3873/aaecbe , https://ui.adsabs.harvard.edu/abs/2019PASP..131a8002B 131, 018002

    2019 doi

  3. [11]

    B., Sanchez B., 2020, Astroalign: A Python module for astronomical image registration ( @eprint arXiv 1909.02946 )

    Beroiz M., Cabral J. B., Sanchez B., 2020, Astroalign: A Python module for astronomical image registration ( @eprint arXiv 1909.02946 )

    2020 arXiv

  4. [12]

    351, Astronomical Data Analysis Software and Systems XV

    Berthier J., Vachier F., Thuillot W., Fernique P., Ochsenbein F., Genova F., Lainey V., Arlot J.-E., 2006, in Gabriel C., Arviset C., Ponz D., Enrique S., eds, Astronomical Society of the Pacific Conference Series Vol. 351, Astronomical Data Analysis Software and Systems XV. pp 367--+

    2006

  5. [13]

    F., Hickson P., Surdej J., 2009, @doi [Opt

    Borra E. F., Hickson P., Surdej J., 2009, @doi [Opt. Photon. News] 10.1364/OPN.20.4.000028 , 20, 28

    2009 doi

  6. [14]

    Bradley L., et al., 2016, Photutils: Photometry tools , Astrophysics Source Code Library, record ascl:1609.011 ( @eprint ascl 1609.011 )

    2016

  7. [15]

    M., 2008, @doi [Monthly Notices of the Royal Astronomical Society: Letters] 10.1111/j.1745-3933.2008.00464.x , 386, L77

    Bramich D. M., 2008, @doi [Monthly Notices of the Royal Astronomical Society: Letters] 10.1111/j.1745-3933.2008.00464.x , 386, L77

    2008

  8. [16]

    W., Poznanski D., Bloom J

    Brink H., Richards J. W., Poznanski D., Bloom J. S., Rice J., Negahban S., Wainwright M., 2013, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/stt1306 , 435, 1047

    2013 doi

  9. [17]

    A., Maureira J.-C., 2017, @doi [The Astrophysical Journal] 10.3847/1538-4357/836/1/97 , 836, 97

    Cabrera-Vives G., Reyes I., Förster F., Estévez P. A., Maureira J.-C., 2017, @doi [The Astrophysical Journal] 10.3847/1538-4357/836/1/97 , 836, 97

    2017 doi

  10. [18]

    E., Kasliwal M

    Cao Y., Nugent P. E., Kasliwal M. M., 2016, @doi [ ] 10.1088/1538-3873/128/969/114502 , https://ui.adsabs.harvard.edu/abs/2016PASP..128k4502C 128, 114502

    2016 doi

  11. [19]

    Carrasco-Davis R., et al., 2021, @doi [The Astronomical Journal] 10.3847/1538-3881/ac0ef1 , 162, 231

    2021 doi

  12. [20]

    C., et al., 2016, @doi [arXiv e-prints] 10.48550/arXiv.1612.05560 , https://ui.adsabs.harvard.edu/abs/2016arXiv161205560C p

    Chambers K. C., et al., 2016, @doi [arXiv e-prints] 10.48550/arXiv.1612.05560 , https://ui.adsabs.harvard.edu/abs/2016arXiv161205560C p. arXiv:1612.05560

    2016 doi arXiv

  13. [21]

    Chollet F., et al., 2015, keras

    2015

  14. [22]

    Denneau L., et al., 2013, @doi [ ] 10.1086/670337 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..357D 125, 357

    2013 doi

  15. [23]

    A., et al., 2019, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/stz2357 , 489, 3582

    Duev D. A., et al., 2019, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/stz2357 , 489, 3582

    2019 doi

  16. [24]

    A., et al., 2021, @doi [ ] 10.3847/1538-3881/abea7b , https://ui.adsabs.harvard.edu/abs/2021AJ....161..218D 161, 218

    Duev D. A., et al., 2021, @doi [ ] 10.3847/1538-3881/abea7b , https://ui.adsabs.harvard.edu/abs/2021AJ....161..218D 161, 218

    2021 doi

  17. [25]

    Dukiya N., et al., 2024, @doi [Bulletin de la Societe Royale des Sciences de Liege] 10.25518/0037-9565.11899 , https://ui.adsabs.harvard.edu/abs/2024BSRSL..93..837D 93, 837

    2024

  18. [26]

    A., Egret D., eds, Astrophysics and Space Science Library Vol

    Egret D., Wenger M., Dubois P., 1991, in Albrecht M. A., Egret D., eds, Astrophysics and Space Science Library Vol. 171, Databases and On-line Data in Astronomy. pp 79--88, @doi 10.1007/978-94-011-3250-3_9

    1991 doi

  19. [27]

    Gaia Collaboration et al., 2016, @doi [ ] 10.1051/0004-6361/201629272 , https://ui.adsabs.harvard.edu/abs/2016A&A...595A...1G 595, A1

    2016 doi

  20. [28]

    Gavrikov P., 2020, visualkeras, https://github.com/paulgavrikov/visualkeras

    2020

  21. [29]

    K., Hickson P., 1992, @doi [ ] 10.1093/mnras/258.3.543 , https://ui.adsabs.harvard.edu/abs/1992MNRAS.258..543G 258, 543

    Gibson B. K., Hickson P., 1992, @doi [ ] 10.1093/mnras/258.3.543 , https://ui.adsabs.harvard.edu/abs/1992MNRAS.258..543G 258, 543

    1992 doi

  22. [30]

    Gieseke F., et al., 2017, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/stx2161 , 472, 3101

    2017 doi

  23. [31]

    Ginsburg A., et al., 2019, @doi [ ] 10.3847/1538-3881/aafc33 , https://ui.adsabs.harvard.edu/abs/2019AJ....157...98G 157, 98

    2019 doi

  24. [32]

    H., 1998, @doi [ ] 10.1086/316230 , https://ui.adsabs.harvard.edu/abs/1998PASP..110.1081H 110, 1081

    Hickson P., Richardson E. H., 1998, @doi [ ] 10.1086/316230 , https://ui.adsabs.harvard.edu/abs/1998PASP..110.1081H 110, 1081

    1998 doi

  25. [33]

    E., 2014, @doi [MNRAS] 10.1093/mnras/stu835 , 442, 259

    Huckvale L., Kerins E., Sale S. E., 2014, @doi [MNRAS] 10.1093/mnras/stu835 , 442, 259

    2014 doi

  26. [34]

    Khazov D., et al., 2016, @doi [The Astrophysical Journal] 10.3847/0004-637X/818/1/3 , 818, 3

    2016 doi

  27. [35]

    Kingma D., Ba J., 2014, International Conference on Learning Representations

    2014

  28. [36]

    L., Pandey S

    Kumar B., Pandey K. L., Pandey S. B., Hickson P., Borra E. F., Anupama G. C., Surdej J., 2018, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/sty298 , 476, 2075

    2018 doi

  29. [37]

    Kumar B., et al., 2022a, @doi [Journal of Astronomical Instrumentation] 10.1142/S2251171722400037 , https://ui.adsabs.harvard.edu/abs/2022JAI....1140003K 11, 2240003

    doi

  30. [38]

    Kumar B., Negi V., Ailawadhi B., Mishra S., Pradhan B., Misra K., Hickson P., Surdej J., 2022b, @doi [Journal of Astrophysics and Astronomy] 10.1007/s12036-021-09795-3 , https://ui.adsabs.harvard.edu/abs/2022JApA...43...10K 43, 10

    doi

  31. [39]

    M., et al., 2009, @doi [ ] 10.1086/648598 , https://ui.adsabs.harvard.edu/abs/2009PASP..121.1395L 121, 1395

    Law N. M., et al., 2009, @doi [ ] 10.1086/648598 , https://ui.adsabs.harvard.edu/abs/2009PASP..121.1395L 121, 1395

    2009 doi

  32. [40]

    Lecun Y., Bottou L., Bengio Y., Haffner P., 1998, @doi [Proceedings of the IEEE] 10.1109/5.726791 , 86, 2278

    1998 doi

  33. [41]

    Mahabal A., et al., 2019, @doi [Publications of the Astronomical Society of the Pacific] 10.1088/1538-3873/aaf3fa , 131, 038002

    2019 doi

  34. [42]

    Turpin, D

    Makhlouf, K. Turpin, D. Corre, D. Karpov, S. Kann, D. A. Klotz, A. 2022, @doi [A&A] 10.1051/0004-6361/202142952 , 664, A81

    2022 doi

  35. [43]

    T., Stockman H

    McGraw J. T., Stockman H. S., Angel J. R. P., Epps H., Williams J. T., 1982, in Crawford D. L., ed., Vol. 0331, Instrumentation in Astronomy IV. SPIE, pp 137 -- 145, @doi 10.1117/12.933448 , https://doi.org/10.1117/12.933448

    1982 doi

  36. [44]

    J., 1978, in , Vol

    Mor \'e J. J., 1978, in , Vol. 630, Lecture Notes in Mathematics, Berlin Springer Verlag. pp 105--116, @doi 10.1007/BFb0067700

    1978 doi

  37. [45]

    pp 807--814

    Nair V., Hinton G., 2010. pp 807--814

    2010

  38. [46]

    Negi V., et al., 2024, @doi [Bulletin de la Societe Royale des Sciences de Liege] 10.25518/0037-9565.11904 , https://ui.adsabs.harvard.edu/abs/2024BSRSL..93..863N 93, 863

    2024

  39. [47]

    Pranshu K., et al., 2023, Transient Name Server Discovery Report, https://ui.adsabs.harvard.edu/abs/2023TNSTR3062....1P 2023-3062, 1

    2023

  40. [48]

    Pranshu K., et al., 2024a, @doi [Bulletin de la Societe Royale des Sciences de Liege] 10.25518/0037-9565.11895 , https://ui.adsabs.harvard.edu/abs/2024BSRSL..93..828P 93, 828

  41. [49]

    Pranshu K., et al., 2024b, Transient Name Server Discovery Report, https://ui.adsabs.harvard.edu/abs/2024TNSTR.964....1P 2024-964, 1

    2024

  42. [50]

    S \' a nchez-S \' a ez P., et al., 2021, @doi [The Astronomical Journal] 10.3847/1538-3881/abd5c1 , 161, 141

    2021 doi

  43. [51]

    O., Cabral J

    S \'a nchez B. O., Cabral J. B., Beroiz M., Dom \' nguez M., Lares M., 2019, Properimage: Image coaddition and subtraction , Astrophysics Source Code Library, record ascl:1904.025

    2019

  44. [52]

    S \'a nchez B., Beroiz M., Chalela M., Cabral J., 2020, Two co-addition and image subtraction codes, @doi 10.13140/RG.2.2.11455.23203

    2020

  45. [53]

    Santos A., et al., 2024, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/stae466 , 529, 59

    2024 doi

  46. [54]

    Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R., 2014, Journal of Machine Learning Research, 15, 1929

    2014

  47. [55]

    Steeghs D., et al., 2022, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/stac013 , 511, 2405

    2022 doi

  48. [56]

    M., ed., Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol

    Surdej J., et al., 2006, in Stepp L. M., ed., Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 6267, Ground-based and Airborne Telescopes. p. 626704, @doi 10.1117/12.671695

    2006 doi

  49. [57]

    Surdej J., et al., 2018, @doi [Bulletin de la Société Royale des Sciences de Liège] 10.25518/0037-9565.7498 , pp 68--79

    2018

  50. [58]

    L., et al., 2018, @doi [ ] 10.1088/1538-3873/aabadf , https://ui.adsabs.harvard.edu/abs/2018PASP..130f4505T 130, 064505

    Tonry J. L., et al., 2018, @doi [ ] 10.1088/1538-3873/aabadf , https://ui.adsabs.harvard.edu/abs/2018PASP..130f4505T 130, 064505

    2018 doi

  51. [59]

    Turpin D., et al., 2020, @doi [ ] 10.1093/mnras/staa2046 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.497.2641T 497, 2641

    2020 doi

  52. [60]

    Virtanen P., et al., 2020, @doi [Nature Methods] 10.1038/s41592-019-0686-2 , 17, 261

    2020 doi

  53. [61]

    M., et al., 2004, @doi [ ] 10.1016/j.newar.2003.12.056 , https://ui.adsabs.harvard.edu/abs/2004NewAR..48..637W 48, 637

    Wood-Vasey W. M., et al., 2004, @doi [ ] 10.1016/j.newar.2003.12.056 , https://ui.adsabs.harvard.edu/abs/2004NewAR..48..637W 48, 637

    2004 doi

  54. [62]

    Ye Q., et al., 2019, @doi [ ] 10.1088/1538-3873/ab1b18 , https://ui.adsabs.harvard.edu/abs/2019PASP..131g8002Y 131, 078002

    2019 doi

  55. [63]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.stat...

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.