What is the powerful statistic in Neyman-Pearson lemma?

According to the Neyman-Pearson lemma (Neyman and Pearson, 1933a), the most powerful (following the definition given in (Neyman and Pearson, 1933b)) statistic is the likelihood ratio test:L(α, q) = P(N,R |H0)P(N,R |H1(α, q)) , (9)which can be calculated without prior information on T .

What is the common technique used to estimate relative importance?

To estimate this relative importance the authors can use a wide range of techniques that involve from data visualization, to Principal Component Analysis (PCA, Pearson (1901)).

What is the disadvantage of euclidean distance?

Although this method has the advantage of fast training, a drawback is that in a high number of dimensions, euclidean distance can be quite computationally expensive and at the same time inaccurate in terms of the feature space real metric.

What can be done to quantify performance metrics for different strategies?

Using the values of the confusion matrix the authors can compute more sophisticated scores, useful to quantify performance metrics for different optimization strategies.•

What is the reason why the authors have more sources of confusion in the real images?

It is clear that the authors have more sources of confusion in the real images, this could explained by the presence of instrumental defects on the CCD camera used, poor flat field calibration, and correlated noise coming from the stacking procedure.

Open AccessJournal Article10.1016/J.ASCOM.2019.05.002

Machine Learning on Difference Image Analysis: A comparison of methods for transient detection

Bruno Sanchez, +17 more

- 26 Dec 2018

- arXiv: Instrumentation and Methods for A...

TL;DR: The effects of the different ML techniques, and the relative feature importances for classification of transient candidates, are studied, and an optimal combined strategy is proposed, which constitutes the basic elements of pipelines that could be applied in searches of electromagnetic counterparts to GW sources.

Abstract: We present a comparison of several Difference Image Analysis (DIA) techniques, in combination with Machine Learning (ML) algorithms, applied to the identification of optical transients associated with gravitational wave events. Each technique is assessed based on the scoring metrics of Precision, Recall, and their harmonic mean F1, measured on the DIA results as standalone techniques, and also in the results after the application of ML algorithms, on transient source injections over simulated and real data. This simulations cover a wide range of instrumental configurations, as well as a variety of scenarios of observation conditions, by exploring a multi dimensional set of relevant parameters, allowing us to extract general conclusions related to the identification of transient astrophysical events. The newest subtraction techniques, and particularly the methodology published in Zackay et al. (2016) are implemented in an Open Source Python package, named properimage, suitable for many other astronomical image analyses. This together with the ML libraries we describe, provides an effective transient detection software pipeline. Here we study the effects of the different ML techniques, and the relative feature importances for classification of transient candidates, and propose an optimal combined strategy. This constitutes the basic elements of pipelines that could be applied in searches of electromagnetic counterparts to GW sources.

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Most frequently asked questions

1. What contributions have the authors mentioned in the paper "Machine learning on difference image analysis: a comparison of methods for transient detection" ?

The authors present a comparison of several Difference Image Analysis ( DIA ) techniques, in combination with Machine Learning ( ML ) algorithms, applied to the identification of optical transients associated to gravitational wave events.. This together, with the ML libraries the authors describe, provides an effective transient detection software pipeline.. Here the authors study the effects of the different ML techniques, and the relative feature importances for classification of transient candidates, and propose an optimal combined strategy.

2. What future works have the authors mentioned in the paper "Machine learning on difference image analysis: a comparison of methods for transient detection" ?

A future extension of this work is to tackle the genuine transient time-series astrophysical classification for large amounts of data.

3. What is the main quality of the algorithm?

A major quality of this algorithm is that the procedure to find this hyperplane only depends on inner products of feature vectors (in the linear algebra sense), and so, non-linear transformation kernels can be used to make this classifier able to work in a wider range of problems.•

4. What is the method for generating a catalog of real objects in the field?

First the authors used Stuff to produce a catalog of real objects in the field, including galaxies and stars, containing their positions and real photometric properties, as well as shape parameters.

Figure 4: Cumulative Luminosity functions for Transient Sources (TS) (left column), Artifacts (Ar) (center column) and Misses (right column) object classes. In the first row we have the simulated data and in the second row the real dataset. The cumulative luminosity function of the injected sources is displayed in every panel as the shaded area. The line code for DIA techniques is: Zackay’s is always in lines with white dots, where Bramich’s is in lines with filled dots, Alard-Lupton is in dotted lines, as well as S corr statistics is in black solid line.

Figure 7: Radial plot of normalized feature importances. The left panel corresponds to the feature selection for the RandomForest algorithm in the simulated dataset. The right panel corresponds to the feature selection specific to the kNN algorithm as explained in Sec. 2.2.2, in the real dataset. The details on the calculation and meaning of each feature are included in Appendix A.

Figure 2: Distribution of α values for the artifacts and transient sources for S corr detection technique. Notice the logarithmic scales in both axes. In vertical lines we include the position of thresholds for α = 3.5σ and α = 5σ. Left: simulated dataset. Right: real dataset.

Table 1: Parameter space for simulated images to be explored for transient detection.

Figure 1: Diagram showing the combined DIA and ML analysis process developed in this work. Reference (R) and new (N) simulated images are processed using DIA by applying Alard and Lupton (H), Zackay et al. (Z), Bramich (B) methods, and S corr (S) image. We show for comparison, an example of a real object, detected from an injected transient source (TS) and an example of a bogus object arising from an artifact (Ar) in the image difference. In the bottom of this figure, we show three bars, splitted into several blocks, each one representing different classification results at different stages of our multi-stage classification process. The first bar represents the results of source detection after the DIA algorithms have performed the image subtraction. The first two blocks of this bar represent the injected sources, which are splitted into Missed and TS detected, and the left block is the resulting set of artifacts Ar. The ML algorithms attempt to learn the classification of TS and Ar subsets into Real and Bogus for each DIA technique. The results of ML are displayed in the second bar, where the sum of the blocks TS and Ar are splitted into Real and Bogus blocks of data. The combination of the DIA+ML results is represented in the last horizontal bar, showing the final quantities of False Negatives (FN), True Positives (TP), False Positives (FP) and the True Negatives (TN). As a final figure of merit we calculate the F1 for each combination of DIA+ML algorithms.

Figure 8: Maps of Recall R and F1, for A&Lupton+RandomForest, for the simulations in two different projections. The left panel shows the values of Recall R, as a projection of distance of the injected transient relative to the center of the host galaxy (in scale radius units) vs the difference in brightness between the transient and the host galaxy. The right panel in turn, shows values of F1 in the plane of slope of the stellar luminosity function (STARCOUNT SLOPE) on the images, vs the total density of stellar sources (STARCOUNT ZP). Notice that greyscale is not the same for both panels.

Citations

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D. Turpin, +36 more

- 22 Feb 2019

- arXiv: Instrumentation and Methods for A...

TL;DR: In this article, the authors present the mini-GWAC pipeline that is set up to respond to the GW alerts and report their optical follow-up observations of 8 GW alerts detected during the O2 run.

...read moreread less

References

•Journal Article•10.1023/A:1010933404324

Random Forests

Leo Breiman

- 01 Oct 2001

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113.1K

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Elements of information theory

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- 01 Jan 1991

TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.

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The Elements of Statistical Learning

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- 01 Jan 2001

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Journal Article•10.1145/358669.358692

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Martin A. Fischler, +1 more

- 01 Jun 1981

- Communications of The ACM

TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.

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Machine Learning on Difference Image Analysis: A comparison of methods for transient detection

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AI Agents for this Paper

Most frequently asked questions

1. What contributions have the authors mentioned in the paper "Machine learning on difference image analysis: a comparison of methods for transient detection" ?

2. What future works have the authors mentioned in the paper "Machine learning on difference image analysis: a comparison of methods for transient detection" ?

3. What is the main quality of the algorithm?

4. What is the method for generating a catalog of real objects in the field?

5. What is the powerful statistic in Neyman-Pearson lemma?

6. What is the common technique used to estimate relative importance?

7. What is the disadvantage of euclidean distance?

8. What can be done to quantify performance metrics for different strategies?

9. What is the reason why the authors have more sources of confusion in the real images?

Figures

Citations

The mini-GWAC optical follow-up of the gravitational wave alerts: results from the O2 campaign and prospects for the upcoming O3 run.

References

Random Forests

Scikit-learn: Machine Learning in Python

Elements of information theory

The Elements of Statistical Learning

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

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