# Exploring the Relationship Between Algorithm Performance, Vocabulary, and
Run-Time in Text Classification

Wilson Fearn
Brigham Young University
<EMAIL_ADDRESS>
&Orion Weller
Brigham Young University
<EMAIL_ADDRESS>
&Kevin Seppi
Brigham Young University
<EMAIL_ADDRESS>

###### Abstract

Text classification is a significant branch of natural language processing,
and has many applications including document classification and sentiment
analysis. Unsurprisingly, those who do text classification are concerned with
the run-time of their algorithms, many of which depend on the size of the
corpus’ vocabulary due to their bag-of-words representation. Although many
studies have examined the effect of preprocessing techniques on vocabulary
size and accuracy, none have examined how these methods affect a model’s run-
time. To fill this gap, we provide a comprehensive study that examines how
preprocessing techniques affect the vocabulary size, model performance, and
model run-time, evaluating ten techniques over four models and two datasets.
We show that some individual methods can reduce run-time with no loss of
accuracy, while some combinations of methods can trade 2-5% of the accuracy
for up to a 65% reduction of run-time. Furthermore, some combinations of
preprocessing techniques can even provide a 15% reduction in run-time while
simultaneously improving model accuracy.111Our code and results are publicly
available at https://github.com/wfearn/preprocessing-paper

##  1 Introduction

With the increasing amount of text data available, text analysis has become a
significant part of machine learning (ML). Many problems in text analysis use
ML methods to perform their task, ranging from classical problems like text
classification and topic modeling, to more complex tasks like question
answering. Although neural networks have become increasingly common in the
research field, many industry NLP problems can be well served by less complex
but more efficient and explainable models, such as Support Vector Machines
(SVMs) or K-Nearest Neighbors (K-NN).

We focus on the text classification problem, where the dominant approach to
using these non-neural models is to first calculate the number of unique terms
in the dataset (the vocabulary, size $V$) and encode each instance of the
dataset into a bag-of-words (BoW) representation Joachims (1998); Zhang et al.
(2010). This results in a high-dimensional vector of size $V$ that indicates
whether each given word of the vocabulary was used in this instance.

However, the vanilla approach to the BoW representation can lead to sub-par
performance, as shown by numerous studies that have examined how preprocessing
techniques affect the BoW w.r.t. performance and vocabulary size. These
studies have examined this representation in fields such as information
retrieval Chaudhari et al. (2015); Patil and Atique (2013); Beil et al.
(2002); Senuma (2011), text classification Yang and Pedersen (1997); Caragea
et al. (2012); Uysal and Gunal (2014); Vijayarani et al. (2015); Kumar and
Harish (2018); HaCohen-Kerner et al. (2020); Symeonidis et al. (2018) and
topic modeling Schofield and Mimno (2016); Blei et al. (2003). They suggest a
myriad of preprocessing techniques that could improve performance, ranging
from choosing features that have high mutual information, low frequency, or
simply remove punctuation.

Another related problem of the BoW representation is that this sparse high-
dimensional vector does not scale well to datasets with large vocabularies. As
preprocessing techniques help contribute to a reduced vocabulary, they should
also help alleviate this scaling problem, at least according to folklore.
However, to the best of our knowledge, no previous study of preprocessing
techniques have examined how they contribute to reduced run-time costs,
leading to uncertainty about what these techniques do to mitigate the
computational complexity in practice.

To remedy this, we analyze how these preprocessing methods affect not only
vocabulary size and performance, but also how they affect training and
inference time. To do this, we contribute a comprehensive analysis of 10
different preprocessing methods applied to four machine learning models,
evaluated on two datasets with widely varying vocabularies (Figure 1).

Figure 1: Comparing vocabulary size (in millions) vs the total number of words
(in 10s of millions) for the AP News and Amazon corpora. Note that the
vocabulary size of AP News w.r.t. the number of documents plateaus much faster
than the noisier Amazon corpus.

Our results show that the individual preprocessing methods provide widely
different effects on run-time, with some methods (i.e. rare word filtering and
stopword removal) providing significant run-time reductions without losing any
performance. We also show that some combinations of preprocessing methods both
improve performance and reduce run-time.

##  2 Experimental Setup

### Datasets

To see how preprocessing affects run-time, we examine two datasets (in
English): the Amazon He and McAuley
(2016)222http://jmcauley.ucsd.edu/data/amazon/ and AP News corpora MacIntyre
(1998). These datasets were chosen because of the wide disparity between their
vocabularies. The Amazon corpus comes from user product reviews and contains a
much higher vocabulary relative to the number of documents, due to its noisy
text. The AP News corpus contains professionally-edited news articles and its
vocabulary plateaus much faster than the Amazon corpus (Figure 1). We perform
sentiment analysis on Amazon and year classification on AP News and report
scores with the accuracy metric. We note that we also computed the F1 score
alongside accuracy and found our results to be similar; thus we report
accuracy since it is easier to understand.

Group | Method | Vocab Size $\downarrow$ | Train Time $\downarrow$ | Test Time $\downarrow$ | Accuracy $\uparrow$
---|---|---|---|---|---
| stop | 99.8 ± 0.2 |  69.5 ± 1.4 | 79.0 ± 3.5 | 97.4 ± 2.2
| rare |  1.0 ± 0.0 | 80.6 ± 3.0 |  70.3 ± 3.2 |  99.3 ± 2.8
| seg | 24.6 ± 0.2 | 93.7 ± 2.4 | 80.3 ± 2.5 |  100.6 ± 1.6
| spell | 57.8 ± 0.2 | 95.1 ± 2.6 | 89.7 ± 2.6 |  99.4 ± 2.3
Individual Methods | hash | 10.1 ± 0.0 | 97.1 ± 4.0 | 75.7 ± 4.0 |  99.2 ± 1.1
| nopunct | 61.9 ± 0.2 | 97.5 ± 2.2 | 89.5 ± 2.0 |  100.7 ± 1.6
| stem | 81.7 ± 0.4 | 97.8 ± 2.0 | 95.0 ± 2.6 |  99.8 ± 1.0
| lower | 88.7 ± 0.3 | 101.7 ± 7.5 | 100.1 ± 6.6 |  99.1 ± 3.0
| nrem | 96.2 ± 0.7 | 101.7 ± 4.0 | 100.7 ± 5.3 |  99.7 ± 1.2
| lemma | 98.1 ± 0.5 | 102.2 ± 5.3 | 101.5 ± 5.1 |  100.3 ± 1.1
| spell+seg+nrem+stop+rare |  0.8 ± 0.0 |  44.6 ± 1.0 | 56.8 ± 1.1 | 95.4 ± 2.0
| stop+rare | 0.9 ± 0.0 |  46.5 ± 3.5 | 44.5 ± 2.0 |  99.8 ± 0.8
Lowest Train/Test Time | spell+seg+nrem+stop+hash | 7.6 ± 0.0 | 53.9 ± 2.0 |  39.6 ± 1.4 |  97.7 ± 2.6
| spell+seg+nrem+stop | 14.1 ± 0.0 | 54.2 ± 1.6 | 50.9 ± 2.3 |  97.6 ± 2.2
| spell+seg+nrem+stop+lemma | 11.9 ± 0.0 | 55.1 ± 0.9 | 50.1 ± 1.7 | 97.6 ± 1.3
| nopunct+rare |  0.9 ± 0.0 |  82.6 ± 1.9 |  70.2 ± 1.7 |  101.0 ± 1.8
| lower+nopunct+nrem+rare |  0.9 ± 0.0 |  87.1 ± 5.5 | 88.8 ± 5.1 | 101.1 ± 0.3
Highest Accuracy | lower+nopunct+rare |  0.9 ± 0.0 | 86.1 ± 2.7 | 86.7 ± 2.2 |  101.3 ± 0.6
| seg+rare |  0.9 ± 0.0 |  86.3 ± 5.8 |  73.3 ± 3.9 |  101.4 ± 1.5
| spell+seg+rare |  0.9 ± 0.0 | 89.6 ± 5.6 | 88.4 ± 5.3 |  101.8 ± 0.5
Table 1: Effect of preprocessing techniques on Amazon. Scores are the relative
performance of each method over the no preprocessing baseline (e.g. stopword
removal takes only 69.5% of the baseline’s training time). Results are the
average (and std) relative performance of the four models, across the five
dataset seeds. Bold indicates statistical similarity to the best score, from a
two-sample t-test with $\alpha=0.05$. For brevity, this table only includes
individual methods and those with the highest accuracy or lowest train/test
time. All results are in Appendix C.

To test the effect of document size on preprocessing, we sampled various-
sized3335k, 10k, 20k, 30k, 40k, 50k, and 100k instances datasets from the
original corpus and ran our analysis on each, sampling 5 different times with
differing random seeds.444Although the Amazon corpus contains many more
documents, we keep our sampling consistent with the AP News corpus, as AP News
has only 600k instances. However, we found that our results were nearly
identical across the differing corpus sizes and thus, only report numbers for
the 100k size.

### Preprocessing Methods

We analyze 10 different methods (with their shortened names in parenthesis):
lowercasing (lower), rare word filtering (rare), hashing (hash), punctuation
removal (nopunct), stopword removal (stop), number removal (nrem), word
stemming (stem), lemmatization (lemma), spelling correction (spell), and word
segmentation (seg). We choose these methods because of their prevalence in
previous work Symeonidis et al. (2018); Kumar and Harish (2018); HaCohen-
Kerner et al. (2020) and their use in industry Li et al. (2013); Sanchez-Pi et
al. (2014).

Due to the exponential number of possible preprocessing combinations, we run
all individual methods but restrict the search space of combinations of these
methods. For rare word filtering and word hashing, we first conduct
experiments for 9 different levels of filtering individually, using only the
best level in future combinations with other methods. Results for all levels
of filtering and hashing are in Appendices A and B. We then conduct
experiments for all 24 combinations of spelling correction, word segmentation,
number removal, and stopword removal, using the best outcome (the pipeline of
all four) to combine with other methods. We note that while this is not an
exhaustive search of all combinations, our analysis includes the standard
preprocessing pipelines as well as many more.

### Models

We use Scikit-Learn Pedregosa et al. (2011) for three of the base algorithms,
including K-NN Altman (1992), Naive Bayes Rish et al. (2001), and the Support
Vector Machine (SVM, Suykens and Vandewalle (1999)). We also employ Vowpal
Wabbit Langford et al. (2007); Karampatziakis and Langford (2010), due to its
strong performance and frequent use in industry. All models use default
hyperparameters and our document representations use the BoW representation,
consisting of a sparse vector format. These four models provide a wide range
of algorithms that might be used, allowing us to show how preprocessing
methods generalize across models.

### Compute

All experiments were performed using 14-core Intel Broadwell processors
running at 2.4GHz with 128GB of DDR4 2400 MT/s RAM.

##  3 Results

We format our results relative to the algorithm with no preprocessing, to
easily show how preprocessing changes this baseline performance. We first run
each algorithm with no preprocessing, measuring the run-time, vocabulary size,
and accuracy. We then report the scores of each preprocessing pipeline
relative to the algorithm’s baseline (e.g. a model with preprocessing that
scores 75% of the no-preprocessing baseline’s accuracy has a relative accuracy
of 0.75).

As the cross product of the number of methods vs. the number of models is
still far too large to include in this paper, we show the average of each
model’s relative proportion to its respective baseline performance.555We first
compute each algorithm’s relative score to its baseline (e.g. SVM with rare
word filtering vs SVM with no preprocessing) and then take the average of the
models for that method (e.g. average the relative performance of rare word
filtering on models {K-NN, Naive Bayes, SVM, and Vowpal Wabbit} for the final
score for rare word filtering). This aggregation shows us the average relative
performance across the four models, helping us generalize our results to be
model-independent. For full tables detailing specific model results, see
Appendix C. Bold scores in tables indicate statistical similarity to the best
score in the column (two-sample t-test, $\alpha=0.05$).

Group | Method | Vocab Size $\downarrow$ | Train Time $\downarrow$ | Test Time $\downarrow$ | Accuracy $\uparrow$
---|---|---|---|---|---
| rare |  0.1 ± 0.0 |  51.4 ± 1.2 |  59.1 ± 2.2 |  99.8 ± 2.0
| stop | 99.5 ± 0.3 | 82.5 ± 2.9 | 86.4 ± 2.0 |  99.0 ± 1.4
| hash | 32.8 ± 0.0 | 98.5 ± 4.7 | 84.7 ± 4.0 |  99.5 ± 1.7
| spell | 65.6 ± 0.2 | 98.6 ± 4.7 | 95.2 ± 7.4 |  99.7 ± 0.9
Individual Methods | lower | 92.3 ± 0.3 | 98.9 ± 1.9 | 97.6 ± 3.5 |  99.8 ± 1.6
| stem | 82.7 ± 0.4 | 99.1 ± 5.2 | 95.2 ± 4.0 |  100.1 ± 1.4
| seg | 47.5 ± 0.2 | 99.5 ± 2.4 | 88.5 ± 2.9 |  100.3 ± 1.3
| nrem | 89.8 ± 0.4 | 99.8 ± 3.9 | 98.5 ± 5.0 |  99.2 ± 1.1
| nopunct | 65.6 ± 0.2 | 99.9 ± 4.6 | 92.9 ± 4.9 |  99.6 ± 1.4
| lemma | 97.4 ± 0.3 | 100.5 ± 1.2 | 98.6 ± 1.6 |  99.6 ± 1.7
| spell+seg+nrem+stop+rare |  0.1 ± 0.0 |  29.2 ± 0.5 |  49.0 ± 0.8 |  99.3 ± 1.7
| spell+nopunct+nrem+stop | 39.9 ± 0.1 | 71.0 ± 1.8 | 69.4 ± 1.8 |  100.1 ± 1.0
Lowest Train/Test Time | spell+nopunct+nrem+stop+lemma | 36.2 ± 0.1 | 71.3 ± 1.3 | 68.4 ± 1.4 |  99.1 ± 1.6
| spell+seg+nrem+stop | 29.3 ± 0.0 | 72.1 ± 1.8 | 65.4 ± 3.2 |  100.1 ± 1.4
| spell+nopunct+nrem+stop+stem | 29.6 ± 0.1 | 72.4 ± 1.2 | 68.2 ± 1.8 |  98.7 ± 1.8
| spell+seg+nrem+stop+stem |  19.3 ± 0.1 | 74.2 ± 2.3 |  66.9 ± 1.5 |  99.7 ± 1.6
| spell+nopunct+nrem+stop | 39.9 ± 0.1 |  71.0 ± 1.8 | 69.4 ± 1.8 |  100.1 ± 1.0
Highest Accuracy | spell+seg+nrem+stop | 29.3 ± 0.0 |  72.1 ± 1.8 |  65.4 ± 3.2 |  100.1 ± 1.4
| spell+seg+nrem+stop+hash | 19.8 ± 0.0 |  73.1 ± 2.7 |  66.6 ± 3.4 |  100.2 ± 1.5
| lower+nopunct+nrem+stop+stem | 39.4 ± 0.4 | 75.5 ± 2.4 | 72.7 ± 3.0 |  100.3 ± 1.0
Table 2: Effect of preprocessing techniques on AP News. Scores are the
relative performance of each method over the no preprocessing baseline (e.g.
stopword removal takes only 82.5% of the baseline’s training time). Results
are the average (and std) relative performance of the four models, across the
five dataset seeds. Bold indicates statistical similarity to the best score,
from a two-sample t-test with $\alpha=0.05$. For brevity, this table only
includes individual methods and those with the highest accuracy or lowest
train/test time. All results are in Appendix C.

### Individual Techniques

We see results for the Amazon corpus in Table 1 and for the AP News corpus in
Table 2. On Amazon, each individual preprocessing method performs
statistically similar to the baseline’s accuracy, while three algorithms
(stopword removal, rare word filtering, and word segmentation) also provide a
moderate decrease (20-30%) in train and test time. Rare word filtering and
stopword removal are effective across both corpora (with rare word filtering
being even more effective on AP News, reducing the training time in half),
while the other methods do not significantly impact either train-time or
accuracy on AP News. We hypothesize that these techniques are more effective
on the AP corpus because of its much smaller (and less varied) vocabulary.

### Combination Techniques

The combination techniques also show a mild impact on accuracy, with most
methods on both corpora performing statistically similar to the baseline. On
the Amazon corpus, a handful of methods trade 2-5% of accuracy for up to a 65%
reduction in training and testing time (“Lowest Train/Test Time” section in
Table 1). Those that do not reduce accuracy (such as stop+rare) can still
reduce the training and testing time by up to 55%. We see in the “Highest
Accuracy” section that some methods (i.e. spell+seg+rare, etc.) can even
improve performance by almost 2% while also reducing run-time by 10-15%.
Similarly, when we examine the results on AP News we can find combinations
with reduced run-time (up to 70% and 50% reductions in train and test time
respectively) with no accuracy loss (but also no gains).

Figure 2: Pearson correlation between the relative performance variables
(train time, test time, accuracy, and vocabulary size) from the results of the
different preprocessing methods.

### Correlations

In order to show the correlation between run-time and the other variables, we
show a heatmap of these correlations in Figure 2. Most of these variables are
highly correlated with each other, as expected (training time is highly
correlated with testing time, etc.). However, although testing time is highly
correlated with vocabulary size (0.8 correlation), training time is not highly
correlated (0.17), We hypothesize that a low vocabulary directly leads to
faster inference, while which words are removed from the vocabulary has a
bigger role in how quickly the algorithm converges during training. This
hypothesis is also supported by the low correlation between vocabulary size
and accuracy, indicating that what is in the vocabulary is more important than
its size.

##  4 Related Work

These experiments relate to a large body of work that considers how
preprocessing methods affect the downstream accuracy of various algorithms,
ranging from topics in information retrieval Chaudhari et al. (2015); Patil
and Atique (2013); Beil et al. (2002), text classification and regression
Forman (2003); Yang and Pedersen (1997); Vijayarani et al. (2015); Kumar and
Harish (2018); HaCohen-Kerner et al. (2020); Symeonidis et al. (2018); Weller
et al. (2020), topic modeling Blei et al. (2003); Lund et al. (2019);
Schofield and Mimno (2016); Schofield et al. (2017a, b), and even more complex
tasks like question answering Jijkoun et al. (2003); Carvalho et al. (2007)
and machine translation Habash (2007); Habash and Sadat (2006); Leusch et al.
(2005); Weller et al. (2021); Mehta et al. (2020) to name a few. With the rise
of noisy social media, text preprocessing has become important for tasks that
use data from sources like Twitter and Reddit Symeonidis et al. (2018); Singh
and Kumari (2016); Bao et al. (2014); Jianqiang (2015); Weller and Seppi
(2020); Zirikly et al. (2019); Babanejad et al. (2020).

The closest lines of work to ours are those that examine how preprocessing
affects text classification accuracy, where recent works like Symeonidis et
al. (2018) and HaCohen-Kerner et al. (2020) analyze and cross-compare up to 16
different techniques for four machine learning algorithms. In contrast, our
work is the first to examine these preprocessing techniques beyond accuracy,
examining them in tandem with how they affect vocabulary size and run-time.

##  5 Conclusion

In this work we conduct the first study that examines the relationship between
vocabulary size, run-time, and accuracy across different models and corpora
for text classification. In general, we find that although vocabulary size is
highly correlated with testing time, it is not highly correlated with training
time or accuracy. In these cases, the specifics of the preprocessing algorithm
(the content of what it removes) matter more.

Our experiments show that rare word filtering and stopword removal are
superior to many other common preprocessing methods, both in terms of their
ability to reduce run-time and their potential to increase accuracy. By using
these methods, we show that it is possible to reduce training and testing time
by up to 65% with a loss of only 2-5% of accuracy, or in some cases, to
provide accuracy and run-time improvements simultaneously. We hope that this
study can help both researchers and industry practitioners as they design
machine learning pipelines to reach their end-goals.

## References

  * Altman (1992) Naomi S Altman. 1992.  An introduction to kernel and nearest-neighbor nonparametric regression.  _The American Statistician_ , 46(3):175–185.
  * Babanejad et al. (2020) Nastaran Babanejad, Ameeta Agrawal, Aijun An, and Manos Papagelis. 2020.  A comprehensive analysis of preprocessing for word representation learning in affective tasks.  In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_ , pages 5799–5810.
  * Bao et al. (2014) Yanwei Bao, Changqin Quan, Lijuan Wang, and Fuji Ren. 2014.  The role of pre-processing in twitter sentiment analysis.  _Intelligent Computing Methodologies Lecture Notes in Computer Science_ , page 615–624.
  * Beil et al. (2002) Florian Beil, Martin Ester, and Xiaowei Xu. 2002.  Frequent term-based text clustering.  In _Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining_ , pages 436–442. Association for Computing Machinery.
  * Blei et al. (2003) David M Blei, Andrew Y Ng, and Michael I Jordan. 2003.  Latent Dirichlet allocation.  _Journal of Machine Learning Research_ , 3(Jan):993–1022.
  * Caragea et al. (2012) Cornelia Caragea, Adrian Silvescu, and Prasenjit Mitra. 2012.  Combining hashing and abstraction in sparse high dimensional feature spaces.  In _Association for the Advancement of Artificial Intelligence_.
  * Carvalho et al. (2007) Gracinda Carvalho, David Martins de Matos, and Vitor Rocio. 2007.  Document retrieval for question answering: a quantitative evaluation of text preprocessing.  In _Proceedings of the ACM first Ph. D. workshop in CIKM_ , pages 125–130.
  * Chaudhari et al. (2015) Anagha Chaudhari, Pravin M Phadatare, Pranil S Kudale, Raviraj B Mohite, Rohan P Petare, Yogesh P Jagdale, and Amitabh Mudiraj. 2015.  Preprocessing of high dimensional dataset for developing expert ir system.  In _2015 International Conference on Computing Communication Control and Automation_ , pages 417–421. IEEE.
  * Forman (2003) George Forman. 2003.  An extensive empirical study of feature selection metrics for text classification.  _Journal of Machine Learning Research_ , 3.
  * Habash (2007) Nizar Habash. 2007.  Syntactic preprocessing for statistical machine translation.  _MT Summit XI_ , pages 215–222.
  * Habash and Sadat (2006) Nizar Habash and Fatiha Sadat. 2006.  Arabic preprocessing schemes for statistical machine translation.  In _Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers_ , pages 49–52.
  * HaCohen-Kerner et al. (2020) Yaakov HaCohen-Kerner, Daniel Miller, and Yair Yigal. 2020.  The influence of preprocessing on text classification using a bag-of-words representation.  _PloS one_ , 15(5):e0232525.
  * He and McAuley (2016) Ruining He and Julian McAuley. 2016.  Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering.  In _Proceedings of the 25th International Conference on World Wide Web_ , pages 507–517. International World Wide Web Conferences Steering Committee.
  * Jianqiang (2015) Zhao Jianqiang. 2015.  Pre-processing boosting twitter sentiment analysis? _2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)_.
  * Jijkoun et al. (2003) Valentin Jijkoun, Gilad Mishne, Maarten de Rijke, et al. 2003.  Preprocessing documents to answer dutch questions.  In _Proceedings of the 15th Belgian-Dutch Conference on Artificial Intelligence (BNAIC’03)_ , volume 434. Citeseer.
  * Joachims (1998) Thorsten Joachims. 1998.  Text categorization with support vector machines: Learning with many relevant features.  In _European conference on machine learning_ , pages 137–142. Springer.
  * Karampatziakis and Langford (2010) Nikos Karampatziakis and John Langford. 2010.  Online Importance Weight Aware Updates.  _arXiv e-prints_ , page arXiv:1011.1576.
  * Kumar and Harish (2018) HM Keerthi Kumar and BS Harish. 2018.  Classification of short text using various preprocessing techniques: An empirical evaluation.  In _Recent Findings in Intelligent Computing Techniques_ , pages 19–30. Springer.
  * Langford et al. (2007) John Langford, Lihong Li, and Alex Strehl. 2007.  Vowpal wabbit online learning project.
  * Leusch et al. (2005) Gregor Leusch, Nicola Ueffing, David Vilar, and Hermann Ney. 2005.  Preprocessing and normalization for automatic evaluation of machine translation.  In _Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization_ , pages 17–24.
  * Li et al. (2013) Huiying Li, Qiang Ye, and Rob Law. 2013.  Determinants of customer satisfaction in the hotel industry: An application of online review analysis.  _Asia Pacific Journal of Tourism Research_ , 18(7):784–802.
  * Lund et al. (2019) J. Lund, Piper Armstrong, Wilson Fearn, Stephen Cowley, Emily Hales, and K. Seppi. 2019.  Cross-referencing using fine-grained topic modeling.  In _NAACL-HLT_.
  * MacIntyre (1998) Robert MacIntyre. 1998.  North american news text supplement.  _LDC98T30, Linguistic Data Consortium_.
  * Mehta et al. (2020) Sneha Mehta, Bahareh Azarnoush, Boris Chen, Avneesh Saluja, Vinith Misra, Ballav Bihani, and Ritwik Kumar. 2020.  Simplify-then-translate: Automatic preprocessing for black-box machine translation.  _arXiv preprint arXiv:2005.11197_.
  * Patil and Atique (2013) Leena H Patil and Mohammed Atique. 2013.  A novel approach for feature selection method tf-idf in document clustering.  In _2013 3rd IEEE International Advance Computing Conference (IACC)_ , pages 858–862. Institute of Electrical and Electronics Engineers.
  * Pedregosa et al. (2011) F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011.  Scikit-learn: Machine learning in Python.  _Journal of Machine Learning Research_ , 12:2825–2830.
  * Rish et al. (2001) Irina Rish et al. 2001.  An empirical study of the naive bayes classifier.  In _IJCAI 2001 workshop on empirical methods in artificial intelligence_ , volume 3, pages 41–46.
  * Sanchez-Pi et al. (2014) Nayat Sanchez-Pi, Luis Martí, and Ana Cristina Bicharra Garcia. 2014.  Text classification techniques in oil industry applications.  In _International Joint Conference SOCO’13-CISIS’13-ICEUTE’13_ , pages 211–220. Springer.
  * Schofield et al. (2017a) Alexandra Schofield, Måns Magnusson, and D Mimno. 2017a.  Understanding text pre-processing for latent dirichlet allocation.  In _Proceedings of the 15th conference of the European Chapter of the Association for Computational Linguistics_ , volume 2, pages 432–436.
  * Schofield et al. (2017b) Alexandra Schofield, Måns Magnusson, and David Mimno. 2017b.  Pulling out the stops: Rethinking stopword removal for topic models.  _Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers_.
  * Schofield and Mimno (2016) Alexandra Schofield and David Mimno. 2016.  Comparing apples to apple: The effects of stemmers on topic models.  _Transactions of the Association for Computational Linguistics_ , 4:287–300.
  * Senuma (2011) Hajime Senuma. 2011.  K-means clustering with feature hashing.  _Proceedings of the ACL-HLT_ , page 122–126.
  * Singh and Kumari (2016) Tajinder Singh and Madhu Kumari. 2016.  Role of text pre-processing in twitter sentiment analysis.  _Procedia Computer Science_ , 89:549–554.
  * Suykens and Vandewalle (1999) Johan AK Suykens and Joos Vandewalle. 1999.  Least squares support vector machine classifiers.  _Neural processing letters_ , 9(3):293–300.
  * Symeonidis et al. (2018) Symeon Symeonidis, Dimitrios Effrosynidis, and Avi Arampatzis. 2018.  A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis.  _Expert Systems with Applications_ , 110:298–310.
  * Uysal and Gunal (2014) Alper Kursat Uysal and Serkan Gunal. 2014.  The impact of preprocessing on text classification.  _Information Processing & Management_, 50(1):104–112.
  * Vijayarani et al. (2015) S Vijayarani, Ms J Ilamathi, and Ms Nithya. 2015.  Preprocessing techniques for text mining-an overview.  _International Journal of Computer Science & Communication Networks_, 5(1):7–16.
  * Weller et al. (2020) Orion Weller, J. Hildebrandt, I. Reznik, Christopher Challis, E. S. Tass, Q. Snell, and K. Seppi. 2020.  You don’t have time to read this: An exploration of document reading time prediction.  In _ACL_.
  * Weller and Seppi (2020) Orion Weller and K. Seppi. 2020.  The rjokes dataset: a large scale humor collection.  In _LREC_.
  * Weller et al. (2021) Orion Weller, Matthias Sperber, Christian Gollan, and Joris Kluivers. 2021.  Streaming models for joint speech recognition and translation.  _arXiv preprint arXiv:2101.09149_.
  * Yang and Pedersen (1997) Yiming Yang and J. Pedersen. 1997.  A comparative study on feature selection in text categorization.  In _ICML_.
  * Zhang et al. (2010) Yin Zhang, Rong Jin, and Z. Zhou. 2010.  Understanding bag-of-words model: a statistical framework.  _International Journal of Machine Learning and Cybernetics_ , 1:43–52.
  * Zirikly et al. (2019) Ayah Zirikly, Philip Resnik, Ozlem Uzuner, and Kristy Hollingshead. 2019.  Clpsych 2019 shared task: Predicting the degree of suicide risk in reddit posts.  In _Proceedings of the sixth workshop on computational linguistics and clinical psychology_ , pages 24–33.

##  Appendix A Rare Word Filtering

Tables 4 and 4 show the results of rare word filtering on the Amazon and AP
News datasets. We filtered at levels corresponding to the geometric
progression of values from 1 to half the size of the corpus (we refer to these
as levels 1 to 9, with higher numbers being more filtered).

# | Vocab Size | Train Time | Test Time | Accuracy
---|---|---|---|---
9 |  0.0 ± 0.0 |  40.2 ± 1.4 |  51.8 ± 2.0 | 92.8 ± 0.7
8 | 0.2 ± 0.0 | 64.6 ± 2.5 | 64.0 ± 2.8 | 96.9 ± 1.1
7 | 1.0 ± 0.0 | 80.6 ± 3.0 | 70.3 ± 3.2 |  99.3 ± 2.8
6 | 4.4 ± 0.0 | 87.4 ± 2.7 | 72.4 ± 2.7 |  100.3 ± 1.8
5 | 16.4 ± 0.0 | 92.4 ± 3.6 | 76.2 ± 3.8 |  99.7 ± 2.4
4 | 41.4 ± 0.1 | 94.8 ± 1.9 | 83.2 ± 2.4 |  100.0 ± 2.1
3 | 63.3 ± 0.1 | 98.1 ± 3.2 | 90.8 ± 3.1 |  99.7 ± 1.8
1 | 100 ± 0.2 | 100.8 ± 2.5 | 100.1 ± 2.0 |  100.0 ± 1.1
2 | 77.5 ± 0.2 | 101.4 ± 5.2 | 97.1 ± 5.9 |  100.3 ± 1.9

Table 3: Rare word filtering on the Amazon dataset, across various levels.
Scores are the relative performance of each method over the no preprocessing
baseline. Results are the average (and std) relative performance of the four
models, across the five dataset seeds. Bold indicates statistical similarity
to the best score, from a two-sample t-test with $\alpha=0.05$.

# | Vocab Size | Train Time | Test Time | Accuracy
---|---|---|---|---
9 |  0.0 ± 0.0 |  33.9 ± 1.2 |  61.1 ± 3.0 |  99.3 ± 1.2
8 | 0.1 ± 0.0 | 51.4 ± 1.2 |  59.1 ± 2.2 |  99.8 ± 2.0
7 | 1.1 ± 0.0 | 69.6 ± 2.0 | 68.0 ± 2.0 |  100.0 ± 1.5
6 | 7.2 ± 0.0 | 83.8 ± 3.7 | 76.6 ± 5.7 |  99.5 ± 0.7
5 | 31.8 ± 0.0 | 94.0 ± 4.8 | 83.0 ± 3.7 |  99.4 ± 1.4
4 | 76.9 ± 0.1 | 98.2 ± 3.4 | 96.0 ± 5.5 |  99.7 ± 1.3
1 | 100.0 ± 0.3 | 99.4 ± 3.3 | 98.2 ± 2.7 |  99.8 ± 1.3
2 | 99.7 ± 0.2 | 100.3 ± 4.3 | 99.2 ± 3.9 |  99.6 ± 1.1
3 | 96.2 ± 0.2 | 100.6 ± 4.3 | 98.7 ± 2.4 |  100.0 ± 0.9

Table 4: Rare word filtering on the AP News dataset, across various levels.
This table is formatted the same as Table 4 (see the caption there for more
information).

We find that rare word filtering at higher levels provides increased
vocabulary and run-time reductions, while also reducing accuracy, in general.

##  Appendix B Word Hashing

Tables 6 and 6 show the effect of different levels of word hashing on model
accuracy (where “Size” indicates the number of hash buckets used). We find
that word hashing with small numbers of buckets reduces vocabulary and run-
time, while also decreasing accuracy in general.

Size | Vocab Size | Train Time | Test Time | Accuracy
---|---|---|---|---
500 |  0.1 ± 0.0 |  92.7 ± 3.7 |  76.9 ± 3.1 | 91.7 ± 1.7
60000 | 15.1 ± 0.0 |  96.1 ± 1.5 | 76.7 ± 2.4 |  99.3 ± 1.0
10000 | 2.5 ± 0.0 |  96.2 ± 1.6 |  73.2 ± 2.2 |  97.9 ± 1.3
1000 | 0.3 ± 0.0 |  96.2 ± 4.1 |  76.5 ± 3.5 | 93.5 ± 1.6
40000 | 10.1 ± 0.0 |  97.1 ± 4.0 |  75.7 ± 4.0 |  99.2 ± 1.1
4000 | 1.0 ± 0.0 | 97.3 ± 1.1 |  75.0 ± 1.0 | 96.7 ± 1.6
20000 | 5.0 ± 0.0 |  97.6 ± 3.5 |  74.6 ± 3.1 |  98.7 ± 1.2
8000 | 2.0 ± 0.0 |  98.2 ± 6.8 |  74.5 ± 4.8 |  97.9 ± 1.1
2000 | 0.5 ± 0.0 | 99.2 ± 3.7 | 77.2 ± 2.6 | 95.1 ± 1.6
6000 | 1.5 ± 0.0 | 100.6 ± 6.0 |  76.7 ± 4.9 | 97.1 ± 1.5

Table 5: Word Hashing on the Amazon dataset, across various levels. Scores are
the relative performance of each method over the no preprocessing baseline.
Results are the average (and std) relative performance of the four models,
across the five dataset seeds. Bold indicates statistical similarity to the
best score, from a two-sample t-test with $\alpha=0.05$.

Size | Vocab Size | Train Time | Test Time | Accuracy
---|---|---|---|---
500 |  0.4 ± 0.0 |  93.5 ± 2.5 | 81.4 ± 1.8 |  99.5 ± 1.3
1000 | 0.9 ± 0.0 |  94.7 ± 2.7 |  79.9 ± 2.6 |  99.8 ± 1.0
4000 | 3.5 ± 0.0 |  94.8 ± 2.8 |  77.5 ± 2.0 |  99.4 ± 1.2
10000 | 8.7 ± 0.0 |  95.4 ± 3.1 |  78.9 ± 3.9 |  98.9 ± 1.8
6000 | 5.2 ± 0.0 |  95.6 ± 2.9 |  78.5 ± 1.8 |  99.0 ± 0.9
2000 | 1.7 ± 0.0 |  96.2 ± 4.8 |  79.9 ± 4.0 |  98.9 ± 1.2
20000 | 17.3 ± 0.0 |  96.6 ± 3.4 | 81.2 ± 2.4 |  99.1 ± 1.4
8000 | 7.0 ± 0.0 |  96.8 ± 5.0 |  80.3 ± 4.7 |  99.0 ± 1.8
40000 | 32.8 ± 0.0 |  98.5 ± 4.7 | 84.7 ± 4.0 |  99.5 ± 1.7
60000 | 44.5 ± 0.1 |  99.0 ± 5.3 | 88.5 ± 6.3 |  99.1 ± 1.3

Table 6: Word Hashing on the AP News dataset, across various levels. This
table is formatted the same as Table 6 (see the caption there for more
information).

##  Appendix C Full Tables for Method Combinations

In Tables 7 and 8 we show the complete table for preprocessing method
combinations on Amazon and AP News respectively.

In Tables 9, 10, 11, and 12, we show the complete results for individuals
models (K-NN, Naive Bayes, Vowpal Wabbit, and SVM respectively). All results
are similar to the main conclusions found in the body of the paper. However,
Naive Bayes in particular shows strong accuracy gains and run-time reductions
for preprocessing methods, in comparison to other models.

Method | Vocab Size | Train Time | Test Time | Accuracy
---|---|---|---|---
spell+seg+nrem+stop+rare |  0.8 ± 0.0 |  44.6 ± 1.0 | 56.8 ± 1.1 | 95.4 ± 2.0
stop+rare | 0.9 ± 0.0 |  46.5 ± 3.5 | 44.5 ± 2.0 | 99.8 ± 0.8
spell+seg+nrem+stop+hash | 7.6 ± 0.0 | 53.9 ± 2.0 |  39.6 ± 1.4 | 97.7 ± 2.6
spell+seg+nrem+stop | 14.1 ± 0.0 | 54.2 ± 1.6 | 50.9 ± 2.3 | 97.6 ± 2.2
spell+seg+nrem+stop+lemma | 11.9 ± 0.0 | 55.1 ± 0.9 | 50.1 ± 1.7 | 97.6 ± 1.3
seg+nrem+stop+lemma | 18.5 ± 0.1 | 55.1 ± 2.3 | 54.3 ± 4.3 | 96.3 ± 3.7
spell+nopunct+nrem+stop+lemma | 31.9 ± 0.2 | 55.3 ± 1.5 | 56.7 ± 1.8 | 96.9 ± 1.2
spell+nopunct+nrem+stop | 34.1 ± 0.2 | 55.8 ± 1.6 | 56.4 ± 2.1 | 97.8 ± 1.6
seg+nrem+stop | 21.0 ± 0.1 | 55.9 ± 1.3 | 52.5 ± 1.6 | 97.5 ± 1.4
lower+nopunct+nrem+stop+lemma | 47.0 ± 0.3 | 56.0 ± 2.2 | 61.2 ± 1.8 | 96.0 ± 1.7
lower+nopunct+nrem+stop | 48.4 ± 0.3 | 56.5 ± 1.3 | 59.4 ± 1.8 | 96.0 ± 2.3
seg+nrem+stop+stem | 14.0 ± 0.1 | 58.5 ± 1.5 | 56.8 ± 1.8 | 97.9 ± 0.7
spell+seg+nrem+stop+stem | 8.5 ± 0.0 | 58.7 ± 1.6 | 54.8 ± 2.3 | 97.8 ± 1.4
lower+nopunct+nrem+stop+stem | 39.3 ± 0.2 | 59.0 ± 1.6 | 63.2 ± 2.9 | 96.2 ± 2.4
spell+nopunct+nrem+stop+stem | 27.7 ± 0.1 | 59.1 ± 2.6 | 61.4 ± 4.0 | 97.5 ± 1.3
stop | 99.8 ± 0.2 | 69.5 ± 1.4 | 79.0 ± 3.5 | 97.4 ± 2.2
lower+rare | 1.0 ± 0.0 | 80.5 ± 2.0 | 69.6 ± 1.7 |  99.8 ± 3.1
rare | 1.0 ± 0.0 | 80.6 ± 3.0 | 70.3 ± 3.2 |  99.3 ± 2.8
spell+rare | 0.9 ± 0.0 | 80.7 ± 2.2 | 70.9 ± 2.6 |  99.7 ± 1.8
stem+rare | 0.9 ± 0.0 | 81.4 ± 2.2 | 69.9 ± 1.7 | 99.4 ± 1.6
nrem+rare | 1.0 ± 0.0 | 82.0 ± 5.6 | 70.1 ± 3.2 | 99.6 ± 1.2
nopunct+rare | 0.9 ± 0.0 | 82.6 ± 1.9 | 70.2 ± 1.7 |  101.0 ± 1.8
lemma+rare | 1.0 ± 0.0 | 82.7 ± 6.5 | 69.6 ± 1.8 |  100.1 ± 1.5
lower+nopunct+rare | 0.9 ± 0.0 | 86.1 ± 2.7 | 86.7 ± 2.2 |  101.3 ± 0.6
seg+rare | 0.9 ± 0.0 | 86.3 ± 5.8 | 73.3 ± 3.9 |  101.4 ± 1.5
lower+nopunct+nrem+rare | 0.9 ± 0.0 | 87.1 ± 5.5 | 88.8 ± 5.1 | 101.1 ± 0.3
spell+seg+rare | 0.9 ± 0.0 | 89.6 ± 5.6 | 88.4 ± 5.3 |  101.8 ± 0.5
seg | 24.6 ± 0.2 | 93.7 ± 2.4 | 80.3 ± 2.5 |  100.6 ± 1.6
spell | 57.8 ± 0.2 | 95.1 ± 2.6 | 89.7 ± 2.6 |  99.4 ± 2.3
hash | 10.1 ± 0.0 | 97.1 ± 4.0 | 75.7 ± 4.0 | 99.2 ± 1.1
nopunct | 61.9 ± 0.2 | 97.5 ± 2.2 | 89.5 ± 2.0 |  100.7 ± 1.6
stem | 81.7 ± 0.4 | 97.8 ± 2.0 | 95.0 ± 2.6 | 99.8 ± 1.0
lower | 88.7 ± 0.3 | 101.7 ± 7.5 | 100.1 ± 6.6 |  99.1 ± 3.0
nrem | 96.2 ± 0.7 | 101.7 ± 4.0 | 100.7 ± 5.3 | 99.7 ± 1.2
lemma | 98.1 ± 0.5 | 102.2 ± 5.3 | 101.5 ± 5.1 | 100.3 ± 1.1
Table 7: Full results of preprocessing methods on Amazon. Scores are the relative performance of each method over the no preprocessing baseline. Results are the average (and std) relative performance of the four models, across the five dataset seeds. Bold indicates statistical similarity to the best score, from a two-sample t-test with $\alpha=0.05$. Method | Vocab Size | Train Time | Test Time | Accuracy
---|---|---|---|---
spell+seg+nrem+stop+rare |  0.1 ± 0.0 |  29.2 ± 0.5 |  49.0 ± 0.8 |  99.3 ± 1.7
rare |  0.1 ± 0.0 | 51.4 ± 1.2 | 59.1 ± 2.2 |  99.8 ± 2.0
spell+nopunct+nrem+stop | 39.9 ± 0.1 | 71.0 ± 1.8 | 69.4 ± 1.8 |  100.1 ± 1.0
spell+nopunct+nrem+stop+lemma | 36.2 ± 0.1 | 71.3 ± 1.3 | 68.4 ± 1.4 |  99.1 ± 1.6
spell+seg+nrem+stop | 29.3 ± 0.0 | 72.1 ± 1.8 | 65.4 ± 3.2 |  100.1 ± 1.4
spell+nopunct+nrem+stop+stem | 29.6 ± 0.1 | 72.4 ± 1.2 | 68.2 ± 1.8 |  98.7 ± 1.8
spell+seg+nrem+stop+hash | 19.8 ± 0.0 | 73.1 ± 2.7 | 66.6 ± 3.4 |  100.2 ± 1.5
spell+seg+nrem+stop+lemma | 25.4 ± 0.1 | 73.6 ± 1.9 | 67.6 ± 1.7 |  99.4 ± 1.5
lower+nopunct+nrem+stop+lemma | 49.1 ± 0.1 | 73.7 ± 1.3 | 72.8 ± 2.0 | 98.4 ± 1.4
seg+nrem+stop | 40.7 ± 0.2 | 74.1 ± 3.4 | 73.1 ± 3.3 |  99.5 ± 1.3
spell+seg+nrem+stop+stem | 19.3 ± 0.1 | 74.2 ± 2.3 | 66.9 ± 1.5 |  99.7 ± 1.6
lower+nopunct+nrem+stop | 51.4 ± 0.2 | 74.2 ± 3.5 | 74.0 ± 4.4 |  99.6 ± 1.3
seg+nrem+stop+lemma | 36.4 ± 0.1 | 75.2 ± 2.1 | 68.1 ± 3.5 |  99.1 ± 1.2
lower+nopunct+nrem+stop+stem | 39.4 ± 0.4 | 75.5 ± 2.4 | 72.7 ± 3.0 |  100.3 ± 1.0
seg+nrem+stop+stem | 29.3 ± 0.1 | 76.9 ± 2.1 | 63.9 ± 1.5 |  99.4 ± 1.7
stop | 99.5 ± 0.3 | 82.5 ± 2.9 | 86.4 ± 2.0 |  99.0 ± 1.4
hash | 32.8 ± 0.0 | 98.5 ± 4.7 | 84.7 ± 4.0 |  99.5 ± 1.7
spell | 65.6 ± 0.2 | 98.6 ± 4.7 | 95.2 ± 7.4 |  99.7 ± 0.9
lower | 92.3 ± 0.3 | 98.9 ± 1.9 | 97.6 ± 3.5 |  99.8 ± 1.6
stem | 82.7 ± 0.4 | 99.1 ± 5.2 | 95.2 ± 4.0 |  100.1 ± 1.4
seg | 47.5 ± 0.2 | 99.5 ± 2.4 | 88.5 ± 2.9 |  100.3 ± 1.3
nrem | 89.8 ± 0.4 | 99.8 ± 3.9 | 98.5 ± 5.0 |  99.2 ± 1.1
nopunct | 65.6 ± 0.2 | 99.9 ± 4.6 | 92.9 ± 4.9 |  99.6 ± 1.4
lemma | 97.4 ± 0.3 | 100.5 ± 1.2 | 98.6 ± 1.6 |  99.6 ± 1.7
Table 8: Full results of preprocessing methods on AP News. Scores are the relative performance of each method over the no preprocessing baseline. Results shown are the average (and std) relative performance of the four models, across the five dataset seeds. Bold indicates statistical similarity to the best score, from a two-sample t-test with $\alpha=0.05$. Method | Vocab Size | Train Time | Test Time | Accuracy
---|---|---|---|---
spell+seg+nrem+stop+rare |  0.8 ± 0.0 |  47.0 ± 0.8 |  46.7 ± 1.0 | 92.3 ± 3.3
seg+nrem+stop+lemma | 18.5 ± 0.1 | 56.7 ± 0.9 | 52.2 ± 1.6 |  89.3 ± 12.1
lower+nopunct+nrem+stop+lemma | 47.0 ± 0.3 | 56.8 ± 1.2 | 50.9 ± 1.0 | 90.9 ± 4.2
spell+seg+nrem+stop | 14.1 ± 0.0 | 57.0 ± 0.9 |  48.2 ± 1.5 | 92.2 ± 6.8
spell+seg+nrem+stop+lemma | 11.9 ± 0.0 | 57.2 ± 1.1 | 53.8 ± 0.9 | 92.8 ± 2.7
spell+seg+nrem+stop+hash | 7.6 ± 0.0 | 57.2 ± 1.4 | 48.3 ± 0.8 | 92.1 ± 6.3
spell+nopunct+nrem+stop+lemma | 31.9 ± 0.2 | 57.5 ± 1.4 | 52.9 ± 1.1 | 92.0 ± 2.2
seg+nrem+stop | 21.0 ± 0.1 | 57.8 ± 0.7 |  47.7 ± 1.2 | 94.7 ± 2.5
spell+nopunct+nrem+stop | 34.1 ± 0.2 | 58.2 ± 1.6 | 48.4 ± 0.8 | 95.5 ± 3.3
lower+nopunct+nrem+stop | 48.4 ± 0.3 | 58.8 ± 1.8 |  47.6 ± 1.3 | 89.2 ± 6.0
lower+nopunct+nrem+stop+stem | 39.3 ± 0.2 | 59.6 ± 1.2 | 61.7 ± 4.2 | 88.0 ± 5.8
spell+nopunct+nrem+stop+stem | 27.7 ± 0.1 | 60.5 ± 2.2 | 62.8 ± 4.3 | 95.1 ± 2.5
spell+seg+nrem+stop+stem | 8.5 ± 0.0 | 61.4 ± 2.0 | 62.4 ± 4.7 | 94.2 ± 2.8
seg+nrem+stop+stem | 14.0 ± 0.1 | 61.8 ± 2.0 | 65.2 ± 0.8 | 94.2 ± 0.8
stop | 99.8 ± 0.2 | 69.3 ± 1.8 | 52.8 ± 3.6 |  93.4 ± 6.6
rare | 1.0 ± 0.0 | 80.5 ± 1.5 | 96.4 ± 0.5 |  95.5 ± 9.3
lower+rare | 1.0 ± 0.0 | 82.7 ± 2.2 | 100.6 ± 2.4 |  93.6 ± 8.8
spell+rare | 0.9 ± 0.0 | 82.9 ± 1.4 | 104.2 ± 6.1 | 94.0 ± 4.2
lemma+rare | 1.0 ± 0.0 | 83.2 ± 3.2 | 99.3 ± 1.8 |  97.4 ± 3.5
stem+rare | 0.9 ± 0.0 | 84.4 ± 1.9 | 101.1 ± 2.2 | 95.8 ± 3.1
nopunct+rare | 0.9 ± 0.0 | 85.3 ± 2.7 | 101.0 ± 2.6 |  99.4 ± 4.4
nrem+rare | 1.0 ± 0.0 | 91.1 ± 16.3 | 102.7 ± 8.7 | 95.6 ± 2.7
spell | 57.8 ± 0.2 | 95.9 ± 2.7 | 103.8 ± 3.3 |  96.4 ± 5.7
seg+rare | 0.9 ± 0.0 | 96.7 ± 14.5 | 112.5 ± 9.7 |  100.6 ± 3.5
seg | 24.6 ± 0.2 | 98.0 ± 3.5 | 105.0 ± 2.7 |  99.3 ± 3.0
stem | 81.7 ± 0.4 | 99.0 ± 1.6 | 102.4 ± 1.9 |  98.5 ± 2.0
lower | 88.7 ± 0.3 | 99.4 ± 2.9 | 102.9 ± 2.8 |  96.0 ± 6.5
hash | 10.1 ± 0.0 | 99.7 ± 1.3 | 100.7 ± 1.9 |  99.4 ± 1.2
lemma | 98.1 ± 0.5 | 101.2 ± 2.5 | 101.9 ± 2.9 |  100.7 ± 1.3
nopunct | 61.9 ± 0.2 | 101.6 ± 3.5 | 103.5 ± 3.0 |  100.1 ± 3.9
nrem | 96.2 ± 0.7 | 102.1 ± 1.5 | 101.7 ± 2.4 |  99.7 ± 1.8
Table 9: Effect of preprocessing techniques on Amazon with the K-NN model. Scores are the relative performance of each method over the no preprocessing baseline. Results shown are the average (and std) relative performance across the five dataset seeds. Bold indicates statistical similarity to the best score, from a two-sample t-test with $\alpha=0.05$. Method | Vocab Size | Train Time | Test Time | Accuracy
---|---|---|---|---
stop+rare |  0.9 ± 0.0 |  44.7 ± 5.4 |  5.8 ± 0.2 |  104.5 ± 0.8
spell+seg+nrem+stop+hash | 7.6 ± 0.0 |  49.7 ± 1.4 | 12.3 ± 0.0 |  103.5 ± 0.8
spell+seg+nrem+stop | 14.1 ± 0.0 |  50.2 ± 1.0 | 18.7 ± 0.6 |  104.4 ± 0.2
spell+seg+nrem+stop+lemma | 11.9 ± 0.0 |  50.4 ± 0.4 | 16.6 ± 0.3 |  104.0 ± 0.4
seg+nrem+stop+lemma | 18.5 ± 0.1 | 52.0 ± 2.3 | 22.7 ± 0.6 |  103.7 ± 0.5
spell+nopunct+nrem+stop+lemma | 31.9 ± 0.2 | 52.1 ± 0.7 | 34.8 ± 0.3 | 103.1 ± 0.9
spell+nopunct+nrem+stop | 34.1 ± 0.2 | 52.8 ± 0.8 | 36.9 ± 0.4 | 102.3 ± 1.0
lower+nopunct+nrem+stop+lemma | 47.0 ± 0.3 | 53.1 ± 1.4 | 49.1 ± 0.8 | 101.7 ± 1.4
seg+nrem+stop | 21.0 ± 0.1 | 53.1 ± 1.6 | 25.3 ± 1.0 |  103.6 ± 1.7
spell+seg+nrem+stop+stem | 8.5 ± 0.0 | 53.3 ± 2.3 | 13.3 ± 0.3 | 102.7 ± 0.9
spell+nopunct+nrem+stop+stem | 27.7 ± 0.1 | 54.2 ± 0.5 | 31.1 ± 0.4 | 101.1 ± 1.4
seg+nrem+stop+stem | 14.0 ± 0.1 | 54.3 ± 0.8 | 18.7 ± 0.3 | 102.9 ± 0.7
lower+nopunct+nrem+stop | 48.4 ± 0.3 | 54.5 ± 0.7 | 50.2 ± 0.6 | 101.5 ± 1.5
lower+nopunct+nrem+stop+stem | 39.3 ± 0.2 | 55.6 ± 1.2 | 41.9 ± 0.4 | 101.5 ± 1.3
rare | 1.0 ± 0.0 | 69.0 ± 0.8 | 6.5 ± 0.1 | 100.6 ± 1.2
lower+rare | 1.0 ± 0.0 | 69.9 ± 1.5 | 6.4 ± 0.0 |  102.3 ± 1.9
stop | 99.8 ± 0.2 | 69.9 ± 1.2 | 99.2 ± 1.2 | 101.1 ± 0.7
nrem+rare | 1.0 ± 0.0 | 70.3 ± 1.2 | 6.5 ± 0.1 | 101.6 ± 0.9
spell+rare |  0.9 ± 0.0 | 70.5 ± 0.9 | 6.4 ± 0.1 | 101.5 ± 0.9
stem+rare |  0.9 ± 0.0 | 70.7 ± 1.7 | 6.4 ± 0.2 | 100.4 ± 1.7
lemma+rare | 1.0 ± 0.0 | 71.2 ± 0.8 | 6.5 ± 0.2 | 100.7 ± 0.7
nopunct+rare |  0.9 ± 0.0 | 73.2 ± 1.6 | 6.5 ± 0.2 | 101.2 ± 0.7
seg+rare |  0.9 ± 0.0 | 73.3 ± 2.2 | 6.5 ± 0.1 | 101.5 ± 1.2
seg | 24.6 ± 0.2 | 87.2 ± 1.6 | 29.1 ± 0.3 | 101.0 ± 0.7
hash | 10.1 ± 0.0 | 90.1 ± 1.1 | 15.4 ± 0.2 | 99.3 ± 1.5
spell | 57.8 ± 0.2 | 90.4 ± 1.7 | 59.9 ± 0.5 | 99.6 ± 1.3
nopunct | 61.9 ± 0.2 | 94.8 ± 2.4 | 64.1 ± 0.8 | 100.6 ± 0.8
stem | 81.7 ± 0.4 | 96.0 ± 1.0 | 82.7 ± 1.0 | 99.8 ± 1.2
lower | 88.7 ± 0.3 | 97.7 ± 2.3 | 89.9 ± 1.5 | 100.4 ± 0.9
lemma | 98.1 ± 0.5 | 99.5 ± 1.1 | 98.4 ± 0.9 | 100.8 ± 1.2
nrem | 96.2 ± 0.7 | 99.8 ± 1.5 | 97.0 ± 1.6 | 99.6 ± 1.2
Table 10: Effect of preprocessing techniques on Amazon with the Naive Bayes model. Scores are the relative performance of each method over the no preprocessing baseline. Results shown are the average (and std) relative performance across the five dataset seeds. Bold indicates statistical similarity to the best score, from a two-sample t-test with $\alpha=0.05$. Method | Vocab Size | Train Time | Test Time | Accuracy
---|---|---|---|---
spell+seg+nrem+stop+rare |  0.8 ± 0.0 |  42.1 ± 1.5 |  46.6 ± 1.4 | 97.0 ± 1.3
stop+rare | 0.9 ± 0.0 | 47.7 ± 4.0 |  51.4 ± 4.1 | 97.5 ± 0.9
lower+nopunct+nrem+stop | 48.4 ± 0.3 | 53.3 ± 1.6 | 56.1 ± 1.2 | 97.5 ± 0.8
spell+seg+nrem+stop | 14.1 ± 0.0 | 54.3 ± 3.2 | 57.2 ± 3.3 | 97.9 ± 0.9
spell+nopunct+nrem+stop+lemma | 31.9 ± 0.2 | 54.4 ± 2.3 | 57.9 ± 2.7 | 97.3 ± 1.2
seg+nrem+stop+lemma | 18.5 ± 0.1 | 54.5 ± 4.0 | 59.9 ± 9.5 | 97.3 ± 1.2
lower+nopunct+nrem+stop+lemma | 47.0 ± 0.3 | 54.9 ± 5.7 | 58.0 ± 4.8 | 96.7 ± 0.7
spell+nopunct+nrem+stop | 34.1 ± 0.2 | 54.9 ± 3.1 | 57.8 ± 2.8 | 97.9 ± 0.8
spell+seg+nrem+stop+hash | 7.6 ± 0.0 | 55.0 ± 3.0 | 58.3 ± 3.4 | 97.5 ± 0.7
seg+nrem+stop | 21.0 ± 0.1 | 55.4 ± 2.3 | 58.5 ± 2.5 | 97.2 ± 0.8
seg+nrem+stop+stem | 14.0 ± 0.1 | 55.5 ± 1.8 | 57.8 ± 2.0 | 98.1 ± 0.6
spell+seg+nrem+stop+stem | 8.5 ± 0.0 | 57.2 ± 0.8 | 59.6 ± 0.9 | 98.1 ± 0.9
lower+nopunct+nrem+stop+stem | 39.3 ± 0.2 | 57.2 ± 3.1 | 59.9 ± 3.2 | 98.4 ± 1.4
spell+nopunct+nrem+stop+stem | 27.7 ± 0.1 | 58.9 ± 5.9 | 61.9 ± 4.6 | 98.1 ± 0.5
stop | 99.8 ± 0.2 | 66.9 ± 1.5 | 69.2 ± 2.4 | 97.9 ± 0.7
nrem+rare | 1.0 ± 0.0 | 78.5 ± 3.5 | 79.1 ± 3.4 |  100.6 ± 0.6
spell+rare | 0.9 ± 0.0 | 79.1 ± 3.6 | 79.7 ± 2.8 |  101.7 ± 1.1
lemma+rare | 1.0 ± 0.0 | 79.8 ± 4.4 | 80.0 ± 3.9 |  101.5 ± 1.0
stem+rare | 0.9 ± 0.0 | 79.9 ± 3.3 | 80.2 ± 3.0 |  100.8 ± 0.8
lower+rare | 1.0 ± 0.0 | 80.2 ± 2.8 | 80.5 ± 2.3 |  101.7 ± 0.7
nopunct+rare | 0.9 ± 0.0 | 80.5 ± 2.2 | 80.7 ± 2.1 |  101.8 ± 1.2
lower+nopunct+rare | 0.9 ± 0.0 | 80.9 ± 3.6 | 81.7 ± 3.5 |  101.4 ± 0.7
seg+rare | 0.9 ± 0.0 | 82.9 ± 4.8 | 83.4 ± 5.1 |  101.8 ± 0.5
rare | 1.0 ± 0.0 | 84.1 ± 9.0 | 84.9 ± 8.3 |  100.6 ± 0.3
lower+nopunct+nrem+rare | 0.9 ± 0.0 | 84.4 ± 9.5 | 85.7 ± 9.1 |  101.3 ± 0.2
spell+seg+rare | 0.9 ± 0.0 | 86.1 ± 9.4 | 86.9 ± 8.9 |  102.1 ± 0.5
seg | 24.6 ± 0.2 | 94.5 ± 1.9 | 94.6 ± 2.6 |  102.2 ± 1.4
nopunct | 61.9 ± 0.2 | 95.3 ± 2.6 | 95.8 ± 2.0 |  101.9 ± 0.5
spell | 57.8 ± 0.2 | 98.6 ± 5.0 | 98.1 ± 6.0 |  101.5 ± 1.0
stem | 81.7 ± 0.4 | 98.9 ± 4.6 | 97.7 ± 4.3 |  100.9 ± 0.5
hash | 10.1 ± 0.0 | 102.8 ± 12.2 | 102.1 ± 12.3 |  100.5 ± 0.8
nrem | 96.2 ± 0.7 | 104.3 ± 11.9 | 103.3 ± 11.4 | 100.0 ± 0.8
lower | 88.7 ± 0.3 | 107.9 ± 17.2 | 107.7 ± 15.5 |  100.9 ± 1.7
lemma | 98.1 ± 0.5 | 109.4 ± 16.3 | 107.8 ± 14.9 | 100.0 ± 1.0
Table 11: Effect of preprocessing techniques on Amazon with the Vowpal Wabbit model. Results shown are the average (and std) relative performance, across the five dataset seeds. Bold indicates statistical similarity to the best score, from a two-sample t-test with $\alpha=0.05$. Method | Vocab Size | Train Time | Test Time | Accuracy
---|---|---|---|---
spell+seg+nrem+stop+rare |  0.8 ± 0.0 |  44.7 ± 0.6 |  77.1 ± 0.9 | 96.7 ± 1.4
stop+rare | 0.9 ± 0.0 | 47.1 ± 1.1 |  76.2 ± 1.6 | 97.4 ± 0.8
spell+seg+nrem+stop | 14.1 ± 0.0 | 55.4 ± 1.5 |  79.3 ± 3.8 | 95.7 ± 0.7
seg+nrem+stop+lemma | 18.5 ± 0.1 | 57.1 ± 1.8 |  82.2 ± 5.6 | 94.9 ± 1.0
spell+nopunct+nrem+stop | 34.1 ± 0.2 | 57.2 ± 0.8 | 82.7 ± 4.4 | 95.7 ± 1.1
seg+nrem+stop | 21.0 ± 0.1 | 57.4 ± 0.5 |  78.5 ± 1.7 | 94.5 ± 0.4
spell+nopunct+nrem+stop+lemma | 31.9 ± 0.2 | 57.4 ± 1.5 | 81.4 ± 3.2 | 95.1 ± 0.6
spell+seg+nrem+stop+lemma | 11.9 ± 0.0 | 57.6 ± 1.3 |  80.1 ± 3.9 | 96.1 ± 0.9
lower+nopunct+nrem+stop+lemma | 47.0 ± 0.3 | 59.3 ± 0.6 | 86.7 ± 0.6 | 94.8 ± 0.5
lower+nopunct+nrem+stop | 48.4 ± 0.3 | 59.5 ± 1.3 | 83.7 ± 4.1 | 95.7 ± 0.9
seg+nrem+stop+stem | 14.0 ± 0.1 | 62.6 ± 1.3 | 85.7 ± 4.2 | 96.6 ± 0.6
spell+seg+nrem+stop+stem | 8.5 ± 0.0 | 62.7 ± 1.2 | 84.1 ± 3.2 | 96.2 ± 1.1
spell+nopunct+nrem+stop+stem | 27.7 ± 0.1 | 62.8 ± 1.6 | 89.8 ± 6.8 | 95.9 ± 0.7
lower+nopunct+nrem+stop+stem | 39.3 ± 0.2 | 63.5 ± 0.9 | 89.5 ± 4.0 | 96.8 ± 1.3
stop | 99.8 ± 0.2 | 71.9 ± 1.3 | 94.6 ± 6.9 | 97.3 ± 0.8
nrem+rare | 1.0 ± 0.0 | 88.3 ± 1.5 | 92.1 ± 0.7 | 100.4 ± 0.5
rare | 1.0 ± 0.0 | 88.7 ± 0.9 | 93.6 ± 4.0 | 100.5 ± 0.5
lower+rare | 1.0 ± 0.0 | 89.1 ± 1.4 | 90.7 ± 1.8 |  101.5 ± 0.8
lower+nopunct+nrem+rare | 0.9 ± 0.0 | 89.9 ± 1.5 | 92.0 ± 1.2 |  100.9 ± 0.3
spell+rare | 0.9 ± 0.0 | 90.3 ± 2.8 | 93.3 ± 1.5 |  101.5 ± 1.2
stem+rare | 0.9 ± 0.0 | 90.5 ± 2.0 | 91.9 ± 1.6 |  100.7 ± 0.7
nopunct+rare | 0.9 ± 0.0 | 91.3 ± 1.1 | 92.6 ± 1.9 |  101.6 ± 0.8
lower+nopunct+rare | 0.9 ± 0.0 | 91.3 ± 1.9 | 91.8 ± 0.8 |  101.2 ± 0.6
seg+rare | 0.9 ± 0.0 | 92.4 ± 1.9 | 90.9 ± 0.7 |  101.6 ± 0.6
spell+seg+rare | 0.9 ± 0.0 | 93.2 ± 1.8 | 89.9 ± 1.6 |  101.5 ± 0.6
seg | 24.6 ± 0.2 | 95.0 ± 2.6 | 92.7 ± 4.5 |  100.0 ± 1.5
spell | 57.8 ± 0.2 | 95.6 ± 1.2 | 97.0 ± 0.8 | 99.9 ± 1.2
hash | 10.1 ± 0.0 | 95.6 ± 1.4 | 84.7 ± 1.5 | 97.7 ± 0.8
lemma+rare | 1.0 ± 0.0 | 96.9 ± 17.6 | 92.5 ± 1.4 |  101.0 ± 0.7
stem | 81.7 ± 0.4 | 97.3 ± 0.8 | 97.4 ± 3.2 | 99.9 ± 0.2
nopunct | 61.9 ± 0.2 | 98.5 ± 0.4 | 94.8 ± 2.1 | 100.2 ± 1.0
lemma | 98.1 ± 0.5 | 98.7 ± 1.5 | 97.9 ± 1.8 | 99.8 ± 0.8
nrem | 96.2 ± 0.7 | 100.8 ± 1.2 | 100.9 ± 5.9 | 99.5 ± 1.0
Table 12: Effect of preprocessing techniques on Amazon with the SVM model.
Scores are the relative performance of each method over the no preprocessing
baseline. Results shown are the average (and std) relative performance across
the five dataset seeds. Bold indicates statistical similarity to the best
score, from a two-sample t-test with $\alpha=0.05$.


11institutetext: 1INAF – Osservatorio Astronomico di Brera, Via E. Bianchi 46,
23807 Merate (LC), Italy 11email<EMAIL_ADDRESS>
2Max-Planck-Institut für extraterrestrische Physik, Gießenbachstraße 1, 85748,
Garching, Germany
3Columbia Astrophysics Laboratory, Columbia University, New York, NY 10027,
USA
4Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans
s/n, E-08193 Barcelona, Spain
5Institut d’Estudis Espacials de Catalunya (IEEC), Carrer Gran Capità 2–4,
E-08034 Barcelona, Spain

# An intermediate polar candidate toward the Galactic plane

S. Mondal 11 G. Ponti 1122 F. Haberl 22 K. Anastasopoulou 11 S. Campana 11 K.
Mori 33 C. J. Hailey 33 and N. Rea 4455

(Received XXX; accepted YYY)

###### Abstract

Context. For the past decade, it has been suggested that intermediate polars
(IPs), a subclass of magnetic cataclysmic variables (CVs), are one of the main
contributors to the hard diffuse X-ray emission from the Galactic center (GC)
and Galactic ridge.

Aims. In our ongoing XMM-Newton survey of the central region of the Galactic
disk ($20\degr\times 2\degr$), we detected a persistent IP candidate, 1.7°
away from the GC. In this work, we better characterize the behavior of this
source by looking at the new and archival XMM-Newton data.

Methods. We performed a detailed X-ray spectral modeling of the source.
Furthermore, we searched for X-ray pulsations in the light curve as well as
its counterpart at other wavelengths.

Results. The _XMM-Newton_ spectrum (0.8–10 keV) of the source is described by
a partial covering collisionally ionized diffuse gas with plasma temperature
$kT=15.7^{+20.9}_{-3.6}$ keV. In addition, the spectrum shows the presence of
iron lines at $E=6.44$, 6.65, and 6.92 keV with equivalent widths of
$194^{+89}_{-70}$, $115^{+79}_{-75}$, and $98^{+93}_{-74}$ eV, respectively.
The X-ray light curve shows a coherent modulation with a period of
$P=432.44\pm 0.36$ s, which we infer is the spin period of the white dwarf.
The white dwarf mass estimated from fitting a physical model to the spectrum
results in $M_{\rm WD}=1.05^{+0.16}_{-0.21}\ M_{\odot}$. We were able to find
a likely optical counterpart in the Gaia catalog with a G magnitude of 19.26,
and the distance to the source derived from the measured Gaia parallax is
$\sim$4.3 kpc.

Conclusions. We provide an improved source localization with subarcsec
accuracy. The spectral modeling of the source indicates the presence of
intervening circumstellar gas, which absorbs the soft X-ray photons. The
measured equivalent width of the iron lines and the detection of the spin
period in the light curve are consistent with those from IPs.

###### Key Words.:

X-rays:binaries – Galaxy:center – Galaxy:disk – white dwarfs – novae,
cataclysmic variables

##  1 Introduction

Intermediate polars (IPs) are a subclass of magnetic cataclysmic variables
(CVs) in which the white dwarf (WD) accretes mass from a late-type main-
sequence star (see Cropper, 1990; Patterson, 1994; Mukai, 2017, for review).
The matter from the companion star forms an accretion disk. The accreted
matter is funneled onto the WD polar cap region following the magnetic field
lines. It releases gravitational energy, creating an intensely bright spot at
or above the magnetic poles. The ionized matter follows the magnetic field
lines, and as it approaches the WD surface, the radial infall velocity
increases to a supersonic speed of 3000–10000 km s-1. Therefore, a shock front
is developed above the star, and the infalling gas releases its energy in the
shock resulting in hard X-ray photons. If the WD spin and the magnetic axis
are not aligned, then the star is an oblique rotator and produces the
modulation in X-ray emission as it sweeps its lighthouse beam around the sky
(Watson et al., 1985; Kim & Beuermann, 1995). The X-ray and optical emission
from the IPs also show a periodic variation on the timescale of its orbital
period around its donor star (Zharikov et al., 2001; Scaringi et al., 2011).
So far, around 50 IPs have been identified, and more than 100 candidates are
known (Downes et al., 2001).

Recently, IPs have been discussed in the context of hard X-ray emission in the
Galactic center (GC), bulge, and ridge regions (Krivonos et al., 2007;
Revnivtsev et al., 2009; Hong, 2012; Ponti et al., 2013; Perez et al., 2015;
Hailey et al., 2016). Early X-ray observations of the GC pointed out the
presence of diffuse X-ray emission (Yamauchi et al., 1996), so-called Galactic
ridge emission with a soft $\sim 0.8$ keV and a hard $\sim 8$ keV component
(Koyama et al., 1996; Tanaka et al., 2000). The soft component is thought to
be produced by the large-scale shock waves of supernova explosions, which heat
the interstellar medium (Kaneda et al., 1997). The origin of the 8 keV plasma
is less certain. On the other hand, the gravitational well of the Galactic
disk is too shallow to confine such a hot plasma, which would escape at
velocities of thousands of km/s. Therefore, the energy required to sustain it
could be equivalent to the release of kinetic energy from one supernova
occurring every 30 years (Kaneda et al., 1997; Yamasaki et al., 1997).
However, such a rate appears higher than current estimates (Ponti et al.,
2015). An alternative hypothesis is that a hot plasma is associated with a
multitude of faint X-ray sources. The Deep Chandra observations of the central
region of the GC ($2\degr\times 0.8\degr$; Wang et al. 2002) and a region
south of the GC ($16\arcmin\times 16\arcmin$; Revnivtsev et al. 2009) pointed
out that the observed hard ($kT\sim 8$ keV) diffuse emission is indeed
primarily produced by a population of unresolved magnetic CVs.

Intermediate polars located at the GC are faint with typical 0.2–10 keV un-
absorbed fluxes of $10^{-12}$ erg s-1 cm-2. The high extinction toward the GC
makes it difficult to detect them in soft X-rays. Therefore, deep observations
are needed. Currently, we are performing an XMM-Newton survey of the Galactic
disk (central $20\degr\times 2\degr$). Our XMM-Newton observations have
exposures of $\sim$20 ks per tile, which enable us to detect faint point
sources. In early 2021, a persistent X-ray point source in the Galactic disk,
1.7° away from the GC, was detected by XMM-Newton in our ongoing survey.
Previously, this field was observed by Swift, Suzaku, and XMM-Newton and the
source Suzaku J174035.6–301416 has been covered multiple times. Suzaku
observed this source twice in 2008 and 2009 (Uchiyama et al., 2011). The 2009
observation was not reliable for obtaining the source location as the
satellite’s altitude was not locked due to a star tracker problem. Uchiyama et
al. (2011) analyzed the 2008 Suzaku observation; their reported source
location is $50.5^{\prime\prime}$ away from our XMM-Newton position.
Considering a 14″ positional uncertainty for Suzaku including the statistical
and systematic errors (Uchiyama et al., 2008), the Suzaku position is off from
XMM-Newton at more than a 3$\sigma$ level. These _Suzaku_ detections need to
be followed up on for better source localization, better statistics, and
higher quality spectral and timing data in the X-ray band. Uchiyama et al.
(2011) performed a detailed spectral and timing analysis of the 2008 _Suzaku_
observation. They detected the pulsation of period $432.1\pm 0.1$ s and
equivalent width of 210, 190, and 130 eV for the 6.4, 6.7, and 6.9 keV lines,
respectively. Furthermore, their spectral fitting revealed that a partial
covering model is required to fit the spectrum. This paper provides a better
source location using the new XMM-Newton observations with improved data
quality and reports the results from a detailed spectral and timing modeling.

##  2 Observations and data reduction

This work is primarily based on the 23 ks XMM-Newton observation of the Milky
Way plane (ObsID: 0886010601, 2021 March 18). The observation data files were
processed using the XMM-Newton (Jansen et al., 2001) Science Analysis System
(SASv19.0.0)111https://www.cosmos.esa.int/web/xmm-newton/sas. We used the SAS
task barycen to apply the barycenter correction to the event files. We only
selected events with PATTERN$\leq 4$ and PATTERN$\leq 12$ for EPIC-pn and
EPIC-MOS1/MOS2 detectors, respectively. The source and background products
were extracted from circular regions with a 25″ radius. The background
products were extracted from a source-free area. The spectrum from each
detector (pn, MOS1, and MOS2) was grouped to have a minimum of 20 counts in
each energy bin. Previously, the source had been observed by XMM-Newton on
2018 September 29 (ObsID: 0823030101, 4.1 ks). To gain statistics, we have
included the spectra of both observations. The spectral fitting was performed
in xspec (Arnaud, 1996) and we applied the $\chi^{2}$ statistic. The spectra
from both observations of the EPIC-pn, MOS1, and MOS2 detectors were fitted
simultaneously. While fitting the data simultaneously, we added a constant
term for cross-calibration uncertainties, which is fixed to unity for EPIC-pn
and allowed to vary for MOS1 and MOS2. The best-fit parameters are listed in
Table 1 with the quoted errors at the 90% significance level.

##  3 Results

###  3.1 X-ray spectra

####  3.1.1 Fitting with phenomenological models

Figure 1: Top panel: XMM-Newton time-averaged spectra fitted with a model
tbabs*tbpcf*(apec+g1). The small insert in panel A shows the iron line complex
in the 6–7.5 keV region for the EPIC-pn detector of ObsID: 0886010601. The
bottom panels show the data-to-model ratio plot obtained from different model
fits. The black, red, green, blue, magenta, and cyan data points are from
EPIC-pn, MOS1, and MOS2 detectors of ObsID: 0886010601 and 0823030101,
respectively.

First, we fit the spectra with a simple absorbed power-law model (panel B of
Fig. 1). A simple power law does not fit the data well $\chi^{2}_{\rm
red}=1.28$. Panel A and B of Fig. 1 show that the spectra exhibit multiple
iron lines. To fit the iron-line complex, three Gaussian components g1, g2,
and g3 at 6.44, 6.65, and 6.92 keV, respectively, are required. We froze the
width of the Gaussian lines at 0 eV as the current X-ray instruments are
unable to resolve the lines. Adding the Gaussian components significantly
improved the fit with $\Delta\chi^{2}=67$ for six additional degrees of
freedom ($dof$; panel C of Fig. 1). The simple model tbabs*(po+g1+g2+g3)
describes the data quite well with $\chi^{2}/dof=228/225$ ($\chi^{2}_{\rm
red}=1.01$). Fitting with the power-law model indicates that the source has a
very hard spectrum with photon index $\Gamma=0.5\pm 0.1$. The hard X-ray
spectral index indicates the IP nature of the source. The equivalent widths of
the lines at $E=6.44$, 6.65, and 6.92 keV are $194^{+89}_{-70}$,
$115^{+79}_{-75}$, and $98^{+93}_{-74}$ eV, respectively. The centroid of the
neutral iron $K_{\alpha}$ line is offset from 6.4 keV at the 1$\sigma$
confidence level.

Typically IP spectra are not well described by a power-law model when data
beyond 10 keV are available. Therefore, we incorporate more physically
motivated models in the following. The spectra from most IPs resemble
bremsstrahlung emission with plasma temperatures $\sim$20 keV (Ezuka & Ishida,
1999). Therefore, we fit the spectra with the model tbabs*(brems+g1+g2+g3).
The temperature of the bremsstrahlung model is not constrained due to a lack
of data above 10 keV. Therefore, we froze the temperature to 20 keV. A simple
bremsstrahlung model cannot fit the continuum ($\chi^{2}/dof=413/229$),
leaving an excess below 2 keV and above 7 keV. This is illustrated in panel D
of Fig. 1. Adding another bremsstrahlung component significantly improves the
fit by $\Delta\chi^{2}=113$ for two additional $dof$. However, the excess at
around 1 keV is still present (see panel E of Fig. 1). The best-fit
temperature of the second bremsstrahlung component is much lower than the
first one, around $kT=0.17\pm 0.02$ keV. This model does not reproduce the
data adequately and can be rejected with $P_{\rm Null}=8.8\times 10^{-4}$ with
$\chi^{2}/dof=300/227$. The spectrum at higher energies is slightly curved.
This curvature cannot be fitted by two bremsstrahlung components absorbed by
the same column density of neutral material. We noticed that adding a
blackbody component to the bremsstrahlung model gives an excellent fit with
$\chi^{2}/dof=228/224$ (panel F of Fig. 1). However, the blackbody component
has a very high temperature of $2.98^{+0.73}_{-0.46}$ keV, as a consequence of
trying to reproduce the high energy part of the spectra (¿5 keV), which we
deem as unlikely. Indeed, a blackbody component is present in some IPs, which
have a very soft component in their spectrum. This can be fitted by a
blackbody with temperatures of 40–60 eV (Haberl & Motch, 1995), which is not
the case here. Next, we added a partial covering absorption component to the
bremsstrahlung model to mimic a scenario in which the continuum emission is
partially covered by the intervening medium. The model
tbabs*tbpcf*(brems+g1+g1+g3) provides an acceptable fit with
$\chi^{2}/dof=245/224$ ($\chi^{2}_{\rm red}=1.09$; panel F of Fig. 1). The
$N_{\rm H,pcf}$ value is almost an order higher than the Galactic absorption.
To constrain the plasma temperature, we fit the data with a partially covered
apec model plus a Gaussian line at 6.4 keV. This model may provide a better
estimate of the plasma temperature by using both the continuum as well as the
6.7 and 6.9 keV line ratio. This model provides a better fit than the
bremsstrahlung model with $\chi^{2}/dof=244/227\ (\chi^{2}_{\rm red}=1.07$;
panel H) and the plasma temperature obtained from this model is
$kT=15.65^{+20.87}_{-3.58}$ keV.

Figure 2: Same as Fig. 1, but for the model tbabs*tbpcf*( mcvspec+g1).

####  3.1.2 Fitting with the physical model mcvspec

We also fit the data with the mcvspec model. This is a physical model which
reproduces the emission from the surface of a magnetized WD. The model is
available in xspec and is based on the one-dimensional accretion column model
of Saxton et al. 2005 (Mori et al. in preparation). The model includes lines
produced by collisionally ionized diffuse gas in the accretion column of the
WD. While doing the fitting, we noticed that there is a degeneracy between
magnetic field $B$ and the mass accretion flux $\dot{m}$. Therefore, we froze
the $B$ and $\dot{m}$ to values 10 MG and 5 g cm-2 s-1, respectively, which is
typically observed in IPs (Mori et al.). The mcvspec model includes the line
from ionized Fe; however, it does not include the neutral 6.4 keV Fe
$K_{\alpha}$ line. Therefore, we added a Gaussian at 6.4 keV with the width of
the line frozen to zero. In the fitting procedure, the abundance value was
frozen at the solar value.

Initially, we tried to fit the data with this model with a single absorption
component. As we saw for the bremsstrahlung and apec models, the partial
covering of the source is also required for the mcvspec model. The neutral 6.4
keV line is also clearly visible in panels A and B of Fig. 2. When we added a
Gaussian component at 6.4, the excess between 6–7 keV was resolved (shown in
panel C of Fig. 2). The spectral fit is shown in Fig. 2. The WD mass estimated
from fitting this model is $1.05^{+0.16}_{-0.21}\ M_{\odot}$.

Table 1: Best-fit parameters of the fitted models.

tbabs*tbpcf*(apec+g1) | tbabs*tbpcf*(mcvspec+
---|---
g1)
$N_{\rm H}$ | $1.29^{+0.19}_{-0.16}$ | $N_{\rm H}$ | $1.30^{+0.20}_{-0.16}$
$pcf$ | $0.69^{+0.04}_{-0.06}$ | $pcf$ | $0.67^{+0.05}_{-0.04}$
$N_{\rm H,pcf}$ | $15.13^{+8.42}_{-3.68}$ | $N_{\rm H,pcf}$ | $15.82^{+7.15}_{-4.17}$
$kT$ | $15.65^{+20.87}_{-3.58}$ | $B$ |  $10\times 10^{6}$ (frozen)
Abun | 1.0 (frozen) | Abun | 1.0 (frozen)
Redshift | 0.0 (frozen) | $\dot{m}$ | 5 (frozen)
$N_{\rm apec}$ | $1.94^{+0.30}_{-0.19}\times 10^{-3}$ | $M$ | $1.05^{+0.16}_{-0.21}$
$E_{\rm g1}$ | $6.44^{+0.05}_{-0.03}$ | $N_{\rm mcv}$ | $1.58^{+0.71}_{-0.20}\times 10^{4}$
$N_{\rm g1}$ | $9.81^{+2.38}_{-2.37}\times 10^{-6}$ | $E_{\rm g1}$ | $6.44^{+0.04}_{-0.03}$
|  | $N_{\rm g1}$ | $7.68^{+1.90}_{-1.02}\times 10^{-6}$
$F_{\rm x}$ | $1.92^{+0.15}_{-0.15}\times 10^{-12}$ | $F_{\rm x}$ | $1.90^{+0.14}_{-0.14}\times 10^{-12}$
$\chi^{2}_{\rm red}\ (dof)$ | 1.07 (227) | $\chi^{2}_{\rm red}$ | 1.07 (227)
222The $N_{\rm H}$ is given in units of $10^{22}$ cm-2. The metal abundance
value is frozen to 1.0. Due to degeneracy, we froze the $B$ and $\dot{m}$ to
10 MG and 5 g cm-2 s-1, respectively. The observed X-ray flux $F_{\rm x}$ is
given in the 0.8–10 keV band.

###  3.2 Periodicity search

For our timing analysis, we focused on the data from the EPIC-pn detector only
as it has the shortest frame time, which allows us to examine the highest
possible frequency range. We extracted the light curve with a time bin of 74
ms. We computed the fast Fourier transform from the longest continuous segment
length of 12.5 ks; with this setup, we could probe frequencies from $8.1\times
10^{-5}$ Hz to 13.5 Hz. A narrow peak at a frequency of $2.3\times 10^{-3}$ Hz
is clearly visible in the power spectral density (PSD) (Fig. 3). We applied
the Bayesian formalism of Gregory & Loredo (1992) to refine the period further
and estimate the error. This method is optimized for the detection of a
periodic signal by comparing different periodic models for a given frequency
and phase bins with a model of a constant count rate. We selected energies
between 0.2 keV and 8 keV and set the maximum number of phase bins to ten.
This resulted in the most probable period of 432.44$\pm$0.36 s. We folded the
light curves in different energy bands with the period derived from the
Bayesian method. The pulse profiles at various energy bands are shown in Fig.
4. The energy dependence of the pulse profile is complex. The pulse profiles
at lower energies appear to be nonsinusoidal and nearly sinusoidal at higher
energies. Additionally, the pulsed fraction ($\rm PF=\frac{F_{\rm max}-F_{\rm
min}}{F_{\rm max}+F_{\rm min}}$) is higher at energies below 5.5 keV.

Figure 3: Fast Fourier transform computed from the longest continuous good
time interval (GTI) of the EPIC-pn data. The PSD shows a clear peak at
$2.3\times 10^{-3}$ Hz, which is likely associated with the spin frequency of
the WD. Figure 4: Complex shape of the pulse profile at different energy
bands. The light curves were folded with a period of 432.44 s.

###  3.3 The long-term X-ray flux variability and multiwavelength counterpart

The source is located 1.71° away from the Galactic center. The source position
after the bore-sight correction is $(\alpha,\delta)_{\rm J200}$ = (17h 40m
33$\aas@@fstack{s}$85, –30° 15′ 01″) which is designated as 4XMM
J174033.8–301501 with a 1$\sigma$ positional uncertainty of
$0.5^{\prime\prime}$. A counterpart in Swift-XRT was also detected in 2008
June 25 observations. A bright source with an offset of 4″ from the XMM-Newton
position is listed in the Swift-XRT point source catalog (D’Elia et al.,
2013). Fig. 5 shows the long-term light curve of the source. The Swift-XRT
2–10 keV observed flux is $2.8\times 10^{-12}$ erg s-1. Suzaku also observed
this source on 2008 October 05 and 2009 February 26. Uchiyama et al. (2011)
reported that the Suzaku 2–10 keV flux in the 2008 observation is around $\sim
2\times 10^{-12}$ erg s-1 cm-2. The 2–10 keV flux in 2018 and the 2021 XMM-
Newton observations are $1.38\times 10^{-12}$ erg s-1 cm-2 and $1.87\times
10^{-12}$ erg s-1 cm-2, respectively. This indicates that even though the
source has been detected multiple times spanning more than a decade, it shows
very little to no flux variability.

Figure 5: Long-term light curve of the source. The 2–10 keV flux given here is
the observed flux. The source does not exhibit significant flux variability.

We searched for counterparts at other wavelengths. Our XMM-Newton observation
has simultaneous UV observation with filters UVW1 (291 nm), UVW2 (212 nm), and
UVM2 (231 nm). We did not find any counterparts in our UV observation within a
3$\sigma$ positional uncertainty (1.5″) of the EPIC detection. The 2008 Swift
observation was done with UVM2 filter (224.6 nm). No counterpart was found in
the Swift UVM2 observation either. Furthermore, we searched for counterparts
in the Gaia and 2MASS catalog. A likely associated Gaia counterpart is 0.48″
away with a G magnitude of $19.262\pm 0.005$. The parallax and proper motion
obtained from Gaia are 0.23 milli-arcsec and 1.92 milli-arcsec yr-1,
respectively. Given the measured parallax by Gaia, the distance to the source
is 4.3 kpc. No counterpart was found in the infrared 2MASS catalog.

##  4 Discussion

The spectrum of the source is complex and shows the presence of iron lines at
energies 6.44, 6.65, and 6.92 keV with an equivalent width of
$194^{+89}_{-70}$, $115^{+79}_{-75}$, and $98^{+93}_{-74}$ eV, respectively.
The measured equivalent width of the lines is similar to the values found in
magnetic CVs (Hellier & Mukai, 2004). We also detected a clear, coherent
pulsation with a pulse period of 432.44 s. The detected spin period is well
within the typical range for magnetic CVs, which is 30 s to $2\times 10^{4}$ s
(Scaringi et al., 2010). The equivalent width and spin period based on the
Suzaku observation are consistent with our measurements. The spin period, iron
line complex, and the equivalent width of the iron lines indicate the source
is a strong candidate for intermediate polar. The source is less likely to be
polar, as polars have a very high magnetic field of 10–240 MG, which
synchronizes the spin and orbital period. The measured 432.44 s period is too
short to be the synchronized spin-orbital period; in polars, the spin-orbital
period typically ranges from (0.3–6)$\times 10^{4}$ s (Scaringi et al., 2010).
For intermediate polars, the spin-to-orbital period ratio is around 0.1
(Barrett et al., 1988); therefore, in this case, the orbital period would be
on the order of 4500 s. However, we do not see a large peak at the
corresponding frequency in the PSD, but this might be because the longest
continuous GTI only covers two cycles for the orbital period.

Two absorption-component models are required to fit the overall spectrum of
the source. This is also noted by Uchiyama et al. (2011) who, while fitting
Suzaku data, required two absorption components to mimic the partial covering
scenario. Uchiyama et al. (2011) first tried to fit the data with a single
apec model, which indicates a plasma temperature of $9.5^{+1.5}_{-0.2}$ keV.
The authors further noticed that adding another apec component significantly
improves the fit. For the two-temperature model, the temperature of the two
apec components are $6\pm 1$ keV and $kT\sim 64$ keV. Our spectral fitting
results are fully consistent with the findings of Uchiyama et al. (2011). Our
best-fit apec model gives plasma temperature $kT=15.13^{+20.87}_{-3.58}$ keV,
which is similar to the value found by Uchiyama et al. (2011). The centroid of
the neutral iron $K_{\alpha}$ line is $6.44^{+0.05}_{-0.03}$, which is shifted
from 6.4 keV at a $1\sigma$ confidence level. The shift is at the level of
1$\sigma$. This shift can be explained by assuming that the material is not
completely cold and is slightly ionized with $\xi\sim$ 200–300 (Matt et al.,
1993). We tested a physically motivated model to constrain the mass of the
central compact object. The mass of the central compact object obtained from
this model is $1.05^{+0.16}_{-0.21}\ M_{\odot}$. It is pretty clear that no
matter which physical model we use, a partial covering with high absorption
column density $N_{\rm H}\sim 1.5\times 10^{23}$ cm-2 is required to fit the
continuum. We suggest this large amount of absorption is due to circumstellar
gas.

From an evolutionary point of view, IP will have a cool main-sequence
companion star, which should be identified as a blue optical counterpart (Hong
et al., 2009). Estimating the X-ray-to-optical flux ratio using Gaia G band
magnitude is possible. We used the formula ${\rm Log}(F_{\rm x}/F_{\rm
G})={\rm Log}(F_{\rm x})+m_{\rm G}/2.5+5.37$ (Maccacaro et al., 1988), where
$F_{\rm x}=1.87\times 10^{-12}\ \rm erg\ cm^{-2}\ s^{-1}$ is 2–10 keV flux and
$m_{\rm G}=19.26$ is the Gaia G band magnitude. This gives ${\rm Log}(F_{\rm
x}/F_{\rm G})=1.35$ which is consistent with the values found in Galactic
bulge CVs (Bahramian et al., 2021). Previously, other X-ray satellites have
detected the source many times; however, the source shows no flux variability.
In general, IPs display some level of long-term variability in both the X-ray
and optical. The expected luminosity range of IPs are in between $3\times
10^{29}$–$5\times 10^{33}$ erg s-1 (Ruiter et al., 2006). The 0.8–10 keV
observed luminosity of the source is $4.05\times 10^{33}$ erg s-1, which
places it in the brightest class of IPs. The low-luminosity IPs are difficult
to detect due to the large absorption toward the GC. The measured X-ray
interstellar medium absorption $1.3\times 10^{22}$ cm-2 is consistent with the
expected total column density of the Galaxy ($N_{\rm H}=N_{\rm HI}+N_{\rm
H2}=1.37\times 10^{22}$ cm-2 , Willingale et al. 2013) toward the source
position. A better characterization of the population of IPs toward the GC can
help us to solve the mystery of Galactic diffuse hard X-ray emission.

##  5 Conclusions

In this work, we have provided a better positional accuracy of the source and
identified an optical counterpart in the Gaia catalog. It is a source located
at a distance of 4.2 kpc. The spin period of the WD is $432.44\pm 0.36$ s, and
the orbital period was not recovered from the X-ray light curve. Previously,
the source had been observed on other occasions, but it did not show any X-ray
flux variability. A partial covering collisionally ionized diffuse gas with
plasma temperature $15.13^{+20.87}_{-3.58}$ keV fits the data well. We
estimated the WD mass to be $1.05^{+0.16}_{-0.21}\ M_{\odot}$. We did not find
any counterpart in the UV detector of XMM-Newton and Swift, and also, no IR
counterpart was found in the 2MASS catalog.

###### Acknowledgements.

SM, GP, and KA acknowledge financial support from the European Research
Council (ERC) under the European Union’s Horizon 2020 research and innovation
program HotMilk (grant agreement No. 865637).

## References

  * Arnaud (1996) Arnaud, K. A. 1996, in Astronomical Society of the Pacific Conference Series, Vol. 101, Astronomical Data Analysis Software and Systems V, ed. G. H. Jacoby & J. Barnes, 17
  * Bahramian et al. (2021) Bahramian, A., Heinke, C. O., Kennea, J. A., et al. 2021, MNRAS, 501, 2790
  * Barrett et al. (1988) Barrett, P., O’Donoghue, D., & Warner, B. 1988, MNRAS, 233, 759
  * Cropper (1990) Cropper, M. 1990, Space Sci. Rev., 54, 195
  * D’Elia et al. (2013) D’Elia, V., Perri, M., Puccetti, S., et al. 2013, A&A, 551, A142
  * Downes et al. (2001) Downes, R. A., Webbink, R. F., Shara, M. M., et al. 2001, PASP, 113, 764
  * Ezuka & Ishida (1999) Ezuka, H. & Ishida, M. 1999, ApJS, 120, 277
  * Gregory & Loredo (1992) Gregory, P. C. & Loredo, T. J. 1992, ApJ, 398, 146
  * Haberl & Motch (1995) Haberl, F. & Motch, C. 1995, A&A, 297, L37
  * Hailey et al. (2016) Hailey, C. J., Mori, K., Perez, K., et al. 2016, ApJ, 826, 160
  * Hellier & Mukai (2004) Hellier, C. & Mukai, K. 2004, MNRAS, 352, 1037
  * Hong (2012) Hong, J. 2012, MNRAS, 427, 1633
  * Hong et al. (2009) Hong, J. S., van den Berg, M., Grindlay, J. E., & Laycock, S. 2009, ApJ, 706, 223
  * Jansen et al. (2001) Jansen, F., Lumb, D., Altieri, B., et al. 2001, A&A, 365, L1
  * Kaneda et al. (1997) Kaneda, H., Makishima, K., Yamauchi, S., et al. 1997, ApJ, 491, 638
  * Kim & Beuermann (1995) Kim, Y. & Beuermann, K. 1995, A&A, 298, 165
  * Koyama et al. (1996) Koyama, K., Maeda, Y., Sonobe, T., et al. 1996, PASJ, 48, 249
  * Krivonos et al. (2007) Krivonos, R., Revnivtsev, M., Churazov, E., et al. 2007, A&A, 463, 957
  * Maccacaro et al. (1988) Maccacaro, T., Gioia, I. M., Wolter, A., Zamorani, G., & Stocke, J. T. 1988, ApJ, 326, 680
  * Matt et al. (1993) Matt, G., Fabian, A. C., & Ross, R. R. 1993, MNRAS, 262, 179
  * Mukai (2017) Mukai, K. 2017, PASP, 129, 062001
  * Patterson (1994) Patterson, J. 1994, PASP, 106, 209
  * Perez et al. (2015) Perez, K., Hailey, C. J., Bauer, F. E., et al. 2015, Nature, 520, 646
  * Ponti et al. (2013) Ponti, G., Morris, M. R., Terrier, R., & Goldwurm, A. 2013, in Astrophysics and Space Science Proceedings, Vol. 34, Cosmic Rays in Star-Forming Environments, ed. D. F. Torres & O. Reimer, 331
  * Ponti et al. (2015) Ponti, G., Morris, M. R., Terrier, R., et al. 2015, MNRAS, 453, 172
  * Revnivtsev et al. (2009) Revnivtsev, M., Sazonov, S., Churazov, E., et al. 2009, Nature, 458, 1142
  * Ruiter et al. (2006) Ruiter, A. J., Belczynski, K., & Harrison, T. E. 2006, ApJ, 640, L167
  * Saxton et al. (2005) Saxton, C. J., Wu, K., Cropper, M., & Ramsay, G. 2005, MNRAS, 360, 1091
  * Scaringi et al. (2010) Scaringi, S., Bird, A. J., Norton, A. J., et al. 2010, MNRAS, 401, 2207
  * Scaringi et al. (2011) Scaringi, S., Connolly, S., Patterson, J., et al. 2011, A&A, 530, A6
  * Tanaka et al. (2000) Tanaka, Y., Koyama, K., Maeda, Y., & Sonobe, T. 2000, PASJ, 52, L25
  * Uchiyama et al. (2011) Uchiyama, H., Koyama, K., Matsumoto, H., et al. 2011, PASJ, 63, S865
  * Uchiyama et al. (2008) Uchiyama, Y., Maeda, Y., Ebara, M., et al. 2008, PASJ, 60, S35
  * Wang et al. (2002) Wang, Q. D., Gotthelf, E. V., & Lang, C. C. 2002, Nature, 415, 148
  * Watson et al. (1985) Watson, M. G., King, A. R., & Osborne, J. 1985, MNRAS, 212, 917
  * Willingale et al. (2013) Willingale, R., Starling, R. L. C., Beardmore, A. P., Tanvir, N. R., & O’Brien, P. T. 2013, MNRAS, 431, 394
  * Yamasaki et al. (1997) Yamasaki, N. Y., Ohashi, T., Takahara, F., et al. 1997, ApJ, 481, 821
  * Yamauchi et al. (1996) Yamauchi, S., Kaneda, H., Koyama, K., et al. 1996, PASJ, 48, L15
  * Zharikov et al. (2001) Zharikov, S. V., Tovmassian, G. H., Echevarría, J., & Cárdenas, A. A. 2001, A&A, 366, 834