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# The first neural machine translation system for the Erzya language

David Dale
<EMAIL_ADDRESS>
The research was conducted between the author’s employments at Skolkovo
Institute of Science and Technology (Skoltech) and at Meta AI.

###### Abstract

We present the first neural machine translation system for translation between
the endangered Erzya language and Russian and the dataset collected by us to
train and evaluate it. The BLEU scores are 17 and 19 for translation to Erzya
and Russian respectively, and more than half of the translations are rated as
acceptable by native speakers. We also adapt our model to translate between
Erzya and 10 other languages, but without additional parallel data, the
quality on these directions remains low. We release the translation models
along with the collected text corpus, a new language identification model, and
a multilingual sentence encoder adapted for the Erzya language.

##  1 Introduction

Out of the 7 thousand languages spoken around the world, only a minor fraction
is covered by machine translation tools. For example, Google
Translate111https://translate.google.com supports only 133 languages, and a
recent model by NLLB Team et al. (2022) supports 202 languages. Most other
languages are often considered ‘‘low-resource’’, although some of them have
millions of native speakers. In the context of machine translation, the
resources that are low are, primarily, parallel and monolingual text corpora.
In this work, we create a machine translation system for the previously
uncovered Erzya language with only publicly available resources, a very small
budget, and limited human efforts. We hope that it will inspire researchers
and language activists to enlarge the coverage of existing NLP resources, and
in particular, translation systems.

Our language of choice is Erzya (myv), which is spoken primarily in the
Republic of Mordovia, located in the center of the European part of the
Russian Federation. The language, along with its close relative Moksha (mdf),
belongs to the Mordvinic branch of the Uralic language family. These two
languages, although not mutually intelligible Janurik (2017), are often
referred to under the common name ‘‘Mordovian’’. Erzya has had a written
tradition since the beginning of the 19th century Rueter (2013). Its most
widely used alphabet is Cyrillic, although there is a Latin-based alternative
alphabet222http://valks.erzja.info (currently blocked in Russia). Erzya has
supposedly 300 thousand speakers333In the 2010 census, 430 thousand people
reported speaking Erzya or Moksha, but their proportions are unclear., and it
is one of the three official languages in Mordovia. According to the UNESCO
classification, the Erzya language has a status of ‘‘definitely endangered’’
UNESCO (2010). Some researchers Janurik (2017) put it between the levels 6b
(‘‘threatened’’) and 7 (‘‘shifting’’) on the EGIDS scale Lewis and Simons
(2010), as it is widely used and transmitted between generations in rural
communities but is being gradually displaced by Russian in urban areas. More
details about the use of Erzya are given by Rueter (2013), who is also a major
current contributor to Erzya NLP resources.

As far as we know, prior to this work, no neural machine translation (NMT)
systems for Erzya have been published. To fill this gap, we create and
publicly release444The source code and links to other resources are provided
at https://github.com/slone-nlp/myv-nmt.  the following deliverables:

  * •

A language identification model with enhanced recall for Erzya and Moksha
languages;

  * •

A sentence encoder for Erzya compatible with LaBSE Feng et al. (2022);

  * •

A small parallel Erzya-Russian corpus and a larger monolingual Erzya corpus;

  * •

Two neural models for translation between Erzya and 11 other languages.

For translation between Russian and Erzya, we validate our models both by
automatic metrics and with judgments of native speakers. More than half of the
translations are rated as acceptable.

##  2 Related Work

Low-resource NLP and, in particular, machine translation, have attracted a lot
of attention. Among the recent ambitious projects are Bapna et al. (2022) and
NLLB Team et al. (2022) that aim at creating NMT systems for hundreds of
languages and rely heavily on collection of large online corpora and transfer
learning. Other works, such as Hämäläinen and Alnajjar (2019), focus on
efficient use of existing vocabularies and morphosyntactic tools to train
machine translation systems for very low-resourced languages.

As far as we know, there are no published large parallel corpora or NMT
systems for Erzya. Rueter and Tyers (2018) develop an Erzya treebank with a
few hundred translations to English and Finnish. Архангельский (2019) present
an Erzya web corpus555http://erzya.web-corpora.net/ along with the way it was
collected, but the corpus is available only via the web interface. For other
published corpora, the situation is similar. There exists a half-finished
rule-based machine translation system between Erzya and
Finnish666https://github.com/apertium/apertium-myv-fin, and a grammar parser
for Erzya777https://github.com/timarkh/uniparser-grammar-erzya. The software
package UralicNLP Hämäläinen (2019) supports Erzya among other languages.

There have been a few attempts to transfer machine learning-based NLP
resources to Erzya from high-resource languages. Alnajjar (2021) adapt
Finnish, English, and Russian word embeddings to Erzya. Muller et al. (2021),
Ács et al. (2021) and Wang et al. (2022) evaluate the performance of
multilingual BERT-like models on natural language understanding tasks for new
languages, including Erzya.

None of the works known to us train machine learning-based models that are
capable of generating Erzya language.

##  3 Methodology and Experiments

###  3.1 Data Collection

As there are no large open-access corpora for Erzya, we compile Erzya and
Erzya-Russian data from various sources:

  * •

12K parallel sentences from the Bible888http://finugorbib.com;

  * •

3K parallel Wikimedia sentences from OPUS Tiedemann (2012);

  * •

42K parallel words or short phrases collected from various online
dictionaries;

  * •

the Erzya Wikipedia and the corresponding articles from the Russian Wikipedia;

  * •

18 books, including 3 books with Erzya-Russian
bitexts999http://lib.e-mordovia.ru;

  * •

Soviet-time books and
periodicals101010https://fennougrica.kansalliskirjasto.fi;

  * •

The Erzya part of
Wikisource111111https://wikisource.org/wiki/Main_Page/?oldid=895127;

  * •

Short texts by modern Erzya authors121212https://rus4all.ru/myv/;

  * •

News articles from the Erzya Pravda website131313http://erziapr.ru;

  * •

Texts found in LiveJournal141414https://www.livejournal.com by searching with
the 100 most frequent Erzya words.

A more detailed account of the data sources is given in Appendix A.

After filtering these texts with the language identification model (Section
3.2), we gathered 330K unique Erzya sentences. A bilingual part of the texts
was used for mining additional parallel sentences in Section 3.4.

###  3.2 Language Identification

To make sure that the extra collected data is in the Erzya language, we train
a FastText Joulin et al. (2016) language classifier for the 323 languages
present in Wikipedia. The 267 thousand training texts were sampled from
Wikipedia with probabilities proportional to $n_{lang}^{1/5}$, where
$n_{lang}$ is the size of Wikipedia in that language151515We adopted the idea
of temperature sampling with T=5 from Tran et al. (2021) and several other
works.. To increase the recall for Erzya and Moksha languages, we augment this
training dataset with Erzya and Moksha Bible texts. The resulting model has
89% accuracy and 86% macro F1 score on the Wikipedia test set (sampled with
the same temperature). For Erzya, it has 97% precision and 82% recall.
Hyperparameters for all trained models are listed in Appendix B.

###  3.3 Erzya Sentence Encoder

To compute sentence embeddings, we use an encoder based on LaBSE Feng et al.
(2022), with an extended vocabulary. First, we use the BPE algorithm Sennrich
et al. (2016) over a monolingual Erzya corpus to add 19K extra merged tokens
to the vocabulary. Then, we fine-tune the model on the limited initial
parallel data (the Bible, OPUS, and dictionaries): we update only the token
embeddings matrix, using the contrastive loss from Feng et al. (2022) over
computed sentence embeddings. Finally, after collecting more parallel
sentences, we fine-tune the full model on a mixture of tasks: contrastive loss
over sentence embeddings, standard masked language modeling loss, and sentence
pair classification to distinguish correct translations from random pairs.

###  3.4 Mining Parallel Sentences

When mining parallel sentences, we strive for high precision. To compensate
for the questionable quality of our sentence encoder, we apply the following
procedure161616For more details on the mining procedure, please read the
source code in the repository that we release..

  * •

We perform only local mining, i.e. we compare sentences only across paired
documents (for Wikipedia and translated books), or within one document (for
the web sources).

  * •

To evaluate similarity of two sentences, we multiply the cosine similarity
between their LaBSE embeddings by the ratio of the length of the shortest
sentence to that of the longest one.

  * •

We further penalize the similarities by partially subtracting from them the
average similarities of each sentence to its closest neighbors, similarly to
using distance margin from Artetxe and Schwenk (2019).

  * •

Given two documents in Russian and Erzya, we use dynamic programming to select
a sequence of sentence pairs that have the maximal sum of pairwise similarity
scores and go in the same order in both documents.

  * •

We accept only the sentence pairs with a score above a threshold, which was
manually tuned for each source of texts.

In total, this approach yielded 21K more unique parallel sentence pairs. The
manual inspection found that more than 90% of them were matched correctly.

###  3.5 Training Machine Translation Models

To benefit from transfer learning, we base our model on the mBART50 model Tang
et al. (2020) pretrained on multiple languages, including two Uralic ones
(Finnish and Estonian). We extend its BPE vocabulary with 19K new Erzya
tokens, using the same method as in Section 3.3, and add a new myv_XX language
code to it. Embeddings for the new tokens are initialized as the averages of
the embeddings of the Russian tokens aligned with them in the parallel
corpus171717We compute alignments with a naive formula: the alignment weight
between tokens $t_{i}$ and $t_{j}$ is estimated as
$\frac{n_{ij}^{2}}{n_{j}n_{j}}$, where $n_{i}$ and $n_{j}$ are their
respective frequencies in the parallel corpus, and $n_{ij}$ is the number of
sentence pairs with $t_{i}$ in one sentence and $t_{j}$ in another., inspired
by Xu and Hong (2022).

We make two copies of this model and train them to translate in the myv-ru and
ru-myv directions, respectively. The myv-ru model is trained on the joint
parallel corpus of sentences and words. The ru-myv model is trained on the
union of this corpus and the back-translated corpus generated by the myv-ru
model from the monolingual myv data.

After training the models on these two languages, we adapt them to 10 more
languages: ar, de, en, es, fi, fr, hi, tr, uk, and zh, resulting in the myv-
mul and mul-myv models (below, by mul we denote any of these 10 languages). We
fine-tune the two models jointly, using a version of online-back translation
and self-training. Specifically, we generate the training pairs in four
alternating steps:

  1. 1.

Sample a ru-mul sentence pair from the CCMatrix Schwenk et al. (2021) dataset,
translate from ru to myv with the mul-myv model;

  2. 2.

Sample a ru-mul pair from the CCMatrix, translate from mul to myv with the
mul-myv model;

  3. 3.

Sample a ru-myv pair from our parallel corpus, translate from myv to mul with
the myv-mul model;

  4. 4.

Sample a myv text from the monolingual myv corpus, translate from mul to myv
and ru with the myv-mul model.

At each step, we update both models on the myv-mul and myv-ru pairs in both
directions. For the self-training updates, we multiply the loss by the
coefficient $\lambda_{ST}=0.05$ to decrease the impact of self-training
relatively to back-translation (the choice of the coefficient is suggested by
experiments in He et al. (2022)).

During the initial experiments, we noticed that, when translating from Russian
to Erzya, the model often just copied Russian phrases with only word endings
sometimes changed. Sometimes this is acceptable because Erzya has multiple
Russian loanwords, but often there exist native words that are preferable. To
alleviate this problem, in step 1 we generate 5 alternative ru-myv
translations using diverse beam search Vijayakumar et al. (2016), and choose
the one with the largest proportion of words recognized as myv by our language
identification model. This problem was also the reason why we chose to train
two different models from translation to Erzya and from Erzya: this way, the
decoder and encoder of a model never work with the same language.

##  4 Evaluation

###  4.1 Data

For model evaluation, we prepare a held-out corpus of 3000 aligned Erzya-
Russian sentences from 6 diverse sources: the Bible, Erzya folk tales
Sheyanova (2017), the Soviet 1938 constitution, descriptions of folk
children’s games Брыжинский (2009), modern Erzya fiction and poetry, and
Wikipedia. To evaluate English and Finnish translation, we use translations
from the Erzya universal-dependency treebank Rueter and Tyers (2018): 441
sentence pairs for en, and 309 for fi. We split all these sets into
development and test parts, and report the results on the test set.

###  4.2 Automatic Metrics

For all evaluated directions (between myv and ru, en, fi) we calculate BLEU
Papineni et al. (2002) and ChrF++ Popović (2017). Both these metrics estimate
the proportion of common parts in the translation and the reference, but BLEU
is calculated as precision over word n-grams, whereas ChrF++ aggregates
precision and recall of word and character n-grams (which is more suitable for
morphologically rich languages such as Erzya and Russian). The values of these
metrics on the test set are given in Table 1. For translation from and to
Russian, the BLEU scores are 17 and 19 points, respectively. For English and
Finnish, however, BLEU is well below 10. We hypothesize that the low quality
may be attributed to the domain mismatch between the Erzya-origin and English-
or Finnish-origin training corpora, but without detailed test sets we cannot
verify this.

Direction | BLEU | ChrF++
---|---|---
ru-myv | 17.71 | 41.16
myv-ru | 19.68 | 38.63
en-myv | 2.77 | 28.03
myv-en | 5.44 | 25.99
fi-myv | 4.79 | 27.42
myv-fi | 3.02 | 22.34
Table 1:  Reference-based scores on the test sets. | ru-myv | myv-ru
---|---|---
Source | BLEU | ChrF++ | BLEU | ChrF++
bible | 10.00 | 36.92 | 10.71 | 33.55
tales | 7.00 | 33.90 | 7.30 | 28.42
constitution | 27.82 | 62.96 | 33.31 | 60.60
games | 10.33 | 31.19 | 9.85 | 26.57
fiction | 8.68 | 30.59 | 5.95 | 26.60
wiki | 28.39 | 48.56 | 32.24 | 47.55
Table 2:  Scores by section on the myv-ru test set.

For the Russian test set, the performance varies greatly depending on the
domain (Table 2). The constitution has the highest scores because its Erzya
text is saturated with Russian loanwords and is easy to generate and
understand. For Wikipedia, the scores are also high, probably because its
Erzya articles are often translated from Russian in a rather literal way. The
other domains have a more artistic style, and the translations are on average
much less literal.

Some examples of the translations and references are given in Table 3.

Type |  Text
---|---
source (ru) |  И вот что рассказывают наши русские старики: «Когда здесь не было этого села, в этом овраге были разбойники; у них были землянки.
source (myv) |  Вана мезе ёвтнить миненек рузонь атятне: зярдо велесь тесэ арасель, се латксонть эрясть розбойникть, эрясть землянкасо.
translation (ru-myv) |  Ды вана мезе ёвтнить минек эрзянь атятне: «Зярдояк те велесэнть арасель, тосо оврагасо ульнесть розбойникть; сынст ульнесть землянкаст.
translation (myv-ru) |  Вот что нам говорят русские старцы: когда деревня здесь не была, то там жилибойники, жили на земле.
source (myv) |  Кода авазо, анокстась лапужа кирькст, истя жо педявтнинзе, валаськавтнинзе педявтома таркатнень начко кедьсэ.
source (en) |  Like his mother, he prepared flat rings, and stuck them onto the patty in the same way, and smoothed out the seams with his wet hands.
source (fi) |  Samalla tavalla kuin äitinsä Ketšai valmisti litteitä rinkuloita liitti ne samalla tavalla, ja siloitti liitoksen märällä kädellä.
translation (myv-en) |  Like his mother, he prepared flat circles, and also filled the canvas with a needle.
translation (myv-fi) |  Kuten äiti, valmistelee tasa-alaiset kentät, myös venytetään, lyödään venyttäjän käsillä.
translation (en-myv) |  Кода аванзо, сон анокстыль валаня суркст, теке ладсо педявтызе сынст пацьказонзо ды вадяшась кедень летькенть марто.
translation (fi-myv) |  Истя жо, кода авазо Кетшай анокстыль лаҥгсо кевпанть, сон солодиль сынст теке ладсо ды солодиль эйсэст кедьлапушкасо.
Table 3:  A few examples of translations and references.

###  4.3 Manual Evaluation

We recruit three native speaker volunteers to evaluate some translations
manually. The evaluation protocol is similar to XSTS NLLB Team et al. (2022),
but evaluates fluency in addition to semantic similarity. The scores are
between 1 (a useless translation) and 5 (a perfect translation), with 3 points
standing for an acceptable translation without serious errors. Criteria for
each score are given in Appendix C.

Each of the 3 annotators rated a few randomly sampled translations from the
dev split of each source: 12 pairs in the ru-myv and 17 pairs in the myv-ru
directions, which amounts to 87 sentence pairs annotations in total. The
average length of the labelled texts was 97 characters, or 14 words.

It turned out that, despite the specified annotation criteria, the annotators
were calibrated very differently: their average ratings were 2.9, 3.5, and
4.1. We chose a pessimistic aggregation strategy: for each of the 29 evaluated
sentence pairs, we took the worst of the scores by our 3 volunteers.

For translation to Erzya, the average pessimistic score was 2.75, and 58%
translations were rated as acceptable (i.e. all the 3 reviewers rated them
with at least 3 points). For translation to Russian, the average score was
2.71, with 53% acceptable translations.

An additional comment from the annotators was that some of the source Erzya
texts were inadequate. In particular, some games sentences contained
grammatical errors181818We are not certain whether these errors are due to the
low quality of the source text, or to the natural variations within the Erzya
language., and most constitution sentences contained Russian words with Erzya
endings instead of their Erzya equivalents. This suggests that one of the next
steps in improving our NMT system might be to filter the training and
evaluation data for better language quality.

##  5 Conclusions and Future Work

In this paper, we present the first NMT system for the endangered Erzya
language, capable of translating between it and 11 diverse languages,
primarily Russian. During its development, we have collected about 30K
parallel Russian-Erzya sentences and 300K monolingual Erzya sentences, and
trained a language identification model and a BERT-based sentence encoder that
support Erzya. All the resources are publicly released. These efforts have
occupied about two man-weeks of working time and almost no expenses191919All
the expenses incurred totalled $9.99 for the paid subscription to the Google
Colab system (https://colab.research.google.com/signup).. We hope that these
results will inspire the NLP community to develop resources for other
endangered languages.

The quality of our system may be improved by collecting more texts in Erzya
and filtering them better than we did. Another promising direction is a more
efficient usage of the vocabularies and parsers that are already available for
the language, e.g. for generating synthetic training data. Finally, we hope to
attract more native speakers for creating larger and cleaner train and test
datasets.

One open research question is that of transfer between languages: whether
Erzya translation benefits from knowledge of, for example, Hungarian or
Estonian, and whether knowledge of Erzya can bring improvements to other
languages, such as Moksha. In further studies, we hope to shed some light on
this direction as well.

##  6 Acknowledgements

We gratefully acknowledge support from the volunteers who participated in the
manual evaluation of translation quality: Semyon Tumaikin, Zinyoronj Santyai,
and Evgenia Chugunova. We are also grateful to the reviewers for their
suggestions which helped to improve this work.

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##  Appendix A Data sources

Source | Type | Size
---|---|---
Erzya-Russian dictionaries: marlamuter.com , mordovians.ru , mordvarf.com , Рябов et al. (2011), Щанкина (2011), Эрюшов  | phrase pairs | 47860
The myv-ru Wikimedia corpus on OPUS Tiedemann (2012) | sentence pairs | 3202
The Bible finugorbib.com  | sentence pairs | 12483
Sheyanova (2017) (aligned) | sentence pairs | 1023
Брыжинский (2009) (aligned) | sentence pairs | 4203
Erzya and Russian Wikipedia wik (2022a) (aligned) | sentence pairs | 11479
Livejournal lj  (aligned) | sentence pairs | 1799
Modern Erzya fiction and poetry rus  (aligned) | sentence pairs | 916
The Soviet 1938 constitution con (1938) (aligned) | sentence pairs | 304
Mordovian tales and riddles Евсевьев (1964) (aligned) | sentence pairs | 3776
Various Erzya fiction books lib.e mordovia.ru  | sentences | 52870
Various Soviet-time books and periodicals fen  | sentences | 54798
Erzya Wikisource, filtered by language wik (2022b) | sentences | 120470
Articles from the Erzya Pravda website pra  | sentences | 43772
Livejournal lj  | sentences | 36584
Erzya Wikipedia wik (2022a) | sentences | 59569
Брыжинский (2009) | sentences | 5194
Table 4:  The sources used to construct the training and evaluation datasets.
The ‘‘size’’ column denotes the number of sentences or phrases in the source.

##  Appendix B Models’ hyperparameters

###  B.1 Language identification

For the language identification model, we use the official FastText
implementation202020https://github.com/facebookresearch/fastText. We train it
with initial learning rate of 0.05 for 100 epochs, using minimum word count of
100, 64-dimensional embeddings and 200K hash buckets for character n-grams
with n from 1 to 4. Then we quantize the model with retraining on the same
dataset, a cutoff of 50000, and norm pruning.

###  B.2 Sentence encoder

For the sentence encoder model, we use a PyTorch port of
LaBSE212121https://huggingface.co/sentence-transformers/LaBSE, in which we
remove tokens for all languages, except Russian and English, and add Erzya
tokens. For vocabulary extension, we set the minimal token count for stopping
BPE at 30.

After extending the vocabulary, we fine-tune the model on the initial parallel
sentences and phrases using the LaBSE contrastive loss with margin 0.3 and
batch size 4 for 500K steps, updating only the embeddings, and passing the
gradient only through the encoded myv sentence. We use the Adafactor optimizer
with learning rate of $10^{-5}$ and clipping the gradient norm at 1. Then we
update the model for 500K steps with learning rate $2\times 10^{-6}$, updating
all the parameters, and alternating batches with the LaBSE loss, MLM loss, and
the loss of classifying the correct and incorrect sentence pairs. Incorrect
pairs are generated either by sampling one of the sentences randomly, or by
randomly inserting, deleting, or swapping words in one of the sentences in a
correct parallel pair.

###  B.3 Machine translation models

Both myv-ru and ru-myv models were initialized from
mBART50222222https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt
with the vocabulary extended with Erzya tokens. They were trained with
Adafactor optimizer using batch size of 8 and learning rate of $10^{-6}$ for 4
epochs: on the first epoch, only token embeddings were updated, and on the
remaining epochs, all parameters were updated.

The myv-mul and mul-myv models were initialized from myv-ru and ru-myv,
respectively. They were jointly trained for 40K updates with batch size of 1.

For inference, we used beam size of 5 and repetition penalty of 5.0.

Both the sentence encoder and the translation models were trained using the
PyTorch232323https://pytorch.org and
Transformers242424https://huggingface.co/docs/transformers/ Python packages.

##  Appendix C Quality annotation guidelines

The following annotation criteria (in Russian) were suggested to the
annotators in Section 4.3.

  * •

5 points: a perfect translation. The meaning and the style are reproduced
completely, the grammar and word choice are correct, the text looks natural.

  * •

4 points: a good translation. The meaning is reproduced completely or almost
completely, the style and the word choice are natural for the target language.

  * •

3 points: an acceptable translation. The general meaning is reproduced; the
mistakes in word choice and grammar do not hinder understanding; most of the
text is grammatically correct and in the target language.

  * •

2 points: a bad translation. The text is mainly understandable and mainly in
the target language, but there are critical mistakes in meaning, grammar, or
word choice.

  * •

1 point: a useless translation. A large part of the text is in the wrong
language, or is incomprehensible, or has little relation to the original text.


11institutetext: Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69,
53121 Bonn, Germany
11email<EMAIL_ADDRESS>22institutetext: Dept. of Physics, Earth Science,
and Space System Engineering, Morehead State University, 235 Martindale Dr,
Morehead, KY 40351, USA 33institutetext: Department of Astronomy and Physics,
Saint Mary’s University, 923 Robie Street, Halifax, NS, B3H 3C3, Canada
44institutetext: MIT Kavli Institute for Astrophysics and Space Research,
Cambridge, MA 02139, USA 55institutetext: Space Telescope Science Institute,
3700 S. Martin Drive, Baltimore, MD 21218, USA 66institutetext: Instituto
Nacional de Astrofísica, Óptica y Electrónica INAOE- CONACyT, México
77institutetext: European Space Agency (ESA), European Space Astronomy Centre
(ESAC), E-28691 Villanueva de la Canada, Madrid, Spain 88institutetext: Kavli
Institute for Particle Astrophysics and Cosmology, Stanford University, 452
Lomita Mall, Stanford, CA 94305, USA

# Lifting the curtain: The Seyfert galaxy Mrk 335 emerges from deep low-state
in a sequence of rapid flare events

S. Komossa 11 D. Grupe 22 L.C. Gallo 33 P. Poulos 22 D. Blue 33 E. Kara 44 G.
Kriss 55 A.L. Longinotti 66 M.L. Parker 77 D. Wilkins 88

(Received 3 August 2020; accepted 28 September 2020; journal reference: A&A
643, L7)

###### Abstract

Context. The narrow-line Seyfert 1 galaxy Mrk 335 was one of the X-ray
brightest active galactic nuclei, but it has systematically faded since 2007.

Aims. We report the discovery with Swift of a sequence of bright and rapid
X-ray flare events that reveal the emergence of Mrk 335 from its ultra-deep
multiyear low state.

Methods. Results are based on our dedicated multiyear monitoring of Mrk 335
with Swift.

Results.  Unlike other bright active galactic nuclei, the optical–UV is
generally not correlated with the X-rays in Mrk 335 on a timescale of days to
months. This fact either implies the absence of a direct link between the two
emission components; or else it implies that the observed X-rays are
significantly affected by (dust-free) absorption along our line of sight. The
UV and optical, however, are closely correlated at the 99.99% confidence
level. The UV is leading the optical by $\Delta t=1.5\pm{1.5}$d. The Swift
X-ray spectrum shows strong deviations from a single power law in all
brightness states of the outbursts, indicating that significant absorption or
reprocessing is taking place. Mrk 335 displays a softer-when-brighter
variability pattern at intermediate X-ray count rates, which has been seen in
our Swift data since 2007 (based on a total of 590 observations). This pattern
breaks down at the highest and lowest count rates.

Conclusions. We interpret the 2020 brightening of Mrk 335 as a decrease in
column density and covering factor of a partial-covering absorber along our
line of sight in the form of a clumpy accretion-disk wind that reveals an
increasing portion of the intrinsic emission of Mrk 335 from the disk and/or
corona region, while the optical emission-line regions receive a less variable
spectral energy distribution. This then also explains why Mrk 335 was never
seen to change its optical Seyfert type (not ‘changing look’) despite its
factor $\sim$50 X-ray variability with Swift.

###### Key Words.:

galaxies: active – galaxies: nuclei – galaxies: individual (Mrk 335) –
galaxies: Seyfert – quasars: supermassive black holes – X-rays: galaxies

##  1 Introduction

Active galactic nuclei (AGN) in extreme minima or maxima states provide us
with important insight into the physics of the black hole and accretion disk
region. The group of narrow-line Seyfert 1 galaxies (NLS1 hereafter) is known
to exhibit particularly strong flux and spectral variability in X-rays
(reviews by Gallo (2018a) and Komossa (2018), and references therein). Mrk 335
(Markarian, 1977) is a member of the class of NLS1 galaxies at redshift
$z=0.0258$ with a black hole mass of $M_{\rm BH}=2.7\times 10^{7}$ M⊙ (Grier
et al., 2012). Its optical spectrum is characterized by strong high-ionization
coronal emission lines with ionization states of up to [FeX] (Grupe et al.,
2008) that imply the presence of a strong EUV-X-ray continuum. Mrk 335 was
detected as a bright and variable UV emitter in early International
Ultraviolet Explorer (IUE) and Hubble Space Telescope (HST) observations
(e.g., Dunn et al., 2006; Longinotti et al., 2013).

For a time, this was one of the X-ray brightest AGN (Tananbaum et al., 1978;
Pounds et al., 1987; Grupe et al., 2001; Bianchi et al., 2001). Then the X-ray
flux of Mrk 335 dropped dramatically in 2007 (Grupe et al., 2007, 2008).
Multiple epochs of exceptional flaring and dipping on its way to and beyond
this low state motivated several deeper spectroscopic observations with XMM-
Newton, Suzaku, NuSTAR, and the HST (Grupe et al., 2012; Gallo et al., 2013;
Kara et al., 2013; Longinotti et al., 2013; Parker et al., 2014; Gallo et al.,
2015; Wilkins et al., 2015; Wilkins & Gallo, 2015; Komossa et al., 2017; Gallo
et al., 2018, 2019; Longinotti et al., 2019; Choudhury et al., 2019; Parker et
al., 2019). These observations have shown that Mrk 335 is well fit with
varying contributions of blurred reflection and (ionized) absorption. The
reflection model explains diminished X-ray continuum emission with changes in
a corona that has collapsed in toward the black hole, and sometimes forms a
collimated outflow in X-ray flare states. The absorption model explains
several epochs of flares and fades with changes in ionization state and
covering factor of the absorbers, which were detected with the XMM-Newton
reflection grating spectrometer (RGS) and in the UV with the HST.

We report the emergence of Mrk 335 from its recent ultra-deep low state (which
lasted from late 2017 to at least January 2020), based on 45 new observations
with the Neil Gehrels Swift observatory (Gehrels et al., 2004) each in all
seven wavebands from optical to X-rays. These are part of our long-term
monitoring of Mrk 335, which started in May 2007. When Mrk 335 was observed on
2020 May 16, it was found already at a flux level that exceeded all
observations since February 2018. In particular, it was very bright in the UV
as well and appeared at a similar UV flux level as seen last during the flare
at the end of 2011. Because of this outburst in May-June 2020, we started an
intensive monitoring campaign with Swift, and we report the results here. We
use a cosmology (Wright, 2006) with $H_{\rm 0}$=70 km s-1 Mpc-1, $\Omega_{\rm
M}$=0.3 and $\Omega_{\rm\Lambda}$=0.7 throughout this paper.

##  2 Data analysis

###  2.1 Swift XRT

We have monitored Mrk 335 with Swift for more than 13 years since May 2007
with a cadence of typically 1-14 days, depending on the brightness and
variability state of Mrk 335. Long gaps of several months occur each year when
Mrk 335 is unobservable with Swift because of its proximity to the Sun. Mrk
335 came out of the Swift Sun constraint on 16 May 2020 (Grupe et al., 2020)
and we resumed our monitoring (Fig. 1 and Fig. 2). The Swift X-ray telescope
(Burrows et al., 2005) was operating in photon-counting mode (Hill et al.,
2004) with typical exposure times of 1-2 ks (Tab. 2).

X-ray count rates were determined using the online XRT product tool at the
Swift data centre in Leicester (Evans et al., 2007)111
https://www.swift.ac.uk/user_objects/. For a dedicated spectral analysis of
select epochs, source photons were extracted within a circle of radius 59″,
and the background was determined in a nearby circle with a radius of 236″.
During the faintest states, spectral fitting of individual spectra is not
possible, and we instead measured hardness ratios, defined as $HR=\frac{hard-
soft}{hard+soft}$, where soft and hard are the counts in the 0.3-1.0 and
1.0-10 keV energy bands, respectively. $HR$ was determined by applying the
Bayesian estimation of hardness ratios, BEHR (Park et al., 2006)222
http://hea-www.harvard.edu/AstroStat/BEHR/.

In order to obtain a high-state spectrum for spectral fitting of a better
signal-to-noise ratio (S/N), we combined the 2020 June 23 – July 1 Swift data
when Mrk 335 was brightest (Target ID 13544, segments 011-016). For
comparison, we created a low-state spectrum when Mrk 335 was in its lowest
state with data from May 2019 (Target ID 33420, segments 218-221). These are
the two primary data sets used for spectral fitting. In both cases we created
new ancillary response files (arfs) by adding the arfs of the single spectra
weighted by their exposure time using the ftool command addarf. The coadded
X-ray spectra in the band (0.3-10) keV were then analyzed with the software
package xspec (version 12.10.1f; Arnaud, 1996).

###  2.2 Swift UVOT

Mrk 335 was observed with the UV-optical telescope (UVOT; Roming et al., 2005)
either in W2 or in all six filters in order to obtain spectral energy
distribution (SED) information of this rapidly varying AGN. Observations in
each filter were coadded using the task uvotimsum. Source counts in all
filters were then extracted in a circular region with a radius of 5″ , and the
background was selected in a nearby region of radius 20″. The background-
corrected counts were then converted into VEGA magnitudes and fluxes based on
the latest calibration as described in Poole et al. (2008) and Breeveld et al.
(2010). The task  uvotsource was used to measure the magnitudes and flux
densities. A Galactic reddening correction was applied to the UVOT data, with
a value of $E_{\rm{(B-V)}}$=0.035 (Schlegel et al., 1998), with a correction
factor in each filter according to Eq. (2) of Roming et al. (2009) and making
use of the reddening curves of Cardelli et al. (1989).

Figure 1: Long-term Swift XRT and UVOT light curves of Mrk 335 from 2007 May
to 2020 July. From top to bottom: XRT count rate, hardness ratio, and UVOT
Vega magnitudes corrected for Galactic extinction. The vertical red lines mark
the beginning of each second year from 2008 to 2020.

##  3 Results

###  3.1 Light curves and DCF analysis

Fig.1 displays the long-term light curve of Mrk 335 in X-rays as well as in
the optical/UV since the start of our Swift monitoring program in May 2007,
when it was discovered in an unusually low X-ray flux state (Grupe et al.,
2007). Figure 2 highlights the 2020 evolution in all bands.

Mrk 335 exhibited several short-duration X-ray flares over the last 13 years
when it was in a low state overall. The new rise in X-ray flux after the long
period of very low activity of more than two years is remarkable; it is
unprecedented in Mrk 335. The systematic rise in the UV flux since about
mid-2018 is even more remarkable.

Fig. 2 shows the Swift results since we resumed our monitoring program on 2020
May 16. This light curve shows multiple rapid flare events in X-rays,
including one rapid rise by a factor of $\sim$5 within a week, and another
rise by a factor of 2 within a day. Changes in the X-ray spectral shape as
shown in the hardness ratio light curve are apparent. The XRT count rate
before May 2020 was at a very low level around 0.05 cts s-1 for more than two
years (and it is still variable, therefore not host dominated), and now (at
the peak of the flare in late June 2020) it reached a value that is a factor
of 10 higher. The brightest state in the UV (W2) was detected on 2020 May 31
with a Galactic-reddening corrected magnitude of 12.68$\pm$0.04. This is the
highest state since the UV flare seen in December 2011.

In order to search for correlations and time delays between different bands
during the 2020 outbursts, a discrete correlation function (DCF; Edelson &
Krolik, 1988) analysis of the UVW2 and B and X-ray light curves was carried
out following Gallo et al. (2018) with Monte Carlo confidence intervals. The
UV band is closely correlated with the optical band, such that the optical is
following and the UV leading by $\Delta t\simeq 1.5$d with a confidence
interval of 3 to 0 days at the 99.99% level. However, no correlation between
the UV and X-rays is found.

Figure 2: Swift outburst light curve of Mrk 335 since 2020 May 16 (units as in
Fig. 1). The horizontal dashed red line marks an X-ray count rate of 0.05
cts/s (the lowest recorded during the 2018-2019 low state was 0.03 cts/s),
while the horizontal dashed blue line marks the lowest value of the W2
magnitude measured with Swift (in 2018). The vertical red lines mark 2020 June
1 and July 1.

###  3.2 X-ray spectral analysis

Spectra were first fit with single power laws of photon index $\Gamma_{\rm X}$
adding Galactic absorption with a column density $N_{\rm H,Gal}=3.96\times
10^{20}$ cm-2 (Kalberla et al., 2005). Because this model did not provide good
spectral fits and left strong systematic residuals in all fits (Fig. 3), we
then applied either an ionized or neutral partial covering absorber (Tab. 1).
The single-component partial covering model consistently requires a lower
column density and covering fraction at high state. Alternatively, the high-
state spectrum is well fit by an ionized absorber. The 2019 low-state spectrum
can also be well described by a simple powerlaw plus black body model, where
the black body is taken to be representative of a soft excess, which is
dominated by a number of emission lines detected with the RGS in low state
(Longinotti et al., 2019; Parker et al., 2019) and not individually resolved
with Swift. We have added such a component (with its parameters fixed to the
low-state values) when fitting the high-state spectrum, but it does not
provide a significant change in the overall fit results. Based on the simple
partial covering absorber fit to the 2020 high state, a (0.3-10) keV X-ray
luminosity of $8.2\times 10^{43}$ erg/s is obtained, or an X-ray Eddington
ratio of $L_{\rm X}/L_{\rm Edd}\approx 0.02$, while for the 2019 low state,
the intrinsic X-ray luminosity is a factor of 1.6 lower when the same model
description is adopted.

Spectral fits were also performed on a 2020 low state and intermediate state
(Fig. 3). However, as Mrk 335 varies strongly during the 2020 time interval
(unlike during the deep May 2019 low state), spectral fits are less reliable
and still come with large errors, and at any given time interval a mix of cold
and ionized absorbers may contribute. These cannot be disentangled with Swift,
however. Applying the simple partial covering model to the 2020 low state, we
find $N_{\rm H}=11.32^{+4.62}_{-2.97}$ cm-2 , while for the intermediate
state, we find $N_{\rm H}=4.32^{+1.22}_{-0.88}$ cm-2.

###  3.3 Hardness ratio variability

We inspected the long-term (since 2007) and short-term (2020) $HR$ variability
of Mrk 335 (Fig. 4) including all data points until 2020 July 19. Three
regimes are apparent: a softer-when-fainter pattern at lowest count rate $CR$,
a softer-when-brigther trend at intermediate $CR$, and an almost constant $HR$
at highest $CR$. These three regimes have also been identified in other
absorbed AGN that are intrinsically bright (e.g., Connolly et al., 2014).
Results of a regression analysis (available in midas) of the three variability
regimes are shown in Fig. 4. A correlation analysis at $CR<0.1$ cts/s gives
$r_{s}=+0.5,T_{S}=8.7$, and $P<10^{-8}$ ($N$=234). At $0.1<CR<0.25$ cts/s,
$r_{s}=-0.27,T_{S}=-3.7$, and $P=0.00014$ ($N$=177), while at $CR>0.25$ cts/s,
$r_{s}=-0.205,T_{S}=-2.7$, and $P=0.0038$ ($N$=174), where $r_{s}$ is the
Spearman rank order correlation coefficient, $T_{S}$ is the Student T-test,
$P$ is the probability that the distribution is just random, and $N$ is the
number of data points.

Table 1:  X-ray spectral fit results of Mrk 335 (see Sect. 3.2). Columns are
as follows: (1) Models: pl = powerlaw, bbdy = black body, zpcf = partial
covering absorber, and zxipcf = ionized absorber model. (2) Absorption in
units of 1022 cm-2; (3) power-law photon index; (4) $kT$ in units of keV of
the black body, or ionization parameter log $\xi$ of the ionized absorber; (5)
covering factor; and (6) goodness of fit, reduced $\chi^{2}$. If no errors are
reported, the quantity was fixed. For all fits the column density at $z=0$ was
fixed to the Galactic value of 3.96$\times 10^{20}$ cm-2.

model | $N_{\rm H}$ | $\Gamma_{\rm X}$ |  $kT$ or log $\xi$ | $\eta$ | $\chi{{}^{2}}_{\rm red}$
---|---|---|---|---|---
(1) | (2) | (3) | (4) | (5) | (6)
high state, 2020 June 23 – July 1
pl |  |  2.49$\pm$0.06 |  |  | 3.2
zpcf * pl |  6.3${}^{+3.3}_{-1.9}$ |  2.95${}^{+0.14}_{-0.14}$ |  |  0.86${}^{+0.04}_{-0.05}$ | 1.4
bbdy+(zpcf * pl)1 |  4.3${}^{+1.7}_{-1.1}$ |  2.97$\pm$0.20 | 0.15 |  0.88${}^{+0.04}_{-0.05}$ | 1.5
zxipcf * pl |  16.1${}^{+4.2}_{-4.9}$ |  2.3${}^{+0.1}_{-0.1}$ |  2.1${}^{+0.2}_{-0.1}$ |  0.87${}^{+0.07}_{-0.06}$ | 1.1
deep low state, 2019 May2
pl |  |  2.34$\pm$0.20 |  |  | 1.6
zpcf * pl |  13.95${}^{+8.62}_{-5.06}$ |  3.07$\pm$0.21 |  |  0.94${}^{+0.03}_{-0.06}$ | 1.0
pl + bbdy |  |  0.50${}^{+0.51}_{-0.55}$ |  0.15$\pm$0.02 |  | 0.9

1 bbdy parameters fixed at low-state value

2 Fits using Cash statistics (Cash, 1979)

Figure 3:  Selected Swift spectra of Mrk 335 in 2020 May–July. The 2020 June
high state is plotted in black, an intermediate state from June 2 and 3 in
red, and a low state from 2020 (data from May 16, 23, and 27, and June 7) in
green. All spectra are fit with a single power-law model with Galactic
absorption, which displays strong fit residuals especially at low and high
energies.

###  3.4 Spectral energy distribution

The SEDs during the high state in May-June 2020 in comparison with a low
-state data set in May 2019 are shown in Fig. 5. The results show that the
optical-UV spectrum has become bluer. While the W2-U color during the low
state in 2019 was –0.03$\pm$0.06, it was –0.61$\pm$0.06 on 2020 May 31. In
X-rays, Mrk 335 has not only become brighter, but the spectrum has changed as
well (see Sect. 3.2).

##  4 Discussion

The long-term Swift light curve of Mrk 335 reveals that its deepest minimum-
state in the UV was reached in 2018, when the X-ray emission was also at a
deep and long-lasting low state, with little variability left in X-rays (Fig.
1). Except for one deviating epoch in 2019, and with some short-time
variability superposed, the UV has been on a steady rise since its 2018
minimum, reaching and exceeding its former Swift peak value (2011) again in
2020 May-June (Fig. 1)333at no time during the Swift monitoring did the UV
emission of Mrk 335 reach the highest brightness level recorded with IUE in
the 1980s (e.g., Dunn et al. (2006), Fig. 1 of Grupe et al. (2008), Longinotti
et al. (2013), Fig. 14 of Tripathi et al. (2020)). Instead, the long-term
X-ray light curve remained flat throughout 2018-2019, and we only detected a
significant rise since 2020 May, with multiple peaks. Except that both reached
a deep low-state in 2018 (Parker et al. (2019), which was relatively short-
lived in the UV, however; Fig. 1), the variability observed with Swift in the
UV and X-rays is not closely correlated on the timescale of days to months
(see also Grupe et al., 2012; Gallo et al., 2018; Tripathi et al., 2020):
multiple flares in the UV are not seen in X-rays and vice versa, as confirmed
by our DCF analysis of the 2020 data. This behavior is markedly different from
that of other bright type 1 AGN (Shappee et al., 2014; Gardner & Done, 2017;
Buisson et al., 2017; McHardy et al., 2018; Edelson et al., 2019) and demands
an explanation (see below).

Figure 4: Hardness ratio vs. X-ray count rate for Swift XRT data of Mrk 335
since 2007 (black). Three regimes are apparent: a softer-when-fainter trend at
lowest count rate, a softer-when-brigther pattern at intermediate count rate,
and a near-constant HR at highest count rate. The upper panel shows the full
count-rate range. Green circles represent the 2020 outbursts. The lower panel
zooms into the count-rate range up to 0.5 cts/s. The red lines represent the
fits to the three regimes (Sect. 3.3), and the blue squares mark the median in
each bin.

Several mechanisms are known to cause strong changes in the X-ray flux and
spectral states of AGN: The first mechanism is changes in ionization state or
geometry of ionized or neutral absorption that fully or partially covers the
continuum source (e.g., Abrassart & Czerny, 2000; Turner et al., 2011; Leighly
et al., 2015). Varying absorption is frequently seen in intermediate-type
Seyfert galaxies whose variability timescales can be as short as hours or
days, but can also occur in NLS1s (Risaliti et al., 2011) when the absorber is
part of a clumpy accretion-disk wind or resides within the BLR. The second
mechanism is changes in the X-ray reflection of photons off the (inner)
accretion disk (Ross & Fabian, 2005). This model has frequently been applied
to explain short-time spectral changes in type 1 Seyfert galaxies by changes
in the location and geometry of the corona. Third, on longer timescales,
changes in the accretion rate including disk instabilities can cause high or
low states in AGN (e.g., Ross et al., 2018; Czerny, 2019). This last mechanism
has been favored to explain some of the extreme changing-look AGN that also
switch their Seyfert types.

Figure 5: Observed Swift SED of Mrk 335 during 2020 June 2, June 27, and the
deep low-state of 2019 May. Note the X-ray flux deficit around (0.7-1.2) keV
on June 2. For comparison, the Swift SED during the highest X-ray state from
2009 January is shown (purple).

In Mrk 335, the variability of the UV band is not correlated with the X-ray
band on the timescale of days to months (with the exception of an epoch in
2014; Gallo et al., 2018). This rejects models that predict a close link
between UV and X-rays: For instance, models where the observed X-rays are
upscattered UV photons; or models where the UV is reprocessed emission from an
X-ray corona heating the disk, which have successfully been applied to several
well-observed AGN that show a close UV–X-ray correlation (Shappee et al.,
2014; McHardy et al., 2018); or models where UV and X-rays are due to
synchrotron radiation (unlikely for Mrk 335 even though it is a radio
emitter).

Furthermore , we can also exclude models of distant dusty absorbers that
diminish the UV by extinction and simultaneously diminish the X-rays by
absorption. However, dust-free absorption, arising in the accretion-disk
region for instance in the form of a clumpy wind, is an efficient mechanism to
cause X-ray spectral and flux changes while leaving the broad-band UV
unaffected. And our spectral fits of a partial covering absorber consistently
imply a decrease in covering factor and column density as the source brightens
in X-rays. Our fit parameters of the ionization parameter, column density, and
covering factor are consistent with predictions from models of clumpy disk
winds (Takeuchi et al., 2013) that form via the Rayleigh-Taylor and radiation-
hydrodynamic instability (Shaviv, 2001) and which predict variability
timescales on the order of weeks.

A partial covering scenario can also explain why Mrk 335 only shows a mild
trend of softer-when-brighter (Fig. 4) because a partial coverer would
preserve the spectral shape as it leaks different fractions of the same
intrinsic continuum when the covering factor changes. The softer-when-fainter
pattern at the very lowest count rates is likely due to a soft X-ray component
in the form of multiple emission lines (Longinotti et al., 2019; Parker et
al., 2019) and a (distant) reflection component (Gallo et al., 2019) which
become increasingly apparent as the primary continuum diminishes. While the
X-ray covering factor obtained from the current and previous (intermediate to
low state) observations of Mrk 335 is high ($\sim$ 80-95%), a partial covering
scenario for Mrk 335 is independently favored by previous UV observations that
revealed variable absorption lines, including Ly$\alpha$ at 20–30% covering
fraction of the UV-emitting site (Longinotti et al., 2019; Parker et al.,
2019).

There is other evidence that the X-rays we observe along our line of sight are
not representative of the 4$\pi$ emission: Optical reverberation mapping
(Grier et al., 2012) has shown that the Balmer line H$\beta$ and the high-
ionization line HeII$\lambda$4686 of Mrk 335 follow the amplitude of the
optical continuum variability444the Balmer line emission is driven by the EUV
beyond the Lyman limit, and the ionization potential of HeII is 54 eV and not
the higher amplitude seen in X-rays. While $R_{\rm max}$ = 1.53 in optical
continuum variability, and $R_{\rm max}$ = 1.55 in HeII (Grier et al. 2012;
where $R_{\rm max}$ is the ratio of maximum to minimum flux in each light
curve), we measured during the same time interval $R_{\rm max}$ = 5.44 in
X-rays.

Furthermore, no significant variability of the optical coronal lines in Mrk
335 was detected (Grupe et al., 2008) despite strong changes in observed
X-rays. As those lines are driven by the EUV-to-soft-X-ray part of the SED,
the observations indicate that the emission lines saw a different (less
variable) continuum. An absorption scenario can also explain the lack of
intrinsic variability of the photoionized soft X-ray emission lines (Parker et
al., 2019), and is consistent with the lack of IR variability of Mrk 335
(Wright et al., 2010).

The question remains why Mrk 335 is no optical Seyfert-type changer555as
observed in an increasing number of other AGN (MacLeod et al., 2016) despite
its high amplitude of X-ray variability (a factor $\sim$50 between highest and
lowest state in our long-term Swift light curve). As described above, the
emission-line regions seem to see a less variable EUV-X-ray SED. Furthermore,
because the broad Balmer lines are intrinsically bright, even a drop of a
factor of 2 still preserves the type 1 nature of Mrk 335.

As Mrk 335 is a very bright AGN at high state, it may turn out to be a Rosetta
stone in deciphering the various contributions of reflection, absorption, and
intrinsic emission, which imprint their presence on AGN X-ray spectra.
Following Mrk 335 closely as it emerges from low state will therefore provide
us with an important opportunity of understanding the physical processes that
drive long-term AGN state changes.

###### Acknowledgements.

We would like to thank Brad Cenko for approving our Swift observing requests
and the Swift team for carrying out our observations, and our anonymous
referee for very useful comments. This work made use of data supplied by the
UK Swift Science Data Centre at the University of Leicester. ALL acknowledges
support from CONACyT grant CB-286316.

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##  Appendix A Log of observations

Table 2: Summary of Swift observations of Mrk 335 in 2020 May – July.

Target ID | Segment | obs start (UT) | obs end (UT) | MJD1 | $t_{\rm XRT}^{2}$ | $t_{\rm V}^{2}$ | $t_{\rm B}^{2}$ | $t_{\rm U}^{2}$ | $t_{\rm W1}^{2}$ | $t_{\rm M2}^{2}$ | $t_{\rm W2}^{2}$
---|---|---|---|---|---|---|---|---|---|---|---
33420 | 240 | 2020-05-16 15:15 | 2020-05-16 15:25 | 58985.63905 | 637 | — | 66 | 86 | 79 | — | 266
| 241 | 2020-05-23 09:34 | 2020-05-23 09:42 | 58992.43683 | 480 | 38 | 38 | 38 | 78 | 105 | 154
| 242 | 2020-05-27 06:12 | 2020-05-27 06:29 | 58996.26405 | 995 | 79 | 79 | 79 | 157 | 249 | 315
| 243 | 2020-05-29 16:56 | 2020-05-29 17:10 | 58998.71010 | 834 | 70 | 70 | 70 | 141 | 171 | 280
| 244 | 2020-05-30 00:55 | 2020-05-30 01:09 | 58999.04275 | 834 | 69 | 69 | 69 | 139 | 178 | 277
| 245 | 2020-05-31 18:19 | 2020-05-31 18:34 | 59000.76809 | 897 | 73 | 73 | 73 | 145 | 205 | 292
| 246 | 2020-06-02 10:05 | 2020-06-02 10:15 | 59002.42337 | 587 | 45 | 45 | 45 | 90 | 149 | 181
| 247 | 2020-06-03 18:11 | 2020-06-03 18:28 | 59003.76332 | 999 | 81 | 81 | 81 | 163 | 226 | 328
| 248 | 2020-06-04 19:55 | 2020-06-04 23:11 | 59004.89769 | 974 | 77 | 77 | 77 | 155 | 215 | 311
| 249 | 2020-06-05 03:47 | 2020-06-05 04:01 | 59005.16214 | 844 | 69 | 69 | 69 | 137 | 192 | 273
| 250 | 2020-06-06 02:04 | 2020-06-06 02:20 | 59006.09137 | 954 | 79 | 79 | 79 | 157 | 210 | 314
| 003 | 2020-06-11 20:23 | 2020-06-11 20:40 | 59011.85493 | 1009 | 82 | 82 | 82 | 165 | 229 | 330
| 005 | 2020-06-13 18:36 | 2020-06-13 18:54 | 59013.78176 | 932 | 77 | 77 | 77 | 154 | 207 | 307
| 004 | 2020-06-13 20:23 | 2020-60-13 20:38 | 59013.85416 | 904 | 74 | 74 | 74 | 146 | 208 | 293
| 006 | 2020-06-15 15:18 | 2020-06-15 15:31 | 59015.64189 | 739 | 56 | 81 | 81 | 164 | — | 329
| 007 | 2020-06-17 23:05 | 2020-06-17 23:16 | 59017.96548 | 629 | 50 | 50 | 50 | 101 | 143 | 203
| 008 | 2020-06-19 18:12 | 2020-06-19 18:29 | 59019.76407 | 989 | 83 | 83 | 83 | 163 | 216 | 328
| 009 | 2020-06-20 03:44 | 2020-06-20 03:59 | 59020.16048 | 889 | 73 | 73 | 73 | 146 | 196 | 293
| 010 | 2020-06-21 14:54 | 2020-06-21 15:11 | 59021.62666 | 974 | 81 | 81 | 81 | 160 | 213 | 322
| 011 | 2020-06-23 05:04 | 2020-06-23 05:21 | 59023.21670 | 1014 | 81 | 81 | 81 | 161 | 252 | 323
| 012 | 2020-06-25 14:18 | 2020-06-25 14:29 | 59025.59944 | 637 | 52 | 52 | 52 | 105 | 138 | 210
| 013 | 2020-06-27 15:39 | 2020-06-27 15:53 | 59027.65675 | 814 | 66 | 66 | 66 | 132 | 190 | 263
| 014 | 2020-06-27 20:22 | 2020-06-27 20:39 | 59027.85434 | 991 | 81 | 81 | 81 | 161 | 229 | 323
| 015 | 2020-06-29 20:11 | 2020-06-29 20:27 | 59029.84626 | 946 | 77 | 77 | 77 | 153 | 219 | 307
| 016 | 2020-07-01 23:09 | 2020-07-01 23:24 | 59031.96965 | 864 | 71 | 71 | 71 | 142 | 194 | 283
| 017 | 2020-07-02 04:03 | 2020-07-02 04:11 | 59032.17113 | 492 | 39 | 39 | 39 | 79 | 107 | 157
| 018 | 2020-07-03 08:37 | 2020-07-03 08:52 | 59033.36409 | 864 | 70 | 70 | 70 | 140 | 202 | 279
| 019 | 2020-07-02 22:51 | 2020-07-04 23:06 | 59034.95704 | 887 | 72 | 72 | 72 | 143 | 206 | 287
| 020 | 2020-07-05 10:07 | 2020-07-05 10:15 | 59035.42399 | 477 | 37 | 37 | 37 | 76 | 110 | 151
| 021 | 2020-07-06 17:59 | 2020-07-06 18:07 | 59036.75174 | 482 | 37 | 37 | 37 | 75 | 116 | 148
| 022 | 2020-07-07 01:56 | 2020-07-07 02:05 | 59037.08344 | 524 | 40 | 40 | 40 | 80 | 135 | 159
| 023 | 2020-07-08 09:57 | 2020-07-08 10:14 | 59038.42019 | 1011 | 81 | 81 | 81 | 160 | 253 | 320
| 024 | 2020-07-09 19:32 | 2020-07-09 19:48 | 59039.81892 | 944 | 79 | 79 | 79 | 157 | 224 | 315
| 025 | 2020-07-01 01:51 | 2020-07-10 02:07 | 59040.08225 | 964 | 78 | 78 | 78 | 156 | 229 | 312
| 026 | 2020-07-11 12:38 | 2020-07-11 12:54 | 59041.53151 | 979 | 78 | 78 | 78 | 155 | 244 | 310
| 028 | 2020-07-05 15:34 | 2020-07-15 15:51 | 59045.65441 | 991 | 79 | 79 | 79 | 157 | 247 | 315
| 027 | 2020-07-16 21:56 | 2020-07-16 22:09 | 59046.91742 | 894 | 72 | 72 | 72 | 144 | 212 | 287
| 029 | 2020-07-17 13:56 | 2020-07-17 14:04 | 59047.58317 | 452 | 36 | 36 | 36 | 74 | 92 | 147
| 030 | 2020-07-18 23:17 | 2020-07-18 23:32 | 59048.97506 | 889 | 73 | 73 | 73 | 146 | 198 | 293
| 031 | 2020-07-19 23:13 | 2020-07-19 23:28 | 59049.97230 | 884 | 72 | 72 | 72 | 144 | 206 | 287
| 032 | 2020-07-20 00:46 | 2020-07-20 00:54 | 59050.03457 | 450 | 35 | 35 | 35 | 70 | 104 | 141
| 033 | 2020-07-22 10:01 | 2020-07-22 10:16 | 59052.42186 | 864 | 73 | 73 | 73 | 145 | — | 290
| 034 | 2020-07-23 02:16 | 2020-07-23 02:26 | 59053.09802 | 649 | 51 | 51 | 51 | 102 | 153 | 205
| 035 | 2020-07-26 22:37 | 2020-07-26 22:54 | 59056.94811 | 989 | 80 | 80 | 80 | — | 239 | 317
| 036 | 2020-07-30 03:10 | 2020-07-30 03:32 | 59060.13607 | 789 | 62 | 62 | 62 | 125 | 190 | 250

1 The Modified Julian Date is given for the middle of the observation.

2 All exposure times $t$ are given in seconds.